indian journal of economics and development print issn

Print ISSN 2277-5412Online ISSN 2322-0430

Indian Journal of Economics and Development

Volume 14No. 2

April - June, 2018

I an m cndi Journal of Econo i s

pmand Develo ent 2277-5412

nde d in SCI Thomson Reuters)I xe E (

CABI

SOED

NAAS rated

www.soed.inIndexed inCite Factor

N A J E DI DI N CON EV

2322-0430

Society of Economics and Developmentwww.soed.in

Society of Economics and DevelopmentObjectives of the Society

i. to promote awareness on the issues relating to economic development,ii. to promote better social and ethical values to promote development, iii. to promote economic prosperity and serve as a tool to create the consciousness for development,iv. to conduct research and publish reports on economic issues,v. to organize seminars, symposia, workshops to discuss the economic problems, andvi. to offer consultancy, liaison and services as a facilitator.

PresidentDr. M.S.Toor

Vice Presidents

Finance SecretaryDr. Satnam Singh Chahel

General SecretaryDr. Parminder Kaur

Joint SecretaryDr. Pankaj Kumar

Members

Indian Journal of Economics and DevelopmentChief EditorDr. S.S. Chahal (India)

EditorsDr. S.S. Chhina (India), Dr. Mini Goyal (India), Dr. Shalini Sharma (India), Dr. Anupama Uppal (India) and Dr. Sanjay Kumar (India)

MembersDr. Cather ine Neumeyer (Canada) Dr. Sendhi l , R.(India) Dr. Jasdeep Singh Toor (India)

Dr. Inderpal Singh (Australia) Dr. Timothy J. Colwill (Canada) Dr. M.K. Randhawa (India) Dr. Dalip Kumar Bishnoi (India) Dr. Ardi Gunardi (Indonesia), Dr. Richard Kwasi Bannor (Ghana), Dr. Sadiq Mohammed Sanusi (Nigeria), Dr. Rohit Singla (USA), Dr. M.K.Sekhon (India), Dr. Renjini, V.R. (India), Dr. Jasdev Singh (India), Ms. Isha Chawla (USA), Dr. R.B.Hile (India), Dr. Baljinder Kaur Sidana (India), Dr. Sangeet Ranguwal (India), Dr. Varinderpal Singh (India), Mr. Ravinderpal Singh Gill (Canada), Dr. Deepak Upadhya (Nepal), and Ms. Donata Ponsian Kemirembem (Tanzania)

Dr. D.K. Grover, Dr. J.M. Singh, Dr. Pratibha Goyal, Dr. Simran K. Sidhu, and Dr. Narinder Pal Singh

Dr. Gian Singh, Dr. Jitender Kumar Bhatia, Dr. J.S. Dhiman, Dr. Prabhjot Kaur, Dr. M. Javed, Dr. Seema Sharma, Dr. Anil Bhat, Dr. Arjinder Kaur, Dr. Lavleen Kaur, Dr. Virender Kumar, Dr. Jatinder Sachdeva, Dr. H.S. Kingra, Dr. Deepak Shah, Dr. Sadhika Berry, Ms. Mehak Jain, and Mr. Baldeep Singh.

, , Dr. Lovlesh Garg (India), , , ,

,

EXECUTIVE COMMITTEE

EDITORIAL BOARD

Membership type

Academics (Faculty and Students)

Annual

Life

Institutional

Annual

Corporate

Annual

Subscription Rates

India (`) Other Countries ($)

1000.00

5000.00

3000.00

5000.00

30.00

300.00

300.00

500.00

Kindly login www.soed.in to remit membership fee via CCAvenue

Society of Economics and Developmentwww.soed.in

Print ISSN 2277-5412Online ISSN 2322-0430


Indian Journal of Economics and DevelopmentVolume 14 (2) April-June, 2018

©Society of Economics and Development

Printed and Published by Dr. Parminder Kaur on the behalf of the Society of Economics and Development

Email: [email protected]: http://soed.in/Journal is available on www.indianjournals.com

Printed at FOIL PRINTERS2051, Gobind NagarLudhiana-141 001Phone: 0161-2404093Email: [email protected]

Indian Journal of Economics and Development(Journal of the Society of Economics and Development)

Research Articles

Financial feasibility analysis of pomegranate production in Solapur district of Maharashtra 199G.D. Rede and K. Bhattacharyya

Agricultural development and food security in Odisha 213 Bidhan Kumar Mohapatra

Evaluation of adoption of precision farming and its profitability in banana crop 225Denny Franco, Dharam Raj Singh and K.V. Praveen

Farm mechanization in Himachal Pradesh: Constraints, status and its role in augmenting farm incomes 235Virender Kumar, S K Chauhan, Harbans Lal, Rajesh Thakur and Divya Sharma

Comparison of financial literacy among women self help group members engaged in weaving and 243coir activities in Salem district of Tamil NaduN. Jayashree, N. Deepa, and A. Raj Shravanthi

Comparative study of bullish option payoffs in USD-INR market 252Avneet Kaur, Sandeep Kapur and Mohit Gupta

An economic analysis of traditional and evolved basmati in Punjab (India) 260 Navdeep Kaur and V.K. Sharma

Marketing pattern and price spread of green fodder in Punjab state 267Harparteet Singh, Varinder Pal Singh and Inderpreet Kaur

An economic analysis of precision agriculture-A case study of paddy in north eastern Karnataka 274K. Shruthi, G.M. Hiremath, Amrutha T. Joshi and Suresh S. Patil

Marketing pattern and efficiency of Khasi Mandarin in Meghalaya 281Sukheimon Passah and A.K. Tripathi

Status and determinants of crop insurance in Gujarat 288Shiv Raj Singh, K.P. Thakar, and C. Soumya

Nature and extent of industrial employment of migrant labourers in Punjab 295Jasdeep Singh Toor and Ketanpreet Kaur

Indebtedness among marginal and small farmers in rural Punjab 302Rupinder Kaur, Sukhvir Kaur, Anupama, Gurinder Kaur and Gian Singh

â-Convergence of real per capita GDP in BRICS economies 309Sayel Basel and R. Prabhakara Rao

Seasonal migration and livelihood of marginalised communities-A case study in Boudh district of Odisha 316Chittaranjan Nayak and Chinmaya Ranjan Kumar

Indebtedness among weaker sections in rural areas of south-west Punjab 324Manvir Kaur, R.K. Mahajan and Rupinder Kaur

Cointegration among major cauliflower markets in Punjab 330Shruti Mohapatra, Jasdev Singh and Sanjay Kumar

Performance appraisal of working capital funding products and risk management practices in private banking sector 336– A case study of Kotak Mahindra BankIsha Chawla

Economics of atta chaki enterprise in Punjab: A study of an agro-based industry 342Pardeep Kaur and Mini Goyal

Volume 14 April-June 2018 No. 2

Contents

Compositional shift analysis of Gujarat livestock population 348Mahammadhusen Khorajiya, R. L. Shiyani, N. J. Ardeshna, M. G. Dhandhalya and B. Swaminathan

Research Notes

Agricultural credit availed and its utilization on large sampled households in Punjab 354Sukhdeep Singh, Arjinder Kaur, and Poonam Kataria

Growth and instability in area, production and productivity of mango crop in Gujarat 359Hamidullah Younisi and J.J. Makadia

An economic analysis of food consumption pattern in West Bengal with special reference to dairy products 364Arnab Roy and Ravinder Malhotra

Consumers' willingness to pay for organic fruits and vegetables and its market potential in Bengaluru 369district, KarnatakaL.K. Adarsha, M. Mohan Kumar and D. Jennie Samuelnavaraj

Problems faced by the borrowers in utilization and acquiring of co-operative bank loans in Nagaland 374Keviu Shuya and Amod Sharma

The policy analysis matrix of maize cultivation in Andhra Pradesh 379M. Srikala

Economic analysis of gerbera cultivation in protected condition in south Gujarat 383Smita Kumari, Narendra Singh, D.J. Chaudhari and V.M. Thumar

Economic sustainability of systems of rice intensification (SRI) in Gumla district of Jharkhand 387Tulika Kumari, Binita Kumari, Priyanka Lal and Ritu Rathod

Review Article

Perception, utilization, and management of renewable natural resources by rural women: A brief review 390Jaspreet Kaur, Ritu Mittal, Varinder Randhawa

Theses Abstract 397

ABSTRACTThe present study aims at examining the financial feasibility analysis of pomegranate production in Solapur district of Maharashtra which happens to be the leading district in terms of production and area under cultivation of that crop. Data collected from primary sources have been analyzed with the set objectives using appropriate techniques and statistical tools. Pomegranate requires a high investment of capital for establishment of orchard which is estimated as ̀ 348473.17 per hectare. The establishment cost is found to be more in small-sized farms as compared to medium and large size groups. The maintenance cost during the gestation period has

rdbeen estimated as ̀ 184357.27 per hectare. The annual maintenance cost termed as recurring cost which has been incurred from 3 year onwards for maintenance of plants which comprises of the expenditure towards the variable factors and fixed factors. The small farmers are ahead of medium and large farmers in terms of application of variable factors whereas the total labour cost varies directly with the size of farms. The average yield of sample farms is found to be 9.38 tonnes per hectare which varies from year to

rd thyear during the period 3 to 12 year of the orchard age. The mean yield of small size farms is found to be highest at 10.70 tons per hectare and it is declining with the increase in size of pomegranate farms. The financial feasibility of investment in pomegranate orchards has been studied with four relevant criteria namely, net present value (NPV), benefit-cost ratio (B-C Ratio), the internal rate of returns (IRR) and payback period (PBP). The NPV, B-C ratio, IRR and PBP for all farms are ̀ 921035.57 per ha, 2.61, 48.23 percent and 4.1 years respectively which confirm that investment on pomegranate orchard is highly profitable and economically feasible in the study area.

KeywordsB-C ratio, financial feasibility, IRR, NPV, PBP, pomegranate.

JEL CodesC82, D24, D61.

1* 2G.D. Rede and K. Bhattacharyya

1 2Ph.D. Research Scholar and Professor, Department of Agricultural Economics, BCKV, Mohanpur-741252, (W.B).

*Correspondence author's email: [email protected]

Received: October 04, 2017 Revision Accepted: May 05, 2018

Financial Feasibility Analysis of Pomegranate Production in Solapur District of Maharashtra

Indian Journal of Economics and Development (2018) 14(2), 199-212

DOI: 10.5958/2322-0430.2018.00122.1

Indexed in Clarivate Analytics (ESCI)

www.soed.in

NAAS Score: 4.82 www.naasindia.org

UGC Approved

Manuscript Number: MS-17197

INTRODUCTIONPomegranate (Punica granatum) belonging to the

family Punicaceae is a very popular and remunerative fruit crop in arid and semi-arid regions of India (Jadhav & Sharma, 2007) with area and production is increasing with a faster pace in India (Chandra et al., 2006). Every part of pomegranate viz., fruit, flowers, leaves, sprouts, root, trunk bark, rind and seeds has economic value. Recent studies strengthen the grade of pomegranate as most important medicinal fruit crop (Seeram et al., 2006; Holland & Bar-Yaakov, 2008) and has pharmaceutical and nutraceutical values (Levin, 2006; Holland et al., 2009). In India during 2014-15 the area and production of pomegranate was 143140 ha and 1789310 metric tonnes

respectively (APEDA). Maharashtra accounts for maximum area about 99140 ha under this crop and 69.26 percent of the country's total pomegranate production. Solapur district of Maharashtra is the leading district in pomegranate cultivation and also known as the “Pomegranate City” for its enormous production. Pomegranate has its chief characteristics like drought resistant, hardy, low water requirement, less gestation period; it is deciduous in temperate region, while it is evergreen or partially deciduous in tropical and sub-tropical regions. It also gives good response for irrigation and modern package of practices. It flourishes in three bahars namely Mrug, Hast and Ambe in the year. Farmer may take any one of it, hence, this crop gives employment

199

throughout the year. A well-managed orchard gives better yield and net returns per hectare which is nearly 8 to 10 times more than the average per hectare income received from traditional cereal dominated cropping pattern of arid and semi-arid regions of the country, as well as in the Maharashtra state. The present study provides information about the various financial feasibility criteria's namely, Net Present Value (NPV), Benefit-Cost Ratio (B-C Ratio), Internal Rate of Returns (IRR) and Pay Back Period (PBP) of pomegranate production for different size groups, namely Group-I (small), II (medium) and III (large). This information would be helpful to the pomegranate growers in general and for decision making related to pomegranate production. In view of these evidences, the current study was undertaken to study the investment pattern in pomegranate orchard, to compute the costs and return structure in pomegranate cultivation and to examine cash flow analysis pomegranate orchard.METHODOLOGY

In the present dissertation work required data have been collected for the agricultural year 2014-15 in Solapur district of Maharashtra. The data was collected from primary and secondary sources to achieve the stated objectives. The primary data were collected from the selected pomegranate cultivators with intensive visits at farm level through personal interviews with the help of well-structured and pre-tested schedules. The sample farmers are further distributed into different farm size groups according to their size of operational holding, that is, small (less than or equal to 2 ha), medium (greater than 2 ha and less than or equal to 5 ha) and large (greater than 5 ha). Financial Feasibility Analysis

The discounted cash flow techniques have been employed to find out the technical feasibility and economic viability of investment on pomegranate orchard on the basis of criteria which are Net Present Value (NPV), Benefit-Cost Ratio (BCR), Internal Rate of Return (IRR) and Pay Back Period (PBP).a) Net present value (NPV)

Net present value is one of the measures of financial viability of the project. The difference between the present value of investment outlays and that of future net cash inflows is known as the net present worth. The criterion is presented below:

Where, Y = Net cash inflows in the year 'n'.n

r = Discount factor.I = Initial investmentn = Economic life of the pomegranate orchard.The decision rule associated with the Net Present

Value is, the project will be accepted if its value is positive and reject if its value is negative.

b) Benefit-Cost Ratio (BCR)Benefit-Cost ratio is the ratio of the discounted net

benefits to the discounted investment of the project. The benefit-cost ratio (BCR) will be worked out by using following formula:

It measures the present value of returns per rupee invested and it is a relative measure. The decision rule is that the project will be accepted if BCR is greater than one and will be reject when BCR is less than one. c) Internal rate of return (IRR)

The Internal Rate of Return can be defined as that discounted rate of return which makes the NPV is equal to zero. It represents the earning power of money used in the project over its project lifespan. The internal rate of return is estimated by interpolation of two cash flow streams with positive and negative net worth. The internal rate of return can be worked as follows.

The Internal Rate of Return is a relative measure. To accept the project, the calculated IRR should be greater than the ongoing opportunity cost of capital.d) Pay back period (PBP)

Pay Back Period is the one of the traditional and undiscounted measure of project analysis technique. It represents the length of time required for the stream of cash proceeds produced by the investment to be equal to the original cash outlay. When the cash flows are equal in all the years of the project life period, the following method will be used for calculation of payback period. RESULTS AND DISCUSSION

Collected data have been analysed with appropriate techniques and statistical tools for the set objectives. The results of the study are presented and discussed in this section.Investment Pattern and Establishment Cost of Pomegranate Orchards

Pomegranate is a perennial fruit crop and the plants start bearing two years after planting. During the productive life of pomegranate, it continues to bear fruits and yields which generates sizeable income to the growers. Establishment of basic infrastructure is the most essential aspect in pomegranate cultivation. It not only includes the expenses incurred for initial investment but also the nourishment of trees for two years till it starts bearing the fruits. Initially pomegranate crop requires the high investment of capital for establishment of orchard followed by maintenance cost up to bearing period. The

å=

--+=n

in IrYNPV

1

1)1(

outflowcashDiscounted

flowcashDiscountedBCR =

÷÷÷÷÷÷÷÷

ø

ö

çççççççç

è

æ

´÷÷÷

ø

ö

ççç

è

æ

+÷÷÷

ø

ö

ççç

è

æ

=

ratesdisvounttwotheat

flowscashtheofworthpresentthe

betweendifferenceAbsolute

ratediscount

loweratflowcash

ofworthPercent

ratesdiscount

twobetween

Difference

rate

discount

Lower

IRR

200

Indian J Econ Dev 14(2): 2018 (April-June)

total cost invested in the pomegranate orchard up to the bearing period (first two years) forms the establishment cost of orchard. Hence the expenditures made at different points of period makes it essential to split the total establishment cost into two subparts viz. initial costs and maintenance costs. The initial investment includes the expenditure incurred on the rental value of the land, costs on bore well and accessories, sprayer, planting material (pomegranate seedlings) and charges for digging of pits and planting, staking, fencing, installation of drip set etc. The maintenance costs are the costs incurred in the maintenance of the orchard till the time of bearing that is up to two years after planting and these includes expenditure on labour, fertilizers, manures, plant protection chemicals, irrigation, etc. The per hectare cost of establishment in pomegranate orchard is presented in Table 1. The overall cost of establishment of per hectare of pomegranate orchard has been estimated as `348473.17. Out of the total establishment cost (A+B), the initial investment constitutes 47.10 percent and the maintenance cost up to the bearing period (two years) accounted for 52.90 percent respectively. The borewell and accessories with drip irrigation system, as a basic input in water scarcity areas, emerge the main cash component in the initial investment. The initial costs of investment, the cost on bore well and accessories accounted for major share of 18.01 percent (`62746.08) followed by installation of drip irrigation set (12.41 percent), digging of pits and planting (7.02 percent), Planting material (4.82 percent), Rental value of the land (3.09percent) and staking and fencing (1.02 percent) respectively.

The cost on sprayer contributes meagre share in the initial investment with 0.73 percent. The perusal of Table 1 also indicates that the average establishment cost per

hectare declines with increase in size of holding. It is usually, the cultivators with small holdings have a tendency of setting maximum plants per unit of area to get higher yields. This leads to increase the expenses on all items except the rental value of the land. The maintenance cost upto bearing period (two years) constitutes major part in the total establishment cost (52.90 percent) of pomegranate orchard. The maintenance cost for year I and II inat the overall level are estimated to be ̀ 85444.67 and 98912.61 accounting for 24.52 and 28.38 percent of the total establishment cost, respectively. Among the three categories of pomegranate growers total maintenance cost upto bearing period (two years) for small farm category is highest for obvious reason. The similar results were also reported by Khunt et al. (2003); Patel and Pundir (2016).Annual Maintenance Cost of Pomegranate Orchard during Gestation Period

The average annual maintenance cost per hectare incurred on pomegranate orchards during gestation period in the study area has been studied for different farm categories.Small farms

It is observed from the Table 2, that the average per hectare total cost incurred on maintenance of pomegranate orchard by the growers during the establishment period is `191212.82 for selected small farms in Solapur district of Maharashtra. The share of variable cost and fixed cost to the total cost has been estimated to be 82.66 and 17.34 percent respectively. The percentage share of material cost and labour cost under the variable cost are found to be 47.15 and 33.12 percent respectively. Among the labour cost, the cost on loosening of soil around the trunk, and formation of bed for drip

Particulars Category of respondents Overall Percent

Small Medium Large

Initial investment

Rental value of the land 10550.00 10658.00 10732.47 10752.27 3.09

Bore well and accessories 65054.25 60140.00 60258.00 62746.08 18.01

Planting material (Seedlings) 18850.00 15505.00 14847.00 16803.78 4.82

Sprayer 3025.00 2185.00 2065.00 2542.30 0.73

Digging of pits and planting 30842.00 23300.00 21785.00 24465.82 7.02

Staking and fencing 4020.00 3325.00 3141.20 3564.14 1.02

Installation of drip set 45245.00 41682.00 40745.80 43241.51 12.41

Total 177586.25 156795.00 153574.47 164115.90 47.10

Maintenance cost up to bearing period

I year 87913.84 86426.55 82289.72 85444.67 24.52

II year 103298.98 100417.90 96393.69 98912.61 28.38

Subtotal (I+II) 191212.82 186844.45 178683.41 184357.27 52.90

Total establishment Cost (A+B) 368799.07 343639.45 332257.88 348473.17 100.00

Table 1. Cost of establishment in pomegranate orchards(`/ha)

201

Rede and Bhattacharyya: Financial feasibility analysis of pomegranate production in Solapur district of Maharashtra

irrigation accounts for a major share with 5.14 percent (`9827.88) followed by FYM application 5.08 percent (`9706.42), weeding 4.34 percent (`8291.72), Plant protection chemical sprayings 3.66 percent (`6996.80), intercultural operations 2.98 percent (`5691.97), training and staking of plants also 2.98 percent (`5696.70), fertilizer application 2.72 percent (`5207.69), irrigation, watch ward 2.38 percent (`4544.55) and removal of flowers and suckers 2.14 percent (`4093.22) to the total maintenance cost. The cost of miscellaneous expenditure and gap filling are minor shares with 1.05 percent and 0.66 percent, respectively.

In the material cost, the expenditure incurred on fertilizers is found to have a major share with 19.50 percent (`37284.58) followed by cost on manures 15.54

percent (`29705.92). The cost incurred on plant protection chemicals (insecticides, fungicides, bactericides and weedicides etc.) accounted 10.01 percent (`19130.87) of total cost is an important part of pomegranate cultivation. Pomegranate seedlings cost for gap filling operation in II year of gestation period is accounted a fewer share of 1.15 percent to the total maintenance cost. Interest on working capital is estimated as 2.39 percent of the total cost. In the fixed cost, the cost on the rental value of land is found to be a major share with 14.60 percent followed by interest on fixed capital with 1.77 percent. The cost on depreciation and land revenue were very less share with 0.86 percent and 0.13 percent, respectively. The total fixed cost accounted for ̀ 16550.45 and `16615.20 for the year I and II, comprises 17.34

Sl. No.

Particulars Year I Year II Total Percent

I. Variable cost

A. Labour cost

1. Intercultural operations 2602.63 3089.34 5691.97 2.98

2. Earthing up and formation of bed for drip irrigation 4421.03 5406.85 9827.88 5.14

3. Gap filling 0.00 1265.34 1265.34 0.66

4. FYM application 4136.54 5569.88 9706.42 5.08

5. Fertilizer application 2327.69 2880.00 5207.69 2.72

6. Weeding 3698.52 4593.20 8291.72 4.34

7. Training and staking of plants 2546.10 3150.60 5696.70 2.98

8. Removal of flowers and suckers 1812.53 2280.69 4093.22 2.14

9. Plant protection chemical sprayings 3220.50 3776.30 6996.80 3.66

10. Irrigation, watch and ward 2085.61 2458.94 4544.55 2.38

11. Miscellaneous 902.65 1105.00 2007.65 1.05

Total labour cost (A) 27753.8 35576.14 63329.94 33.12

B. Material cost

1. Seedlings for gap filling 0.00 2200.00 2200.00 1.15

2. Manure 14561.1 15144.77 29705.92 15.54

3. Fertilizers 16697.9 20586.65 37284.58 19.50

4. Plant protection chemicals (Insecticides, fungicides, bactericides and weedicides)

9280.00 9850.87 19130.87 10.01

5. Others 950.00 880.00 1830.00 0.96

Total material cost (B) 41489.0 48662.29 90151.37 47.15

C. Interest on working capital @ 7.0 percent 2120.50 2445.36 4565.86 2.39

Total variable cost (A + B + C) 71363.3 86683.78 158047.1 82.66

II. Fixed cost

1. Rental value of land 13956.0 13956.07 27912.14 14.60

2. Land revenue 120.00 120.00 240.00 0.13

Sub total 14076.0 14076.07 28152.14 14.72

3. Interest on fixed capital @ 12 percent 1689.13 1689.13 3378.26 1.77

4. Depreciation 785.25 850.00 1635.25 0.86

Total fixed cost 16550.4 16615.20 33165.65 17.34

III Total cost (I+II) 87913.8 103298.9 191212.8 100.00

Table 2. Maintenance cost of pomegranate orchard during gestation period in small farms (`/ha)

202


percent to total cost of maintenance during the gestation period in small farms.Medium farms

The average per hectare total cost incurred on maintenance of pomegranate orchard by the growers during the establishment period is estimated as `186844.45 for selected medium farms in Solapur district of Maharashtra (Table 3). The share of the variable costs and fixed costs to the total cost have been estimated as 83.30 percent (`155644.18) and 16.70 percent (`31200.27) respectively. In the case of variable cost, the expenditure incurred on material cost alone was 45.18 percent (`84417.31) followed by labour cost to carry out different operations in the orchard with 35.70 percent (`66704.84).Among the labour cost, the cost on FYM application accounted for major share 5.54 percent

(`10342.32) followed by earthing up and formation of bed for drip irrigation with 5.46 percent (`10193.68), weeding 4.64 percent (`8661.16), Plant protection chemical sprayings 3.96 percent (`7405.70), training and staking of plants 3.29 percent (`6155.42), intercultural operations 3.19 percent (`5960.65), fertilizer application 2.89 percent (`5408.32), irrigation, watch ward 2.55 percent (`4769.88) and removal of flowers and suckers 2.34 percent (`4378.90). The cost of miscellaneous expenditure and gap filling were minor share with 1.04, and 0.80 percent, respectively. In the material cost, the expenditure incurred on fertilizers accounted major share with 18.96 percent(`35420.89) followed by cost on manures 15.22 percent (`28435.10). The cost incurred on plant protection chemicals (insecticides, fungicides, bactericides and weedicides etc.) accounts for 8.81

Sr. No. Particulars I Year II Year Total Percen

I. Variable Cost

A. Labour cost

1. Intercultural Operations 2784.65 3176.00 5960.65 3.19


3. Gap filling 0.00 1489.64 1489.64 0.80

4. FYM application 4376.90 5965.42 10342.32 5.54


6. Weeding 3975.96 4685.20 8661.16 4.64

7. Training and Staking of Plants 2790.00 3365.42 6155.42 3.29

8. Removal of flowers and Suckers 2056.36 2322.54 4378.90 2.34

9. Plant protection chemical Sprayings 3485.20 3920.50 7405.70 3.96


11. Miscellaneous 864.58 1074.59 1939.17 1.04


B. Material cost


2. Manure 13434.36 15000.73 28435.10 15.22

3. Fertilizers 16469.18 18951.71 35420.89 18.96

4. Plant protection chemicals (Insecticides, Fungicides, Bactericides and Weedicides)

8055.75 8400.00 16455.75 8.81

5. Others 1025.56 880.00 1905.56 1.02




II. Fixed Cost


2. Land revenue 120.00 120.00 240.00 0.13

Sub Total 13331.47 13331.47 26662.94 14.27

3. Interest on Fixed Capital @ 12 percent 1599.78 1599.78 3199.55 1.71

4. Depreciation 657.28 680.50 1337.78 0.72

Total fixed cost 15588.53 15611.75 31200.27 16.70

III Total cost (I+II) 86426.55 100417.9 186844.4 100.00

Table 3. Maintenance cost of pomegranate orchard during gestation period in medium farms(`/ha)

203


percent (`16455.75) of total cost. The cost on seedlings for gap filling were meagre with 1.18 percent and interest on working capital accounted for 2.42 percent of the total cost. Among the fixed costs, the cost on the rental value of land accounts for a major share with 14.14 percent followed by interest on fixed capital with 1.71 percent. The cost on depreciation and land revenue were minor share with 0.72, and 0.13 percent, respectively. The total fixed cost incurred duringthe year I and II, the period of gestation is `15588.53 and 15611.75 per hectare respectively. This accounts for 16.70 percent (`31200.27) of the total maintenance cost of pomegranate orchard in medium farms. The similar results were also reported by Sahana et al. (2017).Large farms

It could be observed from the Table 4, that the average per hectare total cost incurred on maintenance of pomegranate orchard by the growers during the

establishment period is `178683.41 for selected large farms in Solapur district of Maharashtra. In the total cost, the share of variable costs and fixed costs is estimated as 83.92 percent (`149946.96) and 15.97 percent (`28536.45) respectively. In the case of variable cost, the expenditure incurred on material cost alone is 45.18 percent (`84417.31) followed by labour cost to carry out different operations in the orchard with 35.70 percent (`66704.84).Among the labour cost, the cost on FYM application accounts for a major share 6.29 percent (`11237.94) followed by earthing up and formation of bed for drip irrigation with 5.81 percent (`10733.63), weeding 5.01 percent (`8949.75), plant protection chemical sprayings 4.21 percent (`7515.19), training and staking of plants 3.63 percent (`6493.26), intercultural operations 3.45 percent (`6172.25), fertilizer application 3.31 percent (`5921.70), irrigation, watch ward 2.81

Sr. No. Particulars Year I Year II Total Percent

I. Variable costA. Labour cost1. Intercultural operations 2892.25 3280.00 6172.25 3.452. Earthing up and formation of bed for drip irrigation 4758.74 5614.54 10373.28 5.813. Gap filling 0.00 1658.63 1658.63 0.934. FYM application 4695.20 6542.74 11237.94 6.295. Fertilizer application 2669.10 3252.60 5921.70 3.316. Weeding 4084.12 4865.17 8949.29 5.017. Training and staking of plants 3078.65 3414.61 6493.26 3.638. Removal of flowers and suckers 2086.54 2563.21 4649.75 2.609. Plant protection chemical sprayings 3825.00 3890.19 7515.19 4.2110. Irrigation, watch and ward 2278.65 2741.00 5019.65 2.8111. Miscellaneous 950.00 1235.62 2185.62 1.22

Total labour cost (A) 31318.2 39058.3 70176.56 39.27B. Material cost1. Seedlings for gap filling 0.00 2200.00 2200.00 1.232. Manure 12431.8 14011.00 26442.83 14.803. Fertilizers 13450.5 15465.9 28916.46 16.184. Plant protection chemicals (Insecticides, fungicides, bactericides

and weedicides)7842.11 8149.83 15991.94 8.95

5. Others 850.00 980.00 1830.00 1.02Total material cost (B) 34574.4 40806.7 75381.23 42.19

C. Interest on working capital @ 7.0 percent 2147.69 2241.48 4389.17 2.46Total variable cost (A + B + C) 68040.4 82106.5 149946.9 83.92

II. Fixed cost1. Rental value of land 12025.9 12025.9 24051.89 13.462. Land revenue 120.00 120.00 240.00 0.13

Sub total 12145.9 12145.9 24291.89 13.593. Interest on fixed capital @ 12 percebt 1457.51 1457.51 2915.03 1.634. Depreciation 645.86 683.67 1329.53 0.74

Total fixed cost 14249.3 14287.1 28536.45 15.91III. Total cost (I+II) 82289.7 96393.6 178683.4 100.00

Table 4. Maintenance cost of pomegranate orchard during gestation period in large farm(`/ha)

204


percent (`5019.65) and removal of flowers and suckers 2.60 percent (`4649.75).

The cost of miscellaneous expenditure and gap filling were minor share with 1.22 and 0.93 percent, respectively. In the material cost, the expenditure incurred on fertilizers accounted major share with 16.18 percent (`28916.46) followed by cost on manures 14.80 percent(`26442.83). The cost incurred on plant protection chemicals (insecticides, fungicides, bactericides and weedicides, etc.) accounted 8.95 percent (`15991.94) of total cost. The cost on seedlings for gap filling were meagre with 1.23 percent and interest on working capital accounted for 2.46 percent of the total cost. Among the fixed costs, the cost on the rental value of land accounts for a major share with 13.46 percent(`24051.89) followed

by interest on fixed capital with 1.63 percent (`2915.03). The cost on depreciation and land revenue were minor share with 0.74 and 0.13 percent, respectively. The total fixed cost incurred during year I and II of gestation period was `14249.32 and 14287.13 per hectare respectively accounts for comprising 15.97 percent (`28536.45) of the total cost of maintenance of pomegranate orchard in large farms.Overall farms

It could be observed from the Table 5, that the average per hectare total cost incurred on maintenance of pomegranate orchard by the growers during the establishment period was ̀ 184357.27 for selected overall 150 farms in Solapur district of Maharashtra. Among the total cost, share of variable cost 83.14 percent

Sr. No. Particulars Year I Year II Total Percent

I. Variable cost

A. Labour cost

1. Intercultural Operations 2745.08 3021.29 5766.37 3.13


3. Gap filling 0.00 1460.32 1460.32 0.79

4. FYM application 4562.14 6235.85 10797.99 5.86


6. Weeding 3892.77 4665.69 8558.46 4.64

7. Training and staking of plants 2811.25 3264.16 6075.41 3.30

8. Removal of flowers and suckers 1960.20 2294.58 4254.78 2.31

9. Plant protection chemical sprayings 3492.00 3780.21 7272.21 3.94


11. Miscellaneous 893.47 1085.15 1978.62 1.07


B. Material cost


2. Manure 13247.32 14518.20 27765.52 15.06

3. Fertilizers 15632.91 17845.26 33478.17 18.16

4. Plant protection chemicals (Insecticides, fungicides, bactericides and weedicides)

8275.65 8749.24 17024.89 9.23

5. Others 964.21 905.04 1869.25 1.01




II. Fixed cost


2. Land revenue 120.00 120.00 240.00 0.13

Sub Total 13245.47 13245.47 26490.94 14.37

3. Interest on fixed capital @ 12 percent 1589.46 1589.46 3178.91 1.72

4. Depreciation 689.31 725.14 1414.45 0.77

Total fixed cost 15524.24 15560.07 31084.30 16.86

III Total cost (I+II) 85444.67 98912.61 `184357.27 100.00

Table 5. Maintenance cost of pomegranate orchard during gestation period in overall farms(`/ha)

205


(`153272.97) followed by the fixed cost with 16.86 percent (`.31084.30). In the variable cost, the expenditure on material cost alone is 44.66 percent (`82337.83) followed by labour cost with 36.05 percent (`66461.85). Among the labour cost, the cost on FYM application accounts for a major share 5.86 percent (`10797.99) followed by earthing up and formation of bed for drip irrigation with 5.48 percent (`10102.51), weeding 4.64 percent (`8558.46), plant protection chemical sprayings 3.94 percent (`7272.21), training and staking of plants 3.30 percent (`6075.41), intercultural operations 3.13 percent (`5766.37), fertilizer application 2.98 percent (`5485.64), irrigation, watch ward 2.55 percent (`4709.54) and removal of flowers and suckers 2.31 percent (`4254.78). The cost of the miscellaneous expenditure and gap filling were minor share with 1.07 and 0.79 percent, respectively.

In the material cost, the expenditure incurred on fertilizers is found to have a major share with 18.16 percent (`33478.17) followed by cost on manures 15.06 percent (`27765.52). The cost incurred on plant protection chemicals (insecticides, fungicides, bactericides and weedicides etc.) accounted 9.23 percent (`17024.89) of total cost. Pomegranate seedlings cost for gap filling operation in II year of gestation period accounts fewer share 1.19 percent to total cost on maintenance. Interest on working capital accounted for 2.43 percent of the total cost. In the fixed cost, the cost on the rental value of land accounts for a major share with 14.24 percent followed by interest on fixed capital with 1.72 percent. The cost on depreciation and land revenue were very less share with 0.77, and 0.13 percent, respectively. The total fixed cost accounts for `15524.24 and 15560.07 for year I and II, comprising 16.86 percent to total cost of maintenance during the gestation period in overall farms.Annual Maintenance Cost of Pomegranate Orchard during the Bearing Period

The maintenance cost is the recurring cost which will be incurred after the establishment of the orchard normally from third year onwards for maintenance of the plants so that good yield can be obtained over the economic lifespan of the plants. The maintenance cost comprised of the expenditure towards the use of labour and other material inputs along with fixed costs. The average annual maintenance cost incurred for pomegranate orchard in the different categories of pomegranate growers in Solapur district during bearing period is presented in Table 6.

The average annual maintenance cost incurred by pomegranate growers is estimated as ̀ 121341.06. Among the total maintenance cost, the total variable costs in overall constituted a major share of 84.00 percent(`104348.08) followed by the fixed cost with 14.00 percent(`16992.98) during the bearing period in Solapur district. Between three categories of

pomegranate growers the total variable cost of small farmers (`110842.60) shows the highest cost followed by medium (`10297.60) and large (`98619.20) categories. In the total variable costs of overall farms, the material cost is accounts for a major share of 49.21 percent (`59706.60) followed by labour cost with 35.31 percent (`42850.07) of the total maintenance cost. Among the labour costs of overall farms, the cost on pruning operation (main + light) is found to be maximum with 10.51 percent (`12751.47) followed by harvesting 3.86 percent (`4687.37), weeding 3.84 percent (`4657.59), FYM application 2.94 percent (`3565.12), earthing up 2.88 percent (`3491.04), intercultural operations 2.72 percent (`3296.52), fertilizer application 1.97 percent (`2394.10) and miscellaneous cost 0.83 percent to the total cost. The study reveals that the expenses on labour input vary directly with the size of farms. In fact, the small size farms utilize their family members intensively for maintenance of orchard as well as timely application of scare inputs which limit the pomegranate production.

The expenditure incurred on fertilizers accounts for 18.93 percent (`22974.28) is found to be the major share to the total maintenance cost followed by manures with 15.07 percent (`18285.98) and cost of plant protection chemicals 14.29 percent (`17342.00). The interest on working capital accounts for a share of 1.48 percent of the total cost. The input pattern used by pomegranate growers reveals that the total variable cost is accounted for 49 percent of total cost. Cost of variable inputs per hectare of pomegranate crop is inversely proportion to the size of farms. Farmers in small size group relatively aggressive in adopting modern technologies and practices compared to the farmers belong to the higher size groups. The fixed cost per hectare is found to be maximum (`17665.40) in small size group. Among the fixed costs, the rental value of land accounted a major share with 11.78 percent(`1491.66) followed by interest on fixed capital 1.46 percent(`1765.80), depreciation 0.67 percent and land revenue 0.10 percent. The total fixed cost also decreases as size of holding increases, that is, the total fixed cost for small farms was found to be maximum (`17665.40) followed by medium (`16279.70) and large (`15822.73). The average per hectare maintenance cost (total cost) incurred by pomegranate growers during bearing period was `121341.06 in overall farms of Solapur district. Between the three farm categories of pomegranate growers, the maintenance cost decreases as size of holding increases, that is, total cost for small farm was highest (`128508.00) followed by medium (`119237.30) and for large farms (`114441.93).Yield and Return Structure of Pomegranate Orchards

Yield and return structure of selected pomegranate orchards in Solapur district is presented in Table 7. The economic yield starts from third year after planting of pomegranate and it continues upto the twelfth year of orchard establishment in the study area. It is revealed that

206


the overall average yield of pomegranate is 9.38 tonnes per hectare. The overall per hectare average yield increased from third year up to the eight-year and then onwards the yield stated decreasing up to twelfth year. The farms in the study area have received stable yield

th thfrom 5 to 10 year beyond which the yield has declined. The average yield of the sample farms was found to be as

thhigh as 12.02 tonnes per hectare at 8 year. Average yield of different size-groups at different years have been estimated. The mean yield in small size group is found to be 10.70 tonnes per hectare which is highest compared to the medium and large size groups. The average yield is inversely related to the size of the farms in the study area which can be explained as the farmers in lower size group are ahead of the growers in medium and large groups in terms of adoption of improved technologies and practices. The overall average return per hectare has been estimated as ̀ 401801.34. The average return of small size

Sr. No Particulars Category of Respondents Overall Percent

Small Medium Large

I. Variable cost

A. Labour cost

1 Pruning (Main + Light) 13361.36 12856.40 12350.30 12751.47 10.512 Intercultural Operations 3086.13 3347.77 3745.62 3296.52 2.723 Earthing up 3165.20 3458.50 3887.54 3491.04 2.88

4 FYM application 2960.85 3728.95 4080.14 3565.12 2.94

5 Fertilizer application 2056.42 2266.32 2774.50 2394.10 1.976 Weeding 4174.65 4930.54 5124.89 4657.59 3.84

7 Plant protection chemical Sprayings 3742.67 4150.32 4776.10 4196.50 3.46

8 Irrigation, watch and ward 2209.83 2712.47 3547.28 2801.15 2.31

9 Harvesting 4097.30 4885.50 5385.70 4687.37 3.86

10 Miscellaneous 981.57 1020.85 1082.39 1009.21 0.83Total labour cost (A) 39835.96 43357.62 46754.46 42850.07 35.31

B. Material cost

1 Manure 21928.00 17245.36 15982.47 18285.98 15.07

2 Fertilizers 26582.41 22456.98 20047.21 22974.28 18.93

3 Plant protection chemicals ( Insecticides, Fungicides, Bactericides and Weedicides)

19145.63 17254.00 13056.14 17342.00 14.29

4 Others 1352.00 984.68 1024.36 1104.34 0.91

Total material cost (B) 69008.04 57941.02 50110.18 59706.60 49.21

C. Interest on working capital @ 7 percent 1998.60 1658.96 1754.56 1791.41 1.48

Total variable cost (A + B + C) 110842.6 102957.6 98619.20 104348.0 86.00II. Fixed cost

1 Rental value of land 14956.07 13765.00 13247.26 14291.66 11.78

2 Land revenue 120.00 120.00 120.00 123.33 0.10Sub Total 15076.07 13885.00 13367.26 14414.99 11.88

3 Interest on Fixed Capital @ 12 percent 1809.13 1666.20 1604.07 1765.8 1.46

4 Depreciation 780.20 728.50 851.40 812.19 0.67Total fixed cost 17665.40 16279.70 15822.73 16992.98 14.00

III. Total cost (I+II) 128508.0 119237.3 114441.9 121341.0 100.00

rdTable 6. Annual maintenance cost of pomegranate orchard during the bearing period (3 year onwards)(`/ha)

group has been estimated to be `481297.81 per hectare which is highesr compared to other size groups for obvious reason.Cash Flow Analysis for Different Size Farms of Pomegranate OrchardSmall farms

The cost structure and returns obtained in small farms of pomegranate orchards is presented in Table 8. The initial investment on pomegranate per hectare is `177586.25. The maintenance cost in the first of gestation period has been estimated as `87913.84 which is increased up to `103298.98 in second year. The maintenance cost per ha in pomegranate orchard from the third year is `128508.00 which is assumed to be constant until the twelfth year of orchard establishment. The cash flows are detracted with the estimated values of per hectare of pomegranate yields which are varied due to variation of yields at different years. For evaluation of Net

207


Year Cashoutflow

Cash inflow Net cash flow Discounted factor(12 percent)

Discountednet cash flow

1 265500.09 0.00 -265500.09 0.89 -236295.08

2 103298.98 0.00 -103298.98 0.79 -81606.203 128508.00 303139.53 174631.53 0.71 123988.39

4 128508.00 443720.93 315212.93 0.63 198584.14

5 128508.00 476330.23 347822.23 0.56 194780.45

6 128508.00 517077.91 388569.90 0.50 194284.95

7 128508.00 562433.72 433925.72 0.45 195266.57

8 128508.00 626058.14 497550.13 0.40 199020.059 128508.00 602441.86 473933.86 0.36 170616.19

10 128508.00 539448.84 410940.83 0.32 131501.07

11 128508.00 387906.98 259398.97 0.28 72631.71

12 128508.00 354420.00 225912.00 0.25 56478.00

Table 8. Cash flow analysis of pomegranate orchard in Solapur district for small farms(`/ha)

Age of the orchard (Years)

Small Medium Large Overall yield

Yield(t/ha)

Value(`)

Yield(t/ha)

Value(`)

Yield(t/ha)

Value(`)

Yield(t/ha)

Value(`)

rd3 6.74 303139.53 5.93 243594.87 5.22 212065.01 5.66 242578.65th4 9.86 443720.93 7.58 311368.52 6.46 262488.58 7.73 331320.54th5 10.59 476330.23 9.80 402607.13 8.85 359996.13 9.37 401490.79th6 11.49 517077.91 10.23 420447.07 10.62 431919.48 10.42 446497.00th7 12.50 562433.72 11.19 459944.30 12.28 499396.81 11.77 504273.86th8 13.91 626058.14 11.49 472239.00 11.65 473697.40 12.02 514970.90th9 13.39 602441.86 12.27 504270.56 10.61 431517.93 11.90 509793.48

th10 11.99 539448.84 10.76 442381.92 9.62 391127.13 9.77 418453.84th11 8.62 387906.98 8.60 353460.00 8.25 335248.86 8.23 352868.65th12 7.88 354420.00 6.72 276077.43 6.52 265103.20 6.90 295765.70

Average 10.70 481297.81 9.46 388639.08 9.01 366256.05 9.38 401801.34

Table 7. Yield and return structure of pomegranate orchards in Solapur district

Present Value (NPV), a rate of 12 percent has been assumed to represent the cost of capital for pomegranate cultivation which is generally changed by the commercial banks for advancing loans to the farmers. The streams of costs and benefits during establishing period (two years) and bearing period (third to twelfth years) have been annualized at 12 percent discount rate. These present values of the future return are also known as opportunity cost of capital.Medium farms

The cash flows in medium farms of pomegranate orchards is presented in Table 9. The initial investment on pomegranate per hectare is ̀ 156795.00. The maintenance cost in the first of gestation period has been estimated as `86426.55 which increased up to `100417.90 in the second year. In the bearing period of orchard it is `119237.30 in third year and it remained same until the

twelfth year of orchard establishment. The cash flows are diminished with the estimated values of per hectare of pomegranate yields which are varied due to variation of yields at different years. The discounted net return at 12 percent in second year is negative ̀ -79330.14 and during third year it is positive `121042.67. Later it decreased up to ̀ 39210.03 during twelfth year.Large farms

The streams of costs and benefits during establishing period (two years) and bearing period (third to twelfth years) in large farms of pomegranate orchards is presented in Table 10. The initial investment on pomegranate per hectare is ̀ 153574.47. The maintenance cost in the first of gestation period has been estimated as `82289.72 which is increased up to `96393.69 in the second year. The cost per ha in pomegranate orchard from the third year is ̀ 114441.93 which remains same until the

208


Year Cash outflow Cash inflow Net cash flow Discounted factor(12 percent)


1 235864.1862 0.00 -235864.19 0.89 -209919.13

2 96393.69 0.00 -96393.69 0.79 -76151.02

3 114441.93 212065.01 97623.08 0.71 69312.38

4 114441.93 262488.58 148046.64 0.63 93269.39

5 114441.93 359996.13 245554.20 0.56 137510.35

6 114441.93 431919.48 317477.55 0.50 158738.77

7 114441.93 499396.81 384954.88 0.45 173229.70

8 114441.93 473697.40 359255.46 0.40 143702.19

9 114441.93 431517.93 317076.00 0.36 114147.36

10 114441.93 391127.13 276685.20 0.32 88539.26

11 114441.93 335248.86 220806.93 0.28 61825.94

12 114441.93 265103.20 150661.27 0.25 37665.32

Table 10. Cash flow analysis of pomegranate orchard in Solapur district for large farms

(`/ha)

Year Cash outflow Cash inflow Net cash flow Discounted factor (12 percent)


1 243221.55 0.00 -243221.55 0.89 -216467.182 100417.90 0.00 -100417.90 0.79 -79330.143 119237.30 243594.87 124357.57 0.71 88293.874 119237.30 311368.52 192131.22 0.63 121042.675 119237.30 402607.13 283369.84 0.56 158687.116 119237.30 420447.07 301209.77 0.50 150604.897 119237.30 459944.30 340707.01 0.45 153318.158 119237.30 472239.00 353001.70 0.40 141200.689 119237.30 504270.56 385033.26 0.36 138611.98

10 119237.30 442381.92 323144.62 0.32 103406.28

11 119237.30 353460.00 234222.70 0.28 65582.36

12 119237.30 276077.43 156840.13 0.25 39210.03

Table 9. Cash flow analysis of pomegranate orchard in Solapur district for medium farms(`/ha)

twelfth year of orchard establishment. The cash inflows from third-year increased up to seventh year when it is `49939.81. The cash flows are lessened with the estimated values of per hectare of pomegranate yields which are varied due to variation of yields at different years. The discounted net return at 12 percent in second year is found to be negative (`-76151.02). During third year it is ̀ 69312.38 and later it decreased up to ̀ 37665.32 during twelfth year.Overall farms

The cash flow for overall farms of pomegranate orchards is presented in Table 11. In the overall farms, the initial investment on pomegranate per hectare is `164115.90. The maintenance cost in the first of gestation period is estimated as ̀ 85444.67 which is increased up to `98912.61 in the second year. The maintenance cost per

ha during the bearing period of pomegranate orchard from the third year is `121341.06 which is assumed to be constant until the twelfth year of orchard establishment. The cash flows are reduced with the estimated values of per hectare of pomegranate yields which are varied due to variation of yields at different years. The discounted net return at 12 percent in second year is found to be negative (`-78140.96). During third year it is `86078.69 which is increased up to `172319.76 in the seventh year and later on which is decreased up to `43606.16 during twelfth year. The present value of the future return was calculated at twelfth percent discount rate (opportunity cost of capital).Financial Feasibilities of Investments in Pomegranate Orchard

Pomegranate is a perennial fruit crop, once

209


established continues to bear up to 12 to 15 years. Regular returns are expected only after two years of gestation period. Further this requires enormous resources and income is spread over a number of years. Therefore, costs and returns have to be analyzed carefully to test the worthiness of investment in pomegranate enterprise. This analysis helps farmers in decision making.

The economic worthiness of an investment on pomegranate orchard has been tested by estimated values of various parameters along with sensitivity analysis of an investment under varying situation. Hence, to analyse the financial feasibility of pomegranate orchards, the parameters of project appraisal such as net present value, benefit-cost ratio, internal rate of return and payback period, have been employed in pomegranate crop. In analyzing the investment feasibility the establishment costs, maintenance costs and gross returns are considered at 12 percent discount rate representing the opportunity cost of capital. Net present value (NPV)

The net present value is the present value of net returns of the project discounted at the opportunity cost of capital. In other words, the net present worth of an investment is the difference between the present value of series of inflows (returns) and outflows (costs) over the economic life period of the pomegranate enterprise. It helps to evaluate the benefits accrued and costs incurred during the project life. One advantage of NPV is that it gives an idea about surplus money that would be generated by a project at a given discount rate. It is an absolute measure and varies with level of investment and discount rates. The overall NPV of pomegranate crop on per hectare at 12 percent discount rate is found to be `921035.57. It is also revealed from the Table 12 that the magnitude of NPV is inversely related to the size of farms. The formal selection criterion of NPV is to accept all the projects with positive values. Applying this principle, net present value of pomegranate crop clearly indicates its financial reliability and economic feasibility in the study area. Similar results were found by Ravikumar et al. (2011); Koujalgi et al. (2013).Benefit-cost ratio (B-C Ratio)

Benefit-cost ratio is another tool for appraising the worthiness of investment and it helps to ascertain the profitability of an enterprise. This criterion indicates the rate of return per rupee invested in pomegranate enterprise. In pomegranate cultivation, initial investment was to be made to establish the orchard and maintenance costs are to be incurred during subsequent years. During these years of maintenance, the cash inflows or benefits have exceeded the cash outflows or costs and therefore the costs in the coming years will be met out of returns obtained. The decision in the B-C ratio framework is to select the projects where the ratio is more than one. In the overall farms B-C ratio has been estimated as 2.61 at 12 percent discount rate which satisfies the rule indicating the worthiness of investment on pomegranate orchard.

The benefit-cost ratio indicates expected returns for each rupee of investment in pomegranate crop. Thus, it can be concluded that investment in pomegranate orchard is economically viable and financially feasible in Solapur district, the area under study.Internal rate of return (IRR)

Internal rate of return is suggested to be very suitable measure for evaluating the profitability of investment on different projects. The IRR is the rate at which the net present worth of project is zero or the discounted costs are equal to the discounted returns. This represents the rate of return over the life period of the project. This criterion measures the rate of return that can be realized by investment in pomegranate orchard. Hence, the IRR indicates an important basis of investment and better than other criteria of evaluation. The value of IRR generally depends on the magnitude of returns realized in each year over the economic life period and more particularly in the initial years of pomegranate enterprise. The internal rate of return has been computed by interpolating two discount rates (lower and higher discount rates). It is superior over the other measures since it takes into consideration the reinvestment opportunities of enterprises during the lifespan.

The formal selection criterion of IRR is to accept the projects with IRR more than the opportunity cost of capital. The internal rate of return of the overall farms has been estimated as 48.23 percent. Similar results were found by Patel and Pundir (2016). The IRR represents the maximum rate of interest at which the growers can borrow from lending agencies and invest on pomegranate orchard. In other words, it is the earning power of money invested on pomegranate during its lifespan. Since IRR is more than the opportunity cost of capital it clearly indicates that investment on pomegranate orchard is a financially sound and economically viable proposition in the study area.Payback period (PBP)

The payback period refers to the time required for the project to pay for itself. In other words, it is the period required to recover the establishment cost of the orchard. In the present study the payback period for pomegranate orchards is about 4.1 years in the overall farms. This clearly indicates that about four years will require getting back the establishment cost of orchard. Thus on the basis of criteria like NPV, B-C Ratio, IRR and PBP used for judging the financial worth whileness of pomegranate cultivation it can be concluded the investment on pomegranate is highly profitable and economically feasible in the study area. It is also observed that from Table 12 that small farm has an edge over medium and large farms showing relative aggressiveness in adopting improved technologies and practices. CONCLUSIONS

The total establishment cost of pomegranate orchard is divided into two parts, that is, initial costs where expenses are incurred for preparation of land and

210


Year Cash outflow Cash inflow Net cash flow Discounted factor(12 percent)

Discounted net cash flow

1 249560.56 - -249560.57 0.89 -222108.90

2 98912.61 - -98912.61 0.79 -78140.96

3 121341.06 242578.65 121237.59 0.71 86078.69

4 121341.06 331320.54 209979.48 0.63 132287.07

5 121341.06 401490.79 280149.73 0.56 156883.85

6 121341.06 446497.00 325155.94 0.50 162577.97

7 121341.06 504273.86 382932.80 0.45 172319.76

8 121341.06 514970.90 393629.84 0.40 157451.94

9 121341.06 509793.48 388452.42 0.36 139842.87

10 121341.06 418453.84 297112.78 0.32 95076.09

11 121341.06 352868.65 231527.59 0.28 64827.73

12 121341.06 295765.70 174424.64 0.25 43606.16

Table 11. Cash flow analysis of pomegranate orchard in Solapur district for overall farms(`/ha)

Particulars Units Small Medium Large Overall

Net present value `/ha 1232457.45 873968.57 801118.08 921035.57Benefit cost ratio Ratio 3.31 2.51 2.38 2.61Internal rate of return Percent 56.22 47.39 44.97 48.23

Pay-back period Years 3.6 4.4 4.8 4.1

Table 12. Financial feasibility of pomegranate orchards in Solapur district(Discount rate = 12 percent)

nourishment of seedlings and maintenance cost which are incurred in maintenance of orchard till the time of bearing the fruits i.e. up to two years. This crop requires a high investment of capital for establishment of orchard which is estimated as `348473.17 per hectare. The establishment cost is found to be more in small-sized farms compare to medium and large size groups. The overall maintenance cost during the gestation period

st nd (1 and 2 year) has been estimated as `184357.27 per hectare. Like establishment cost, small farms are spending more in maintaining the orchard during the gestation period compare to the growers of other groups. The annual maintenance cost can be termed as recurring

rdcosts which is incurred from 3 year for maintenance of plants so that good yield can be obtained over the economic lifespan of the pomegranate plants which is assumed to be 12 years. The annual maintenance cost comprises of the expenditure towards the variable factors and fixed factors. The per year variable cost of pomegranate crop for one hectare of small, medium and large farms have been estimated as `69008.00, `57941.00, and 50110.00 respectively. The small farmers are ahead of medium and large farmers in terms of application of variable factors whereas the total labour cost varies directly with the size of farms. Per hectare of total labour costs for small, medium and large size-groups

have been estimated as `39835.00, 43357.00 and 46754.00 respectively. In fact the small farms utilize their family labour on different operational activities of this orchard crop. The average yield of sample farms is found to be 9.38 tons per hectare which varies from year to year

rd thduring the period 3 to 12 year of the orchard age. Yield is thfound to be highest at 8 year which has been estimated as

12.02 tons per hectare. Inter size group variation in yield level of pomegranate has observed in the study area. The mean yield of small size farms is found to be maximum at 10.70 tons per hectare and it is declining with the increase in size of pomegranate farms. The financial feasibility of investment in pomegranate orchards has been studied with four relevant criteria namely, Net Present Value (NPV), Benefit-Cost Ratio (B-C Ratio), Internal Rate of Returns (IRR) and Pay Back Period (PBP). For calculation of these parameters 12 percent rate have been assumed to represent the cost of capital which is charged by the commercial banks for advancing loans to the farmers for cultivation of orchard crop. The costs which are incurred for production of pomegranate and the accrued returns over years have been properly discounted at 12 percent rate of interest in order to have a meaningful financial analysis. The NPV, B-C ratio, IRR and PBP for all farms are `921035.57 per ha, 2.61, 48.23 percent and 4.1 years respectively which confirm that investment on

211


pomegranate orchard is highly profitable and economically feasible in the study area. The study also reveals that on the basis of four criteria the small farms showing maximum NPV as `1232457.45 per hectare, B-C ratio as 3.31, IRR as 56.22 percent and PBP as 3.6 years have an edge over medium and large farms.REFERENCESChandra, R, Marathe, R.A., & Kumar, P. (2006). Present status

of pomegranate and its scope for crop diversification in arid and semi-arid region of Maharashtra. In Proceedings of the National Symposium on Agroforestry for livelihood Security Environment Protection and Biofuel Production, Jhansi, India, pp 77-78.

Holland, D., & Bar-Yaakov, I. (2008). The pomegranate: New interest in an ancient fruit. Chronica Horticulture, 48, 12-15.

Holland, D., Hatib, K., & Bar-Yaakov, I. (2009).The pomegranate: Botany, horticulture, breeding. In Janick J. (Ed).Horticultural Reviews, 35, 127-191. New Jersey: John Wiley and Sons.

Jadhav, V.T., &Sharma, J. (2007).Pomegranate cultivation is very promising. Indian Horticulture, 52, 30-31.

Khunt, K.A., Gajipara, H.M., Gadhavi, B.K., & Vekaria, S.B. (2003). Economics of production and marketing of pomegranate. Indian Journal of Agricultural Marketing, 17(1), 100-107.

Koujalgi, C.B., Mundinamani, S.M., & Kulkarni, B.S. (2013). Analysis of pomegranate orchards for financial feasibility and economic viability sustainable cultivation in Karnataka, Research Journal of Agricultural Sciences, 4(2), 202-206.

stLevin, G.M. (2006). Pomegranate (1 ed.), Third Millenium Publishing, East Libra Drive Tempe, AZ.

Patel, S.K., & Pundir, R.S. (2016). An economic analysis of production of pomegranate in middle Gujarat. International Journal of Forestry and Crop Improvement, 7(1), 101-107.

Ravikumar, K.T., Hosamani, S.B., Mamledesai, N.R.,Suresh, Ekbote, D. & Ashalatha, K.V. (2011). Investment pattern and maintenance cost in pomegranate orchards: An economic analysis. Karnataka Journal of Agricultural Sciences, 24(2), 164-169.

Sahana, R.T., Venkatamana, M.N., & Anitha, S. (2017), Economic and financial feasibility of pomegranate cultivation in Chitradurga district of Karnataka. International Journal of Agricultural, Science and Research, 7(1), 127-134.

Seeram, N .P. , Schu lman , R .N. , & Herber, D . (2006).Pomegranates: Ancient roots to modern medicine. Florida: CRC Press, Taylor and Francis Group, Boca Raton.

212


ABSTRACTFood security has different dimensions. To understand the food security situation in the eastern Indian state of Odisha, this paper studied various agricultural development indicators and population growth including the workforce in the agriculture subsector over a 20-year period (pre- and post-globalisation). Data came from secondary sources and regression models were used to analyse the time series data for different crops. The marketable surplus/deficit of crops over a long period was also studied. The results showed comparatively good development in agriculture in Odisha. However, food insecurity still exists making it a serious issue in Odisha irrespective of various welfare schemes and subsidizations programs. The results also lead to the conclusion that beyond this welfare and subsidy/free programs, emphasis must be given on providing farmers with generous economic incentives to optimise their production from their existing arable lands and to increase their purchasing power. The poor should be given work and allowed to earn. After all, food security for all in Odisha can be obtained through the synergy of people having income and a government that supports farmers in crop productivity and intensification enhancement and ensures an effective public distribution system.

KeywordsAgricultural development, food security, Odisha, population, pre-and post-globalisation period.

JEL CodesQ10,Q11, Q18, Q56.

Bidhan Kumar Mohapatra

International Rice Research Institute, Agri-food Policy Platform, IRRI India, New Delhi

Email: [email protected]

Received: December 11, 2017 Revision Accepted: June 05,2018

Agricultural Development and Food Security in Odisha


DOI: 10.5958/2322-0430.2018.00123.3


www.soed.in


UGC Approved


213

INTRODUCTIONAgriculture is the main source of life sustenance for

the human population. However, though there has been significant growth in other sectors, the agriculture sector still continues to be the mainstay of livelihood for human civilization. The growth of the agricultural sector is important not only for ensuring food security and reduction of poverty in rural areas, but also sustaining growth of rest of the economy. Agriculture is essential for inclusive development because it produces food as well as economic wealth for many of the world's poorest people (Sustainable Development Solutions Network, 2013). The aforesaid facts are more relevant in case of states like Odisha where nearly 60 percent people earn their livelihood through agriculture and allied activities (The Government of Odisha, 2015). Agriculture sector experienced a decelerating trend in the State in recent years with less than 16 percent contribution to GSDP, but it continues to remain a priority sector for the State because of its high potential in employment generation, inclusiveness, sustainable growth, etc. (The Government

of Odisha, 2015). Agriculture is the mainstay of the LDC economies, underpinning their food security, export earnings and rural development (Food and Agriculture Organization, 2002). Fortunately, at present we are not facing the prospect of large-scale famine-but we are at a crossroads. About 842 million people remain chronically hungry because they cannot afford to eat adequately, despite the fact that the world is no longer short of food. In a disconcerting paradox, more than 70 percent of the world's food-insecure people live in rural areas in developing countries. Many of them are low-paid farm labourers or subsistence producers who may have difficulty in meeting their families' food needs. (Food and Agriculture Organization, 2014).

India at present finds itself in the midst of a paradoxical situation, endemic mass-hunger coexisting with mounting foodgrain stocks. The food grain stocks available with the Food Corporation of India stand at an all-time high of 62 million tonnes (MT) against an annual requirement of around 20 MT to ensure food security. Still, an estimated 200 million people are underfed and 50

million are on the brink of starvation, both of which result in high mortality. The paradox lies in the inherent flaws in the existing policy and implementation bottlenecks (Sarabu, 2014). From 330 million in 1947 to 1.25 billion today, India's population increased by almost 3.8 times. During this period, India's food grain production increased from 50MT in 1951 to 257 MT in 2015 (IASPOINT, 2015).

For Odisha, a combination of economic, social, ecological, and institutional factors contributes to food security. However, some other factors give rise to food insecurity, together with its negative impacts, such as a high level of income poverty, a large tribal population living in remote areas with poor connectivity, and the periodic recurrence of drought and floods. This makes food insecurity chronic in Odisha in spite of its fairly comfortable food availability situation. A comprehensive measure of food access, taking into account several direct and indirect indicators, places Odisha in the category of 'very low' food access (Food Insecurity Atlas of Rural India, 2001). This is mainly due to poor entitlement on account of high incidences of poverty, inadequate employment opportunities in lean seasons, and poor economic access to the public distribution of subsidised food grains. Odisha, previously known as Orissa, has been placed in the category of 'severely food insecure' regions in terms of food availability, food access, and food absorption indicators. Severe food insecurity in Odisha was found to be primarily due to the presence of a vulnerable rural population with poor livelihood access or with livelihood susceptible to natural disasters(The Government of Orissa, 2004).

To meet the demands of an increasing population while maintaining self-sufficiency, the present rice production level of around 89 MT needs to be increased up to 120 MT by the year 2020(Kumar, Dwivedi, Narain, Rawat, & Chauhan, 2015). The challenge for agricultural scientists is to ensure a balanced, sustainable, and ecologically-sound agricultural development suitable to supply the requirements for micro and macronutrients (Goplan, 2001). Food security at both the national and household levels has been the focus of agricultural development strategy in India since the mid-1960's when import dependence for cereals had gone up to 16 percent and the country faced severe droughts continuously for two years (Acharya, 2009). The concept of sustainable agriculture should, therefore, be viewed in the context of the need to boost agricultural productivity, production, and profitability vis-à-vis the need to improve the economic conditions of farmers and ensure food security in general.

Keeping this in mind, the growth profile of Odisha's agriculture was studied in this paper by using time series data on some aggregate indices of agricultural output viz., index of net area sown (NAS), index of gross cropped area (GCA), and index of intensity of cultivation or cropping. The performance of various crops was also studied in detail. Besides, area coverage and production of various crops in pre and post globalisation period was studied. Marketable surplus/deficit of important crop categories has been analysed. Also, population growth and workforce have been briefly discussed.REVIEW OF LITERATURE

The issues of sustainable development and economic

growth are intertwined, and issues of poverty alleviation and agricultural growth cannot be isolated from each other. Therefore, food security has to be aligned with the formulation of agricultural trade strategies. It emphasizes the complementary nature of key variables and the respective roles of state, market and civil society institutions in ensuring sustainable and equitable growth (Acharya, Singh, & Sagar, 2002). At the national level, some measures of food security would be production, trade, and availability of food grains (Parikh, 1997). Institutional innovation can be a major source of productivity growth. Three major groups of institutions are important for agriculture, namely (a) institutions to make markets work better such as market regulation and information systems; (b) secured property rights for land and water to motivate private investment in agriculture, especially investments with a longer term pay off; and(c) collective action by farmers through well-performing farmer-organizations to reduce transaction costs, connect farmers to markets, and improve their bargaining position in those markets(Das & Mishra, 2010). Joshi and Kumar (2011) cited more factors such as the ballooning food subsidy, the identification of beneficiaries in both rural and urban areas, and leakages in the supply chain.Arable lands are diminishing every year as they are diverted for industrial, residential, recreational, and other uses. Other resources such as water, fertilizers, and labour are also becoming scarce and costly. Crop yields are lost due to biotic factors that include pests, diseases, and weeds even with the heavy use of costly chemical pesticides. Similarly, crop losses due to abiotic stresses such as drought, cold, heat, and salinity remain high and unpredictable. Recent advances in biotechnology offer exciting opportunities to address some of these challenges. When it comes to food accessibility, building warehouses at the village or block level, forming groups, and enabling collective bargaining could be seen as parts of the price policy. On the other hand, consumers' interest can be protected by open market operations and PDS.

Bhatt (2011) stated that food is many layered– from the cosmos to livelihood to ritual to myth. To protect food security, the base of agriculture – small farmers, their produce, and the locality of farming – must be protected as well. Autonomy, diversity, and locality are the fundamentals of food security(Bhatt, 2011).There are mainly three ways of meeting the increasing demand for food and other farm products expand the net area under cultivation intensify cropping over the existing area, and the third raising the yield rate of crops. In recent years, the three primary factors that affected the increases in world crop production include increased cropland and rangeland area (15 percent contribution in 1961-1999), increased yield per unit area (78 percent contribution), and greater cropping intensity (7 percent contribution) (Nellemann et al., 2009). Kalamkar (2009) in his findings explained that the world food situation is currently being rapidly redefined by new driving forces. Income growth, climate change, high energy prices, globalisation, and urbanisation are transforming food consumption production and markets. The need for food security arises primarily due to the fluctuation in food production and

214


non-availability of sufficient food from domestic sources. India has solved the problem of food security which is evidenced by its mounting buffer stocks, but still there are millions of food-insecure and undernourished people in the country. Thus, the limitation is not on food supply but on its distribution. Kumbhar (2012) explained that the paradox of food insecurity with plenty of food is found in Odisha as in any other part of the world. He found that entitlement failure rather than food availability is the detrimental factor causing food deprivation in Odisha. Public support through the Public Distribution System (PDS) could have reduced food insecurity but its poor designing, implementation, and functioning left the poor in the cold. METHODOLOGY

The time series data on agricultural development were collected from various secondary sources such as government publications, websites, journal articles, reports, etc. The data were tabulated and verified for analysis among others, tabulated for analysis. The collected data described how indicators of agriculture and food security have been performing over decades. In order to accomplish these tasks, methods of suitable advanced econometrics tools and techniques were utilised. The regression analysis was used to estimate growth, regression coefficients and statistical significance for various crops over periods. Simple statistical operations were used to compute for the marketable surplus/deficit of various crops.

To find the growth component of agriculture for area, and production at different periods, dummy variable analysis was carried out for which the total time period from 1980-81 to 2012-13 was divided into three periods, 1980-81 to 1989-90 is the pre-globalisation period, 1990-91 to 2000-01 post-globalisation period (first phase), and second phase of the post-globalisation period from 2001-02 to 2012-13.

The change of agricultural production over time was obtained by using exponential model.

The presence of characteristics of different periods of time in the rural economy of Odisha can be captured by the Dummy Variable model. The dummy variable takes one of two possible values, one value signifying the presence of one qualitative possibility and the other value signifying the absence of that possibility. The dummy variable generally assumes a value of zero or unity, unity referring to the occurrence of an event and zero referring to the non-occurrence of the event. Let the model be-

Y = a+ bt + r D + r D + r D t + r D t + u1 1 2 2 3 3 4 2

Where Y= Dependent variablea, b and r = Regression parameters.i

t = Explanatory variable (time)Di = 1 if he characteristics occurred in a period.= 0 if otherwise.r , r = Differential slopes, indicating by how much the 3 4

slope coefficients of the different period differs from the original function.

The dummy variables have been clipped with the 't' (time) to form a new explanatory variable of the function. If 'u' is well behaved then the regression parameters a,b, r ican be estimated taking 'D ' and 't' time as the independent 1

variable and 'Y' as the dependent variable.RESULTS AND DISCUSSIONStatus of Agricultural Development Indicators over the Years

The land area available for agricultural purposes was found declining all over the world-a scenario that was also witnessed in Odisha. As multiple demands for the land increase, the lesser land is devoted to agriculture and its allied sub-sectors.The intensive cultivation of available cultivable lands, wherever feasible, seems to be a viable strategy for increasing the gross area under cultivation and augmenting food production. These trends are discernible in Odisha as well (The Government of Odisha, 2015), where there was a decline in net area sown (NAS) over the years (Table 1).

The perusal of Table 1 showed that the index numbers for NAS depict an increasing behaviour from 93.73 in 1950-51 to 101.57 in 1980-81 and to 104.46 in 1990-91 before it declined to 96.59 in 2001-01. After that, it started to goback to an increasing trend from 89.83 in 2010-11 to 92.36 in 2012-13. The same trend was observed in the index numbers for gross cropped area (GCA) which increased from 70.55 in 1950-51 to 102.99 in 1980-81 and to 112.98 in 1990-91 before it fluctuated to92.77 in 2000-01 and increased again to 106.85 in 2012-13. The index numbers for intensity of cultivation also showed overall an increasing trend but with fluctuating behaviour. It increased from 75.27 in 1950-51 to 101.41 in 1980-81 and to 108.17 in 1990-91. However, after the 90's, it showed quite a fluctuating behaviour.

The perusal of Table 2 presents the changes in the cropping pattern in the state of Odisha over different time periods from 1980-81 to 2012-13. The cropping pattern is presented through the percentage share of different crop groups in the total cropped area.

215

Mohapatra: Agricultural development and food security in Odisha

Indicators 1950-51 1960-61 1970-71 1980-81 1990-91 2000-01 2010-11 2012-13

NAS 93.73 95.89 92.80 101.57 104.46 96.59 89.83 92.36

GCA 70.55 72.27 79.61 102.99 112.98 92.77 106.92 106.85

Intensity of cultivation 75.27 75.38 85.80 101.41 108.17 96.06 119.05 118.70

Table 1. Index numbers of agricultural indicators in different years

Author's calculation. Base period being triennium ending 1982-83=100.

The percentage of area in the case of rice showed a little fluctuation by decreasing to 45.89 percent in 1990-91 from 47.92 percent in 1980-81. In 2002-03, the area increased to 57.18 percent but it declined to 48.21 percent in 2012-13. Similarly, in the case of total cereals, the percentage of area declined from 59.25 percent in 1980-81 to 51.67 percent in 1990-91 but in 2002-03, it showed a sharp increase to 62.58 percent before it again declined to 54.159 percent in 2012-13. The percentage of area for total pulses showed an increasing trend from 19.73 percent in 1980-81 to 22.21 percent in 1991-92. In 2002-03, it declined to 17.59 percent but it increased again to 24.47 percent in 2012-13. The percentage of area under total foodgrains declined from 78.98 percent in 1980-81 to 73.89 percent in 1990-91 and then showed an increasing trend of 80.17 percent in 2002-03. However, after that it again declined to 78.62 percent in 2012-13. In the case of total oilseeds, the percentage of area showed an increasing trend from 8.42 percent in 1980-81 to 12.06 percent in 1990-91 and declined to 7.86 percent in 2002-03, but it again increased to 8.99 percent in 2012-13. The percentage of area under total fibres declined from 1.14 percent in 1980-81 to 0.94 percent in 1990-91 but then increased thereafter to 1.05 percent in 2002-03 and to 1.78 percent in 2012-13. In the case of potato, the percentage of area showed a constant trend (0.09 percent) in 1980-81 to 2002-03, but it increased to 0.17 percent in 2012-2013. The percentage of area under sugarcane showed a marginal decreasing trend from 0.56 percent in 1980-81 to 0.51 percent in 1990-91 and to 0.34 percent in 2002-03. After that, the percentage of area increased to 0.47 percent in 2012-13. The percentage of area under tobacco showed a declining trend from 0.24 percent in 1980-81 to 0.16 percent in 1990-91, to 0.06 percent in 2002-03, and to 0.02 percent in 2012-13. In the case of other crops including fruits, the percentage of area increased to 12.35 percent in 1990-91 from 10.57 percent in 1980-81. Then it showed declining trend from 10.44 percent in 2002-03 to 9.95 percent in 2012-13. These demonstrate clearly that the percentage of area under all crops showed fluctuations over time.

The perusal of Table 3 showed the growth rates of area, yield, and production for different crops at different points of time (1980-81 to 2012-13). It was observed that in terms of area, only rice and fibres showed a positive growth rate as against the other crops / crop groups such as cereals, pulses, food grains, vegetables, condiments and spices, sugarcane, tobacco, and oilseeds which all showed a negative growth rate. Also, the growth rate of the rice area was lower than that of the fibre area. In terms of yield, the crops rice, cereals, foodgrains, vegetables, condiments and spices, sugarcane, and tobacco all showed a positive growth rate of yield while the yield in the case of pulses and fibres showed a negative growth rate. The growth rate of yield for condiments and spices is the highest followed by rice as compared to other crops.

A positive growth rate of production was also found in rice, cereals, foodgrains, vegetables, and condiments &spices. Crops such as pulses, oilseeds, fibres, sugarcane, and tobacco faced a negative growth rate of production. Condiments and spices again had the highest growth rate of production followed by rice. It is gleaned from these results that only rice showed a positive growth rate for the area, yield, and production while only pulses showed a negative growth rate for these three indicators at different points of time. Cereals, foodgrains, vegetables, and condiment and spices showed a positive growth rate for both yield and production but not for the area.Critical Analysis of Agricultural Development Indicators for Various Crops During the Pre- And Post-Globalisation Period

The results of the dummy variable analysis for the area under different crops are reflected in Table 4. During the first period, positive coefficients were noted in rice, cereals, pulses, food grains, oilseeds, vegetables, and

Crops Area (percent)

1980-81 1990-91 2002-03 2012-13

Rice 47.92 45.89 57.18 48.21Total Cereals 59.25 51.67 62.58 54.15Total Pulses 19.73 22.21 17.59 24.47Total Food Grains 78.98 73.89 80.17 78.62Total Oilseeds 8.42 12.06 7.86 8.99Total Fibres 1.14 0.94 1.05 1.78Potato 0.09 0.09 0.09 0.17Sugarcane 0.56 0.51 0.34 0.47Tobacco 0.24 0.16 0.06 0.02Other Crops 10.57 12.35 10.44 9.95

Table 2. Percentage of the area for different crops at different times

Author's computation

216


Growth rate (percent)Crops

Area Yield Production

Rice 0.04 1.81 1.79

Cereals -0.03 1.77 1.57

Pulses -1.00 -0.89 -1.88

Food Grains -0.15 1.39 1.24

Oilseeds -0.88 0.12 -0.79

Fibres 0.34 -2.45 -2.12

Vegetables -1.11 1.31 0.24

Condiments & Spices -0.16 2.61 2.54

Sugarcane -1.24 0.26 -0.97

Tobacco -7.11 1.24 -5.85

Table 3. The growth rate of the area, yield, and production of different crops (1980-81 to 2012-13)

Author's computation. Base data from Directorate of Economics and Statistics, and Directorate of Agriculture and Food Production, Odisha.

condiments and spices. However, only the coefficient for oilseeds was statistically significant, showing that there was stagnancy during this period. A positive increment in the slope coefficient during the second period over the first period was found only for fibres although it is not statistically significant. Negative coefficients were found statistically significant in the case of foodgrains, oilseeds, vegetables, and tobacco. This shows that the second period basically gives a scenario of either stagnancy or deceleration for almost all crops.

For the third period over the first period, there was a positive increment in the slope coefficients for the following crops such as pulses, food grains, fibres, vegetables, and sugarcane, although only the coefficients for fibres, vegetables, and sugarcane were statistically significant. The negative coefficients were observed in rice, cereals, oilseeds, condiments and spices, and tobacco, which were all found to be statistically significant except for condiments and spices. Thus, on the whole, the third period gives a scenario of revival with a tendency of acceleration for fibres, vegetables, and sugarcane, and continuance of tendency of deceleration

2 for rice, oilseeds, and tobacco. The high-value R was observed in the case of oilseeds, vegetables, sugarcane, and tobacco, indicating that the equations are well-fitted equations. However, the low values in the case of the rest of the crops indicate the presence of cyclical fluctuations in the area under these crops.

So far as crop production is concerned was a negative increment in the slope coefficients during the second period over the first period for all crops, although the

coefficients were statistically significant only for rice, cereals, food grains, oilseeds, fibres, and tobacco. The negative coefficients with regard to other crops were found statistically insignificant in the case of pulses, vegetables, condiments& spices, and sugarcane. On the whole, the second period gives a scenario of either stagnancy or deceleration for almost all crops (Table 5). For the third period over the first period, a positive increment in the slope coefficients was observed for all crops except rice, cereals, oilseeds, and tobacco although it was statistically significant only for fibres, vegetables, condiments and spices, and sugarcane. On the whole, the third period gave a scenario of revival with a tendency of acceleration for fibres, vegetables, condiments and

2 spices, and sugarcane. The high-value R was observed in the case of oilseeds, fibres, condiments & spices, and tobacco, indicating that the equations were well-fitted equations. However, the low values in the case of the rest of the crops indicated the presence of cyclical fluctuations in the area under these crops. The above analysis clearly demonstrated a fluctuating behaviour in the case of the cropped area both for NAS and GCA and also for cropping intensity, which was also observed in the percentages of the area for different crops. Only rice exhibited a positive growth rate for the area, and production at different periods of time. In contrast, pulses showed a negative growth rate for the area and production at different points of time. Crops such as cereals, food grains, vegetables, and condiments and spices also showed a positive growth rate for production but not for the area. The period-wise in terms of area, it was observed

Crops a b1ã 2ã 3ã 4ã 2R

Rice 3.619(454.54)

0.002(1.37)

0.029(1.46)

0.120(4.26)

-0.002(-0.89)

-0.005(-3.37)

0.58

Cereals 3.680(146.07)

0.003(1.03)

0.118(0.99)

0.410(2.72)

-0.009(-1.44)

-0.016(-2.52)

0.23

Pulses 3.230(18.45)

0.008(0.29)

0.338(0.78)

-0.847(-1.37)

-0.027(-0.73)

0.020(0.56)

0.13

Food Grains 3.831(343.44)

0.001(0.54)

0.095(3.41)

-0.051(-1.30)

-0.007(-2.81)

0.001(0.28)

0.41

Oilseeds 2.879(116.32)

0.019(4.66)

0.435(7.07)

-0.006(-0.07)

-0.039(-7.31)

-0.018(-3.55)

0.80

Fibres 2.052(56.28)

-0.011(-1.95)

-0.198(-2.19)

-0.434(-3.37)

0.015(1.96)

0.026(3.46)

0.55

Vegetables 1.698(65.62)

0.004(0.88)

0.184(2.86)

-0.704(-7.69)

-0.014(-2.51)

0.018(3.41)

0.85

Condiments and spices 2.149(63.97)

0.007(1.30)

0.136(1.63)

-0.111(-0.93)

-0.012(-1.69)

-0.002(-0.34)

0.21

Sugarcane 1.716(50.07)

-0.007(-1.19)

0.121(1.41)

-0.581(-4.79)

-0.006(-0.87)

0.022(3.10)

0.70

Tobacco 1.322(25.42)

-0.017(-2.02)

0.469(3.63)

0.352(1.91)

-0.035(-3.15)

-0.024(-2.28)

0.95

Table 4. Regression coefficients of “crop area” using dummy variable analysis

Author's computation.Figures in parenthesis are t- Stat; Significant at 1 percent = 2.771; Significant at 5 percent = 2.056

217


that during the pre-globalisation period, a positive and significant growth was noticed in the case of oilseeds. During the second period, acceleration was not observed for all the crops while deceleration was noted for foodgrains, oilseeds, vegetables, and tobacco. During the third period, acceleration was observed in the case of fibres, vegetables, and sugarcane while deceleration was noted for rice, cereals, oilseeds, and tobacco. In terms of production, it was noted that there was a positive growth in cereals and oilseeds during the first period and in fibres, vegetables, condiments & spices, and sugarcane during the third period. Meanwhile, during the second period, there was a deceleration in the case of rice, cereals, food grains, oilseeds, fibres, and tobacco. Rice in Odisha

Rice is the staple food for the people of Odisha. If the rice productivity of the state is compared to that of the whole country of India, Odisha is far behind the national average (Figure 1) data from Odisha Agriculture Statistics, 2013-14. It was only in 2012-13 when the average rice productivity of Odisha approached that of India's average (India 2461 kg/ha and Odisha 2361 kg/ha). In all other years, there is a huge gap in rice productivity between India and Odisha. Moreover, in Odisha, rice productivity is fluctuating intensely, which could be attributed to many biotic and abiotic stresses which happened very often in the state. This implies that there is still a lot to do for agricultural development in Odisha despite the fact that the state is progressing satisfactorily.Population Growth

Population growth is one of the factors influencing

the food security (requirement) situation in the state of Odisha. Figure 2 (Statistical Abstract of Odisha, 2012, Directorate of Economics & Statistics, Odisha) indicated how fast the population of Odisha has been growing since 1951. As per the 1951 census, the population was at 14.65 million and reached 41.97 million in 2011. Decadal population variation in 1971-1991 was at the top level (19.82 to 25.05 percent). Until 1951, the progressive growth rate was 42.15 percent but it has reached 307.14 percent as per the 2011 census. This situation truly alarming as such a high growth in population obviously demands huge foodgrain production to ensure food security.Marketable Surplus or Deficit of Rice, Cereals, Pulses, and Oilseeds

Aside from studying the development indicators of agriculture and population growth, it is also important to compare the foodgra in demand (consumption requirement)against supply (production/availability).The estimates of marketable surplus or deficit of rice, cereals, pulses, and oilseeds over a period of 20 years from 1994-95 to 2013-14are shown in Figure 3 and data provided in Appendix 1 and 2. The total consumption requirement is calculated from factors such as population and food requirement per adult per day, among others. The adult population is estimated at 88 percent of the population. Surplus or deficit is calculated from the total requirement (including seed, feed, and wastage) and production. The food (consumption) requirement per person per day for rice, cereals, pulses, and oilseeds is set at 400 gm, 500 gm, 50 gm, and 45 gm, respectively.

Crops a b1ã 2ã 3ã 4ã 2R

Rice 3.556(61.51)

0.017(1.82)

0.330(2.30)

-0.116(-0.57)

-0.025(-2.07)

-0.003(-0.26)

0.53

Cereals 3.657(100.27)

0.010(2.43)

0.413(2.41)

0.128(0.59)

-0.026(-2.91)

-0.006(-0.65)

0.60

Pulses 2.960(16.51)

0.008(0.28)

0.581(1.30)

-1.292(-2.04)

-0.046(-1.21)

0.032(0.89)

0.27

Food grains 3.722(74.79)

0.012(1.49)

0.320(2.58)

-0.282(-1.60)

-0.025(-2.31)

0.005(0.49)

0.50

Oilseeds 2.712(67.05)

0.025(3.85)

0.694(6.90)

-0.477(-3.34)

-0.062(-7.17)

-0.006(-0.73)

0.76

Fibres 2.812(95.83)

-0.004(-0.77)

0.494(6.77)

-0.895(-8.62)

-0.037(-5.96)

0.027(4.61)

0.92

Vegetables 2.57(65.01)

0.01(0.88)

0.20(1.99)

-0.93(-6.64)

-0.01(-1.78)

0.03(3.62)

0.67

Condiments and spices 2.159(38.91)

0.009(0.99)

0.084(0.61)

-0.956(-4.87)

-0.005(-0.45)

0.035(3.14)

0.75

Sugarcane 3.497(81.54)

-0.002(-0.23)

0.236(2.22)

-0.662(-4.36)

-0.016(-1.70)

0.021(2.46)

0.60

Tobacco 1.038(15.24)

-0.018(-1.63)

0.628(3.71)

0.427(1.78)

-0.039(-2.69)

-0.021(-1.50)

0.89

Table 5. Regression coefficients of “crop production” using dummy variable analysis

Author's computation. Figures in parentheses are t- Stat; Significant at 1 percent = 2.771; Significant at 5 percent = 2.056

218


The rice deficit was observed only in five years, with production being at an all-time low in 2002-03 with a deficit of 2.27million tonnes. Contrastingly, rice production was at an all-time high in 2012-13 with a surplus of 3.21 million tonnes. For cereals, production was similar to that of rice. There was a surplus of cereals over 14 years and a deficit in six years. These deficit years included the five years wherein rice deficits were noted. The pulses production remained always lower than the requirement, with only the two earliest years (1994-95 and 1995-96) having a surplus production. The next 18 years witnessed a deficit for pulses, with the highest deficit being 0.52 million tonnes (in 2002-03) and the lowest being 0.06 million tonnes (in 2008-09). For oilseeds, deficits were noted across all the 20 years, with the maximum deficit being 1.55 million tonnes (in 2002-03) and the minimum being 0.81million tonnes (in 1994-95). These results indicate that food shortage still exists in Odisha and that the state is yet to have a full food surplus. Cultivators and Agricultural Labourers in Odisha

It was observed (Figure 4) that the percentage share of total cultivators continuously declined from 56.82 percent in 1961 to 49.16 percent in 1971, 46.94 percent in 1981, 46.31 percent in 1991, 35.82 percent in 2001, and 30.63 percent in 2011. In contrast, the proportion of agricultural labourers to the total workforce showed an increasing trend i.e. from 17.01 percent in 1961 to 28.28 percent in 1971, although there was a marginal decline in 1981 with 27.75 percent before it increased again to 28.68 percent in 1991. After which it declined to 21.88 percent in 2001 and again increased to 22.61 percent in 2011. Overall, the results of the last three censuses indicated a declining trend. It seems that small farmers are either losing their land and are forced to work as agricultural labourers or are shifting to non-farming activities. Despite this declining trend, however, agriculture is still

the predominant occupation because cultivators and agricultural labourers combined made up 62 percent of the total workers in 2011. An analysis of cultivators based on land holdings found that the percentage of small farmers declined from 32.89 percent in 1970-71 to 19.68 percent in 2010-11. This category accounted for 26.58 percent of the total operated area in 1970-71 which inreased to 30.87 percent in 2010-11.In 1970-71, marginal and small farmers constituted 76.2 percent of the total workforce, which increased to 91.84 percent in 2010-11, thereby indicating a high rise in the number of marginal farmers. Moreover, landholdings of marginal and small farmers increased over the period. This magnifies the need to bring advanced agricultural technologies to marginal and small farmers. Also, it was observed that concentration ratio was high at 0.5071 in 1970-71 and declined to 0.4267 in 2010-11. This high concentration ratio results in inequality in the land distribution pattern in the state, which witnessed a decline over time. The state's agrarian economy is basically a small peasant economy, with more than 91 percent of the cultivators operating less than 1 hectare of land, occupying 70 percent of the operated area. Therefore, the changing land distribution system has serious implications in terms of the state's agricultural growth and degree of underemployment (Mohapatra, 2016).Food Security and Schemes in Odisha

The Government of Odisha Poverty Task Force defines extreme food insecurity in terms of calorie intake as per capita per day stands below 1800 kcal. Using this definition, the proportion of the population considered extremely food insecure has been measured on the basis of various geographical and socioeconomic categories. There is significant variance in food grain production from year to year because of the occurrence of droughts and other natural disasters. The total food grain

20781984

2102 21312202 2178

21252239

23932461 2424

1496 14551554 1557

1720

15531609 1640

1472

2361

1821

1000

1500

2000

2500

3000

2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14

Pro

duct

ivit

y (K

g/H

a)

Figure 1. The productivity of rice (India vs. Odisha) from 2003-04 to 2013-14

India Odisha

219


production has generally been fluctuating: it was 96.32 lakh tonnes in 2013-14 as compared to 113.99 lakh tonnes in 2012-13, 76.17 lakh tonnes in 2011-12,and87.70 lakh tonnes in 2010-11. The concern of seasonal variations and its related consequences for higher prices and lower employment and wages hits poorer and more food insecure households the hardest. Aside from unpredictable shocks, the lean season for Odisha tends to be from July to September, during which time most of the rural population face some food shortages (Behara &

-50

0

50

100

150

200

250

300

350

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

1901 1911 1921 1931 1941 1951 1961 1971 1981 1991 2001 2011

Dec

adal

Po

ln. v

aria

tio

n

& P

rogr

essi

ve g

row

th r

ate

(%)

Pop

ula

tio

n (

Mill

ion

)

CensusPopulation Decadal population variation (%) Progressive growth rate (%)

Figure 2. Population in Odisha from 1991 to 2011

-4

-3

-2

-1

0

1

2

3

4

0 10 20

Mar

keta

ble

Sur

plus

/Def

icit

(m

illi

on t

onne

)

Year

Rice Cereals Pulses Oilseeds

Figure 3. Marketable Surplus or Deficit of 'Rice, Cereals, Pulses and Oilseeds' in Odisha over a 20-year period (1994-95 to 2013-14)

Penthoi, 2017).Though, the food availability in Odisha is fairly

comfortable, food insecurity is chronic and the state has been placed in the category of the 'severely food insecure' regions(Environmental Information System-ENVIS, 2011) (Figure 5). One-third of the total districts of Odisha are 'extremely food insecure', emphasizing the severity of the situation. Of the many parameters for ensuring food security, food availability reached a reasonable level as evidenced from above. However, all other factors need to

220


0

10

20

30

40

50

60

1961 1971 1981 1991 2001 2011

Census

Wo

rker

s (%

)

Cultivator

Agricultural Labour

Figure 4. The trend of agriculture workforce in Odisha

be addressed properly to ensure food for all people.Over the years, there were many state and central

schemes and programs that were introduced in Odisha to ensure food for all. Among these were the scheme for the supply of rice at Re.1 per Kg, the Aahar scheme (lunch at `5 per meal), the Annapurna Yojana, Antyodaya Anna Yojana (AAY), the Targeted Public Distribution System (TPDS), the National Food Security Mission (NFSM) schemes, etc. Despite of such schemes/programs,

evidence still reflects a shortage of food, in particular, nutritious food, which leads to starvation and malnutrition death. CONCLUSIONS

In order to ensure food security on a sustainable basis, the provision of adequate economic incentives for farmers to optimise their production from available arable lands and increase their incomes (purchasing power)should be of high priority. Besides, consumers' interests need to be protected by open market operations and the Public Distribution System. In predominantly agricultural and rural economies like Odisha, accelerated agricultural growth and an efficient agricultural sector are the keys to reducing food insecurity and poverty at a faster rate. Acceleration of agricultural growth and improving the profitability and efficiency of agriculture in general requires interventions such as investments in agricultural research, investments in productivity-raising infrastructure, popularisation of farm mechanisation, availability of an efficient system for the entire chain of marketing activities from farmgate to consumers, retention and attraction to rural youth through innovative farming, and the provision of based crop insurance. Odisha has solved the problem of food insecurity through its mounting buffer stocks, although on the contrary there are still millions of food-insecure and undernourished people in the state. Thus, the limitation is not in foodgrain production but in its distribution to and affordability by poor people. In addition, the provision of highly subsidised foodgrains and/or cooked meals etc., do not

221


Figure 5. Food Security Status in Odisha by Districts

Source: Centre for Environmental Studies, Forest & Environment Department, Government of Orissa, ENVIS, News Letter, Vol-24, January-March 2011.

look like sustainable solution of food insecurity. Such schemes have negative impact on the workforce, de-motivating people to work and thereby leading to labour scarcity.

Above and beyond the numerous welfare schemes and subsidy/free programs that have been provided to farmers, emphasis must be given on income generation for the poor to give them work and allow them to earn. After all, food security for all can be achieved with the synergy of people having income in hand and a government that supports its farmers by increasing their productivity and ensuring a proper public distribution system. DISCLAIMER

The views expressed by the writers do not necessarily reflect the views of the International Rice Research Institute.REFERENCESAcharya, S.S. (2009). Food security and Indian agriculture:

Policies, production performance and marketing environment. Agricultural Economics Research Review, 22(1), 1-19.

Acharya, S.S., Singh, S., & Sagar, V. (2002). Sustainable agriculture, poverty and food security: Agenda for Asian economies. Jaipur: Rawat Publications.

Behera, S., & Penthoi, G.C. (2017). Food insecurity and government intervention for sustainable food access in Odisha. International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS), 6(2), 38-46.

Bhatt, E.R. (2011, December 23). Food must not be reduced to security: Autonomy, diversity and locality should be the fundamentals of policy. Retrieved from The Indian E x p r e s s : h t t p : / / i n d i a n e x p r e s s . c o m / a r t i c l e / opinion/columns/food-must-not-be-reduced-to-security

Centre for Environmental Studies. (2011, January-March). Food security in Odisha. Environmental Information System-ENVIS Newsletter, 24(1), 1-8

Das, T.K., & Mishra, L. (2010). Including agriculture in sustaining economic growth of India. IUP Journal of Applied Economics, 9(4), 69-84.

Food and Agriculture Organization. (2002). The role of agriculture in the development of least-developed countries and their integration into the world economy. Rome: FAO.

Food and Agriculture Organization. (2014). The state of food and agriculture. Rome: FAO.

Food Insecurity Atlas of Rural India. (2001). M S Swaminathan Research Foundation World Food Programme. Retrieved

from https://documents.wfp.org/stellent/groups/ public/documents/ena/wfp076968.pdf?iframe

Goplan, C. (2001). Achieving household nutrition security in societies in transition: An overview. Asia Pacific Journal of Clinical Nutrition, 10, S4-S12.

IASPOINT. (2015, September 8). Trends in foodgrain p r o d u c t i o n i n I n d i a . R e t r i e v e d f r o m https://www.gktoday.in: https://www.gktoday.in/ academy/article/trends-in-foodgrain-production-in-india/

Joshi, P.K., & Kumar, P. (2011). Food demand and supply projections for India, 2010-2030: Trade, Agricultural Policies, and Structural Changes in India's Agrifood System: Implication for National and Global Markets. New Delhi: International Food Policy Research Institute.

Kalamkar, S.S. (2009). Globalisation, food security and sustainable agriculture in India. The Indian Economic Association 92nd IEA Conference Volume, Special Issue, Part II (pp. 90-113). Bhubaneswar: The Indian Economic Association.

Kumar, V., Dwivedi, S., Narain, S., Rawat, S.K., & Chauhan, B. (2015). Assessment of marketable and marketed surplus of rice in relation to farm size. Agro Economist, 2(2), 13-17.

Kumbhar, R.K. (2012). Food security in a regional perspective. Riga: LAP Lambert Academic Publishing.

Mohapatra, B.K. (2016). Time series analysis of sectoral contribution to state income: It's high time to invest in the primary sector in Odisha. International Journal of Advanced Research, 4(7), 344-347.

Nellemann, C., MacDevette, M., Manders, T., Eickhout, B., Svihus, B., Prins, A. G., & Kaltenborn, B. P. (2009). The environmental food crisis: The environment's role in averting future food crises. Arendal: The United Nations Environment Programme (UNEP).

Parikh, K. (1997). Food security-Individual and national: India's economic reform and development. Delhi: Oxford University Press.

stSarabu, V.K. (2014). Sustainable agriculture in 21 Century. New Delhi: Regal Publications.

Sustainable Development Solutions Network. (2013, September 18). Solutions for sustainable agriculture and food systems. The UN Sustainable Development Solutions Network (SDSN).

The Government of Odisha. (2015). Odisha agriculture statistics 2013-14. Bhubaneswar: Directorate of Agriculture and Food Production, Odisha.

The Government of Odisha. (2015). Odisha economic survey 2014-15. Bhubaneswar: Planning and Coordination Department, Government of Odisha.

The Government of Orissa. (2004). Human development report. Orissa. Bhubaneswar: Planning and Coordination Department, Government of Orissa.

222


2000-01 36.42 32.05 5.85 6.68 5.03 -1.66

2001-02 37.05 32.60 5.95 6.80 7.54 0.74

2002-03 37.54 33.03 6.03 6.89 3.59 -3.30

2003-04 38.03 33.47 6.11 6.98 7.11 0.13

2004-05 38.53 33.91 6.19 7.07 6.96 -0.11

2005-06 39.04 34.35 6.27 7.17 7.43 0.26

2006-07 39.55 34.81 6.35 7.26 7.43 0.17

2007-08 40.07 35.26 6.44 7.35 8.35 0.99

2008-09 40.60 35.73 6.52 7.45 7.64 0.19

2009-10 41.13 36.20 6.61 7.55 7.75 0.20

2010-11 41.68 36.68 6.69 7.65 7.77 0.12

2011-12 42.22 37.16 6.78 7.75 6.70 -1.06

2012-13 42.78 37.65 6.87 7.85 10.36 2.51

2013-14 43.35 38.15 6.96 7.96 8.57 0.62

Year Projected population

Adult equivalent to

88 percent

Total consumption requirement

Total requirement

Production Surplus/deficit

Million Nos. Million Nos. Million tonnes Million tonnes Million tonnes Million tonnes

Rice (Requirement 400 gm per adult per day)1994-95 33.07 29.10 4.25 4.86 6.35 1.501995-96 33.60 29.57 4.32 4.93 6.23 1.291996-97 34.15 30.05 4.39 5.01 4.44 -0.581997-98 34.70 30.54 4.46 5.10 6.21 1.111998-99 35.26 31.03 4.53 5.18 5.39 0.211999-00 35.83 31.53 4.60 5.26 5.19 -0.082000-01 36.42 32.05 4.68 5.35 4.61 -0.732001-02 37.05 32.60 4.76 5.44 7.15 1.712002-03 37.54 33.03 4.82 5.51 3.24 -2.272003-04 38.03 33.47 4.89 5.58 6.73 1.152004-05 38.53 33.91 4.95 5.66 6.54 0.882005-06 39.04 34.35 5.02 5.73 6.96 1.232006-07 39.55 34.81 5.08 5.81 6.93 1.122007-08 40.07 35.26 5.15 5.88 7.66 1.772008-09 40.60 35.73 5.22 5.96 6.92 0.962009-10 41.13 36.20 5.29 6.04 7.02 0.982010-11 41.68 36.68 5.36 6.12 6.93 0.812011-12 42.22 37.16 5.43 6.20 5.90 -0.312012-13 42.78 37.65 5.50 6.28 9.50 3.212013-14 43.35 38.15 5.57 6.37 7.61 1.89

Appendix 1. Estimates of marketable surplus of rice and cereals in Odisha, 1994-95 to 2013-14

Cereals (Requirement 500 gm per adult per day)1994-95 33.07 29.10 5.31 6.07 6.83 0.761995-96 33.60 29.57 5.40 6.17 6.73 0.561996-97 34.15 30.05 5.48 6.27 4.78 -1.491997-98 34.70 30.54 5.57 6.37 6.60 0.231998-99 35.26 31.03 5.66 6.47 5.77 -0.701999-00 35.83 31.53 5.76 6.58 5.61 -0.97

223


1997-98 34.70 30.54 0.79 0.90 0.71 -0.19

1998-99 35.26 31.03 0.80 0.92 0.61 -0.31

1999-00 35.83 31.53 0.82 0.93 0.65 -0.28

2000-01 36.42 32.05 0.83 0.95 0.51 -0.44

2001-02 37.05 32.60 0.85 0.97 0.70 -0.27

2002-03 37.54 33.03 0.86 0.98 0.46 -0.52

2003-04 38.03 33.47 0.87 0.99 0.62 -0.37

2004-05 38.53 33.91 0.88 1.01 0.63 -0.38

2005-06 39.04 34.35 0.89 1.02 0.79 -0.22

2006-07 39.55 34.81 0.90 1.03 0.87 -0.17

2007-08 40.07 35.26 0.91 1.05 0.91 -0.14

2008-09 40.60 35.73 0.93 1.06 0.99 -0.06

2009-10 41.13 36.20 0.94 1.07 0.96 -0.11

2010-11 41.68 36.68 0.95 1.09 1.00 -0.09

2011-12 42.22 37.16 0.96 1.10 0.92 -0.18

2012-13 42.78 37.65 0.98 1.12 1.04 -0.08

2013-14 43.35 38.15 0.99 1.13 1.06 -0.07

1994-95 33.07 29.10 1.45 1.65 0.84 -0.81

1995-96 33.60 29.57 1.47 1.68 0.86 -0.84

1996-97 34.15 30.05 1.49 1.71 0.50 -1.21

1997-98 34.70 30.54 1.52 1.73 0.55 -1.19

1998-99 35.26 31.03 1.54 1.76 0.45 -1.31

1999-00 35.83 31.53 1.57 1.79 0.57 -1.22

2000-01 36.42 32.05 1.59 1.82 0.37 -1.44

2001-02 37.05 32.60 1.62 1.85 0.54 -1.31

2002-03 37.54 33.03 1.64 1.87 0.32 -1.55

2003-04 38.03 33.47 1.66 1.90 0.50 -1.40

2004-05 38.53 33.91 1.68 1.92 0.53 -1.39

2005-06 39.04 34.35 1.70 1.95 0.55 -1.40

2006-07 39.55 34.81 1.73 1.97 0.60 -1.37

2007-08 40.07 35.26 1.75 2.00 0.68 -1.32

2008-09 40.60 35.73 1.77 2.03 0.70 -1.32

2009-10 41.13 36.20 1.80 2.05 0.62 -1.43

2010-11 41.68 36.68 1.82 2.08 0.64 -1.44

2011-12 42.22 37.16 1.84 2.11 0.66 -1.44

2012-13 42.78 37.65 1.87 2.14 0.69 -1.45

2013-14 43.35 38.15 1.89 2.16 0.70 -1.46

224


Pulses (Requirement 50 gm (dal) per adult per day)

1994-95 33.07 29.10 0.75 0.86 1.16 0.29

1995-96 33.60 29.57 0.77 0.88 1.19 0.32

1996-97 34.15 30.05 0.78 0.89 0.57 -0.32

Year Projected population

Adult equivalent to

88 percent

Total consumption requirement

Total requirement

Production Surplus/deficit

Million Nos. Million Nos. Million tonnes Million tonnes Million tonnes Million tonnes

Appendix 2. Estimates of marketable surplus of pulses in Odisha, 1994-95 to 2013-14

Oilseeds (Requirement 45 gm per adult per day)

ABSTRACTThe study attempts to assess the determinants of adoption of precision farming and its profitability, based on primary data from Kerala. Logit regression, farm business analysis, data envelopment analysis and Garret's Ranking were employed to analyze the data. Farm size, level of education and attitude of farmers towards risk orientation were the factors which positively and significantly influenced the adoption of precision farm technologies. Precision farming was found to be generating impressively higher returns in comparison to traditional farms and were technically and allocative efficient. Constraints to its adoption identified include rainfall, lack of expertise and credit facilities, etc.

KeywordsData envelopment analysis, farm business analysis, Logit model, precision farming, profitability.

JEL CodesD61, O33, Q13, Q16.

1* 2 3Denny Franco , Dharam Raj Singh and K.V. Praveen

1 2 Scientist, Water Technology Centre, Principal Scientist, and 3 Scientist Division of Agricultural Economics Indian Agricultural Research Institute, New Delhi-110012

*Corresponding author's email: [email protected]

Received: January 23, 2018 Revision Accepted: May 15, 2018

Evaluation of Adoption of Precision Farming and its Profitability in Banana Crop


DOI: 10.5958/2322-0430.2018.00124.5


www.soed.in


UGC Approved


225

INTRODUCTIONPrecision farming is a promising technology for

improving agricultural productivity, decreasing production costs and minimizing the environmental pressure of farming. Precision farming can effectively impart resilience to agriculture from changing climatic conditions thus making the agricultural production system sustainable (Bramley, 2009; Gebbers & Adamchuck, 2010). Farmers in developed countries have been using precision farming technology for several years. In India, however, its adoption has not gained momentum until recently. The inefficiencies in the conventional system and the stress that it puts on the natural resources has led some of the progressive farmers in India to think in favour of precision farming of late. Ever since the adoption of precision farming in India, it has carved a niche for itself among the educated, young and enterprising farmers.

Adoption of precision farming is temporally and geographically uneven (Lowenberg-De Boer, 1998) and will be mostly steered by risk-adjusted profitability (Batte & Arnholt, 2003). Constraints faced by the farmers in

adoption of precision farming include higher initial cost of technologies like drip irrigation, fertigation and chemical pesticides, transportation issues and market tie ups that led to low price fixation for the produce and difficulty in accessing the market information (Kanna, 2006). Lack of finance and credit facilities also acted as important constraints in adoption of precision farming (Maheswari, 2008).

Several studies have analyzed the efficiency of precision farming technologies. Drip irrigation, which is a component of precision farming, was superior in producing higher vegetative growth, fruit yield and quality, besides saving 20percent irrigation water over other methods of irrigation (Rodney et al., 1997). The spacing between plants should be decided to optimize the resources available, both land and inputs. Planting geometry significantly affected yield, capital cost, operating cost and net return for the banana crop planted in one hectare area and irrigated using drip irrigation system. The net return was found to be highest for one plant at 2 m spacing (Tiwari & Reddy, 1997).

Plants receiving drip and sprinkler irrigation had

significantly higher yield than furrow and basin for both amaranthus and okra. Partial budgeting indicated that a minimum of a 2.5 and 3 year period would be needed to pay off fixed assets for drip irrigation for amaranthus and okra. It is recommended that drip irrigation be used for production of amaranthus and okra, especially where labour is neither readily available nor cheap (Okunde et al., 2009). Examination of the allocative and cost efficiency implications of adopting variable-rate fertiliser application using Data Envelopment Analysis revealed higher efficiency scores for precision farming adopters. A positive allocative efficiency effect of precision farming technology is brought about mainly by a farm's ability to better extrapolate the soil's productive potential. They confirmed that precision farming makes farms' efficiency more responsive to production conditions, farm specialisation, legal form and other technological practices (Curtiss & Jelinek, 2012).

Farms that employed precision farming to cut costs, increase product quality and reduce environmental impact have enjoyed a marketing edge (Freestone, 2013). The cluster approach rendered support to collective marketing through mutual consultations and discussions in precision farming in Tamil Nadu (Vadivel & Muthuvel, 2007). Market tie-ups for the crops raised were being negotiated between the Farmers' Forum and the buyers. Using the best equipments, which are most efficient, more environmentally friendly, have better traceability and result in better quality produce, appeal to customers and help build trust and strategic business alliances. Hi-tech polyhouse cultivation is essentially the highest level of precision farming using which crop requirements of the inputs can be precisely met.

Insights into adoption of precision agriculture, constraints in adoption and the prospects will assist the public and private decision makers in leveraging desirable strategies. The stakeholders at different levels will be interested in understanding the process by which the farmers become aware of and adopt new technology like precision agriculture. These micro efforts, in turn, may cause macro impacts in agricultural scene. Even though several studies on efficiency and profitability of precision farming are available in literature, most of them focused on field crops (Maiorano et al., 2009; Sells, 1995; Swinton & Lowenberg-DeBoer, 1998). Horticultural crops have not gained great attention as the field crops (Griffin & Lowenberg-DeBoer, 2005). Some studies dealt with fruit crops like citrus and grapes (Ellison et al., 1997; Whitney et al., 1999), but not many studies examined the economic viability of precision farming in vegetables. With this backdrop, the present study examines the adoption of precision farming, associated constraints and its profitability in Palakkad district of Kerala. Productivity, income and efficiency in precision farming in open field and polyhouses vis-à-vis traditional farming along with the market linkages is analyzed in selected crops.

DATA AND METHODOLOGYThe study is based on the primary data, from the state

of Kerala in India, collected using multistage sampling technique in the year 2012-13. Palakkad district, and Chittoor block from the district, was purposively selected due to the higher concentration of the farmers involved in precision farming in the region. A total number of 40 precision farmers and 40 traditional farmers (80 farmers) were selected randomly from four randomly selected villages and were interviewed. Primary data on fixed and operational costs and returns in cultivation of crops under traditional and precision farming, the additional fixed and maintenance cost involved in precision farming etc. were collected through structured interview schedule. Further, the information on market linkages for inputs and outputs supply in precision farming and farmers' perception about constraints to the adoption of precision farming was also recorded. Logit Model

Logit model was employed to identify the factors influencing the farmers' decision for the adoption of precision farming. The model postulates that the

thprobability (P ,) of i farmer practicing precision farming i

is a function of an index variable Z - a set of the i

explanatory variables. In fact, Z is equal to the logarithm i

of the odds ratio, i.e., ratio of probability that a farmer adopts precision farming to the probability of non-adoption and it can be estimated as a linear function of explanatory variables (X ). These can be mathematically ki

expressed as:

Where, i = 1, 2,..., n. n is the total number of farmers. k = 1, 2,..., m. and m is the total number of

explanatory variables. á = Constant and â = Parameter to be estimatedk

The factors, hypothesized to influence the adoption of farmers, include age, education of head of the household (years), size of operational holding (ha), number of family workers, farming experience, non-farm income, risk orientation and extension agency contact.Cost Concepts

Differences in input use, productivity, income and employment under the precision farming and conventional framing were examined using cost of cultivation and return concepts. Various cost concepts are discussed below:

Cost A = Wages of hired labour, cost of inputs such 1

as seed, manures, fertilizers, insecticides and pesticides, hired machinery charges, imputed value of owned machine power, depreciation on implements like fertigation units and farm buildings, irrigation charges,

iZiiie

XFZFP-+

===1

1)()(

ni

i k kik1i

PZ ln X

1P =

ìü= =a+bíý

-îþå

226


land revenue and interest on working capital. Cost B = Cost A + Interest on value of owned 1 1

fixed capital assets such as fertigation equipments, precision farming structures and other assets of farms (excluding land).

Cost B = Cost B + Rental value of land. 2 1

Cost C = Cost B + Imputed value of family labour. 1 1

Cost C = Cost B + Imputed value of family labour. 2 2

Cost C = Cost C + 10 percent of Cost C accounting 3 2 2

for managerial input.Returns over different costs:Farm business income = Gross revenue – Cost A1

Family labour income = Gross revenue – Cost B2

Net income over Cost C = Gross revenue – Cost C1 1



Cobb-Douglas Production FunctionCobb-Douglas production functions for banana

cultivation under precision and traditional farming were fitted as follows:

ln Y = ln A + a ln S + b ln M + c ln L + d ln P + e ln i i i i i i i i i i i

I + f ln F + U …(1) i i i i

ln Y = ln A + a ln S + b ln M + c ln L + d ln P + e lnI j j j j j j j j j j j j

+ f lnf + U …(2) where, j j j

Y = Yield (kg per ha)S= Seed rate (number of suckers)M = Manures (kg per ha)L = Human labour (days per ha)P = Plant protection chemical (kg neem cake

equivalent per ha)I= Motor pump (hr/ha)F= Fertilizer (plant nutrients kg per ha)u = Error termA, a, b, c, d, e and f are parameters to be estimated and

subscript i denotes precision farming and subscript j denotes non-precision farming.

Taking differences between Equations (1) and (2), adding and subtracting some terms and rearranging these terms, one will get Equation (3):

ln (Y /Y ) = {ln (A / A )}+ {(a – a ) lnS + (b – b ) ln M + i j i j i j j i j j

(c – c ) lnL + (d – d ) ln P + (e –e ) ln I +(f –f ) lnF }+ {a ln i j j i j j i j j i j j i

(S /S ) + b ln (M / M ) + c ln (L /L ) +d ln (P / P ) +e ln (I / I ) i j i i j i i j i i j i i j

+ f ln (F /F )}+ [(U – U )] …(3)i i j i j

The left hand side of Equation (3) denotes the difference in per ha productivity of precision and non-precision methods, while the right hand side (RHS) decomposes the difference in productivity changes due to technology as well as input use. Equation (3) has three major terms on RHS. The first, second and third bracketed terms attribute to neutral technological change, non-neutral technological change, and change due to input-use, respectively.Data Envelopment Analysis

Data Envelopment Analysis (DEA), which is a non-parametric linear programming approach, was used in the estimation of technical, allocative and economic

efficiencies in banana production. The purpose of DEA is to construct a non–parametric envelopment frontier over the data points such that all observed points lie on or below the production frontier. The data were available on P inputs and Q outputs in each of N farms. Input and output vectors are represented by the vectors, x and y , i i

threspectively, for the i farm. For each farm, the efficiency is calculated as the ratio of all outputs over all inputs, denoted as uy /vx where u is a Q1 vector of output weights i i,

and v is a P1 vector of input weights. To select the optimal weights, the mathematical programming problem was specified as:

Max( uy /vx ),i i

Subject to uy /vx ≤1, j= 1, 2,……, Nj j

u,v ≥0 To avoid the problem of infinite number of solutions

in this form, a multiplier form was derived after imposing the constraint vx =1. Again using the duality in linear i

programming, an equivalent envelopment form was derived as:

Min,

Subject to - y +Y ≥0,i

x -X≥0, i

≥0,

Where, is a scalar and is a N1 vector of constants. This envelopment form involves fewer constraints than the multiplier form (P+1< N+1). The value of obtained

this the efficiency score for the i farm. It will satisfy ≤ 1,

with a value of 1 indicating a point on the frontier and hence a technically efficient farm. For the calculation of economic and allocative efficiencies in banana cultivation, the following cost minimization DEA was run using price information about the output and inputs and considering the behavioural objective of cost minimization:

MinSubject to ,

and

Where, w is the vector of input prices for the farm 'i' i *and x is the cost-minimizing vector of input quantities i

for the farm 'i'. The economic efficiency was calculated as the ratio of minimum cost to observed cost as:

Economic efficiency =

For estimation of farm level efficiencies in banana production per farm use of human labour in days, machine charges in hours, fertilizers in kg, plant protection chemicals in kg neem cake equivalent and irrigation hours were considered as inputs and the production in kg as output. The actual wage, fertilizer price and irrigation charges per hour were used for

*` ii xw

0yi

³lU+-

0Xx*i ³l-

0³l

ii

ii

xw

xw

`

` *

227

Franco et al.: Evaluation of adoption of precision farming and its profitability in banana crop

estimation of economic efficiency. The DEAP V2.1 computer programme was employed with the assumption of constant return to scale. Garret's Ranking

Garret's ranking was used to identify and rank the constraints in adoption of precision farming. Garret's formula for converting ranks into percent is given below:

Percent position = 100*(R -0.5)/Nij j

Where, th thR = Rank given for i factor by j individual; ij

th N = Number of factors ranked by the j individual.j

The percent position of each rank was converted into scores referring to the table given by Garrett and Woodworth (1969). For each factor, the score of individual respondent was added together and divided by the total number of the respondents. These mean scores for all the factors were arranged and ranked in descending order to identify the important factors.RESULTS AND DISCUSSION

The study area is dominated by small and marginal farms. Of the total precision farming practitioners, 25, 60, and 15 percent were marginal, small, and medium farmers, respectively (Table 1). In the case of traditional farms, all farms were found to be in small and marginal categories and average size of holding was low compared to the precision farms. It was found that head of the family practicing precision farming had attained high level of education than traditional farmers. Two-fifths of the total head of precision family and only one-fifth head of traditional farming family had senior secondary and higher education. Further, the average age of the family head was comparatively lower in the family practicing precision farming than the traditional farming. Determinants for Adoption of Precision Farming

The adoption of precision farming is affected by several factors which were identified through logistic

regression. Except age, all other parameters viz. working members, farming experience, risk orientation, education and land size positively affected the adoption of precision farming (Table 2). All the parameters except working members and farming experience were found to be statistically significant. The estimated odds ratio indicated that for an increase of one year in the age of the farmer, the probability for adopting precision farming decreases by about 19 percent. Negative relationship between age and adoption of technologies might be due the fact that as the farmer becomes older, their ability and willingness to take up new ventures decreases. On the other hand, the young farmers are full of enthusiasm and

Particulars Precision farmers Traditional farmers

Marginal Small Medium Total Marginal Small Total

Number of farms 10 24 6 40 20 20 40

Sample farms (percent) 25.0 60.0 15.0 100.0 50.0 50.0 100.0

Average size of family 0.92 1.48 2.63 1.52 0.68 1.60 1.14

Male family workers (No.) 1.3 1.5 1.5 1.5 1.3 1.4 1.3

Female family workers (No.) 1.1 1.3 1.3 1.3 1.2 1.3 1.3

Male family workers (partially) (No.) 0.4 0.4 0.5 0.4 0.6 0.8 0.7

Female family workers (partially) (No.) 0.9 0.5 0.7 0.7 0.5 0.5 0.5

Age of head of the family (Years) 41.2 46.3 47.3 45.2 50.5 49.5 50.0

Education of the head of the family

Primary (Percent) - 12.5 16.7 10.0 30.0 25.0 27.5

Secondary (Percent) 40.0 54.2 50.0 50.0 45.0 60.0 52.5

Higher secondary (Percent) 50.0 16.7 16.7 25.0 15.0 10.0 12.5

Graduation (Percent) 10.0 16.7 16.7 15.0 10.0 5.0 7.5

Table 1. General information about the selected farmers

Source: Authors' estimates based on field survey.

Particulars Coefficient Odds ratio

Intercept**-13.481

(5.9541)

Working members 0.0446(0.3852)

1.046

Farming experience 0.0889(0.0793)

1.093

Risk orientation***0.4807

(0.1546)1.617

Age***-0.2185

(0.0856)0.804

Education ***0.3542(0.114)

1.425

Land size **0.8193(0.3602)

2.269

No. of observation 63

Log-Likelihood ratio 46.65

Table 2. Logistic regression of factors affecting adoption of precision farming

Source: Authors' estimates based on field survey.***, ** significant at 1 and 5percent level.

228


Particulars Precisionfarming

Traditionalfarming

Yield (q/ha) 608 439

Cost A 322556 201453Cost B1 329449 204671Cost B2 343222 216852Cost C1 342735 229614Cost C2 356509 241796Cost C3 392160 265975

Gross returns 1216395 684921

Farm business income 893839 483468

Family labour income 873173 468069Net income over Cost C1 873660 455307Net income over Cost C2 859887 443126Net income over Cost C3 824236 418946

Table 3. Comparison of costs and returns in banana cultivation under precision and traditional farming

(`/ha)

Source: Author's estimates based on field survey.

have enterprising mind. Further, as the risk orientation increases by 1 unit, the probability for adoption will increase by 61 percent. Undoubtedly, farmers must be provided with the complete picture of the precision farming technologies. A farmer with a holistic knowledge about precision farming and a good risk orientation will be more successful in adoption of precision farming.

Education was another important factor identified that determined the adoption of precision farming. As the education of the farmer increases by one year, the probability for adoption will increase by 42 percent. It is true that education opens new arena for the farmers from where they can learn, test and adopt new methods of farming technologies suited to their farms. The educated farmers have the capacity to take the farming to new heights by incorporating precision farming and other modern agricultural technologies in their farms. In the study area also, the educated farmers, who had access to newspapers, internet and other communication sources preferred to adopt precision farming more than the uneducated farmers. Similarly, with one acre increase in land size, the probability for adoption of precision farming will increase by 126 percent. Number of members in the family and the farming experience were not found to affect the adoption behaviour of the farmers. Farm Business AnalysisBanana

Comparison of the costs and returns in banana cultivation under precision and traditional farming in the study area is presented in the Table 3. The results indicate higher yield, gross return and net income for precision farming than the traditional. This is a true indication of the higher efficiency of precision farming in utilizing the inputs in a better way to provide higher output. The total variable cost was higher in precision farming compared to traditional farming. This is because the precision farming incorporates modern agricultural technologies which require significant expenses. But, the farmers still practice precision farming because the increased cost is nullified by the effect of increased yield. The increase in yield in the case of precision farming was higher than the increase in cost when compared to traditional farming. Another interesting result from the analysis was the higher family labour income in precision farming. Precision farming utilizes the services of family labour more than the hired labour. The total labour requirement in precision farming was less compared to the traditional farming, thus most of the farm operations can be realized from family labour itself.Farm Level Efficiencies

The elasticities of production of banana derived by fitting the Cobb-Douglas production function under precision and traditional farming are presented in the Table 4. The female labour had a positive and significant impact on the productivity under precision farming, traditional farming and under pooled conditions. The production response to the female labour was more in the

precision farming (0.274) compared to traditional farming (0.077) as indicated by the regression coefficients. This shows the importance of female labour in the farming scenario of Palakkad. The important intercultural operations are done by women in both types of farming. Their importance is still higher in precision farming since many women SHGs participate significantly in this type of farming. Irrigation contributed significantly to the traditional farming (0.145), whereas its contribution to precision farming was insignificant. This means that the level of irrigation is standardized in precision farming and any increase or decrease in this level may not create any difference in the production. Interestingly, the fertilizers contributed negatively and significantly to the production under precision farming. This is an indication that, in Palakkad there is still scope for optimizing the fertilizer use under precision farming. All other factors were not able to contribute significantly to the production. This is because banana production technology has been standardized in the study area. Any deviation in the level of use of these variables, which came insignificant, are not going to affect the yield level under existing production environment.

A perusal of Table 5 reveals that the total productivity difference between precision farming and traditional farming of banana was 40.37 percent. Among various sources responsible for total productivity variation, the contribution of precision farming technology was highest (26.94 percent). The contribution due to differences in input-use level was 13.43 percent. Among various inputs contributing to the productivity difference in precision farming, male labour (8.14 percent ), tractor hours (3.00 percent), farmyard manure (3.90 percent), fertilizers

229


Particulars Change in yield

(percent)

Total change in Productivity (estimated) 40.37

Change due to precision farming technology 26.94

Changes in inputs

Male labour 8.14

Female labour -17.48

Tractor (Hr) 3.00

Planting material -3.73

Farm Yard Manure 3.90

Fertilizers 17.32

Plant Protection Chemicals 3.62

Irrigation -1.33

Total change due to inputs 13.43

Table 5. Decomposition of the productivity difference in precision and traditional farming

Source: Authors' estimates based on Field Survey.

(17.32) and plant protection chemicals (3.62 percent) contributed positively, whereas female labour (-17.48 percent), planting material (-3.73 percent) and irrigation (-1.33 percent) contributed negatively. The effect that precision farming technology made in the productivity, which is greater than the contribution of input use level, is evident from this.

The results of Data Envelope Analysis are presented in the Table 6. The results clearly indicate that precision farming is more efficient compared to traditional farming. The overall technical efficiency in precision farming was found to be 92.42 percent and it was highest for marginal farmers (95.52 percent). In traditional farming, the overall technical efficiency was 88.67 percent. The marginal farmers were more technically efficient (91.53 percent) as compared to small farmer. The technical efficiency of marginal farmers practicing precision farming was but higher than those doing traditional cultivation. The reason for this is that the farmers doing precision farming is having better control over inputs when compared to traditional farmers. The precision farmers have to deal only with specific inputs and that too in specific stages of the crop growth. Thus the wastage of inputs will be very less for them. Again, the high

Particulars Precision farming Traditional farming Pooled

Coefficients Standard Error



Constant *12.074 *14.221 *13.963Male labour -0.148 0.132 -0.001 0.097 -0.08 0.076Female labour

*0.274 0.074***0.077 0.044

*0.174 0.038Tractor (Hrs) 0.03 0.049 -0.038 0.045 0.003 0.031Planting material (No.) 0.292 0.539 -0.266 0.441 0.139 0.338FYM (kg) 0.076 0.081 0.105 0.093 0.067 0.056Fertilizers ***-0.547 0.304 -0.42 0.272 *-0.674 0.102PP chemicals -0.073 0.096 -0.0004 0.09 -0.042 0.066Irrigation (Hr) 0.014 0.064 ***0.145 0.076 0.049 0.041

2 R (percent) 58 50 91F - value *4.09 **2.77 *72.84Number of farmers 33 30 63

Table 4. Production function estimates for banana cultivation under precision farming and traditional farming

Source: Authors' estimates based on Field Survey.*, ** and *** Significant at 1,5 and 10 percent levels.

Farm sizes Precision farming efficiency Traditional farming efficiency

Technical Allocative Economic Technical Allocative Economic

Marginal 95.52 89.88 85.95 91.53 39.07 34.87

Small 93.26 87.16 81.17 86.31 41.78 35.21

Medium 86.40 86.72 74.60 - - -

All 92.42 87.58 80.84 88.67 40.55 35.06

Table 6. Farm efficiencies in banana production system(Percent)

Source: Author's estimates based on Field Survey.

efficiency of marginal farmers is due to their ability to manage the inputs in a better way. When the farm size increases the management ability may decrease.

230


The marginal farmers involved in precision farming were more efficient in allocative and economic efficiency also. The overall allocative efficiency in precision farming was 87.53 percent and economic efficiency was 80.84 percent. In traditional farming the overall allocative efficiency was only 40.55 percent and economic efficiency was 35.06 percent. In the traditional farming the small farmer category has better allocative and technical efficiency compared to marginal farmers. The marginal farmers in practicing precision farming in Palakkad were able to adapt to the market environment, by changing the variety of crops cultivated. Thus their resources were allocated in a better way and which yielded better returns. This led to the better allocative and economic efficiency for this category of farmers. Market Linkages for Inputs and Outputs in Precision Farming and Traditional Farming

Table 7 gives a comparison of precision farming and traditional farming with respect to the market linkages for inputs. For inputs like seeds, fertiliser, machinery, and pesticides Farmers generally depend upon institutions such as government agencies, private agencies, and agricultural university and farmer associations. Farmers

Precision farming Traditional farming

Marginal Small Medium All Marginal Small All

SeedGovt agencies 10.0 12.5 - 10.0 10.0 5.0 7.5Private agencies 40.0 58.3 83.3 57.5 40.0 55.0 47.5Agricultural university 20.0 29.2 16.7 25.0 5.0 10.0 7.5Farmers association 80.0 50.0 50.0 57.5 70.0 50.0 60.0FertilizerGovt agencies 10.0 20.8 16.7 17.5 15.0 10.0 12.5Private agencies 50.0 29.2 66.7 40.0 20.0 45.0 32.5Agricultural university 10.0 - - 2.5 - - -Farmers association 80.0 91.7 83.3 87.5 85.0 65.0 75.0PesticidesGovt agencies - 20.8 16.7 15.0 15.0 15.0 15.0Private agencies 50.0 54.2 83.3 57.5 40.0 35.0 37.5Farmers association 90.0 62.5 66.7 70.0 85.0 85.0 85.0MachineryGovt agencies - - - - - - -Private agencies 60.0 45.8 83.3 55.0 40.0 40.0 40.0Agricultural university - - - - - - -Farmers association 60.0 79.2 66.7 72.5 75.0 75.0 75.0Bio-fertilizersGovernment agencies 10.0 20.8 33.3 20.0 5.0 20.0 12.5Private agencies - 16.7 16.7 12.5 10.0 5.0 7.5Agricultural university - 8.3 50.0 12.5 10.0 5.0Farmers association 100.0 75.0 33.3 75.0 50.0 25.0 37.5

Table 7. Market linkages for inputs in precision farming and traditional farming(Percent)


organization in precision farming held a percentage share of 57.5 in seeds, 87.5 in fertilizer, 72.5 in machinery, 70 in pesticides and 75 in biofertilizers vis a vis a percentage share of 60 in seeds,75 in fertilizer, 75 in machinery, 85 in pesticides and 37.5 in biofertilizers in traditional farming. This suggests that the dependence of traditional and precision farmers on farmers association is high with respect to all the inputs. The marginal, small and large farmers have highest access to the farmers' organization. Farmers' organization is highly successful in meeting the input demands of farmers. The performance of government agencies and agricultural universities was poor as its share didn't go beyond 20 percent for any type of inputs in both the case of precision farming as well as traditional farming. The linkage of farmers in both precision and traditional farming with private agencies is high for all type of inputs except biofertilizers. Even marginal farmers and small farmers have better marketing linkage with private agencies. The role of farmers association was observed to have increased in precision farming model compared to the traditional farming in meeting the input demand of farmers.

231


Precision farming Traditional farming

Marginal Small Medium All Marginal Small All

VFPCK 80.0 54.2 16.7 55.0 60.0 40.0 50.0Farmers' association 10.0 4.2 5.0 25.0 12.5Local traders 10.0 8.3 16.7 10.0 10.0 5.0Super market 70.0 20.8 30.0Primary market 4.2 - 2.5 20.0 15.0 17.5Wholesale market 20.0 33.3 100.0 40.0 15.0 30.0 22.5Co-operative stores 10.0 41.7 0.0 27.5 55.0 45.0 50.0

Table 8. Market linkages for output on precision and traditional farming(Percent)


Table 8 delineates market linkages for output for precision farming and traditional farming. Vegetable and Fruit Promotion Council Keralam (VFPCK), farmers association, local traders, private supermarket, primary market, wholesalers and co-operative stores were the major players whom farmers were dependent for selling out. In precision model, VFPCK has the largest share (55 percent) followed by wholesalers (40 percent), private supermarket (30 percent) and co-operative stores (28 percent) whereas in traditional farming VFPCK and co-operative stores stood first with equal share (50 percent) followed by wholesalers (22 percent) and primary market

(18 percent). Traditional farmers didn't have any linkage with private supermarket, which has good market linkage with precision farmers. In precision farming model, marginal and small farmers mainly depended on VFPCK whereas in traditional farming farmers had very good linkage with co-operatives also. The linkage between medium class precision farmers and wholesalers was found to be very strong.Constraints in Adoption of Precision Farming

Constraints in the adoption of precision farming identified using Garret's ranking technique are presented in the Table 9. The ranking is done for different categories

Environmental constraints Garrett score Rank Infrastructural constraints Garrett score Rank

High rainfall 65.63 1 Lack of credit availability 70.53 1Drainage problem 60.25 2 Lack of marketing facilities 66.58 2Pest attack 51.50 3 Transportation problems 50.67 3High temperature 48.05 4 Lack of packaging facility 41.08 4Drought 44.50 5 Problems in power supply 37.50 5Excessive soil erosion 27.20 6 Lack of storage facility 33.80 6Extension constraints Garrett score Rank Economic constraints Garrett score RankLack of technical expertise 64.08 1 High-cost inputs 75.18 1Lack of resource persons 55.85 2 Price instability 58.77 2Inadequate motivation from officials 55.40 3 Market glut 58.18 3Timely unavailability of weather data 50.28 4 Lack of contractual agreements 52.62 4Lack of demonstration farm 48.08 5 High wage rate 39.85 5Effect of culture 27.90 6 Lack of insurance coverage 35.03 6Administrative constraints Garrett score Rank Social constraints Garrett score RankRed tapism 67.38 1 Lack of self-confidence 64.73 1Lack of support from Krishibhavan 62.48 2 Fear of failure 54.28 2Improper price policies 51.73 3 Lack of support from society 31.00 3License problem 36.55 4Technological constraintsSmall farmsNon-availability of skilled labourHeterogeneity of cropping systemNon-availability of quality inputsNon-availability of implementsComplexity of tools

Garrett score70.6367.8348.4047.5536.2835.50

Rank123456

Table 9. Constraints to the adoption of precision farming

Source: Authors' estimates based on Field Survey.

232


of constraints. There was environmental, infrastructural, extension, economic, administrative, social and technological constraints. The perceived priorities of the farmers showed that high rainfall, drainage problem and pest attack were the major environmental constraints in adoption of precision farming. The study area receives high rainfall for more than four months in a year. Thus, the management of water and moisture in the farm is very difficult for which high expertise is required. This high rainfall will also lead to difficulties in drainage and high incidence of insect pests. The pests and diseases incidences along with high rainfall and drainage problem make the adoption of precision farming very difficult.

Among infrastructural related constraints, lack of credit availability, marketing facilities and transportation problem were identified as the most severe. Availability of credit has an important role in precision farming since the initial investment required for establishing a precision farm is quite higher. The lack of marketing facilities and the transportation issues further aggravate the problems of the farmers. The farmers had to sell their products in markets situated far away to get good price. Better transportation is required for this. Exclusive marketing and transportation facilities for products from precision farms are the need of the hour. The extension services from the government offices like agriculture department have an important role in attracting more farmers to precision farming. Unfortunately the farmers in the sampling area perceived poor support from such offices in terms of providing resource persons and technical expertise. The unavailability of resource persons and technical expertise were the extension constraints that stand in the way of adopting precision farming. The farmers further perceived the high input cost and price instability as the major economic constraints. In administrative constraints, poor support from agriculture department and lengthy and time consuming procedure were the leading constraints. In case of social constraints, the lack of self-confidence in the farmers was identified as the prime constraint. The self-confidence of the farmers cannot be boosted unless the extension offices take proper efforts in this direction. Finally, small size of the farms and non-availability of the skilled labour were serious technological constraints in the adoption of precision farming. Labour shortage is one issue that the farmers in the sampling area were facing. The educated youth have already diverted completely from the agricultural sector. The requirement of skilled labour in the precision farming aggravates the problem.CONCLUSIONS

The field evidence from the Palakkad district of Kerala indicated that the farmers in the region have been adopting precision farming for some years now, but with varying rates of adoption. Age, risk orientation, education and land size were the major factors influencing the adoption of precision farming in the region. Except age, all other determinants positively affected the adoption of

precision farming. Number of working members in the family and the farming experience were not found to affect the adoption behaviour of the farmers. Precision farming gave higher productivity compared to traditional farming. The productivity difference was mainly due to the precision farming technology, and the contribution of male labour and fertilizer which are variable inputs. Data Envelope Analysis revealed the higher technical, allocative and economic efficiency in precision farming than in traditional farming. Farmers associations in Palakkad played important role in providing the inputs to both precision and traditional farms. The Vegetables and Fruits Promotion Council Keralam, farmers' associations and the cooperative stores are the major agencies procuring the produce in the region.

The results of the Garrette's ranking identified high rainfall, drainage problem and pest attack as major environmental constraints to the adoption of precision farming. In the case of infrastructural constraints, lack of credit availability, marketing facilities and the problems in transportation were identified as the most severe constraints. The unavailability of resource persons and technical expertise were the extension constraints, and high input cost, especially in liquid fertilizer cost and sophisticated equipment cost, and price instability were major economic constraints. Poor support from extension departments, high incidence of red-tapism were administrative constraints, and the lack of self-confidence in the farmers was identified as social constraint in precision farming adoption. Small size of the farms and non-availability of the skilled labour was ranked as the top technological constraints to the adoption of precision farming. Overall, the performance of precision farming in the Palakkad district of Kerala is satisfactory. The institutions, both government and private, along with the innovativeness and hard work of the farmers are the factors responsible for its success. This model can be spread to more regions, so that the farming community in those regions can also reap its benefits.

REFERENCESBatte, M.T., & Arnholt, M.W. (2003). Precision farming

adoption and use in Ohio: Case studies of six leading-edge adopters. Computers and Electronics in Agriculture, 38, 125-139.

Bramley, R.G.V. (2009). Lessons from nearly 20 years of precision agriculture research, development, and adoption as a guide to its appropriate application. Crop Pasture Science,60, 197-217.

Curtiss, J., & Jelinek, L. (2012). Cost efficiency and farm self-selection in precision farming: The case of Czech wheat

stproduction. Paper presented at the 131 EAAE Seminar 'Innovation for Agricultural Competitiveness and Sustainability of Rural Areas. Prague, Czech Republic, September 18-19, 2012.

ACKNOWLEDGEMENTThis paper is drawn from the M.Sc. thesis titled "An

economic analysis of precision farming in Palakkad district of Kerala", submitted by the first author to thePost Graduate School, IARI

233


Ellison, P., Ash, G., & McDonald, C. (1997). An expert system for the management of Botrytis cinerea in Australian vineyards. Agricultural Systems, 56, 185-207.

Freestone, J. (2013). Will precision farming change the face of UK agriculture? Oxford Farming Conference. Retrieved from https://connect.innovateuk.org/ c/document/get_ file? p_lpdf.

Gebbers, R., & Adamchuck, V.I.. (2010). Precision agriculture and food security. Science, 327, 828-831.

Griffin, T.W., & Lowenberg-DeBoer, J. (2005). Worldwide adoption and profitability of precision agriculture. Revista de Politica Agricola,14, 20–38.Retrieved from <http:// www.embrapa.br / publ icacoes/ tecnico/revis ta A g r i c o l a / r p a - d e - 2 0 0 5 / p o l _ a g r _ 0 4 - 2 0 0 5 - p -pgembrapa.Pdf>.

Kanna, K. (2006). Tamil Nadu precision farming project, AC&RI, TNAU, Coimbatore. Research Report. Department of Economic History London School of Economics Houghton Street London WC2A 2AE. Retrieved from http://agritech.tnau.ac.in/tnpfp-ENG/pdf/01.percent20Londonpercent20Schoolpercent20ofpercent20Economicspercent20Evaluation.pdf

Lowenberg-DeBoer, J. (1998). Adoption patterns for precision agriculture. SAE Technical Paper Series 982041.Society of Automotive Engineers, St. Joseph, Michigan, USA.

Maheswari, R. (2008). Precision Farming technology, adoption decisions and productivity of vegetables in resource-poor environments. Agricultural Economics Research Review, 21,415-424.

Maiorano, A., Reyneri, A., Magni, A., & Ramponi, C. (2009). A decision tool for evaluating the agronomic risk of exposure to fumonisins of different maize crop management systems in Italy. Agricultural Systems, 102, 17–23.

Okunde, D.A., Olanusi, O.A., & Adekalu, K.O. (2009). Growth, yield and economics of okra and amaranth production under irrigation. International Journal of Vegetable Science, 15, 28-43.

Rodney, D.R., Roth, R.L., & Garner, B.R. (1997). Citrus response to irrigation methods. Proceedings of International Society of Citriculture, 1, 106-110.

Sells, J.E., 1995. Optimizing weed management using stochastic dynamic programming to take account of uncertain herbicide performance. Agricultural Systems, 48 (3), 271–296.

Swinton, S.M., & Lowenberg-DeBoer, J. (1998). Evaluating the profitability of site specific farming. Journal of Production Agriculture, 11 (4), 439-446.

Tiwari, K.N., & Reddy, K.Y. (1997). Economic analysis of trickle irrigation system considering planting geometry, Agricultural Water management, 34, 195-206.

Vadivel, E., & Muthuvel, I. (2007). Tamil Nadu precision farming project: A successful model of market-linked small farmers corporate. Research Report. Tamil Nadu Agricultural University, Coimbatore.

Whitney, J.D., Miller, W.M., Wheaton, T.A., Salyani, M., & Schueller, J.K. (1999). Precision farming applications in Florida citrus. Applied Engineering in Agriculture, 15(5), 399-403.

234


ABSTRACTThe present study sought to assess the status of mechanization in the state, and the concomitant constraints and to examine its role in reducing costs and hence enhance farm incomes. The results of this investigation showed that there is proliferation of the marginal holdings in the state as marginal farmers account for about 70 percent of the total holdings with meagre average holding size of 0.41 ha. The average number of parcels per holding in the state is more than double than that at the national level. These tiny holdings limit the scope of farm mechanization. The use of electricity in agriculture, as a proportion of total energy sold, is around one percent in Himachal Pradesh. The overall extent of farm mechanization in the state in terms of the number of agricultural implements per 1000 ha of gross cropped area, increased continuously from 1.543 machines in 1972-73 to 76.817 in 2011-12. The number of tractors and power tillers per 1000 ha of net sown area also increased from 0.06 in 1972 to 17.74 till 2002. The field evidence suggests that use of power tiller led to the reduction of cost to the tune of about 95 and 208 percent over hired bullock pair and owned bullock pair, respectively for land preparation and sowing purposes. This calls for more incentives towards farm mechanization so as to boost commercial farming in the state by laying more stress on high tech, precision, labour saving, farm machinery and tools.

KeywordsCost saving, farm mechanization, marginal holdings.

JEL CodesA12, O13, Q55.

*Virender Kumar , S.K. Chauhan, Harbans Lal, Rajesh Thakur and Divya Sharma

Department of Agricultural Economics, Extension Education and Rural Sociology, COA, CSK HPAU, Palampur-176062 (HP)

*Corresponding author'sEmail: [email protected]

Received: January 25, 2018 Revision Accepted: May 30,2018

Farm Mechanization in Himachal Pradesh: Constraints, Status and its Role in Augmenting Farm Incomes


DOI: 10.5958/2322-0430.2018.00125.7


www.soed.in


UGC Approved


235

INTRODUCTIONThe adoption and application of package of farm

machinery and technology for agricultural mechanization has significantly contributed to improve the cropping intensity and farm produce during the last 40 years in India (The Government of India, 2016). As such, mechanization of agriculture has attracted the increased attention of farm policy planners, administrators, scientists, industry, field workers and of course of farmers due to several reasons such as cost escalations, scarcity of labour, inefficiency in manually performed farm operations, distraction of modern youth from conventional agriculture due to its toiling nature and non-viability, etc. By using various power sources and improved farm tools and equipment, it reduces the drudgery of both the human beings and draught animals, enhances the cropping intensity, precision and efficiency of utilization of various crop inputs and reduces the losses at various stages of crops production and processing. In the end, farm

mechanization leads to enhanced overall productivity and production with the lower cost of production.

Himachal Pradesh is one of the least urbanised states in the country with 89.96 percent of its population living in rural areas. Agriculture and allied activities dominate the rural livelihoods of the people in the state which is reflected by the fact that this sector provides direct employment to about 62 percent of total workers in the state (The Government of Himachal Pradesh, 2017). The land use features indicate that just around ten percent of the total

thgeographical area is under plough of which 4/5 is rainfed. The state has made notable progress in the production of agricultural commodities, especially fruits and vegetables. From the viewpoint of commercial and diversified agriculture, the production of vegetables at 17.92 lakh tonnes has surpassed the foodgrain production (16.34 lakh tonnes) in 2015-16. The crop diversification towards vegetable crops has engulfed considerable area in the state

and is no longer restricted to select valleys and pockets enjoying apparent agro-climatic advantages. Despite all these glorious achievements, there are serious concerns that need attention in Himachal Pradesh. One of these constraints, being the hilly terrain, pertains to the acute shortage of culturable land. There is growing marginalization of land holdings which are highly fragmented and as such defy any economies of scale. Further, the present socio-economics of nucleus family systems led to general labour scarcity at the farm family level. Combined with the general labour scarcity due to such schemes as MNREGS, commercial vegetable growing, being more labour intensive, is becoming difficult in the state.

With this background, the present study was undertaken with the objective of examining the land constraint and the marginalization of land holdings and the status of farm mechanization in Himachal Pradesh and juxtaposing the same with mechanization scenario at the national level. The study is based on the secondary data culled from different sources both at the state as well as at the national level along with field-level data for studying the role of mechanization in reducing costs and hence increasing farm incomes. METHODOLOGY

In order to accomplish the objectives of the study, both secondary and primary data were collected. The secondary data were culled from different sources both at the state as well as at the national level. The state-level data were collected from such sources as Annual Season and Crop Reports (various issues) and Statistical Outline of Himachal Pradesh (publications of Government of Himachal Pradesh) for different years. The required national-level data were collected from such publications as Agricultural Statistics at a Glance, 2015 and All India Report on Input Survey, 2011-12 (publications of Government of India). The primary data for studying the role of mechanization in reducing costs and increasing farm incomes were taken from the research report 'Extent and Scope of Farm Mechanization in Himachal Pradesh Crop Diversification Promotion Project (HPCDP) Areas: A Pilot Study', Department of Agricultural Economics, Extension Education & Rural Sociology, College of Agriculture, CSK Himachal Pradesh Krishi Vishvavidyalaya, Palampur (HP) and pertained to year 2017.

Suitable statistical tools such as averages, percentages, ratios and compound growth rates were employed to accomplish the objectives of the study and interpret the results. The compound growth rates were computed to examine the growth in power consumption for agricultural purposes and overall consumption of electricity and sale of tractors and power tillers over a time period by using exponential production function. RESULTS AND DISCUSSIONLand Constraint and Growing Marginalisation of Land Holdings

The area under plough is always a serious concern in the hilly and mountainous regions, and it is extremely difficult to bring more area under cultivation due to huge costs involved. Various socio-economic developments have taken a heavy toll on the net sown area in the state. The temporal changes in land use pattern in the state during the period 1990-91 to

2013-14 (Table 1) indicated that the net sown area dwindled by about 33 thousand hectares. In the land under such categories as current fallows, and other fallows has been witnessing an increase in the area considerably. Again over time, such factors as the wild animal menace of monkeys, blue bulls, stray animals, wild boars, etc. and the widespread infestation by obnoxious weeds such as Ageratum, Lantana and Parthenium, etc. have been rendering culturable lands unfit for use and the farmers are being compelled to abandon their main livelihood option of farming in many areas. On the other hand, land put to non-agricultural uses has also witnessed massive increase during this period and most of it came from the cultivated area only. The diversion of quality farmland for constructing houses and shops for small business on and near the road heads and small hamlets/markets is a familiar feature anywhere in the state. Most of these changes might have not been captured in the revenue records also. Such inclusions might accentuate the decline in the net sown area in the state.

There has been a notable increase in the number of holdings in the state overtime. During the period 1990-2010, the number of land holdings in the state increased from 8.33 lakh in 1990-91 to 9.61 lakh in 2010-11 (Table 2), whereas the total area cultivated by these holders decreased from 10.09 lakh hectares to 9.55 lakh hectares. The average land

Land use category Triennium ending

1990-91 2013-14

Reporting area (according to village papers)

3364.23(100.0)

4576(100.0)

Forests 1001.43(29.77)

1126(24.61)

Barren 184.07(5.47)

777(16.98)

Non-agricultural uses 198.67(5.91)

350(7.65)

Culturable waste 126.13(3.75)

122(2.67)

Permanent pastures 1162.27(34.55)

1510(33.00)

Miscellaneous tree crops 45.73(1.36)

64(1.40)

Current fallows 44.00(1.31)

54(1.18)

Other fallows 19.27(0.57)

22(0.48)

Net sown area 582.80(17.32)

550(12.02)

Table 1. Changes in land use in Himachal Pradesh, 1990-91 to 2013-14

('000 ha)

Source: Computed from the data taken from the Annual Season and Crop Report (various issues), Directorate of Land Records, Shimla-9, Government of Himachal Pradesh and www.eands.dac.net.in.Figures in parentheses indicate percentages to the total in each category.

236


Size category 1990-91 2010-11

Number ('000)

Area('000 ha)

Holding size (ha)

Number ('000)

Area('000 ha)

Holdingsize (ha)

Marginal farmers (Below 1.0 ha) 5.32 (63.80)

2.15 (21.30)

0.40 6.70 (69.78)

2.73 (28.63)

0.41

Small farmers (1.0-2.0 ha) 1.66 (20.00)

2.35(28.30)

1.41 1.75 (18.17)

2.44 (25.55)

1.39

Semi-medium farmers (2.0-4.0 ha) 0.94 (11.30)

2.58 (25.50)

2.74 0.85 (8.84)

2.31 (24.14)

2.72

Medium farmers (4.0-10.0 ha) 0.36 (4.30)

2.05 (20.30)

5.73 0.28 (2.87)

1.57 (16.39)

5.61

Large farmers (10.0 ha and above) 0.05 (0.60)

0.97( 9.60)

17.58 0.03 (0.34)

0.51 (5.29)

17.00

Total 8.33 (100.00)

10.09 (100.00)

1.21 9.61 (100.0)

9.55 (100.0)

0.99

Table 2. Distribution of various farm size categories in Himachal Pradesh, 2010-11

Source: Statistical Outline of Himachal Pradesh, 2015-16, Department of Economics and Statistics, Government of Himachal Pradesh, Shimla. Figures in parentheses indicate percentages to the total in each category.

holding decreased from 1.21 hectares in1990-91 to 0.99 hectares in 2010-11 in this hilly state which indicated 18 percent decline during this period. The distribution of land holdings according to 2010-11 Agricultural Census showed that an overwhelming 88 percent of the total holdings belonged to small and marginal categories while about 12 percent of holdings were owned by semi medium, medium and large category farmers. The corresponding shares in the total area operated by these two broad groups were 54 and 46 percent, respectively.

The marginal farmers (having less than 1 ha) account for about 70 percent of the total holdings in the state and operated just 29 percent of the total area, with meagre average holding size of 0.41 ha and during this period, about 1.28 lakh farmers joined the ranks of marginal class. Due to lack of land consolidation, the holdings were scattered and hence, difficult to manage. Consequently, these paltry holdings are being rendered unviable for farming what to

State Average number of parcels per holdings

Average area per parcel (ha)

Average sizeof holding (ha)

Himachal Pradesh

Marginal 5.01 0.08 0.41

Small 5.27 0.26 1.39

Semi-medium 5.57 0.48 2.72

Medium 6.31 0.88 5.61

Large 6.95 3.44 17.00

All groups 5.15 0.19 0.99

Jammu and Kashmir 3.38 0.18 0.61

Uttarakhand 2.47 0.36 0.88

All India 2.01 0.57 1.14

Table 3. Distribution of number of holdings, operated area, parcels and cropped area by major size groups in Himachal Pradesh, 2011-12

Source: All India Report on Input Survey, 2011-12, Department of Agriculture, Cooperation and Farmers WelfareMinistry of Agriculture, Cooperation &Farmers Welfare, Government of India.

speak of mechanized farming (Table 3). The average number of parcels per holding in the state was far higher at 5.15 as compared to national average of 2.01. This difference was again clearly protruding even when seen in relation to even other mountainous states. For example, in J&K and Uttarakhand, it was 3.38 and 2.47, respectively. Further, the average area per parcel in the state was 0.08 ha Thus, the average area per parcel in Uttarakhand and at the country level was about 4.5 and 7 times, respectively, of the same in Himachal Pradesh. This again constitutes one of the major roadblocks in the way of farm mechanization.Status of Farm Mechanization in India

The contribution of agricultural mechanization was well recognized in improving the productivity of this sector along with irrigation, biological and chemical inputs of high yielding seed varieties, fertilizers, pesticides, etc. (Table 4). It was estimated that the efficient farm machine use could enhance the gross income of the farmers by 29-49 percent

237

Kumar et atl.:Farm mechanization in Himachal Pradesh: Constraints, status and its role in augmenting farm incomes

Particulars Percent

Increase in productivity 12-34Seed-cum-fertilizer drill facilitates i) Saving in seeds 20ii) Saving in fertilizer 15-20Enhancement of cropping intensity 5-22

Increase in gross income of the farmers 29-49

Table 4. The contribution of farm mechanization in agriculture

Source: Report of the Sub-Group on Agricultural Implements and Machinery for Formulation of Ninth Five Year Plan, Government of India.

Operation Extent of mechanization

(Percent)

Soil working and seedbed preparation

40

Seeding and planting 29

Plant protection 34

Irrigation 37

Harvesting and threshing 60-70 for wheat and

rice and <5 for others

Table 5. Extent of mechanization by farm operations in India

Source: Farm Mechanization in India, Dept. of Agriculture and Cooperation, Ministry of Agriculture 2013, as quoted in FICCI. Agri-Report (2015), Labour in Indian Agriculture: A Growing Challenge.

Year

Agricultural workers

Draughtanimals

Tractors Power Tillers Diesel engines Electric motors

1971-72 15.40 45.40 6.8 0.30 18.10 14.00

1991-92 8.60 16.60 30.30 0.40 23.30 20.90

2012-13 5.00 5.51 45.80 0.80 16.30 26.80

Table 6. Temporal changes in the composition of farm power availability in India, 1971-72 to 2012-13(Percent)

Source: CR Mehta et al, Project Coordinator, FIM, CIAE, Bhopal, Trends of Agricultural Mechanization in India CSAM Policy Brief, June 2014 as quoted in State of Indian Agriculture, 2015-16, Directorate of Economics & Statistics, Department of Agriculture, Cooperation & Farmers Welfare, Ministry of Agriculture, Cooperation &Farmers Welfare, Government of India.

through savings in seeds, fertilizers, and through increased cropping intensity and ultimately productivity.

As regards the extent of mechanization in India was concerned, the operation-wise details are given in Table 5. While it was fairly high at 60-70 percent in the harvesting and threshing of wheat and rice, it was 40 percent in the case of soil working and seedbed preparation. Similarly, slightly over one-third of the operations of plant protection and irrigation were reported to be mechanized at the country level. The fact that seeding and planting operations are least mechanized among various operations calls for further efforts in mechanizing Indian agriculture.

With the increasing commercialization of Indian agriculture, advancement of technology and declining farm labour availability, the composition of the farm power use in the country has undergone sea change during the past four decades or so (Table 6). During the period (1971-72 to 2012-13), the share of agricultural workers in total farm power decreased from 15.4 to 5.0 percent while that of draught animals reduced from 45.4 to 5.1 percent. Put together, the share of agricultural workers and draught animals reduced from 60.8 percent in 1971-72 to 10.1 percent during 2012-13. On the other hand, the share of tractors increased from 6.8 to 45.8 percent and that of electric motors almost doubled from 14 to 26.8 percent. Despite the fact that Indian farms witnessed further marginalization, the share of power tillers was still under one percent. Also, the share of electric motors nearly doubled from 14 to 26.80 percent during the study period.

Since, electricity is playing an increasing role in agricultural operations, it is worthwhile to study its consumption trend in this sector. The consumption of electricity for agricultural purposes stood at 1.47 lakh Giga

Watt-hour in 2012-13 and it accounted for 20.80 percent of the total electricity consumption in the country (Table 7). During the past three decades (1983-84 to 2012-13) the power consumption for agricultural purposes increased at the rate of 4.85 percent annum while the overall consumption of electricity registered a higher rate of 5.20 percent during the same period. The use of electricity in agriculture, as a proportion of total energy sold, across various states in the country showed a widely varying pattern (Table 8). While it was very low in the hilly states of Himachal Pradesh and NER (1.02 percent), it was quite high in the states of Rajasthan (43.46 percent), Karnataka (33.39 percent), Haryana (31.53 percent), MP (30.30 percent) and Punjab (30.09). The magnificent achievement of agriculture in MP during the period 2010-15 was attributed, inter alia, to the availability of power (Ninan, 2017).

The tractorisation of farm operations is another landmark in the post green revolution period, more so in the last two decades or so. In the case of tractors, the sale increased at an annual rate of 9.81 percent during the period 2004-2014 (Table 9). Further, if one looks at the spatial distribution of sales of tractors in 2014-15, around 60 percent of the tractors were sold only in the five states of UP, MP, Rajasthan, Maharashtra and Gujarat (Table 10). On the contrary, three hilly states of HP, J&K and Uttarakhand, put together, accounted for just 1.15 percent of all the tractors sold in the country during 2014-15. These regional imbalances in the sale of tractors clearly revealed the low extent of mechanization in these hilly areas due to terrain

238


Year Consumption for agriculturalpurposes

Total consumption(TC)

Share of agriculturalconsumption to TC (Percent)

1983-84 18234 102344 17.821992-93 63328 220674 28.702002-03 84486 339598 24.882012-13 147462 708843 20.80CGR (Percent) **4.85

(1.1609)

**5.20(0.4800)

Table 7. Consumption of electricity for agricultural purposes in India, 1983-2012(Gwh)

Source: Agricultural Statistics at a Glance 2015, Directorate of Economics and Statistics, Department of Agriculture, Cooperation &Farmers Welfare, Ministry of Agriculture, Cooperation &Farmers Welfare, Government of India.GWh: Giga watt-hour ** Significant at 5 percent level.Figures in parentheses are standard errors.

State/Region Consumptionfor agriculturalpurposes (GWh)

Total energysold (GWh)

percent share ofconsumption

for agriculture

Himachal Pradesh 74.95 7357.80 1.02

Jammu and Kashmir 296.74 5163.02 5.75

Other Hilly States

Uttarakhand 387.84 8574.11 4.52

North Eastern Region (NER) 79.14 7649.15 1.03

States with the highest use (percent)

Rajasthan 18324.65 42160.22 43.46

Haryana 8279.71 26257.62 31.53

Karnataka 17173.77 51439.47 33.39

Madhya Pradesh 9978.28 32935.62 30.30

Punjab 10779.03 35825.19 30.09

All India 147461.92 708843.39 20.80

Table 8. Consumption of electricity for agriculture purpose in select states, 2012-13

Source: Agricultural Statistics at a Glance 2015, Directorate of Economics & Statistics, Department of Agriculture, Cooperation &Farmers Welfare, Ministry of Agriculture, Cooperation &Farmers Welfare, Government of India.

related constraints. On the other side, though the sale of power tillers registered still higher growth at 12.27 percent per annum during the same period, yet it declined a great deal after 2011-12 when it was maximum. This really is not a healthy sign for farm mechanization, especially in mountainous states where power tillers have to play a crucial role in the agricultural development.Spatio-temporal Changes in Farm Mechanization in Himachal Pradesh

As agriculture progresses, there is a tendency to use more farm machinery to save on drudgery, both human and animal, lower farm costs and increase productivity. Farm mechanization in Himachal Pradesh is confined to the southwest sub-mountain regions where land was relatively flat and less undulating. Though, several farm implements such as small tractors, power tillers and power sprayers, etc. were made available to farmers on subsidy yet mechanization was at a low level in the state and only some medium and large farmers use these machines/implements. The overall extent of farm mechanization in Himachal Pradesh in terms of the number of agricultural implements per one thousand hectares of gross cropped area, increased

continuously from 1.543 machines per one thousand hectare of the gross cropped area in 1972-73 to 76.817 in 2011-12 (Table 11). More specifically, the number of tractors increased significantly from 402 to 15443 between 1972-73

Year Tractor Power tiller

2004-05 248000 17000

2007-08 347000 26000

2011-12 535000 60000

2014-15 551000 46000

Total 4899000 429000

CGR (percent) **9.81 (0.2496)

**12.27 (0.5272)

Table 9. Year-wise sale of tractors and power tillers in India

Source: Agricultural Statistics at a Glance 2015, Directorate of Economics and Statistics, Department of Agriculture, Cooperation &Farmers Welfare, Ministry of Agriculture, Cooperation &Farmers Welfare, Government of India.**Significant at 5 at percent level.Figures in parentheses are standard errors.

239


Agricultural census

Agricultural Implements (numbers) Total Extent of mechanization(machines per '000 ha of the gross cropped area)Tractors Threshers Electric

pumpsOil

engines

1972-73 402 439 338 255 1434 1.5431982-83 936 8847 585 1177 11545 12.0531992-93 3466 19221 1222 1299 25208 25.9181997-98 4205 14048 2530 1295 22078 22.3842006-07* 5711 15443 1275 1558 23987 25.1442011-12* 15363 46891 5008 4321 71583 76.817

Table 11. The extent of mechanization in Himachal Pradesh, 1972-73 to 2011-12

Source: Computed from the data taken from Statistical Outline of Himachal Pradesh (various issues), Department of Economics & Statistics and Annual Season and Crop Report, Directorate of Land Records, Shimla-9, Government of Himachal Pradesh.* All India Report on Input Survey, 2006-07 &2011-12.

State/Region Tractors sold Percent share in total sales

Himachal Pradesh 1519 0.28

Other hilly states

Jammu and Kashmir 2601 0.47

Uttarakhand 2205 0.40

States with the highest share (Percent)

Uttar Pradesh 92219 16.72

Madhya Pradesh 63744 11.56

Rajasthan 64565 11.71

Maharashtra 49839 9.04

Gujarat 46411 8.42

All India 551463 100.00

Table 10. Distribution of sale of tractors in India, 2014-15

Source: Agricultural Statistics at a Glance 2015, Directorate of Economics and Statistics, Department of Agriculture, Cooperation and Farmers Welfare, Ministry of Agriculture, Cooperation and Farmers Welfare, Government of India.

and 2011-12. Similarly, a considerable increase was also observed in the number of threshers in above said period.

The tractorisation of farm operations was the most visible form of mechanization in the Indian context and the hill farming is no exception. The tractorisation here referred to the use of tractors and power tillers in various farm operations. It was expressed in terms of number of tractors and power tillers per thousand hectares of net sown area to capture the variations over space and time. It is evident from Table 12 number of tractors and power tillers per thousand hectares of net sown area (NSA) increased immensely from 0.06 in 1972 to 17.74 till 2002 in the state.

The animal power was substituted by mechanical power for various crop production operations. This was reported in several earlier studies (Kumar et al., 2004; Sharma et al., 1995). The extent of change is clearly topography dependent as has been reflected by the figures in Table 12. In a district like Una district with relatively flat terrain, the figure (53.87) is three times of the state average (17.74). In hilly districts like Shimla and Kinnaur, it was extremely low at less than one tractor per one thousand hectares of net sown area. There

appeared to be an inverse relationship between the use of tractors and power tillers and the population of bulls. As the agriculture progresses, there was a tendency to substitute mechanical power for animal power. In order to examine this issue, firstly the changing ratio of indigenous bulls per crossbred bull was computed over space and time for the state. It was done so because it is an accepted fact that indigenous bulls are far superior to crossbred bulls as far as tilling of land is concerned. Thereafter, it was juxtaposed with the extent of tractorization. It can be clearly concluded that across the districts, the ratio of indigenous bulls to crossbred bulls has opposite relationship with the extent of mechanization. As in Chamba district, the number of indigenous bulls per crossbred bulls was highest among other districts (Table 13) whereas the ratio of tractors and power tillers per thousand hectares of net sown area (NSA) was low with respect to other districts. And in Una, where the ratio of work animals was low, there the extent of mechanization was the maximum. Furthermore, custom hiring of tractor services for field operations was a common feature in these low hill areas.

Another issue pertains to the differential extent of farm mechanization across size classes of land holdings in the state. Since, the mechanization of agriculture is capital intensive, it is normally assumed that as the farm size grows, there was an increasing tendency of mechanization of farm operations. This is so because, in the first place, the resource endowments of small and marginal farmers generally did not permit them to go in for the costly farm machinery and secondly given the small size of the market of custom hiring services, it may not be economically viable for them to own these farm machines. Such tendencies across various farm size groups were clearly revealed by the data of the All India Report on Input Survey, 2011-12 of the Government of India (Table 14). As may be seen in Table 14 for almost all the farm machines considered here, the marginal farmers who constitute about 70 percent of total holdings in the state, their percentage is least among all the farm size categories. It was noticed that less than one percent of the total marginal farmers are estimated to be using power tillers, pump sets (both diesel and electric), raised bed planters (tractor driven), sprinklers and drip irrigation in the state. The use of

240


self-propelled power weeders or vegetable transplanters (tractor driven) was virtually missing from this preponderant majority of the farming community. This fact reiterates the above facts and reinforces the need to have a group based/custom hiring approach towards mechanization in the state.Field Evidence of Cost Saving through Mechanization

The adoption of a particular intervention in crop production system is acceptable to the farming community if it is cost-effective compared to existing practices and contributes to the reduction of drudgery and increased net returns of the crop enterprises. The comparative economics (financial feasibility) of a power tiller in land preparation and sowing operations was estimated vis-a-vis bullock pair (both owned and hired) (Kumar et al., 2017). The costs incurred under these scenarios were estimated and are depicted in Table 15. It can be seen from the Table 14 that the total per hectare cost of field preparation and sowing of crops in the low hill zone was estimated at `50805, 26070 and 16504 in the case of owned block pair, hired bullock pair and power tiller, respectively. The results revealed that the cost of these operations was significantly less in case of power tiller compared to bullock labour. The use of power tiller led to the reduction of cost to the tune of about 95 and 208 per cent in case of hired bullock pair and owned bullock pair, respectively for land preparation and sowing purposes. It was profitable to use hired power tiller as against owning and/or hiring bullocks. This saving in cost led to increased farm incomes ultimately. Thus, in order to promote the use of power tiller in the study area, the power tiller was made available on group basis depending on the cultivated land of the group members.

The plant protection is a crucial component of commercial agriculture (vegetable cultivation) in the study area. The infestation by weeds, insect pests and diseases led to a substantial decline of productivity in these crops. The power operated spray pumps are stated to be more efficient as compared to manual sprayers. Keeping in view the fact that the chemical requirements in both cases (manual knapsack and power operated spray pump) are same, the only difference was with respect to labour used. It was observed that the use of power operated spray pumps reduced the cost of labour to the extent of 233 percent over manual spray pump (Table 16).

Thus, it can be concluded from these findings that the mechanization of crop production operations is the need of the hour both with respect to time as well as cost considerations besides reducing the drudgery of both the humans as well as animals.CONCLUSIONS

The area under plough is continuously declining in the state. The ranks of marginal farmers are swelling overtime making farming unviable and the scatter of holdings, as indicated by the highest number of parcels per holding, in Himachal Pradesh is highest in the country. This constitutes one of the major roadblocks in the way of farm mechanization and calls for consolidation of these holdings. There are noticeable regional imbalances in the use of electricity in agriculture and the sale of tractors. This brings out the low extent of mechanization in these hilly areas vis-a-

Districts Number of tractors/'000 ha of net sown area

1972 1982 1992 2002

Bilaspur 0.20 0.83 3.67 14.31Chamba NA NA NA 7.34Hamirpur 0.03 1.36 3.66 11.08Kangra 0.03 1.94 7.01 21.54Kinnaur NA NA NA 0.41Kullu 0.12 1.82 2.36 2.45Lahaul and Spiti NA 1.61 3.75 35.76Mandi NA 1.14 2.14 4.06Shimla NA 0.03 0.00 0.09Sirmaur 0.28 4.32 11.80 42.07Solan 0.09 1.77 11.78 39.95Una 0.12 4.28 26.06 53.87HP 0.06 1.63 6.05 17.74

Table 12. The district-wise intensity of tractors in H.P, 1972-2012

Source: Computed from the data taken from Statistical Outline of Himachal Pradesh (various issues), Department of Economics & Statistics and Annual Season and Crop Report, Directorate of Land Records, Shimla-9, Government of Himachal Pradesh.

District Number of indigenous bulls per crossbred bull

1982 1992 2002 2012

Bilaspur 38 11 4 4Chamba 93 41 14 8Hamirpur 15 9 2 -Kangra 24 11 5 2Kinnaur 13 6 3 2Kullu 19 12 4 2Lahaul & Spiti 6 7 2 4Mandi 31 14 7 4Shimla 12 9 5 3Sirmaur 26 13 10 7Solan 24 8 5 4Una 15 6 4 1H.P. 22 12 6 4

Table 13. Changes in the number of indigenous bulls in H.P, 1982-2012

Source: Debnath (2016).

vis main states due to terrain related constraints. Though the sale of power tillers registered significant growth during the last decade, yet it lost the growth momentum after 2011-12. This really is not a healthy sign for farm mechanization, especially in mountainous states where power tillers have to play a crucial role in the agricultural development. Within HP, in hilly districts like Shimla and Kinnaur, intensity of tractor/power tiller at less than one tractor per one thousand hectares of net sown area is extremely low. The field evidence indicates that use of power tiller is the best option as it results in substantial cost saving over bullocks, both hired and owned, for land preparation and sowing. Similarly, the use of power operated spray pumps also reduced the cost

241


Machinery Farm size group

Marginal Small Semi medium Medium Large Overall

Hand operated sprayer/duster 20.48 29.28 33.49 39.64 37.5 23.84Power operated sprayer/ 1.61 2.58 2.95 4.00 3.13 1.97Seed-cum fertilizer drill/seed drill 1.76 2.35 1.89 2.18 6.25 1.91Potato and groundnut digger 5.63 6.07 6.37 6.55 3.13 5.79Bund former 2.19 2.23 1.89 2.55 6.25 2.20Diesel engine pump set 0.36 0.46 0.71 1.45 3.13 0.45Electric pumps 0.37 0.52 1.06 2.18 6.25 0.53Power Tiller 0.75 0.69 1.18 1.45 3.13 0.80Vegetable transplanter (tractor driven) - 0.06 - - 3.13 0.02Raised bed planter (tractor drawn) 0.03 0.06 - 0.36 3.13 0.05Power weeder (self- propelled) - 0.06 - - 3.13 0.01Sprinkler used for irrigation purposes 0.96 1.43 1.53 1.82 3.13 1.13Drip irrigation 0.34 0.80 1.42 2.55 3.13 0.59

Table 14. Estimated number of operational holdings using agriculture machinery by major size groups in Himachal Pradesh

(Percent holdings)

Source: All India Report on Input Survey, 2011-12, Directorate of Economics & Statistics, Department of Agriculture, Cooperation &Farmers Welfare, Ministry of Agriculture, Cooperation & Farmers Welfare, Government of India.

Particulars Amount (`/ha)

Cost saving over

owned bullocks (Percent)

Owned bullocks

Cost of maintenance of bullocks 37734 -

Human labour 7500

Total 50805

Hired bullocks

Bullocks charges 18570 94.88

Human labour 7500

Total 26070

With hired power tiller

Power tiller hiring charges 13504 207.83

Human labour 3000

Total 16504

Table 15. The financial efficiency of power tiller over bullocks for field preparation

Particulars Amount(`/ha)

Saving in cost (Percent)

Labour use with the manual sprayer

1500 -

Labour use with power operated the sprayer

450 233.33

Table 16. The financial efficiency of power operated sprayer pump over the manual prayer

substantially over manual spray pump. Thus, it can be concluded that the mechanization of farm operations is the

need of the hour both with respect to time as well as cost considerations. This will result in increased farm incomes which are the ultimate objective of all agricultural planning in the country. The policy interventions need to make these farm machines easily accessible to the peasantry in the mountainous states if agro-climatic niches available in these regions are to be harvested. For this, the experience and lessons of custom hiring of farm machinery need to be adopted with necessary modifications to suit local conditions for the overall development of agriculture in the mountainous regions.REFERENCESDebnath, U. (2016). A spacio-temporal study of livestock economy

of Himachal Pradesh. (Master's Thesis). CSKHPKV Palampur, Himachal Pradesh (unpublished).

Kumar, V., Chauhan S.K., Lal, H. & Thakur, R. (2017). Extent and scope of farm mechanization in Himachal Pradesh Crop Diversification Promotion Project (HPCDP) Areas: A pilot study. Research Report, Department of Agricultural Economics, Extension Education & Rural Sociology, College of Agriculture, CSK Himachal Pradesh Krishi Vishvavidyalaya, Palampur (HP).

Kumar, V., Sharma, H.R. & Sharma, R.K. (2004). Livestock economy of Himachal Pradesh: Growth patterns, ecological implications and state policy. Agricultural Economics Research Review, 17(1), 57-76.

Ninan, T.N. (2017, April 29/30). Weekend ruminations-MP's 'miracle agriculture. Business Standard.

Sharma, V.P., Singh, R.V. & Gajja, B.L. (1995). Livestock situation in India: A spatio-temporal analysis. Indian Journal of Agricultural Economics, 50 (3), 391.

The Government of Himachal Pradesh. (2017). Economic survey, 2016-17. Directorate of Economics and Statistics, Government of Himachal Pradesh, Shimla (HP).

The Government of India. (2016). State of Indian Agriculture,

2015-16. Directorate of Economics and Statistics, Department of Agriculture, Cooperation and Farmers Welfare, Ministry of Agriculture, Cooperation & Farmers Welfare, New Delhi.

242


ABSTRACTFinancial literacy is considered an important adjunct for the promotion of financial inclusion and ultimately financial stability. With this background the study was taken up to assess and compare the financial literacy among the self-help group members engaged in weaving and coir activity of Salem district. 160 sample respondents involved in weaving activity and 40 in coir activity were randomly selected. The data was analyzed using simple conventional analysis and the results revealed that the financial knowledge was found to be low and the financial behavior and financial attitude was average among the women self-help group members engaged in weaving and coir activity. The study suggests that banks could take up proper extension efforts on financial literacy programmes. FLCCC should make efforts in providing financial counseling services to the SHGs through face to face interactions. The members of SHGs should be encouraged to engage in multiple income earning activities to improve their standard of living.

KeywordsFinancial attitude, financial behavior, financial knowledge, financial literacy, women SHG's.

JEL CodesB26, F65, G02, G21, G31.

*N. Jayashree, N. Deepa, and A. Raj Shravanthi

Department of Agricultural and Rural Management, Tamil Nadu Agricultural University, Coimbatore-641003


Received: November 21, 2017 Revision Accepted: May 15, 2018

Comparison of Financial Literacy among Women Self Help Group Members Engaged in Weaving and Coir Activities in Salem District of Tamil Nadu


DOI: 10.5958/2322-0430.2018.00126.9


www.soed.in


UGC Approved


243

INTRODUCTIONGlobally financial inclusion, financial literacy and

consumer protection has been organized as the pursuit of financial stability. It has been universally accepted that the objective of inclusive growth cannot be obtained without financial inclusion. Financial literacy is considered an important adjunct for the promotion of financial inclusions and ultimately financial stability (Nash, 2012).

Financial literacy is the ability to make informed judgments and to take effective decisions regarding the use and management of money (Noctor et al., 1992). Financial literacy not only improves the quality of life for individuals but also improves the quality of financial markets. Financially educated consumers benefit the economy by encouraging genuine competition as well as force the service providers to improve their levels of efficiency. Financial literacy helps in improving the quality of financial services and contributes to economic

growth and development of a country. Self Help Group (SHG) is a group of 12 to 20 women of the same socio-economic background who voluntarily work together for their own upliftment. The unique feature of the SHG is its ability to inculcate among its members sound habits of thrift, savings and banking. Regular savings, periodic meetings, compulsory attendance, and systematic training are the salient features of the SHG concept. In the backdrop of these facts, the present study was carried out to compare the financial literacy among women members of the self-help groups engaged in weaving and coir activities in Salem district of Tamil Nadu.REVIEW OF LITERATURE

Hogarth and Hilgert (2002) surveyed 1000 respondents and asked their experience with any of 12 different financial products or services like savings and checking accounts to credit cards, mortgages, refinancing, and investments etc. Majority of the customers were financially knowledgeable experience

with the product or service. Nearly 56percent of financially knowledgeable respondents had invested in mutual funds and only 26 percent of respondents had less financial knowledge.

Kim (2004) found that workplace 'Cooperative Extension financial education Program' created a significant change in the respondents financial attitude, financial behaviours. It created more effects on their retirement planning, and also improved their personal finances in other key areas such as credit management, general financial planning and cash management. Perceived financial well-being and financial knowledge also had improved.

Borden et al. (2008) indicated that having a positive attitude toward financial management can have a positive effect on an individual's intentions to utilize several types of savings/investment vehicles, limit credit card use, and manage their finances in more beneficial ways.

Lusardi and Mitchell (2008) examined the factors influencing women's retirement planning, relying on a purpose-designed module for that they developed the Health and Retirement Study (HRS) on planning and financial literacy. A sample of 785 women respondents from U.S were taken for the study. They found that older women in the US had very low levels of financial literacy, and the majority of women have undertaken

Scheresberg (2013) analyzed the financial literacy and financial behavior in a sample of approximately 4,500 young adults in the age group of 25 to 34. They found that most young adults lack basic financial knowledge. Financial literacy was especially low among certain demographic groups, such as women, minorities, and lower-income or less-educated people. Only 49 percent of young respondents with a college education and 60percent of young respondents with postgraduate education could correctly answer three simple questions designed to assess financial literacy. They showed that respondents who display higher financial literacy or higher confidence in their math or personal finance knowledge have better financial outcomes, they are more likely to plan savings for emergencies.

Jappelli and Padula (2013) analyzed data for 39 countries and found that financial literacy was a determinant for the level of national savings and that the impact of literacy was potentially large. Increase in standard deviation by one was equal to increase of national savings by 3.6 percent.

Xiao et al. (2014) examined the association of financial literacy and financial behaviour of college students. Financial literacy was measured by both subjective and objective methods. Financial knowledge and financial behaviours were categorized into risky paying and borrowing behaviours. It was found that only subjective knowledge was correlated with a reduction in both composite behaviours. Both subjective and objective knowledge, however, reduced some specific risky paying and borrowing behaviours.

METHODOLOGYThe study was conducted in six villages belonging to

four blocks of Salem district, namely Veerapandi, Mecheri, Omalur and Tharamangalam. A total of 200 respondents were selected randomly as the sample for the study, of which 160 sample respondents were doing weaving activity and 40 respondents were doing coir activity. The primary data were collected using structured interview schedule developed by OECD, through personal interview method during the year 2017 and were analyzed using conventional statistical tools.RESULTS AND DISCUSSIONDemographic Profile of the Weaving and Coir Sample Respondents

The demographic variables of the sample units serve as prerequisite for better understanding of their knowledge level, behavior on finance and attitude on finance. Therefore data on age, marital status, family type, educational status and income was analyzed (Table 1).

The perusal of Table 1 showed that about 37 and 33 percent of the weaving and coir sample respondents belonged to the age group of 30-39 years respectively. Only negligible number of respondents belonged to 60- 69 and more than 70 years of age. In the selected study area most of the sample respondents belonged to the middle age group and fewer number of youngsters were involved in self-help groups in both weaving and coir activity.

Around 90 percent of weaving and 95 percent of coir sample respondents were married. Nearly 16 percent of widowed respondents belonged to weaving activity and only negligible number of widowed respondents belonged to coir activity. Out of 160 sample respondents, about 63 percent of weaving and 57 percent of coir respondents were in nuclear family. The results revealed that 74 percent of weaving and 47.50 percent of coir sample respondents were literates (Table 1). Among the total sample respondents nearly 69 percent respondents were literates. Thus, there was a high literacy level in the study area. This indicated that there was a chance for better utilization of financial resources in achieving meaningful financial literacy rate.

Nearly 47 percent of the total sample respondents had monthly income between `10,000 to `25,000 and 44.50 percent of the total sample respondents with monthly income up to `10,000. Weaving respondents earned higher income than the coir respondents because they also weave silk sarees, therefore their value of business was higher than the coir works. Financial Knowledge

A financially literate person has some basic knowledge on key financial concepts. The core questionnaire therefore includes eight questions to test levels of financial knowledge. The questions were chosen to cover a range of financial topics such as, basic numeracy, simple and compound interest, inflation and

244


return, inflation and prices, risk and return and role of diversification in risk reduction. It was found that, 83 percent of weaving and 85 percent of coir self-help group respondents had knowledge on basic numeracy. About 17 percent of weaving and 15 percent of coir respondents lacked knowledge on basic numeracy. Most of the respondents were literate in that study area so they have

the basic numeracy knowledge. Most of the respondents (99.50 percent) of self-help group have knowledge on inflation and return. Similarly 98 percent of self-help group respondents have knowledge on inflation and prices (Table 2).

About 39 and 23 percent of the weaving and coir making self-help group respondents have knowledge on

Demographic profile Category No. of weaving respondents (n =160)1

No. of coir respondents (n =40)2

Total number of respondents (N=200)

Age (Years) 20-29 15.60 820.00 16.50

30-39 36.90 32.50 36.00

40-49 25.00 22.50 24.50

50-59 14.30 15.00 14.50

60-69 6.90 7.50 7.00

Above 70 1.30 2.50 1.50

Marital status Married 90.00 95.00 91.00

Widowed 10.00 5.00 9.00

Family type Nuclear family 163.00 57.00 162.00

Joint family 37.00 43.00 38.00

Educational status No formal education 26.25 52.50 31.50

Primary school 23.13 12.50 21.00

Secondary school 29.38 35.00 30.50

Higher secondary 6.88 - 5.50

Graduate 14.38 - 11.50

Income level (`) Up to 10,000 45.60 40.00 44.50

10,000 to 25,000 46.25 50.00 47.00

25,000 to 50,000 8.15 10.00 8.20

Table 1. Demographic profile of the sample respondents(Percent)

Financial concept Knowledge level

No. of weaving respondents (n =160)1



Basic numeracy K 83.00 85. .00 83.50

LK 17.00 15.00 16.50

Inflation and return K 99.00 100.00 99.50

LK 1.00 - 0.50

Inflation and prices K 98.00 100.00 98.00

LK 2.00 - 2.00

Simple interest K 39.00 23.00 36.00

LK 61.00 77.00 64.00

Compound interest K 7.00 2.50 6.00

LK 93.00 97.50 94.00

Risk & return K 36.00 23.00 33.00

LK 64.00 77.00 67.00

Risk diversification K 10.00 5.00 9.00

LK 90.00 95.00 91.00

Table 2. Knowledge on financial concepts of the sample respondents(Percent)

K = Knowledge; LK = Lack Knowledge.

245

Jayashree et al.: Comparison of financial literacy among women self help group members engaged

simple interest rate respectively and 61 and 77 percent of the weaving and coir making sample respondents lacked knowledge of simple interest rate. Similarly, 94 percent of the total sample respondents lacked knowledge on compound interest. Only six percent of the respondents have knowledge on compound interest. Hence, it is necessary to update their knowledge on simple and compound interest.

Only 36 and 23 percent of the weaving and coir self-help group respondents have knowledge on risk and return. Most of them lacked knowledge on risk and return. Hence, it is necessary to create awareness on return policies through campaigns and meetings. Similarly, 10 and 5percent of the weaving and coir self-help group respondents have knowledge on risk diversification. Majority (91 percent) of the total respondents lacked knowledge on risk diversification. Financial Behavior

The study attempted to measure the financial behavior of the respondents by collecting the information on the way respondents dealt with money in their daily lives. A total of eight items were asked to capture their behavior that included assessment of affordability of products and expenditures, timely payments of bills, Monitoring the financial affairs, planning and monitoring the off household budgets, active saving habits, borrowing propensity, setting of long-term goals and type of information for choosing a financial products.

Affordability of Products and ExpendituresThe perusal of Table 3 revealed that 78 and 53 percent

of the weaving and coir respondents considered the affordability level before buying something. About 22 and 47 percent of them were not considering the affordability level. Though they could not afford, they were ready to get the money from informal sources and buy the things.Timely Payment of Bills by the Sample Respondents

It can be inferred that 100 percent of weaving and coir self-help group respondents paid their bills on time. They were not likely to delay any bill payments because of penalty for delaying payments (Table 4). Monitoring the Financial Affairs of the Sample Respondents

An examination of Table 5 revealed that nearly 74 and 50 percent of the weaving and coir women self-help group members were monitoring their family financial affairs respectively. About 26 and 20 percent of the weaving and coir respondents were not monitoring their financial affairs and their partners alone monitor their family financial affairs respectively. The respondents who made decisions independently and along with partner were monitoring the financial affairs. Planning and Monitoring the Household Budgets

It could be inferred from the Table 6, about 63 and 55 percent of the weaving and coir making respondents showed responsibility of family budget and planning.

Affordability of products and expenditures




Consider 78.00 53.00 73.00Not consider 22.00 47.00 27.00Total 80.00 20.00 100.00

Table 3. Affordability of products and expenditures of the sample respondents(Percent)

Timely payment of bills No. of weaving respondents (n =160)1



Made payments on time 100.00 100.00 100.00Not on time 0.00 0.00 0.00Total 80.00 20.00 100.00

Table 4. Timely payment of bills by the sample respondents(Percent)

Monitoring financial affairs No. of weaving respondents (n =160)1



Monitor 74.00 50.00 69.00Not monitor 26.00 20.00 31.00Total 80.00 20.00 100.00

Table 5. Monitoring of financial affairs of the sample respondents(Percent)

246


Nearly 37 percent and 45 percent of the weaving and coir respondents do not have the responsibility for their family budget. Respondents who involved in the financial planning and budgeting were also involved in planning and monitoring their household budget.Saving Habits of the Sample Respondents

The perusal of Table 7 revealed that apart from saving their money in self-help group savings account, 53 and 62.50 percent of weaving and coir respondents saved their money in informal clubs. About 16 and 12.50 percent of the weaving and coir respondents invested money in properties, jewels, livestock's etc., Only four percent of the weaving respondents saved through a bank account and two percent of the respondents saved their money at home itself and 25 percent of both weaving and coir respondents were not actively involved in savings. The financial institution has more rules and regulations and it was very difficult to understand and utilize the financial services so most of the respondents had not saved their money at bank. Setting of Long-term Goals of the Sample Respondents

It is found from the Table 8 that nearly 64 and 83 percent of the weaving and coir making respondents had the long-term financial goals, only 36 and 18 percent of the weaving and coir respondents were not having any financial goals. The respondents who had a long-term

goal are in the age group of 20-50 years because of more responsibility like construction of own house, buying property, children education, etc. than the other age group.Financial Problems Faced by the Sample Respondents

From Table 9, it could be inferred that 68 percent (109) of the weaving respondents faced financial problems in the last twelve months. Of the total respondents nearly 26 percent of them were not facing any financial problems related to finance. Most of the respondents faced unexpected financial problems, health issues, etc. And their income level was not enough to handle those situations.Arrangements Made by the Respondents for Financial Problems

The perusal of Table 10 revealed that out of 109 weaving respondents 60 percent of the respondents had taken the loan from local money lenders, while 29 percent of the respondents were cut back their spending and seven percent of the respondents worked overtime and earned extra money and four percent of the respondents took money from their friends to solve the financial problems. It was noticed that as high as 75 percent of the coir respondents took money from local informal sources. About 22.50 percent of the respondents spend less money and 2.50 percent of the respondents worked overtime and earned extra money to solve the problems. Of the total

Planning and monitoring of household budget




Monitor 63.00 55.00 61.00Not monitor 37.00 45.00 39.00Total 80.00 20.00 100.00

Table 6. Planning and monitoring of the household budget(Percent)

Method of savings No. of weaving respondents (n =160)1



Informal clubs 53.00 62.50 54.50Savings account 4.00 0.00 3.00Cash at home 2.00 0.00 2.00Other ways 16.00 12.50 15.50No savings 25.00 25.00 25.00Total 80.00 20.00 100.00

Table 7. Saving habits of the sample respondents(Percent)

Goal setting No. of weaving respondents (n =160)1



Have goals 64.40 82.50 68.00Have no goals 35.60 17.50 32.00Total 80.00 20.00 100.00

Table 8. Setting of long-term goals by the sample respondents(Percent)

247


Types of information No. of weaving respondents (n =160)1



Not choose a product 36.00 58.00 40.50Independent decision 15.00 12.00 14.50Dependent decision 49.00 30.00 45.00Total 80.00 20.00 100.00

Table 11.Type of information for choosing a financial product(Percent)

respondents, majority (64 percent) of them depended on informal sources to solve their financial problems.Type of Information for Choosing Financial Products

The perusal of Table 11 showed that 36 and 58 percent of the weaving and coir making respondents had not chosen any financial products for the last twelve months. About 64 percent of the weaving respondents had choose financial products out of that 15 percent of the respondents made independent decisions and 49 percent of the respondents have collected the information from others and made the dependent decisions. In coir activity, about 42 percent of the respondents were having financial products. Out of which 30 percent of the respondents were making independent decisions and only 12 percent of them were making decisions by their own in choosing the financial products. Of the total respondents, only 45 percent of them were choosing financial products on their own. Hence, creating awareness about different financial products among the respondents would help them to choose the products by their own decisions.Financial Attitude

The financial attitude were measured based on their extent of belief in planning, propensity to save, propensity to consume, attitude towards expenditure and satisfaction level of present financial situation.

Extent of belief in financial planningAccording to Table 12, it could be inferred that of the

total respondents, nearly 90 percent of them were having belief in planning. Only very limited percent of the respondents (9 percent in weaving and 20 percent in coir making) were not having belief in financial planning.Propensity to save

The results presented in Table 13 clearly indicated that 72 and 65 percent of the weaving and coir making respondents were satisfied when they were saving money for future whereas28 and 35 percent of the respondents were satisfied with spending the money. The respondents who had financial planning in their house were satisfied with savings.Propensity to consume

The perusal of Table 14 revealed that 77 and 52 percent of the weaving and coir making respondents have a belief that the money was both for savings and spending. About 23 and 48 percent of the weaving and coir respondents had a thought that money was only for savings purpose. Out of 200 sample respondents majority of them (72 percent) believed that money is for both savings and spending.Attitude towards expenditure

The results presented in Table 15 showed that 51 and

Financial problems No. of weaving respondents (n =160)1



Have problems 68.00 100.00 74.50Have no problems 32.00 0.00 25.50Total 80.00 20.00 100.00

Table 9. Financial problems faced by the sample respondents(Percent)

Arrangements for problems No. of weaving respondents (n =160)1



Loan from local money lenders 60.00 75.00 63.75

Spend less 29.00 22.50 27.50

Work overtime 7.40 2.50 6.00

Borrow from friends 3.60 0.00 2.75

Total 73.00 27.00 100.00

Table 10. Arrangements made by the respondents for financial problems(Percent)

248


Propensity to consume No. of weaving respondents (n =160)1



Money is to spend 23.00 48.00 28.00Money is for saving and spending 77.00 52.00 72.00Total 80.00 20.00 100.00

Table 14. Propensity to consumption of the sample respondents(Percent)

Attitude towards expenditure No. of weaving respondents (n =160)1



Worried 51.00 45.00 49.50Not worried 49.00 55.00 50.50Total 80.00 20.00 100.00

Table 15. Attitude towards expenditure of the sample respondents(Percent)

Planning No. of weaving respondents (n =160)1



Belief in planning 91.00 80.00 88.50No belief in planning 9.00 20.00 11.50Total 80.00 20.00 100.00

Table 12. Extent of belief in financial planning(Percent)

Propensity to save No. of weaving respondents (n =160)1



Satisfied with saving 72.00 65.00 70.50Satisfied with spending 28.00 35.00 29.50Total 80.00 20.00 100.00

Table 13. Savings belief of weaving and coir respondents(Percent)

45 percent of the weaving and coir making respondents tend to be worried about their living expenditures. Nearly half of the total sample respondents were worried about their living expenses because their income was not sufficient to cover the expenditure.Satisfaction Level of Present Financial Situation

The perusal of Table 16 revealed that nearly half of the total sample respondents were satisfied with their present financial situation, because they were not worried

Satisfaction level No. of weaving respondents (n =160)1



Satisfied 55.00 47.50 53.50Not satisfied 45.00 52.50 46.50Total 80.00 20.00 100.00

Table 16. Satisfaction level of present financial situation of the sample respondents(Percent)

about their living expenditures.Comparisons of Dimensions of Financial Literacy between Weaving and Coir Respondents

It could be inferred that compared to coir (8 percent), weaving (30 percent) had high financial knowledge because of more literate people in weaving activity (Table 17). About 24 and 40 percent of the weaving and coir respondents had poor financial knowledge respectively. Thus, it could be concluded that financial knowledge was

249


Response variable Weaving Coir

Financial knowledgeHigh financial knowledge 30.00 8.00Average financial knowledge 46.00 52.00Poor financial knowledge 24.00 40.00Financial behaviorPositive financial behavior 54.00 45.00Average financial behavior 35.00 33.00Indifferent financial behavior 11.00 22.00Financial attitudeHighest positive financial attitude 6.00 17.00Positive financial attitude 75.00 58.00Indifferent financial attitude 19.00 25.00

Table 17. Dimensions of financial literacy of weaving and coir respondents

(Percent)

comparatively high in weaving activity than the coir activity. About 54and 45 percent of the weaving and coir respondents showed positive financial behavior respectively.

It was noticed about 35 and 33 percent of the weaving and coir making respondents showed average financial behavior and 11 and 22 percent of the weaving and coir respondents have poor financial behavior respectively. Most of the respondents resorted to the savings through the self-help group accounts. Both weaving and coir self-help group respondents showed only little difference in the point of financial behavior. Only 6 and 17 percent of the respondents showed highest positive attitude. About 75 and 58 percent of the weaving and coir respondents showed positive financial attitude. Only 19 and 25 percent of the weaving and coir making respondents possessed indifferent financial attitude. Most of the sample respondents possessed favorable financial attitude which led to favorable financial behavior. Though the respondents have good financial attitude they do not have enough income for investments.CONCLUSIONS

Financial literacy among women self-help groups' members engaged in weaving and coir activities in Salem district of Tamil Nadu were compared with respect to financial knowledge, financial behaviour and financial attitude. Weaving respondents have high knowledge on basic numeracy skill than the coir respondents. Only negligible number of respondents (6 percent) has knowledge on compound interest rate. About 36 and 23 percent of weaving and coir making respondents have knowledge on risk and return respectively. Only nine percent of the sample respondents have knowledge on risk diversification. Generally the financial knowledge was high in the weaving respondents than the coir respondents.

About 78 percent of the weaving respondents were considering the affordability level before purchase anything and only 53 percent of the coir sample

respondents were considering the affordability. It was found that 100 percent of both weaving and coir sample respondents were paying their bills on time. It was reported by 100 percent of the coir respondents that they faced financial problems and only 68 percent of the weaving respondents faced financial problems. For solving their financial problems nearly 75 percent of the coir respondents and 60 percent of the weaving respondents were arranging money from local money lenders. Majority of the weaving (54percent) and coir (45percent) sample respondents had positive financial behavior. Weaving sample respondents had higher financial behavior than the coir sample respondents.

Weaving respondents have high believe in financial planning than the coir respondents. About 91 and 80 percent of the weaving and coir respondents respectively have a belief in financial planning. About 72 and 65 percent of the weaving and coir respondents were satisfied when they were saving money for future respectively. About 75 percent of weaving respondents have positive financial attitude than the coir (58percent) sample respondents. SUGGESTIONS AND POLICY IMPLICATIONSSome of the suggestions and policy implications emerged from this study are as follows:·Bank could take up proper extension efforts on

financial literacy programme through special camps to educate the SHG by NGOs, volunteers and college students. They may also encourage adopting small savings schemes.

·The functioning of the Financial Literacy and Credit Counselling Centre (FLCCC) needs to be improved in providing financial counselling services to the Self Help Groups through face to face interactions.

·None of the SHG considered for this study were involved in more than one income gaining activity. The Micro Small Medium Enterprises (MSME), Department of the Government of India must ensure that the risk faced by these Self Help Groups to be reduced by encouraging them to engage in multiple activities.

·The SHG involved in both weaving and coir making activities were having lower awareness on both simple and compound interests. This paves the way for their susceptibility to various losses. This can be overcome by periodic messages using Information and Communication Technology (ICT) tools.

REFERENCESBorden, L.M., Lee, S.A., Serido, J., & Collins, D. (2008).

Changing college students' financial knowledge, attitudes, and behavior through seminar participation. Journal of Family and Economic Issues, 29(1), 23-40.

Hogarth, J.M., & Hilgert, M.A. (2002). Financial knowledge, experience and learning preferences: Preliminary results from a new survey on financial literacy. Consumer Interest Annual, 48(1), 1-7.

Jappelli, T., & Padula, M. (2013). Investment in financial literacy and saving decisions. Journal of Banking and

250


Finance, 37(8), 2779-2792.Kim, J. (2004). Impact of a workplace financial education

program on financial attitude, financial behavior, financial well-being, and financial knowledge. Proceedings of the Association for Financial Counseling and Planning Education, 22, 82-89.

Lusardi, A., & Mitchell, O. S. (2008). Planning and financial literacy: How do women fare? American Economic Review: Papers and Proceedings, 98 (2), 413–417. Retrieved from https://www.dartmouth.edu/~alusardi/ Papers/AER-FinalPublishedVersion.pdf.

Nash, D.R. (2012). Financial literacy: An Indian scenario. Asian Journal of Research in Banking and Finance, 2(4), 79-84.

Noctor, M., Stoney, S., & Stradling, R. (1992). Financial

literacy: A discussion of concepts and competencies of financial literacy and opportunities for its introduction into young people learning. Report Report. National Westminster Bank. London: National Foundation for Education Research.

Scheresberg, Carlo de Bassa. (2013). Financial literacy and financial behavior among young adults: Evidence and implications. Numeracy, 6(2), 1-21. Retrieved from http://scholarcommons.usf.edu/cgi/viewcontent.cgi?article=1138&context=numeracy

Xiao, J.J., Ahn, S.Y., Serido, J., & Shim, S. (2014). Earlier financial literacy and later financial behaviour of college students. International Journal of Consumer Studies, 38(6), 593-601.

251


ABSTRACTEmpirical literature lacks in quantum of studies on option payoffs especially in context of Indian foreign exchange contracts. This study fills this gap and tried to assess the bullish option payoffs with underlying USD-INR contract. The study was done for a period of 68 months from October 2010 to June 2016. Three bullish option strategies namely long call, short put and covered call were applied and their payoffs were assessed for different moneyness. In addition strategies were also compared using multiple comparison tests. Although no statistically significant differences were recorded when compared for different moneyness in the respective strategy, but in comparison between the strategies, long call was found to be a outperformer and the result is statistically significant.

KeywordsBullish, covered call, long call, short put, USD-INR.

JEL CodesG11, G13, G15, M16, N25.

1 2 3*Avneet Kaur , Sandeep Kapur and Mohit Gupta

1 2 3Research Scholar, Professor and Assistant Professor, School of Business StudiesPunjab Agricultural University, Ludhiana -141004


Received: December 29, 2017 Revision Accepted: May 21, 2018

Comparative Study of Bullish Option Payoffs in USD-INR Market


DOI: 10.5958/2322-0430.2018.00127.0


www.soed.in


UGC Approved


252

INTRODUCTIONAmongst the derivative instruments, trading in

options has increased phenomenally on stock exchanges all over the world. Options were first introduced by the Chicago Board Options Exchange in April, 1973 (Business growth in CD segment, 2017). The Philadelphia Stock Exchange initiated option trading on major currencies in 1982. Ever since these beginnings, trading on currency derivatives has attracted the interest from academics, practitioners, and regulators. The heightened price volatility, along with factors such as greater integration of financial markets, volatile risk environment, access to cheap and faster information and better ability to analyze it have led to a greater need for protection against the risk factors such as price risk, counter-party risk, and operating risk(Lodha,2008).

Due to fast growth in cross-border trade, changes in international economic and political landscape, global meltdown in 2008, recession and foreign investment flows has led to increase in volatility in the foreign exchange rates. The need to hedge the foreign exchange risk has increased at higher rate thereby ensuring a secure future financial position. This has led to introduction of

currency derivatives in Indian market which has been growing at high rate. Between the financial years 2005 and 2017, cash market turnover increased by 11.4 percent on annual compounding basis, while Future and option turnover rose by 35 percent compounded annually (Business growth in CD segment, 2017). The turnover in currency options increased by 81 percent to USD 0.2 trillion, or 9 percent of the total forex (FX) segment (Guru, 2009). At present, there are number of specialized financial instruments available to the investors which protect them from the downside market movements. Currency futures and currency options are the two hedging instruments under the currency derivative segment of NSE. For hedging the USD-INR risk, investors, firms, companies have been restricted to forward and future contracts but in the last few years' currency options have emerged as a popular alternative to forward contracts. The foreign exchange options market is the largest, deepest and the most liquid markets for any kind of options across the world.

A foreign exchange option is a contract that allows the holder to buy or sell a designated quantity of foreign currency at a specified exchange rate up to a specified date

(Pasmantier, 1992). Currency options can be either be bought or sold. When an investor buys a currency option the price he pays for the option is called the currency option premium. When an investor sells currency option he will receive the currency option premium. The inherent flexibility in option contracts and predetermined cash outflows make them the most favoured instruments. Options give the traders or investors an opportunity to hedge their risk, when they are trading in stocks or commodities or currencies. The introduction of options markets has allowed the investors to participate in the price movements of stocks or exchange rate without investing the large amount of funds in buying the stock or currency or commodities.

Much of literature is available that studies the performance of option strategies on index, stocks, portfolios but very less research has been done on studying the performance of currency option strategies especially in Indian currency market. This paper focuses on risk and return characteristics of option strategies with particular reference to bullish scenario of US dollar (USD). It is pertinent to mention here that what bullishness is to USD, it is bearishness of the same extent to Indian Rupee (INR). The particular bullish strategies that have been studied and referred in this paper are namely long call strategy, short put strategy and covered call strategy. REVIEW OF LITERATURE

Several studies have been conducted highlighting the mixed results on the performance of option strategies. But most of these studies are limited to stocks, indices and some of them relate to commodities. In fact the studies on the performance of options in the currency market are very few and rarely published. Nevertheless, the payoffs of the options in any kind of assets are similar in nature so the findings become a kind of universal learning.

The results of the study by Kruizenga (1964) depicted that longer length calls produced relatively larger average returns than the shorter calls, which might be expected in a rising market. He studied the buying and selling of options during the period 1953-56, which was one of an extraordinarily bullish stock market. It was found that call buyers made substantial profits (during the period studied) and put buyers had losses. The literature on long call option strategy to hedge the foreign exchange risk presented that the firm can create a riskless position by buying a foreign exchange call and shorting a currency forward contract. If the firm 'wins the contract, then it will not exercise the call option and if the firm loses the contract, it can exercise the call and buy out its short position on the forward market. In both the cases, the firm has to incur the cost equal to the cost of buying call option i.e. call premium (Feiger & Jacquillat, 1979). A similar study by Dash et al. (2007) concluded that the use of long calls exceed the returns on the underlying stocks.

Studies on put-write strategy depicts that put index outperformed the S&P 500 Index, leading to 39 percent

less volatility and generate compounded annual return in excess of S&P 500 total return index. Put-write strategy generated risk adjusted returns superior to those of S&P 500 index. One month volume based protective put strategy (strategy which is favored by investors during volatile market) outperformed the other option strategies. This strategy can be beneficial for the investors who want to increase their income and try to increase risk adjusted returns, in return for risking under performance during bull market (Ungar & Moran, 2009). Similar studies (Chicca & Larcher, 2012) have concluded that writing put options generated high returns. These returns are compensation for bearing investors' aversion to volatility risk. The use of put options also plays an important role in portfolio returns. For the investors' benefit written put positions can be used to leverage other portions of the portfolio. Portfolios comprising written options outperformed the benchmark portfolio even after considering transaction costs and margin calls (Doran & Fodor, 2008). Aggarwal and Gupta (2013) found protective strategies could outperform a simple buy and hold portfolio on risk adjusted basis. The authors also found that portfolio with 2 percent ITM long put had superior performance.

There are many popular option trading strategies, but covered call strategy has been the most popular option trading strategy. A covered call strategy, also called as buy-write strategy, is an investment strategy in which the investor buys a stock or a basket of stocks or any other asset class and writescall options for certain premium that cover the stock position or other asset class (Hoffmann & Fischer, 2012). Many individual investors and investment professionals consider covered call writing as a relatively safe and conservative trading strategy that not only generates extra income (Tergesen, 2001) but also partially hedging downside equity risk (Crawford, 2005). Various researchers have observed that writing of covered call strategy produced substantially larger returns than the traditional buy and hold strategy, although the risk level of the optioned portfolio as measured by the standard deviation of returns was lower (Bookbinder, 1976; Kassouf, 1977; Pounds, 1978; Grube & Panton, 1978; Yates & Kopprasch, 1980). Merton et al. (1978) found that fully covered strategies are less risky than holding the underlying stock. Aggarwal and Gupta (2013) found that covered call strategy could outperform a simple buy and hold portfolio on risk adjusted basis. The study found that portfolio with 5 percent ITM short call had superior performance.

In recent years there have been many articles written about the BXM Index and the buy-write strategy on the S&P 500 index (Roeder, 2004; Crawford, 2005; Feldman & Roy, 2004; Clary, 2007) as well as the buy-write on broader indices including the Russell 2000 (Kapadia & Szado, 2007). Zeikel (1980) observed that common stock, as measured by S & P 500 stock price averages, are expected to produce a return of 8.8 percent, while the

253

Kaur et al.: Comparative study of bullish option payoffs in USD-INR market

covered call would increase the return to 9.8 percent.Whaley (2002) studied the performance of the BXM and found that this passive buy-write strategy not only had a considerably lower risk level but also outperformed the S&P 500. Two follow up studies by Feldman and Roy (2004); Associates (2006) support these findings. A study by Benesh and Compton (2000) showed that during bullish phase the covered call strategy gave low returns but the strategy resulted in low risk as compared to the underlying stocks. Also while purchasing calls or puts the chances of suffering a 100 percent loss are quite high, especially for options that is at-the-money or out-of-the-money at the time of purchase.

Concludingly, it can be observed that the outperformance results of the options in the bullish market are mixed. The results in case of foreign exchange market are largely missing in the literature. It is with this aim, the present study was conducted. Next three sections of the paper deal with the research methodology applied to achieve the objectives of the study, empirical findings and the conclusion. METHODOLOGY

Bullish option strategies are employed when the options trader expects the price of underlying currency will appreciate. In the present case, study is conducted with assumption that trader is expecting that USD will appreciate. It is necessary to assess how high the price of an underlying currency can go and the timeframe in which the rally will occur in order to select the optimum trading strategy. Three bullish strategies namely long call strategy, short put strategy and covered call strategy have been covered in the present paper and the objective of the study is to select the best option strategy and among the strategies deciding upon which moneyness to choose.Long Call Strategy

Long call involves buying call option only. This is simplest hedging strategy to protect against any price rise. An investor will buy the call option if he is bullish in anticipation and is expecting market to give returns in the near future. Payoffs from the long call strategy are:

CT = MAX (ST - X, 0)Where,CT = Payoff to a call option at maturityX= Strike priceST= Price of underlying at maturity

Short Put StrategyShort put involves selling put option only. Trader sell

put option as a protection against any small price increase in the underlying asset. The premium received from the put option sold can be used to offset the increased costs in the cash market due to the price rise. Put options are often sold when trader is expecting mild bullishness in the near future. The payoffs from short out strategy are

PT = MAX (0, X - ST) Where,PT = Payoff to a put option at maturityX= Strike price

ST= price of the underlying at maturityCovered Call Strategy

The covered call strategy trader writes a call option contract on an asset the investor owns. The call option is considered “covered” because the writer of the option owns the underlying asset and has eliminated the unlimited risk associated with the writing of the call option. In this option strategy trader buys the underlying asset and simultaneously sells the call option against the same. This strategy is opted when the trader is anticipating mild bullishness in the underlying asset. The payoffs from the covered call strategy are

Profit from a Covered Call = UT - U0 – max [0, UT - X] + p

Where, UT = Price of the underlying asset at the exercise dateU0 = Price of the underlying asset at the inception of

the strategyX = Exercise pricep = Premium received on writing the call optionThe study was carried out using monthly closing

values of the US Dollar-Indian Rupee current future rates available on National Stock Exchange (NSE) of India for the period starting from 29 October, 2010 (the start of currency option market) to 30 June, 2016. In addition the monthly closing values of call and put options on USD-INR were collected for the same time period as above. Only European style options are available on the indices available on NSE. To better understand the returns on long call, short put and covered call strategy all the moneyness of options were studied. The option strategies were executed in three different ways: using out-of-the-money (OTM) calls, using in-the-money (ITM) calls and using at-the-money (ATM) calls. Specifically five types of moneyness namely ATM, 2 percent ITM, 5 percent ITM, 2 percent OTM and 5 percent OTM were taken up in the study. 2 percent OTM call options is defined as option with an exercise price greater than 102 percent of the prevalent spot price and 2 percent ITM calls have an exercise price less than 98 percent of the prevalent spot price. For put options, 2 percent OTM options have an exercise price 98 percent below the current spot price and 2 percent ITM put options have an exercise above 102 percent of the currency spot price. Similarly 5 percent OTM call options is defined as option with an exercise price greater than 105 percent of the prevalent spot price and 5 percent ITM calls have an exercise price less than 95 percent of the prevalent spot price. For put options, 5 percent OTM options have an exercise price 95 percent below the current spot price and 5 percent ITM put options have an exercise above 105 percent of the currency spot price. For calls and puts, the option is considered an ATM if the exercise price of the underlying currency is close to the spot price. In case strike price as mentioned were not available, nearest strike prices were utilized. The study was restricted to one month expiry options only due to volume considerations

254


In order to execute the option strategy, on starting of every month call/put options on USD-INR were sold at different moneyness. At the end of the month, all the positions were squared-off at the closing prices and a new cycle was started, which was squared-off on the next trading month and so on. The strategy was applied on 5 strike prices namely ATM, 2 percent ITM, 2 percent OTM, 5 percent ITM and 5 percent OTM and thereafter squared-off every month. For each currency-option combination, returns were computed as the excess of the payoff from the investment spent on entering in currency option. Percentage returns have been calculated on monthly basis. However, keeping in mind the high frequency of trade, transaction costs have been ignored. The results have been compared using t-test, ANOVA F-test and multiple range Tukey test.RESULTS AND DISCUSSION

This section includes summary statistics on returns from three bullish option strategies namely long call strategy, short put strategy and covered call strategy using different moneyness of the options. Table 1 depicts frequency distribution of returns, descriptive statistics and inferential statistics on returns from long call strategy. The strategy was applied on 5 strike prices namely ATM, 2 percent OTM, 5 percent OTM, 2 percent ITM and 5

percent ITM. In each case the strategy was applied for 68 th thmonths that is from 29 October, 2010 to 30 June, 2016.

In all the moneyness options, frequency of negative return has been found to be more than the frequency of positive returns. The incidence of no profit and no loss ranged from 1 event (in case of ATM) to 16 events (in case of 5 percent OTM). Among all the 5 moneyness options, the frequency of negative returns (69.12 percent) was found to be highest for 5 percent OTM and the frequency of positive return (32.35 percent) was found to be highest for ATM and 2 percent ITM long call strategy. In 4 out of 5 moneyness options, the mean return was found to be positive but median return was found to be negative in all the cases depicting larger number of high positive returns. Since this is long call strategy, the minimum return in all the 5 cases was found to be -100 percent (loss of entire premium paid on buying the option) but maximum return varied to a large extent. The distribution return in all the 5 cases was found to be non-normal as depicted by the results from Kolmogorov-Smirnov Test Statistics and Shapiro-Wilk. Kurtosis is more than 3 for all the 5 moneyness options which means distribution is leptokurtic.

Among all the five long call strategies, 5 percent OTM strategy was found to yield highest mean return

Measures ATM 2 percent OTM

5 percent OTM

2 percent ITM

5 percent ITM

Negative return 45(66.18)

45(66.18)

47(69.12)

40(58.82)

40(58.82)

Positive return 22(32.35)

11(16.18)

5(7.35)

22(32.35)

15(22.06)

No profit/loss 1(1.47)

12(17.64)

16(23.53)

6(8.83)

13(19.12)

Mean return 26.56 -31.66 267.57 56.92 89.08t-test (H = 0)0 1.05 -2.45 1.28 0.86 1.55

p-value 0.297 0.017 .203 0.389 0.125

F test (H = Returns from all Moneyness are equal) (p value)0 1.236(.295)

Median return -83.70 -50.82 -83.33 -18.03 -11.31

Std. deviation 208.22 106.77 1715.40 541.78 472.57

Minimum return -99.83 -99.91 -99.33 -99.93 -99.33

Maximum return 875.83 656.67 12100.00 4386.21 2433.33

Skewness 2.41 4.38 5.94 7.84 4.06

Kurtosis 6.40 26.03 37.27 63.43 17.21

Coefficient of variation 7.84 -3.37 6.41 9.52 5.30Kolmogorov-smirnov test statistics (p-value) 0.272

(.000)0.261(.000)

0.488(.000)

0.386(.000)

0.404(.000)

Shapiro-wilk(p-value)

0.660(0.000)

0.569(0.000)

0.212(0.000)

0.215(0.000)

0.410(0.000)

Table 1. Returns from long call strategy(n=68)

Source: Authors' own calculation.*p< .05, Figures in parentheses represents percentages.

255


(M=267.570, SD=1715.39) and the lowest return occurred in 2 percent OTM strategy (M= -31.66, SD= 106.77). 5 percent ITM offers the best risk/reward ratio and the lowest volatility percentage per unit of return (CV = 5.30). 2 percent ITM long call strategy yielded highest volatility percentage per unit of return (CV = 9.52). 2 percent OTM carries the least risk/reward ratio, but the mean return was not found to be favourable (CV = -3.37).

Further the returns from all the 5 long call strategies were compared against assumed mean of zero and corresponding t-values and p-values have been presented. It was found that returns from all the strategies were not found to be significantly different from zero at 5 percent level of significance. The returns from all the strategies were further compared with the help of one way ANOVA. It was found that there was no significant difference at 5 percent level of significance (F=1.236, p=0.295) between the returns of all the 5 long call strategies at different strike prices.

The results regarding the short put strategy are presented in Table 2. Short put strategy is often executed by the traders who are mildly bullish as they want to earn the premium obtained from the selling of the put option. The entire premium can be earned if the option expired above the strike price on which the option is entered into.

The strategy was applied on 5 strike prices namely

ATM, 2 percent OTM, 5 percent OTM, 2 percent ITM and 5 percent ITM. In each case the strategy was applied for

th th68 months that is from 29 October, 2010 to 30 June, 2016. In all the cases, the frequency of positive returns has been found to be more than the frequency of negative returns. The incidence of no profit and no loss ranged from 7 events (in case of 2 percent ITM) to 33 events (in case of 5 percent ITM). Among all the 5 short put strategies, the frequency of negative returns (35.29 percent) was highest for 2 percent ITM and frequency of positive return (82.35 percent) was highest for 2 percent OTM.In 3 out of 5 moneyness options, the mean return was found to be positive. The median return ranged from zero return to positive return among all moneyness options. Since this is short put strategy, the minimum return in all the 5 cases varied to a large extent but maximum return was found to be 100 percent (as the entire premium can be earned due to selling of the option). The distribution of return in all the 5 cases was found to be non-normal as depicted by the results from Kolmogorov-Smirnov Test Statistics and Shapiro-Wilk. Kurtosis is more than 3 for 2 percent OTM and 5 percent OTM which means distribution is leptokurtic and kurtosis is negative for 2 percent ITM which means distribution is platykurtic. Among all the five moneyness options under short put strategies, 2 percent ITM strategy was found to yield


5 percent OTM

2 percent ITM

5 percent ITM


12(17.65)

4(5.88)

24(35.29)

14(20.59)


56(82.35)

33(48.53)

37(54.41)

21(30.88)

No Profit/loss 0 0 31(45.59)

7(10.3)

33(48.53)

Mean return 5.71 -44.37 -40.11 13.27 5.39t-test (H = 0)0 0.34 -0.89 -0.56 1.60 1.05

p-value 0.737 0.375 .581 0.114 0.296F test (H = Returns from all Moneyness are equal) (p value)0 0.48

(0.751)Median return 97.81 95.55 0.00 15.48 0.00

Std. deviation 139.53 409.95 595.94 68.28 42.20

Minimum return -415.79 -1900.00 -4845.46 -163.32 -141.78

Maximum return 99.65 99.87 99.87 99.82 99.92

Skewness -1.68 -3.36 -8.06 -0.37 -0.04

Kurtosis 2.15 10.71 65.87 -0.69 2.24

Coefficient of variation 24.43 -9.24 -14.86 5.14 7.83Kolmogorov-Smirnov Test Statistics (p-value)

0.273(.000)

0.416(.000)

0.468(.000)

0.125(0.010)

0.252(.000)

Shapiro-Wilk(p-value)

0.716(.000)

0.398(.000)

0.164(.000)

0.393(0.02)

0.858(.000)

Table 2. Returns from short put strategy (n=68)

Source: Authors' own calculation.*p< .05, Figures in parentheses represents percentages.

256


highest mean return (M=13.27, SD=68.28) and the lowest return occurred in 2 percent OTM strategy (M= - 44.37, SD = 409.95). 2 percent ITM offers the best risk/reward ratio and the lowest volatility percentage per unit of return (CV = 4.20). ATM short put strategy offers highest volatility percentage per unit of return (CV =28.25). 2 percent OTM carries the least risk/reward ratio, but the mean return is not favourable (CV = -18.56). In comparison ATM and ITM strategies yielded higher returns than OTM short put strategies. 2 percent ITM performs better than the other 4 short put strategies with highest mean return. Returns from all the 5 short put strategies were compared against assumed mean of zero and corresponding t-values and p-values have been presented. It was found that returns from all the moneyness options were not found to be significantly different from zero at 5 percent level of significance. The return from all the moneyness options was further compared with the help of one way ANOVA and it was found that there is no significant difference at 5 percent level of significance (F = .48, p = .751) among the returns of all the 5 short put strategies at different strike prices.

Table 3 depicts the result of covered call strategy. In this strategy there is simultaneous buying of asset and selling of call against the same. The trader will like to


5 percent OTM

2 percent ITM

5 percent ITM


20(29.41)

27(39.71)

27(39.71)

29(42.65)


48(70.59)

41(60.29)

41(60.29)

39(57.35)

No Profit/Loss 0 0 0 0 0

Mean Return -0.12 0.50 0.20 0.01 0.18t-test (H = 0)0 1.75 -0.63 0.81 0.05 0.60

p-value 0.085 0.531 0.420 0.959 0.546

F test (H = Returns from all Moneyness are equal) (p value)0 0.85 (.494)

Median return 0.47 0.16 0.31 0.15 0.38

Std. deviation 1.54 2.37 2.05 1.87 2.48

Minimum return -6.36 -6.93 -6.93 -5.27 -6.94

Maximum return 1.62 10.62 3.50 4.22 5.15

Skewness -2.02 1.25 -1.20 -0.45 -0.34

Kurtosis 4.82 6.69 2.08 0.54 0.47

Coefficient of variation -13.08 4.72 10.15 158.21 13.59Kolmogorov-Smirnov Test Statistics (p-value)

0.205(0.000)

0.240(0.000)

0.107(0.000)

0.125(0.000)

0.050(0.000)

Shapiro-Wilk(p-value)

0.795(0.000)

0.787(0.000)

0.916(0.000)

0.969(0.000)

0.984(0.000)

Table 3. Returns from covered call strategy(n=68)

Source: Authors' own calculation.*p<0.05.Figures in parentheses represent percentages.

enter in this strategy when he is mildly bullish. The strategy was applied on 5 strike prices namely

ATM, 2 percent ITM, 5 percent ITM, 2 percent OTM and 5 percent OTM. In each case the strategy was applied for

th th68 months that is from 29 October, 2010 to 30 June, 2016. In all the cases, the frequency of positive return has been found to be more than the frequency of negative returns. There was no incidence of no profit and no loss in the entire sample period of 68 months. Among all the 5 covered call strategies, the frequency of negative returns (42.65 percent) was highest for 5 percent OTM and frequency of positive return (70.59 percent) highest for 2percent ITM. ATM and 2 percent ITM covered call strategies performed better as compared to remaining 3 covered call strategies. In 4 out of 5 cases, the mean return was found to be positive and median return was also found to be positive in all the cases depicting larger number of high positive returns. Since this is covered call strategy, the minimum return and maximum return in all the 5 cases varied but not to a large extent. The distribution return in all the 5 cases was found to be non-normal as depicted by the results from Kolmogorov-Smirnov Test Statistics and Shapiro-Wilk. Four out of five covered call strategies depicted negative skewness which means there is a higher probability for extremely high losses and limited gains.

257


Strategy Compared

with

Mean

difference

p-value

Long call Short put 180.48 **0.027

Covered call 167.55 **0.044

Short put Long call -180.48**0.027

Covered call -12.92NS

0.981

Covered call Long call -167.55 **0.044

Short put 12.92 NS0.981

Table 4. Multiple comparisons of three bull option strategies

Source: Authors' own calculation.**p< .05, Figures in parentheses represents percentages.NS: Non-significant.

Kurtosis is more than 3 for ATM and 2 percent ITM which means distribution is leptokurtic.

Among all the five covered call strategies, 2 percent ITM strategy was found to yield highest mean return (M=0.50, SD=1.54) and the lowest return occurred in ATM strategy (M = -0.12, SD=1.54). 2 percent ITM offers the best risk/reward ratio and the lowest volatility percentage per unit of return (CV = 4.72). 2 percent OTM covered call strategy offers highest volatility percentage per unit of return (CV = 158.21). ATM carries the least risk/reward ratio, but the mean return is not favourable (CV =13.08). Returns from all the five covered call strategies were compared against assumed mean of zero and corresponding t-values and p-values have been presented. It was found that returns from all the moneyness options were not found to be significantly different from zero at 5 percent level of significance. The return from all the cases was further compared with the help of one way ANOVA. It was found that there was no significant difference at 5 percent level of significance (F = 0.85, p = 0.494) between the returns of all the covered call strategies at different strike prices.

Further the comparison was made between different strategies and the results of the multiple range tests are presented in Table 4. The returns of all moneyness options have been combined for each of the strategy. Statistically significant different returns were observed between the pair of strategies. The statistics have been calculated using Tukey test.

The perusal of Table 4 showed that there is a statistically significant difference between returns of long call and short put (p = 0.027) and long call and covered call (p =0.044) and in both the comparisons long call strategy performed better than the others. Returns from short put strategy and covered call strategy are not statistically different from each other (p = 0.981).CONCLUSIONS

The present paper tested the payoffs from bullish option strategies as applied on USD-INR contract. Payoffs from three bullish strategies namely long call, short put, and covered call strategy were calculated and

analyzed. Monthly closing price data of USD-INR was obtained from NSE website for 68 months starting from October 2010 to June 2016. Similarly closing prices of options were also collected from NSE website for the same time period. When compared among different moneyness among the respective option strategies, no significant differences were observed. The strategies employed were also compared among themselves and it was found that long call statistically performed better as compared to short put and covered call, and this is very significant learning for the market participants and other stakeholders. REFERENCESAggarwal, N., & Gupta, M. (2013). Portfolio hedging through

options: Covered call versus protective put. Journal of Management Research, 13(2), 118-126.

Associates C. (2006). An historical evaluation of the CBOE S&P 500 buy write index strategy. Retrieved from http://wwwcboecom/micro/bxm/Callan_CBOEpdf.

Benesh, G.A., & Compton, W.S. (2000). Historical return distributions for calls, puts, and covered calls. Journal of Financial and Strategic Decisions, 13(1), 15-33.

Bookbinder, A.I.A. (1976). Security options strategy. New York: Programmed Press.

Business growth in CD segment.(2017). Business growth in CD segment. Retrieved from https://wwwnseindiacom/ products/content/derivatives/currency/cd_historical_businessGrowthhtm

Chicca, L., & Larcher, G. (2012). A comparison of different families of put-write option strategies. ACRN Journal of Finance and Risk Perspectives,1(1), 1-14.

Clary, I. (2007). Wall street spreading the word on options. Pensions and Investments, 35(4), 41-42.

Crawford, G. (2005). Buy writing makes comeback as way to hedge risk. Pensions and Investments, 33(10), 3-29.

Dash, M., Deepa, K.M., Kavita, V., & Sindhu, S. (2007). A study of optimal stock and options strategies. Retrieved from https://ssrn.com/abstract=1293203 or http://dx.doi.org/ 10.2139/ssrn.1293203.

Doran, M., & Fodor, A. (2008). Is there money to be made investing in options? A historical perspective. Retrieved 2014 from http://ssrncom/abstract=873639.

Feiger, G., & Jacquillat, B. (1979). Currency option bonds, puts and calls on spot exchange and the hedging of contingent foreign earnings. The Journal of Finance, 34(5), 1129-1139.

Feldman, B.E., & Roy, D. (2004). Passive options-based investment strategies: The case of the CBOE S&P 500 buy write index. ETFs and Indexing, 4(1), 72-89.

Grube, R.C., & Panton, D.B. (1978). How well do filter-rule strategies work for options? Journal of Portfolio Management, 4(2), 52-57.

Guru, A. (2009). Forex derivative markets in India: thDevelopments thus far and road ahead. Retrieved on 30

August, 2014 from http://ssrn.com=1420615.Hoffmann, A.O., & Fischer, E.T.S. (2012). Behavioural aspects

of covered call writing: An empirical investigation. Journal of Behavioural Finance, 13(1), 66-79.

Kapadia, N., & Szado, E. (2007). The risk and return characteristics of the buy-write strategy on the Russell 2000 Index. The Journal of Alternative Investments, 9(4), 39-56.

258


Kassouf, S.T. (1977). Option pricing: Theory and practice (Master's Thesis). The Institute for Quantitative Research in Finance, Columbia University, California.

Kruizenga, R.J. (1964). Profit returns from purchasing puts and calls. In P. Cootner (Ed.).The random character of stock market prices (p. 392-411.). Cambridge: MIT Press.

Lodha, K.R. (2008). Derivatives in Indian financial market-Structure and financial concerns an Indian perspective. Retrieved from http://papers.ssrn.com/sol3/papers. cfm?abstract_id=1089967

Merton, R.C., Scholes, M.S., & Gladstein, M.L. (1978). The returns and risk of alternative call option portfolio investment strategies. Journal of Business, 51(2), 183-242.

Pasmantier, A.B. (1992). Currency options: From inception to present. Review of Business, 13(4), 43-48.

Pounds, H.M. (1978).Covered call option writing: Strategies

and results. Journal of Portfolio Management,4(2), 31-42.Roeder, D. (2004, February 25). New funds try options to boost

stock income. Chicago Sun-Times.Tergesen, A. (2001). Taking cover with covered calls. Business

Week, (3733), 132.Ungar, J., & Moran, M.T. (2009). The cash-secured putwrite

strategy and performance of related benchmark indexes. The Journal of Alternative Investments, 11(4), 43-56.

Whaley, R.E. (2002). Return and risk of CBOE buy write monthly index. The Journal of Derivatives, 10(2), 35-42.

Yates, J.W. & Kopprasch, R. W. (1980). Writing covered call options: Profits and risks. Journal of Portfolio Management, 7(1), 74-79.

Zeikel, A. (1980). Covered options writing for institutional investors. Journal of Accounting, Auditing and Finance, 3(3),276-281.

259


ABSTRACTThe present study was conducted to examine the performance and prospects of traditional and evolved basmati rice in Punjab. The study was based on the primary data collected from Amritsar and Tarn Taran districts of Punjab during 2016-17 kharif season. Six villages were selected and 45 farmers from each district was selected growing basmati rice. Costs and returns analysis revealed that per acre total variable costs were high in traditional varieties with`15589 as compared to evolved varieties of basmati with ̀ 14315. The gross returns from traditional varieties were `36959 as compared to evolved varieties (`40920 per acre). The returns over variable costs were `21370 from traditional varieties and `26605 from evolved varieties per acre. Thus, the evolved varieties of basmati were more remunerative as compare to traditional basmati.

KeywordsCosts, evolved, returns, traditional, variables.

JEL CodesC81, D24, Q13.

*Navdeep Kaur and V.K. Sharma

Department of Economics and Sociology, Punjab Agricultural University, Ludhiana-141004

*Corresponding Author's Email:[email protected]


An Economic Analysis of Traditional and Evolved Basmati in Punjab


DOI: 10.5958/2322-0430.2018.00128.2


www.soed.in


UGC Approved


260

INTRODUCTIONRice occupies an important place in our agricultural

economy. For over one half of the global population, rice is the prime and cheapest source of energy and protein. About 95 percent of the rice produced and consumed in Asia. Aromatic rice occupies a prime position in Indian culture because of their high quality. India had an immense wealth of aromatic rice; many have been lost during the last three decades as an aftermath of the green revolution where emphasis was on yield rather than quality. This highly valued rice is collectively called Basmati and is popular not only throughout Asia but also in Europe and USA (Bhattacharjee et al., 2002).

India is one of the world's major producers of rice accounting for 20 percent of all world rice production (Sardar et al., 2016). In India, due to its aroma, long slender grains and excellent cooking properties basmati occupies a unique position in the world rice market. Indian basmati rice which is monopoly of India and Pakistan is known for its excellent quality and commands a high premium in market. It is unique rice, which is exclusively grown in India and Pakistan, in the regions of the foothills of Himalayas. In order to tap the full potential

of export market as well as to secure a good price for the farmers, systematic efforts should be made for the production and export of good quality rice.

Rice is the most dominating kharif crop enterprise in Punjab. Rice crop was an important constituent of an exemplary growth of Punjab agriculture during last four decades. However, it is now considered to be the main enterprise responsible for the present turmoil of overuse of water, depletion of soil health and a cause of air pollution by stubble burning of rice (Singh, 2011). One typical solution of emerging imbalance to increase genetic diversification within rice. Basmati, known as king of rice, uses less water and soil nutrients, has high export potential and its straw is economically used for livestock feed instead of burning and creating environmental pollution. In Punjab, basmati is grown mainly in Amritsar, Tarn Taran, Gurdaspur, Kapurthala, Hoshiarpur and Patiala districts of the state. The total area under basmati was 3.40 lakh ha with production 9.24 lakh tons in 2008-2009 as compared to 7.63 lakh ha with production 21.36 lakh tonnes during 2015-16, the area under traditional varieties is 0.78 lakh ha with production 2.14 lakh tonnes in 2015-16. But in 2016-17 kharif season

Amritsar, Tarn Taran, and Gurdaspur districts witnessed 40 percent lower cultivation of basmati as compared to last kharif season (Sharma, 2017).

The best varieties ever released for cultivation in Punjab are Basmati 370, Punjab Basmati 3, Pusa Basmati 1121 and Pusa Basmati 1509. Basmati 370, basmati 386, Taraori basmati, Dehraduni, Ranbir basmati and kernel (Pakistani) are the traditional varieties and Pusa Basmati 1121, Pusa Basmati 1509 are the evolved varieties of basmati. The traditional varieties are disappearing due to the introduction of new high yielding varieties of basmati. The major reason for the disappearance of many of local rice varieties was their steady replacement with the high yielding varieties introduced in 1960s with the green revolution. Indigenous varieties are hardy, resistant to pests, require lesser farm inputs, yield good amount of fodder and grains have high nutritional and medicinal value (Amudha, 2011). Cultivation of basmati varieties saved around 18, 81, 70, and 39 percent irrigation water, Urea, DAP and Zinc fertilizers respectively as compared to non-basmati rice crop (Grover, 2012). This showed that Basmati rice cultivation is both resources conserving as well as better remunerative.METHODOLOGY

Amritsar and Tarn Taran districts of Punjab were selected due to have maximum area under basmati. These districts of Punjab were representing the traditional belt of basmati as well as evolved varieties of basmati in Punjab. The block-wise data regarding area under traditional and evolved varieties of basmati in Amritsar and Tarn Taran districts were collected from the different sources such as Department of Agriculture, Government of Punjab, Chandigarh and Chief Agricultural Officer of the respective districts. From each of the selected district of Punjab, one block with highest area under basmati was chosen and three villages each were selected randomly from two districts. Three farm size categories were formed, namely, small (< 7 acres), medium (7-14 acres), and large (> 14 acres) through cube root frequency method. Out of six selected villages,15 basmati growers were selected from six villages spread over three farm size categories in probability proportional to size of the farm. Thus, 90 traditional and evolved basmati growers selected from the six selected villages constituted the total sample. Information on all the operations in traditional and evolved varieties of basmati cultivation and output received in physical as well as in monetary terms for the year 2016-17 was collected from the respondent farmers. RESULTS AND DISCUSSION

In crop production process, the output is the integrated result of a number of inputs involved in different proportions. Therefore, in order to study the economics of any enterprise, it is necessary to evaluate the share of various components in the total expenditure involved in production process. An attempt has been made to work out the expenditure on different inputs in cultivation of traditional and evolved varieties of basmati

on different farm size categories in the study area. The gross returns over variable costs on per acre basis for traditional and evolved varieties of basmati along with average yield and prices received have also been discussed.Trends in Area, Production, and Productivity of Traditional and Evolved Basmati

It was essential to go through the trends of area, production and productivity of traditional and evolved varieties of basmati before discussing their economics to know their status in Punjab state. Over the last few years, the Punjab state has undergone numerous changes in cultivation of different varieties of basmati. Therefore, the results corresponding to the growth pattern of area, production and productivity of traditional and evolved varieties of basmati rice in the Punjab state during 2000-01 to 2015-16 is depicted in Table 1. The total area under basmati in Punjab state keeps on fluctuating from the year 2000-01 to 2007-08. But in the year 2008-09, there was terrific change in area, production and productivity of basmati rice due to the introduction of new variety PUSA basmati 1121. The area under this variety in year 2008-09 was 181 thousand hectares with 533 thousand tonnes production and 2945 kg/ha productivity.

Due to the introduction of new variety, the area under total basmati becomes 340 thousand hectares with production 924 thousand tonnes and productivity 2718 kg/ha. After 2008-09 the area under total basmati was increased due to introduction of another variety of basmati PUSA basmati 1509 and it becomes 559 thousand hectares with production 1509 thousand tonnes and 2694 kg/ha productivity. But this trend was only up to the year 2014-15. In 2015-16 the area under PUSA basmati 1509 was declined to 175 thousand hectares with production 496 thousand tonnes and 2834 kg/ha productivity. This decline is due to the reason that the rice millers and exporters association decided against the purchase of PUSA basmati 1509, the decision has been taken because of high incidence of breakage. Again there was tremendous change in year 2014-15 the area under total basmati becomes 863 thousand hectares with production 2371 thousand tonnes and 2747 kg/ha productivity. Due to the introduction of evolved varieties of basmati, the area under traditional varieties was decreased to 24 thousand hectares with production and productivity 60 thousand tonnes and 2500 kg/ha respectively. The maximum area under basmati was noticed in 2014-15 due to the evolved varieties of basmati. In the year 2015-16 the area under total basmati again decreased due to lack of minimum support price in basmati results in variation in price. In this year the area under total basmati was 763 thousand hectares with production 2136 thousand tonnes and productivity was 2799 kg/ha. The details on the compound annual growth rate of different varieties of basmati area, production and productivity during 2000-01 to 2015-16 in Punjab are also given in Table 1. The results indicated that area, production, and productivity of total

261

Kaur and Sharma: An economic analysis of traditional and evolved basmati in Punjab

Yea

rP

usa

Bas

mat

i 11

21P

usa

Bas

mat

i 15

09T

rad

itio

nal

var

ieti

es(B

asm

ati

370,

386)

Tot

al B

asm

ati

Pro

du

ctio

n

Are

a(0

00'h

a)P

rod

uct

ion

(000

'ton

nes

)Y

ield

(kg/

ha)

Are

a(0

00'h

a)P

rod

uct

ion

(000

'ton

nes

)Y

ield

(kg/

ha)

Are

a(0

00'h

a)P

rod

uct

ion

(000

'ton

nes

)Y

ield

(kg/

ha)

Are

a(0

00'h

a)P

rod

uct

ion

(000

'ton

nes

)Y

ield

(kg/

ha)

2000

-01

00

00

00

105

163

1552

105

163

1552

2001

-02

00

00

00

103

162

1573

103

162

1573

2002

-03

00

00

00

157

261

1662

157

261

1662

2003

-04

00

00

00

212

384

1811

212

384

1811

2004

-05

00

00

00

130

202

1554

130

202

1554

2005

-06

00

00

00

100

176

1760

100

176

1760

2006

-07

00

00

00

122

246

2016

122

246

2016

2007

-08

00

00

00

144

317

2201

144

317

2201

2008

-09

181

533

2945

00

015

939

124

5934

092

427

18

2009

-10

395

1079

2732

00

011

828

524

1551

313

6426

59

2010

-11

401

933

2327

00

014

939

626

5755

013

2924

16

2011

-12

450

1145

2545

00

010

820

318

7855

813

4824

16

2012

-13

338

948

2805

00

012

028

323

5845

812

3126

87

2013

-14

507

1348

2659

4212

830

4810

3030

0055

915

0626

94

2014

-15

571

1549

2713

268

762

2842

2460

2500

863

2371

2747

2015

-16

510

1426

2796

175

496

2834

7821

427

4476

321

3627

99

CA

GR

***

11.9

89**

*12

.122

NS

0.11

8N

S10

4.12

4N

S96

.850

*-3

.574

*-8

.191

NS

-4.4

04**

*4.

119

***

16.2

13**

*21

.575

***

4.61

3

Tab

le 1

. Tre

nd

s in

are

a, p

rod

uct

ion

,an

d p

rod

uct

ivit

y of

tra

dit

ion

al a

nd

evo

lved

var

ieti

es o

f b

asm

ati

in P

un

jab

, 20

00-0

1 to

201

5-16

Sou

rce:

Dep

artm

ent

of A

gric

ult

ure

(n

.d.)

.**

**

and

sig

nif

ican

t at

1 a

nd

10pe

rcen

t le

vels

.

262


basmati rice during the period 2000-01 to 2015-16 has increased significantly at the compound growth rate of 16.213, 21.575, and 4.613 percent respectively. During the year 2008-09 to 2015-16 the area and production of PUSA basmati 1121 increased with significant CAGR 11.989, and 12.122 percent. On the other hand, the area, production of traditional varieties during the period 2000-01 to 2015-16 declined with CAGR of -8.191 and -4.404 and productivity increased with significant CAGR of 4.119 percent.Economics of Traditional and Evolved Basmati

The perusal of Table 2 showed that per acre total variable cost on traditional basmati varieties on small, medium and large farms was `16140, 14822, and 14216 and in evolved varieties it was `4997, 13527, and 13112 respectively. The examination of major components of variable cost revealed that expenditure on human labour in traditional basmati cultivation on small, medium and large farms was `10110, 9913, and 9529 per acre respectively constituted 62.64, 66.88, and 67.03 percent of total variable cost respectively and in evolved varieties it was `7774, 7659, and 7327 on respective farm size categories which constitute 51.84, 56.62, and 55.88

percent of the total variable cost. The second major component in cultivation of traditional basmati varieties was machine labour and expenditure on it was `2175, 1228, 1150, and 1819 on small, medium, large and overall farms and constitutes 13.48, 8.28, 8.09, and 11.67 percent of the total variable cost and it was `3199, 2223, 2250, and 2844 in evolved varieties of basmati which constitute 21.33,16.43,17.16, and 19.87 percent of the total variable cost on respective farm size categories. The third major input in the cultivation of traditional varieties of basmati was the insecticide/pesticide which constitutes 8.73, 9.28, 9.60 and 8.95 percent of the total variable cost on small, medium, large and overall farms and expenditure on it was `1409, 1375, 1365, and 1395. On the other hand, category wise expenditure on it was `1179, 1104, and 1079 respectively on small, medium and large farms in evolved varieties of basmati.

Other main components in cultivation of traditional basmati were fertilizer, micronutrient, irrigation, seed and seed treatment and weedicide. On an average the expenditure on these components were `766, 405, 290, 214, and 198 which constitutes 4.91, 2.60, 1.86, 1.37 and 1.27 percent of the total variable cost. In evolved varieties

Particulars Traditional varieties Evolved varieties

Small farms

Medium farms

Large farms

Overall farms

Small farms

Medium farms

Large farms

Overall farms

Farm yard manure 106(0.66)

87(0.59)

57(0.40)

96(0.62)

154(1.03)

122(0.90)

76(0.58)

137(0.96)

Seed and seed treatment 217(1.34)

209(1.41)

209(1.47)

214(1.37)

237(1.58)

212(1.44)

202(1.54)

227(1.59)

Fertilizers 786(4.87)

757(5.11)

669(4.71)

766(4.91)

1016(6.77)

889(6.57)

886(6.76)

970(6.78)

Micronutrients 413(2.56)

393(2.65)

381(2.68)

405(2.60)

438(2.92)

399(2.95)

378(2.88)

421(2.94)

Weedicide 200(1.24)

199(1.34)

190(1.34)

198(1.27)

199(1.33)

194(1.43)

192(1.46)

197(1.38)

Insecticides/Pesticides 1409(8.73)

1375(9.28)

1365(9.60)

1395(8.95)

1179(7.86)

1104(8.16)

1079(8.23)

1049(7.33)

Irrigation 305(1.89)

261(1.76)

275(1.93)

290(1.86)

331(2.21)

297(2.20)

285(2.17)

317(2.21)

Human labour 10110(62.64)

9913(66.88)

9529(67.03)

9995(64.12)

7774(51.84)

7659(56.62)

7327(55.88)

7695(53.75)

Machine labour 2175(13.48)

1228(8.28)

1150(8.09)

1819(11.67)

3199(21.33)

2223(16.43)

2250(17.16)

2844(19.87)

Marketing charges 142(0.88)

145(0.98)

147(1.03)

143(0.92)

212(1.41)

213(1.57)

212(1.62)

212(1.48)

Interest on variable cost 277(1.72)

314(1.72)

304(1.72)

331(1.72)

258(1.72)

232(1.72)

225(1.72)

246(1.72)

Total variable cost 16140(100.00)

14822(100.00)

14216(100.00)

15589(100.00)

14997(100.00)

13527(100.00)

13112(100.00)

14315(100.00)

Table 2. Economics of traditional and evolved varieties of basmati on different farm size categories, 2016-17(`/acre)

Figures in parentheses are percentages to total.

263



Smallfarms

Mediumfarms

Largefarms

Overallfarms

Smallfarms

Mediumfarms

Largefarms

OverallFarms

Family labour 86.29(34.14)

70.83(28.58)

59.22(24.86)

79.33(31.75)

81.92(42.15)

68.19(35.60)

55.54(30.32)

75.48(39.24)

Casual labour 162.77(64.40)

144.15(58.17)

140.66(59.05)

155.56(62.25)

108.06(55.60)

93.79(48.97)

92.80(50.66)

102.70(53.39)

Permanent labour 3.69(1.46)

32.84(13.25)

38.34(16.09)

14.99(6.00)

4.36(2.24)

29.56(15.43)

34.83(19.02)

14.19(7.38)

Total 252.75(100.00)

247.82(100.00)

238.22(100.00)

249.88(100.00)

194.34(100.00)

191.54(100.00)

183.17(100.00)

192.37(100.00)

Table 3. Pattern of human labour use in traditional and evolved varieties of basmati on different farm size categories, 2016-17

(Hours/acre)


of basmati expenditure on these were ̀ 970, 421, 317, 227, and 197 which constitute 6.78, 2.94, 2.21, 1.59, and 1.38 percent respectively of the total variable cost on overall farms.Pattern of human labour use

Human labour, a vital input to conduct various on-farm activities is generally provided by family members, permanent labour,and casual labour. The total human labour hours used for different operations in traditional basmati cultivation has been presented in Table 3. It is evident that on average farm, total human labour used for cultivation of traditional varieties of basmati was higher 249.88 per acre as compared to evolved varieties 192.37 hours per acre. On account of relatively no use of combine harvester or more human labour use in harvesting of traditional basmati as compared to evolved varieties, traditional basmati cultivation was relatively labour intensive. On an average farm family,labour used in traditional varieties of basmati was 79.33 hours per acre constitute 31.75 per cent of the total labour used in traditional varieties of basmati. The use of family labour in evolved varieties was 75.48 hours which constitutes about 39.24 per cent of the total labour used. Permanent labour used in traditional varieties was 14.99 hours and in evolved varieties it was 14.19 hours per acre. On an average farm casual labour was more used in traditional varieties of basmati (155.56 hours per acre) on an average farm as it constitutes 62.25 percent of total labour used of total labour used in traditional varieties of basmati and in evolved varieties the use of casual labour was less 102.70 hours per acre which constitutes 53.39 percent of the total labour used.

Therefore, it is concluded that cultivation of traditional varieties of basmati was labour intensive. The main reason behind higher use of human labour was manual harvesting of traditional varieties and other operations like lopping.Pattern of machine use

Intensive cultivation in Punjab has been realized

through the mechanization of the farms. The timeliness and effectiveness of various agricultural operations require appropriate and efficient use of farm machinery. The use of tractor, combine harvester and irrigation machinery in cultivation of traditional and evolved varieties of basmati has been presented in Table 4. The tractor used in cultivation of traditional verities on an average farm was 5.60 hours per acre and in evolved varieties, it was 5.37 hours per acre. There was no use of combine harvester in traditional varieties due to their manual harvesting and use of combine harvester in evolved varieties was 0.36 hours per acre. In cultivation of traditional and evolved varieties of basmati, the use of tractor hours was more by small farms followed by medium and large farms.

Irrigation is the most critical input for paddy cultivation. The overall machine use on average farm in study area for irrigation in case of traditional cultivation observed at 71.98 hours per acre in traditional varieties and it was 80.87 hours per ace in evolved varieties of basmati. Category-wise total use of machinery for irrigation was 70.75, 73.04, and 76.60 hours per acre by small, medium and large farms respectively in traditional varieties of basmati. In traditional and evolved varieties of basmati the use of electric motor and diesel engine was more by large farms followed by medium and small farms.Returns from traditional and evolved varieties of basmati

Table 5 showed that the yield on small, medium, large and overall farms was 10.12, 9.95, 9.45 and 10.01 quintals per acre respectively in traditional varieties which was less than evolved varieties 17.74, 17.76, 17.70, and 17.74 quintals per acre on respective farm size categories. The gross returns on respective farm size categories were`37086, 37076, and 35965 per acre in traditional varieties and in evolved varieties `40885, 40973, and 41000, respectively.

The return over variable cost per acre was high in

264



Small farms

Medium farms

Large farms

Overall farms

Small farms

Medium farms

Large farms

Overall farms

Productivity (q/acre) 10.12 9.95 9.45 10.01 17.74 17.76 17.70 17.74

Price received (`/q) 3350 3405 3475 3378 2222 2260 2300 2241

Value of main product (`/acre) 33918 33929 32845 33802 39435 40147 40700 39757

Value of by-product (`/acre) 3168 3147 3120 3157 1450 826 300 1163

Gross returns (Main product + By product) (`/acre)

37086 37076 35965 36959 40885 40973 41000 40920

Return over variable cost ( /acre)` 20946 22254 21749 21370 25888 27446 27888 26605

Variable cost of production (`/q) 1594 1490 1504 1557 845 762 740 807

Returns over variable cost (`/q) 2069 2236 2301 2134 1459 1545 1575 1500

Table 5. Returns from traditional and evolved varieties of basmati on different farm size categories, 2016-17


SmallFarms

Mediumfarms

Largefarms

Overallfarms

Smallfarms

Mediumfarms

Largefarms

OverallFarms

Tractor 5.91(7.65)

5.11(6.41)

4.95(4.95)

5.60(7.12)

5.60(6.49)

5.00(5.57)

4.9(5.17)

5.37(6.10)

Electric motor 70.75(91.26)

73.04(91.66)

76.60(92.57)

71.98(91.46)

78.87(91.38)

82.89(92.28)

87.60(92.36)

80.87(91.72)

Generator/Diesel engine 0.91(1.17)

1.54(1.93)

1.2(1.45)

1.12(1.42)

1.83(1.85)

1.71(1.88)

2.00(2.43)

1.82(1.92)

Combine harvester 0.00(0.00)

0.00(0.00)

0.00(0.00)

0.00(0.00)

0.30(0.31)

0.44(0.47)

0.47(0.58)

0.36(0.38)

Total 77.57(100.00)

79.69(100.00)

82.75(100.00)

78.70(100.00)

86.31(100.00)

89.82(100.00)

94.85(100.00)

88.17(100.00)

Table 4. Pattern of machinery use in traditional and evolved varieties of basmati on different farm size categories, 2016-17

(Hours/acre)


evolved varieties of basmati (`26605) on an average farm and less in traditional varieties (`21370). Variable cost of production per quintal was higher in traditional varieties (`1557) on an average farm and lower in evolved varieties (`807). On an average farm the returns over variable cost rupees per quintal was high in traditional varieties (`2134) and lower in evolved varieties of basmati (`1500) due to their very low price. CONCLUSIONS

It was evident from the study that the area under traditional varieties was very less than evolved varieties of basmati. The expenditure on human labour was more on traditional varieties of basmati as `2300.33 more than evolved varieties due to their manual harvesting and other operations like lopping. The expenditure on machinery labour was more in evolved varieties of basmati because of use of combine harvester for their harvesting as `1025 more than traditional varieties. Due to the excessive vegetative growth of traditional varieties, the expenditure on fertilizers was less by `203.67 than traditional

varieties. But the traditional varieties are more prone to insect/ pest and diseases than evolved varieties so that the expenditure on insecticide/pesticide in traditional varieties was more by `346.78 than evolved varieties of basmati. Therefore, it was concluded that the total variable cost of traditional varieties was high by ̀ 1275.65 than evolved varieties of basmati. The use of human labour was more in traditional varieties than evolved varieties of basmati by 57.51 hours. The use of machinery labour was more in evolved varieties than traditional varieties as 9.47 hours more than traditional varieties of basmati.

It has been revealed from the study that the yield of traditional varieties of basmati was lower by 7.73 quintals per acre than evolved varieties. Therefore the gross returns were high from the evolved varieties by ̀ 3961.17 than traditional varieties of basmati due to their very low yield. Overall, the study revealed that the evolved varieties of basmati were more remunerative due to their high yield and less variable cost than traditional varieties

265


despite of their very low price during the study period. Therefore, it is concluded that evolved varieties of basmati are resource conserving and more remunerative than traditional if the farmers get reasonable price for them.REFERENCESAmudha, K. (2011). Traditional rice varieties. Kisan World, 38,

1-12.Bhattacharjee, P. Singhal, R.S., & Kulkarni, P.R. (2002).

Basmati rice: A review. International Journal of Food Science and Technology, 37(1), 1-12.

Department of Agriculture. (n.d.). Agriculture at a glance. Department of Agriculture, Government of Punjab,

Chandigarh.Grover, D.K. (2012). Basmati rice cultivation for resource

conservation and use efficiency in context to sustainable agriculture in Punjab. Indian Journal of Economics and Development, 8, 11-26.

Sardar, M.S., Saran, S.K., & Kaur, A. (2016). Technical efficiency of rice cultivation in West Bengal: An economic analysis . Indian Journal of Economics and Development,12(1), 41-48.

Sharma, A. (2017, July 29). 40% dip in area under basmati cultivation in one year. Hindustan Times, New Delhi.

Singh, J. (2011). Impact assessment study of IPM basmati project for boosting diversification process in Punjab (pp. 1-58.). Mumbai: Sir Rattan Tata Trust.

266


ABSTRACTThe study was conducted in three agro-climatic zones of Punjab state viz. Sub mountainous zone, Central zone and South Western zone for examining the marketing pattern, price spread and marketing efficiency of various marketing channels and marketing problems being faced by the fodder growers. It was observed that out of the total sample size of 120 farmers, only 20 farmers were involved in marketing of fodder. Majority of them were selling fodder occasionally, criteria of payment was daily cash basis and maximum distance covered by majority of the fodder growers for marketing of green fodder was between 5-10 kilometers. The major reason for sale of fodder was fodder production being economical followed by surplus fodder production. For the maize crop, there was only one marketing channel (Channel-I) which was not so popular channel. For sorghum crop, there were two marketing channels (Channel-II and III). For the bajra crop, there existed three channels (Channel-I, II and III) and for berseem crop, there were two marketing channels i.e. Channel-II and III. The marketing efficiency was highest in Channel-III for all the crops. The important marketing problems being faced by the fodder growers were lack of proper market followed by price fluctuations, low output prices and distant market.

KeywordsMarketing channel, marketing cost, marketing efficiency, price spread.

JEL CodesQ12, Q13.

* Harparteet Singh, Varinder Pal Singh and Inderpreet Kaur

College of Dairy Science & Technology, Guru Angad Dev Veterinary & Animal Sciences University, Ludhiana-141 004


Received: August 29, 2017 Revision Accepted: April 10, 2018

Marketing Pattern and Price Spread of Green Fodder in Punjab


DOI: 10.5958/2322-0430.2018.00129.4


www.soed.in


UGC Approved


267

INTRODUCTIONLivestock have been an integral component of India's

agricultural and rural economy since time immemorial. Livestock is an important asset for them which provides employment to millions of rural people. Most often livestock is the only source of cash income for subsistence farmers as well as ensuring family purchasing power in the event of crop failure. Rapid growth of livestock sector is, therefore, most desirable not only to sustain steady agriculture growth but also to reduce rural poverty. Growth in livestock sector has more potential to reduce poverty than a similar growth in crop sector (Mellor 2004).

Presently, livestock sector contributes 26.90 and 35.90 percent of agricultural GDP in India (The Government of India 2016) and Punjab state (The Government of Punjab, 2016). Despite being the leading milk producer nation, the Indian dairy sector is plagued by several hurdles such as low productivity of animals,

inadequate availability of quality green fodder and quality fodder seeds etc. One of the main reasons for low productivity of livestock is malnutrition, under nutrition or both, besides the low genetic potential of animals. The country is highly deficient in respect of availability of green fodder, dry fodder and concentrates. The deficit of green fodder currently is 35 percent (The Planning Commission, 2012). The area under fodder crops in India has stagnated at about 8.5-9.0 million hectares during the past decade and accounts for only about 4.6 percent of the total cultivated area. The projected green fodder and dry fodder demand for 2020 is 1134 and 630 million tonnes, whereas, the availability is expected to stand at 406 and 473 million tonnes leaving a shortage of 64 and 25 percent, respectively (Srivastava, 2017). Standing at 40 percent even today, the availability of good quality upgraded fodder seeds and conservation of fodder either as silage or hay remains a major concern (Narke, 2017).

Among the livestock products, milk is the most

important. The economic viability of milk production heavily depends on source (s) of feed and fodder as feeding cost account for about 70-75 percent of the total variable cost of dairy farming. Profitable livestock farming depends mainly on adequate availability of fodder at reasonable price. Green fodder is the essential component of feeding milch animals to obtain optimum level of milk production which account for about 44 percent of the feed and fodder expenditure (Kaur et al., 2012). Apart from that, green fodder crops are known to be cheaper source of nutrients as compared to concentrates and hence helpful in bringing down the cost of feeding and thereby leading to higher profitability.

When it comes to enhancing the productivity of Punjab's dairy sector, ensuring an adequate supply of reasonable quality feed and fodder is one of the major challenges. The tremendous pressure of livestock on available feed and fodder resources and consequent dismal scenario of fodder inadequacy has been the major impediment in the sustained growth of dairy sector. The deficit of green fodder is estimated to be 46.38 percent in the state (Kaur et al., 2014). Owing to problem of shortage of fodder and feed, the future growth of livestock has to be sustained primarily on enhancement of animal productivity and not on increase in number of animals. The farmers will get the remunerative prices for their surplus green fodder produce only when the effective and efficient processing and marketing system of fodder is in place. The present study was undertaken to examine the marketing pattern of green fodder in the state, the price spread in various marketing channels and to study the marketing problems of green fodder growers.METHODOLOGY

The study was conducted in three agro-climatic zones of Punjab state viz. Sub mountainous zone, Central zone and South Western zone. Three districts, one from each zone, was selected purposively on the basis of highest area under fodder crops. The selected districts were Hoshiarpur from Sub-mountainous Zone, Ludhiana from Central Zone and Ferozepur from South Western Zone. Amongst the selected districts, two blocks from each district, one block near and another distant to the periphery of district headquarter was selected randomly to realize the effect of distance factor. In the next stage, a cluster of two to three villages was selected randomly from each selected block. A sample of twenty fodder growing farmers from each cluster was selected making a total sample of 120 farmers. Different marketing channels for the disposal of fodder crops in the study area were examined to assess the cost and margins of different functionaries involved in the disposal of fodder crops till the produce reaches the consumers. Different marketing channels for the disposal of green fodder crops in the study area were examined by selecting a sample of four to five intermediaries in each channel. The primary data was collected using a specially designed and pre-tested schedule by personal interview method for the

agricultural year 2016-17.In order to examine the marketing efficiency of each

fodder marketing channel, Acharya's formulae of marketing efficiency (Acharya & Agarwal, 2012) was used which is represented as follow:

MME = FP / (MC + MM)Where,

MME = Modified measure of marketing efficiencyFP = Price received by the farmerMC = Marketing costMM = Marketing margins

RESULTS AND DISCUSSIONPattern of Green Fodder Marketing

The perusal of Table 1 brought out that out of the total sample size of 120 farmers; only 20 farmers were involved in marketing of green fodder. The number of the farmers engaged in marketing was highest in Sub-mountainous zone followed by the South Western zone and Central zone. Maximum of them (14) were selling fodder occasionally out of which six were from Sub Mountainous zone followed by South Western zone with five and Central zone with three farmers. Majority of the farmers were selling green fodder through Mandi. For all the farmers, the criteria of payment were daily cash basis. The maximum distance covered by majority of the fodder growers for selling green fodder was between five to ten km.Reasons for Sale of Fodder

The reasons for sale of fodder given by the fodder growers are discussed in detail in this section. A total of four reasons for sale of fodder have been reported in the state. The fodder growers were asked to rank these four reasons. The number and percentage of responses given by different fodder growers for various reasons for sale of fodder is presented in Table 2, which brought out that maximum number of the farmers reported fodder production for sale as economical, followed by surplus fodder production, high demand of green fodder and to earn extra income through sale.

The number of farmers giving various ranks to various reasons for sale of fodder, total score, mean score and ranks assigned to various reasons is presented in Table 3. The reason with highest mean score was given first rank and the reason with next highest mean score was given second rank and so on. According to the Garrett ranking, the most important reason for sale of fodder was fodder production for sale is economical followed by surplus fodder production, high demand of green fodder and to earn extra income through sale.Marketing channels and price spread in marketing of various fodder crops in Punjab state

The marketing of fodder through different channels refers to the path through which green fodder from the producer to the ultimate consumer. The major marketing channels involved in marketing of fodder were summarized as follows:Channel-I: Producer-Consumer (Direct sale in village)

268


Channel-II: Producer-Chaff cutter-ConsumerChannel-III: Producer-Dairy owner (Consumer)

The Channel-I involved direct sale of fodder in the village but it was least important channel. The Channel-II was the longest channel and was least efficient channel. The Channel-III involved sale of green fodder to dairy owner (consumers) through Mandi. The marketing costs, marketing margins, price spread and marketing efficiency in marketing of different green fodder crops in various marketing channels in Punjab state is discussed.

Table 4 highlights the marketing costs, margins, price spread and marketing efficiency of maize and sorghum crops in various marketing channels. The study brought out that for maize crop, there was only one marketing channel (Channel-I) and price spread in this channel was zero and the producer received hundred percent of consumer's rupee. However, this was not so popular as very few farmers are following this channel for the sale of maize fodder.

In sorghum crop, there existed two marketing channels (Channel-II and III). In both the channels, the net price received by the farmer was same (`106.75 per q) which was 67.31 percent of consumer's price. Costs i n c u r r e d b y p r o d u c e r i n c l u d e w e i g h i n g , loading/unloading, transportation out of which

Particulars Central zone Sub-mountainous zone South-western zone Punjab

Total farmers 40 40 40 120

No of farmers marketing fodder 5 9 6 20

Frequency of sale

Always 1 2 0 3

Occasionally 3 6 5 14

Rarely 1 1 1 3

To whom

Dairy owner 1 0 1 2

Mandi 4 8 5 17

Neighbours 0 0 1 1

Criteria of payment on daily basis 5 9 6 20

Distance (km)

0-5 0 0 0 0

5-10 2 9 6 17

10-15 3 0 0 3

Table 1. Pattern of green fodder marketing

Factors Number of

farmers

Percentage of

responses

Surplus fodder production 15 75

High demand of green fodder 13 65

Fodder production is economical 20 100

To earn extra income through sale 2 10

Table 2. Number and percentage of responses for reasons for sale of fodder

(n=20)

Reasons Rank No. of responders Total score Mean score Rank

1 2 3 4

Surplus Fodder production 5 5 3 2 15 831 55.40 II

High demand of green fodder 4 5 2 2 13 714 54.92 III

Fodder production for sale is economical 9 6 4 1 20 1196 59.80 I

To earn extra income through sale 0 0 1 1 2 71 35.50 IV

Table 3. Ranking of reasons for sale of fodder being given by fodder growers in Punjab

transportation charges were the highest followed by harvesting charges and loading/unloading. The total cost incurred by producer was the same in both channels for sorghum crop.

Total marketing cost was observed to be highest in Channel-III followed by Channel-II. The marketing margins were `20 per q in Channel-II and there was no marketing margin in Channel-III as there was no intermediary in this channel. The price spread was highest in Channel-II followed by Channel-III. The marketing efficiency was highest in Channel-III followed by Channel-II. It may be concluded from the above

269

Singh et al.: Marketing pattern and price spread of green fodder in Punjab

Sr. No. Cost items Maize Sorghum

Channel-I Channel-II Channel-III

Amount PCP Amount PCP Amount PCP

1. Net price received by the producer 105.58 100.00 106.75 67.31 106.75 74.96

2. Costs incurred by producers

i. Harvesting charges - - 6.60 4.16 6.60 4.63

ii. Weighing charges - - 1.25 0.79 1.25 0.88

iii. Loading/unloading - - 5.00 3.15 5.00 3.51

iv. Transportation - - 10.00 6.30 10.00 7.02

Sub-total - - 22.85 14.40 22.85 16.05

3. Producer's sale price - - 129.60 81.72 129.60 91.01

4. Purchase price of chaff cutter - - 129.60 81.72 - -

5. Costs incurred by Chaff cutter

i. Loading/unloading - - 2.50 1.58 - -

ii. Chaffing - - 2.50 1.58 - -

Sub total - - 5.00 3.15 - -

Chaff cutters margin - - 20.00 12.61 - -

Sale price of chaff cutter - - 154.60 97.48 - -

6. Costs incurred by Dairy owner(Consumer)

i. Weighing charges - - - - 0.80 0.56

ii. Loading/unloading - - 0.00 - 3.00 2.11

iii. Transportation - - 4.00 2.52 5.00 3.51

iv. Commission charges - - 4.00 2.81

Sub-total - - 4.00 2.52 12.80 8.99

7. Total cost to dairy owner (Consumer) 105.58 100.00 158.60 100.00 142.40 100.00

8. Total marketing cost - - 31.85 20.08 35.65 25.04

9. Total marketing margins - - 20.00 12.61 - -

10. Price spread - - 51.85 32.69 35.65 25.04

11. Marketing efficiency - - 2.06 - 2.99 -

Table 4. Marketing cost and price spread in marketing of maize and sorghum in Punjab(`per q)

PCP: Percent to consumer's price.

discussion that Channel-III was the most efficient channel for sorghum marketing as price spread was lower in this channel and marketing efficiency was higher.

The marketing cost, margins, price spread and marketing efficiency in marketing of bajra crop in different marketing channels is presented in Table 5, which revealed that there existed three channels for marketing of bajra crop. In the Channel-I, the net price received by the farmer was same as price paid by the dairy owner consumer. Here, the price spread was zero but it was used by very few numbers of the farmers for sale of bajra crop. In both the Channel-II and III, net price received by the farmer was the same. The costs incurred by producer were highest on transportation charges followed by harvesting charges and loading/ unloading.

The sale price of the produce was same in both the channels. The purchase price of chaff cutter in Channel-II was `128.95 per q (82.71 percent) and cost incurred by

chaff cutter includes loading/unloading and chaffing which was same (` 2.6 per q) in each. Further, the cost incurred by the dairy owners includes only transportation charges. Similarly, in Channel-III, incurred by dairy owner includes weighing charges, transportation and commission charges out of which maximum was transportation charges followed by commission and loading/unloading charges. Here, price spread was highest in Channel-II and Channel III was the most efficient channel as compared to other channels.

The marketing cost, margins, price spread and marketing efficiency in marketing of berseem crop in different marketing channels has been presented in Table 6, which revealed that there existed two channels for marketing of berseem crop (Channel-II and III). In both these channels, the net price received by the producer was same. Costs incurred by producer were the highest on transportation charges followed by harvesting charges

270


Sr. No. Cost items Channel-I Channel-II Channel-III

Amount PCP Amount PCP Amount PCP

1 Net price received by the producer 102.00 100.00 105.85 67.90 105.85 75.072 Costs incurred by producersi. Harvesting charges - - 6.80 4.36 6.80 4.82ii. Weighing charges - - 0.80 0.51 0.80 0.57iii. Loading/unloading - - 5.00 3.21 5.00 3.55iv. Transportation - - 10.50 6.74 10.50 7.45

Sub-total - - 23.10 14.81 23.10 16.383 Producer's sale price - - 128.95 82.71 128.95 91.454 Purchase price of chaff cutter - - 128.95 82.71 - -5 Costs incurred by Chaff cutteri. Loading/unloading - - 2.60 1.67 - -ii. Chaffing - - 2.60 1.67 - -

Sub total - - 5.20 3.34 - -Chaff cutters margin - - 18.00 11.55 - -Sale price of chaff cutter - - 152.15 97.59 - -

6 Costs incurred by Dairy owner(Consumer)i. Weighing charges - - - - 0.80 0.57ii. Loading/ unloading - - - - 3.00 2.13iii. Transportation - - 3.75 2.41 4.75 3.37iv. Commission charges - - - - 3.50 2.48

Sub-total - - 3.75 2.41 12.05 8.557 Total cost to dairy owner (Consumer) 102.00 100.00 155.90 100.00 141.00 100.008 Total marketing cost - - 32.05 20.55 35.15 24.939 Total marketing margins - - 18.00 11.55 - -10 Price spread - - 50.05 32.10 35.15 24.9311 Marketing efficiency - - 2.11 - 3.01 -

Table 5. Marketing cost and price spread in marketing of bajra in Punjab state

(`per q)


and loading/unloading. The total cost incurred by producer was same in both channels.

The sale price of producer in both the channels was the same. The cost incurred by chaff cutter in Channel-II includes loading/unloading and chaffing which was same in each. Further, the cost incurred by the dairy owners include only transportation charges The total marketing costs and margins for Channel-II were estimated at `31.98 and `18.67 per q respectively. Similarly in Channel-III, the cost incurred by dairy owner was the highest for transportation charges followed by commission and loading/unloading charges. The price spread in Channel-II was higher compared to the Channel-III and the marketing efficiency was higher in Channel III. Therefore, the Channel-III was found to be the most efficient channel for berseem marketing.Marketing problems

The marketing problems being faced by the fodder growers in Punjab state are discussed in detail in this section. A total of eight marketing problems have been reported in the state. The fodder growers were asked to

rank these eight production problems. The number and percentage of responses given by different fodder growers for various marketing problems is presented in Table 7, which showed that maximum number of the farmers reported lack of proper market followed by distant market, costly transportation and low output prices.

The number of farmers giving various ranks to various marketing problems, total score, mean score and ranks assigned to various problems is presented in Table 8. The problem with highest mean score was given first rank and the problem with next highest mean score was given second rank and so on. According to the Garrett ranking, among the marketing problems faced by fodder growers, lack of proper market got the first rank followed by price fluctuations low output prices, distant market, costly transportation, high marketing costs, malpractices by intermediaries and delay in payments.CONCLUSIONS

From the foregoing discussion, it may be concluded that criteria of payment was daily cash basis for all the

271


Sr. No. Cost items Channel-II Channel-III

Amount PCP Amount PCP

1. Net price received by the producer 94.45 65.10 94.45 72.79

2. Costs incurred by producers

i. Harvesting charges 6.73 4.64 6.73 5.19

ii. Weighing charges 0.95 0.65 0.95 0.73

iii. Loading/unloading 5.00 3.45 5.00 3.85

iv. Transportation 10.33 7.12 10.33 7.96

Sub-total 23.01 15.86 23.01 17.73

3. Producer's sale price 117.46 80.96 117.46 90.52

4. Purchase price of chaff cutter 117.46 80.96 - -

5. Costs incurred by Chaff cutter

i. Loading/unloading 2.57 1.77 - -

ii. Chaffing 2.57 1.77 - -

Sub-total 5.14 3.54 - -

Chaff cutters margin 18.66 12.86 - -

Sale price of chaff cutter 141.26 97.36 - -

6. Costs incurred by Dairy owner(Consumer)

i. Weighing charges - - 0.80 0.62

ii. Loading/ unloading - - 3.00 2.31

iii. Chaffing - - - -

iv. Transportation 3.83 2.64 4.83 3.72

v. Commission charges - - 3.67 2.83

Sub-total 3.83 2.64 12.30 9.48

7. Total cost to dairy owner (Consumer) 145.09 100.00 129.76 100.00

8. Total marketing cost 31.98 22.04 35.31 27.21

9. Total marketing margins 18.67 12.87 - -

10. Price spread 50.65 34.91 35.31 27.21

11. Marketing efficiency 1.86 - 2.67 -

Table 6. Marketing cost and price spread in marketing of berseem in Punjab state (` per q)


Factors Number of

farmers

Percentage of

responses

Lack of proper market 20 100

Price fluctuations 19 95

Low output prices 16 80

Distant market 12 60

Delay in payments 1 5

High marketing costs 5 25

Malpractices by intermediaries 14 70

Costly transportation 18 90

Table 7. Number and percentage of responses for marketing problems

(n=20)

farmers. Maximum distance covered by majority of the fodder growers for marketing of green fodder was five to ten km. The main reason for sale of fodder was production being economical followed by surplus production. Regarding the marketing channels, for the maize crop, there was only one marketing channel i.e. Channel-I which was not so popular and price spread in this channel was zero. For sorghum crop, there were two marketing channels (Channel-II and III). The Channel-III (Producer-Dairy owner (Consumer) was found to be most efficient channel. For bajra crop, there existed three channels (Channel-I, II, and III). The marketing efficiency was highest in Channel III. Similarly, for berseem crop, there were two marketing channels (Channel-II and III) and Channel-III was found to be most efficient channel for marketing of berseem crop. The most important marketing problem was observed to be lack of

272


Factors Rank No. of farmers

Total score

Mean score

Rank

1 2 3 4 5 6 7 8

Lack of proper market 9 5 3 2 1 0 0 0 20 1388.00 69.40 I

Distant market 3 4 8 3 0 1 0 0 19 1187.00 62.47 II

Low output prices 2 8 2 1 0 1 1 1 16 961.00 60.06 III

Price fluctuations 1 1 5 4 1 0 0 0 12 706.00 58.83 IV

Delay in payments 0 0 0 0 1 0 0 0 1 47.00 47.00 VIII

High marketing costs 0 2 0 1 2 0 0 0 5 281.00 56.20 VI

Malpractices by intermediaries 1 1 4 3 3 2 0 0 14 767.00 54.79 VII

Costly transportation 4 1 2 4 4 3 0 0 18 1027.00 57.06 V

Table 8. Ranking of marketing problems being faced by fodder growers in Punjab

proper market followed by price fluctuations, low output prices and distant market.REFERENCESAcharya, S.S., & Agarwal, N.L. (2012). Agricultural marketing

thin India (5 ed.). New Delhi: Oxford &IBH Publishing Co. Pvt Ltd.

Kaur, I., Singh, V.P., Kaur, H., & Singh, P. (2012).Cost-benefit analysis of cow milk production in Punjab. Journal of Agricultural Development and Policy, 22(1),67-74.

Kaur, P., Bhullar, A.S., Kaur, I., & Kaur, H. (2014).Growth and performance of the dairy sector in Punjab. In Inderjit Singh, Sukhwinder Singh and Lakhwinder Singh (Eds.). Punjab's economic development in the era of globalization (pp. 141-157). Delhi: LG Publishers Distributors,

Mellor, J. (2004). Agricultural growth and poverty reduction: The rapidly increasing role of smallholder livestock. In V. Ahuja (Ed.) Livestock and livelihoods: Challenges and

opportunities for Asia in the emerging market environment. Anand: NDDB.

Narke, A. (2017). From the president's desk. Indian Dairyman, 69(2), 16-17.

Srivastava, A.K. (2017).Inaugural address. Indian Dairyman, 69(4), 42-45.

The Government of India. (2016). Basic animal husbandry statistics 2016. Department of Animal Husbandry, Dairying and Fisheries, Ministry of Agriculture, Government of India: New Delhi.

The Government of Punjab. (2016). Statistical abstract of Punjab 2016 Economic Advisor to Government, Economic and Statistical Organisation, Chandigarh.

The Planning Commission. (2012). Report of the Working Group on Animal Husbandry and Dairying for Five-Year Plan (2012-2017). New Delhi: Planning Commission, Government of India.

273


ABSTRACTstPrecision farming (PF) is considered as the agricultural system of the 21 century, as it symbolizes a better balance between

reliance on traditional knowledge, information and management-intensive technologies. The present study was undertaken in the Koppal district of Karnataka. The study revealed that there were savings made with respect to chemical fertilizers and plant protection chemical (PPC) under PF. Thus sustainability in use of resources can also be achieved through PF. Paddy yield was higher by 10.68 percent in PF as compared to conventional farming. Conversely, the net returns was observed to be more (56.28 percent) in case of precision farming situation. Hence, there was a net gain of ̀ 16,935.49 per ha was in precision farming over non-precision farming.

KeywordsCosts, economics, net gain, precision farming, returns, sustainability.

JEL CodesC81, D24, M11, O33, O38, Q55.

1 2* 3 3K. Shruthi , G. M. Hiremath ,Amrutha T. Joshi and Suresh S. Patil

1 2 3Ph.D. Scholar, Assistant Professor,and Professors, Department of Agricultural EconomicsCollege of Agriculture, UAS Raichur.


Received: September 01, 2017 Revision Accepted: May 03, 2018

An Economic Analysis of Precision Agriculture-A Case Study of Paddy in North Eastern Karnataka


DOI: 10.5958/2322-0430.2018.00130.0


www.soed.in


UGC Approved


274

INTRODUCTIONAgriculture is a very important sector for the

sustained growth of the Indian economy. Indian agriculture is characterized by agro-ecological diversities in soil, rainfall, temperature and cropping system. It is now widely recognized that soil type and structure can differ over short distances and this variability increases with field size. Many factors like topography, ancient earthworks, drainage patterns and exposure to shade will all influence the soil characteristics in a particular area. Since differences in the soil affect crops and thus yield, it is clear that more accurate agricultural management practices with improved technology have the potential to benefit the farmer financially. In the present days of increasing input costs, decreasing commodity price and environmental concerns, farmers and government authorities are looking for new ways to increase efficiency of resources, cut in costs and subscribe to sustainable agriculture. Currently, agriculture production is facing significant challenges such as escalating costs of production, shortage of irrigation water and increased

public concern about the impacts of agricultural production on the environment. The focus on enhancing the productivity during the green revolution coupled with total disregard of proper management of inputs without considering the ecological impacts has resulted in environmental degradation (Singh, 2010).

Implementation of such management practices has been possible through pertinent agricultural research, education and extension enabling development and infusion of appropriate technologies by ICAR Institutes and Agricultural Universities, and taking them across to farmers through Krishi Vigyan Kendras (KVKs). Precision farming helps in dealing with this challenge by proper and effective management of soil and crop variability with the use of information technology. According to Robert et al. (1995), precision farming is defined as information and technology-based agricultural management system to identify, analyze and manage site-soil, spatial and temporal variability within fields for optimum profitability, sustainability and protection of the environment.

Precision farming is a farm management concept based on observing, measuring and responding to inter and intra-field variability in crop fields. Variability typically has both a spatial and temporal component which makes statistical/computational treatments quite involved. It is considered as the agricultural system of the

st21 century, as it symbolizes a better balance between reliance on traditional knowledge, information and management-intensive technologies. It is based on the philosophy of heterogeneity within homogeneity and requires precise information on the degree of variability within the field management. The aim is to vary the agricultural inputs in response to the varying conditions within the field. The key differentiator between the conventional management system and precision agriculture is in the concept of identification of field variability and the application of modern information technologies towards providing, processing and analyzing the multi-source data of high spatial and temporal resolution for decision-making and operations in the management of crop production. Precision farming provides a new solution using a systems approach for today's agricultural issues such as the need to balance productivity with environmental concerns. It aims at increased economic returns, as well as at reducing the energy input and the environmental impact of agriculture which is the crux of precision farming.Raj Khosla stated that precision agriculture is doing the right thing, in the right place at the right time.

The technology has been currently implemented in Karnataka state under the RKVY funded project on precision farming in selected field crops since 2011. The project was implemented through the three State Agricultural Universities in the state viz., UAS, Raichur, UAS, Dharwad and UAS, Bangalore. Farmers' participatory approach was adopted to execute the project at the farmers' fields of Raichur, Kalaburgi and Koppal districts, covering equivalent of 100 acres each in cotton, pigeonpea and paddy crops respectively, that represent major field crops of the North-Eastern Karnataka zone, along with on-farm research demonstration plots (5.00 acres in each crop) at four research stations of UAS, Raichur (Patil et al., 2013).

With this background, an attempt has been made in the present paper to analyze comparative economics of paddy production under precision and non-precision farming situations. METHODOLOGYLocale of the Study: The study was conducted in Karnataka state with a focus on the North Eastern Karnataka region in the jurisdiction of UAS, Raichur. However, the study confined to Jangamarakalgudi village of Gangavathi taluk, Koppal district of North Eastern Karnataka as RKVY- Precision Farming project has been implemented in this district. Sampling Procedure: The precision farming adopted farmers refers to those who are the beneficiaries of

precision farming project of UAS, Raichur. The precision farming non-adopted farmers refers to those who were not participated in precision farming but growing the same crop in the same area. The number of farmers who adopted precision farming for paddy were 38. An equal number of non-adopted farmers were selected on the same criterion. In all, the total sample size consisted of 76 farmers. Data Source: Primary data were collected from the farmers who adopted precision farming techniques in paddy since last three years and also from conventional farmers with the help of pre-tested interview schedule. The data were collected from the sample farmers by personal interview method using the pretested schedule during the period of January and February for the agricultural year 2014-15.

This technique of tabular presentation was employed to assess the cost, returns and profits of paddy under both precision and non-precision farming. The data were summarized with the aid of statistical tools like percentage, averages etc. to draw meaningful inferences.

The partial budgeting is one of the basic tools in all farm management decision making. In order to compare the costs and returns of precision and non-precision farming, partial budgeting technique was employed. This will reflect difference in quantitative aspects of precision and non-precision farming. A partial budget model was constructed by considering all revenue and expenses that would change with an alteration to the farm operation. The model of the above form was used. In the present study, the components like increase in costs and decrease in returns on debit side. While, decrease in returns and increase in returns were taken on credit side. If the difference in credit and debit side is positive, it is considered as net gain and if it is negative, it is considered net loss.RESULTS AND DISCUSSION

The results of pattern and extent of input usage under precision and non-precision cultivation of paddy indicated that (Table 1) precision farming practicing farmers were found to use more quantity of seeds (68.52 kg/ha), organic manures (3.91 t/ha) and biofertilizers (0.52 kg/ha) which was higher by 7.25, 28.20, and 85.71 percent respectively than that of non-participants of precision farming. This was due to awareness about importance of organic manures and biofertilizers among the participants of precision farming.

The savings in chemical fertilizers under precision farming of paddy was to the extent of 64.30, 56.17, and 47.58 percent of N, P O , and K O, respectively when 2 5 2

compared to non-precision farming situation. This was mainly because of tendency of using more quantity of fertilizers by the farmers under non-precision farming, which was mainly due to lack of awareness about the recommended dose of fertilizer usage among them. Further, there was misconception among the farmers that increased application of fertilizers would lead to higher

275

Shruthi et al.: An economic analysis of precision agriculture-A case study of paddy in north eastern Karnataka

yield. Whereas, precision farming practicing farmers have applied fertilizers based on soil testing report which indicates the variable rate of application of fertilizers across the fields. Thus sustaining the soil health. But they have used more quantity of micronutrients (126.89 percent) such as zinc, boron, gypsum, magnesium and iron as compared to farmers under non-precision farming in order to supply the requirements of soil as per soil test report.

Grid soil sampling and soil analysis were carried out at the beginning of the every season only in case of precision farming. Hence fertilizers were applied as per soil analysis report across the grids. This indicated the variable rate of application of fertilizer under precision farming. Therefore it was observed that there was savings with respect to N, P O and K O and increased application 2 5 2

of micronutrients. The similar kind of observation was

also made by Swinton and Lowenberg-DeBorer (1998). They reported that the application of major nutrients were decreased, while the micronutrients increased slightly. Snyder (1996); Ahmad et al. (1997) concluded in their studies that there was savings with respect to quantity of fertilizers application due to variable rate of application as compared to uniform application of fertilizers across the field.

The labour cost (Table 2) incurred on cultivation of paddy under both precision and non-precision farming were calculated based on the results of labourused to carry out various operations. The cost incurred was found to be higher by 4.80 percent in the case of precision farming (`39115.62/ha) as compared to non-precision farming (`37325.57/ha). Farmers under non-precision farming have incurred higher cost for application of fertilizers (5.10 percent), hand weeding (20.90 percent) and for

Particulars Precision farming Non-precision farming Percent change

Seeds 68.52 63.89 7.25

Organic manures (t/ha) 3.91 3.05 28.20

Biofertilizers 0.52 0.28 85.71

Fertilizers

N 73.21 205.06 -64.30

P O2 5 59.44 135.62 -56.17

K O2 47.40 90.42 -47.58

Micronutrients 26.63 11.74 126.89

Table 1. Comparative material input use pattern in precision and non-precision cultivation of paddy(kg/ha)

Operations Precision farming Non-precision farming Percent change PF over NPF

Nursery bed preparation 1968.75 1750.81 12.45

Removal of stubbles 1142.50 1026.20 11.33

Soil sampling 475.00 - -

Ploughing 1488.12 1445.96 2.92

Puddling 3921.25 3318.23 18.17

Harrowing 1448.75 1474.89 -1.77

Laser levelling 1672.50 - -

Application of manures 550.00 466.70 17.85

Transplanting 6756.25 6632.10 1.87

Application of fertilizers 1612.50 1697.60 -5.01

Hand weeding 5118.75 6471.07 -20.90

Irrigation 2531.25 2489.08 1.69

Application of PPC 2450.00 3507.09 -30.14

Application of growth regulators 112.50 100.98 11.41

Harvesting cum threshing 6867.50 6177.94 11.16

Cleaning and bagging 1000.00 766.92 30.39

Total 39115.62 37325.57 4.80

Table 2. Operation-wise labour cost in precision and non-precision cultivation of paddy

(`/ha)

PF: Precision farming, and NPF: Non-precision farming

276


application of PPC (30.14 percent). This was quite obvious due to use of more quantity of fertilizers and PPC under non-precision farming as compared to precision farming. Under precision farming situation, the higher cost incurred to carry out operations like nursery bed preparation, removal of stubbles, ploughing and puddling. Since input usage (except fertilizers and PPC) was more in precision farming, labour cost was conversely more for the operations such as application of manures and growth hormones, transplanting and irrigation. Higher yield was reported in precision farming as compared to non-precision farming. Hence more labour cost was incurred for harvesting cum threshing,

Sr. No. Particulars Precision farming

Non-precision farming

Percent change PF over NPF

A. Material input cost

Seeds 1918.70(2.59)

1895.70(2.40)

1.21

Organic manures 1173.75(1.59)

917.03(1.16)

27.99

Biofertilizers 525.00(0.71)

366.81(0.46)

43.13

Chemical fertilizers 5335.02(7.21)

10820.52(13.68)

-50.70

Plant protection chemicals 6580.00(8.89)

7945.00(10.04)

-17.18

Growth harmones/ regulators 372.50(0.50)

335.00(0.42)

11.19

Sub total 15904.97(21.49)

22280.06(28.16)

-28.61

B. Labour cost 39115.62(52.84)

37325.57(47.18)

4.80

C. Marketing cost 805.86(1.09)

728.15(0.92)

10.67

D. Interest on working capital @ 9 percent 5024.38(6.79)

5430.04(6.86)

-7.47

E. Total variable cost (A+B+C+D) 60850.83(82.20)

65763.82(83.13)

-7.47

1 Depreciation 792.71(1.07)

947.59(1.20)

-16.34

2 Land rent 10875.00(14.69)

10875.00(13.75)

0.00

3 Land revenue 175.00(0.24)

175.00(0.22)

0.00

4 Interest on fixed capital @ 11.25 percent 1332.30(1.80)

1349.73(1.71)

-1.29

F. Total fixed cost (1+2+3+4) 13175.01(17.80)

13347.32(16.87)

-1.29

Total cost (E+F) 74025.85(100.00)

79111.14(100.00)

-6.43

Table 3. Comparative cost of cultivation of paddy under precision and non-precision farming(`/ha)

Figures in the parentheses indicate percentage to total cost.PF: Precision farming, and NPF: Non-precision farming.

cleaning and bagging operations.The perusal of Table 3 clearly indicated that there was

savings in per hectare cost of cultivation to the extent of 6.43 percent in precision farming (`74025.85) when compared to non-precision farming (`79111.14). Of the total cost of cultivation, the material cost accounted 21.49 percent in precision farming and 28.16 percent in non-precision farming. This was mainly due to the savings in costs on fertilizers and PPC under precision farming. On the other hand, the labour cost was 4.8 percent morein precision farming as compared to non-precision farming.

The operations such as soil sampling and laser levelling contributed for more labour cost in precision

277


farming, which was not carried out in non-precision farming.Similarly, precision farming practicing farmers incurred more marketing cost (10.67 percent) as against farmers under non-precision farming. The one of the reasons quoted for lower marketing cost by the farmers under non-precision farming was due to procurement of output by the paddy millers and hence thereby there was no transportation cost.

The total variable cost under both precision as well as non-precision farming (82.02 to 83.13 percent) constituted the major component of total cost of cultivation. Though the total variable cost formed major component, there was savings to the extent of 7.47 percent under precision farming due to savings made by precision farming practicing farmers in fertilizers and PPC. These results were contradictory to the study of Maheswari et al. (2008) who reported that 42.15 percent of higher total variable cost was incurred under precision farming as compared to non-precision farming in tomato production.

Out of total fixed cost, the cost of land (`10875.00/ha) occupied the major share. The depreciation cost was 16.34 percent higher in case of non-precision farming as compared to precision farming. The reason observed for higher depreciation cost in non-precision farming was due to owning of more number of equipments and machineries as compared to farmers practicing precision farming. This was further substantiated by the fact that higher proportion of large farmers was observed in non-precision farming (13.16 percent) as compared to precision farming (7.89 percent). Hence, higher fixed cost has been incurred by the farmers under non-precision farming due to owning of more number of equipments and machineries.

The profit in paddy cultivation under precision and non-precision farming has been examined by computing per hectare cost of cultivation and gross returns realized (Table 4). The yield realized was higher in precision (75.55 q/ha) compared to non-precision farming (68.26 q/ha) to the extent of 10.68 percent. Hence, gross returns (11.27 percent) and net returns (56.28 percent) were relatively higher in precision farming as compared to non-

precision farming. Overall, it indicated that the costs were higher in non-precision farming (6.43 percent) whereas; net returns were more in precision farming (56.28 percent).

Similar findings were reported by Koch et al. (2004) that N management strategy produced additional net

-1returns from $18.21 to 29.57 ha over the uniform N management strategy. Similarly study conducted in Australia by Robertson et al. (2009) reported that on an average increase in gross margin under precision farming technology over the whole cropping program. In India, the study conducted by Maheswari et al. (2008) reported that precision farming has led to 80 percent increase in yield in tomato and 34 percent in brinjal production. Increase in gross margin has been found as 165 and 67 percent, respectively in tomato and brinjal farming.

The returns per rupee spent were higher in case of precision farming (1.66) as compared to non-precision farming (1.39). This indicated that by spending one rupee, `1.66 of returns can be generated in precision farming and `1.39 rupees in non-precision farming. These results were in line with Snyder (1996); Thomas (2006) who reported that the BC ratio was more than one in precision farming operating firm.

In order to assess the net gain or loss due to adoption of precision farming technology over non-precision farming, the method of partial budgeting was carried out (Table 5). The findings of the partial budgeting technique clearly indicated that there was net gain in precision farming over non-precision farming. Though the majority of the inputs contributed to increase in costs, more credit was observed due to decrease in costs mainly by the inputs like chemical fertilizers, PPC and labour cost for the operations like harrowing, application of fertilizers, hand weeding and application of PPC. Increase in returns due to precision farming was found to be `12,428.10 /ha. Hence, there was a net gain of `16,935.49 /ha was obtained by substituting precision farming over non-precision farming. Similar result was reported by Swinton and Lownberg-DeBorer (1998). They showed that the average benefit of precision farming over three years was about 15bushels/ac.

Particulars Precision farming Non-precision farming Percent change PF over NPF

Total variable cost 60850.83 65763.82 -7.47Total fixed cost 13175.01 13347.32 -1.29Total cost 74025.84 79111.14 -6.43Yield (q) 75.55 68.26 10.68Gross returns 122656.30 110228.20 11.27Net returns 48630.46 31117.06 56.28Returns per rupee spent 1.66 1.39 -

Table 4. Cost and returns structure of paddy under precision and non-precisionfarming conditions(`/ha)

PF: Precision farming, and NPF: Non-precision farming.

278


Debit `/ha Credit `/ha

A. Increase in costs C. Decrease in costs

Seeds 23.00 Chemical fertilizers 5485.50

Organic manures 256.72 Plant protection chemicals 1365.00

Biofertilizers 158.19 Harrowing 26.14

Growth harmones/ regulators 37.50 Application of fertilizers 85.10

Nursery bed preparation 217.94 Hand weeding 1352.32

Removal of stubbles 116.30 Application of PPC 1057.09

Soil sampling 475.00

Ploughing and puddling 645.18

Laser levelling 1672.50

Application of manures and growth harmones 94.82

Transplanting 124.15

Irrigation 42.17

Harvesting cum threshing 689.56

Cleaning and bagging 233.08

Marketing cost 77.71

B. Decrease in returns Nil D. Increase in return

Total (A+B) 4863.82 Total (C+D) 21799.25

Net gain [(C+D)-(A+B)] = 21799.25-4963.82 = 16935.43

Table 5. Assessment of net gain or loss in precision farming over non-precision farming of paddy through partial budgeting approach

(`/ha)

CONCLUSIONSThe study has revealed that the farmers were

benefitted due to adoption of precision farming as compared to non-adopters and farmers under non-precision farming were applying higher dosage of N, P O , 2 5

K O fertilizers without the knowledge of infield variability 2

and application of higher quantity of PPC with the misconception of getting higher yields. This affects soil health and also leads to higher cost of cultivation. Hence, there is a need to strengthen by the extensionagencies efforts to educate the farmers about the consequences of applying overdoses of these inputs. This would also go a long way in maintaining the soil health and reducing other adverse effects on the environment.It was confirmed from the study that the resources were efficiently utilized in precision farming compared to non-precision farming and through variable rate of application of fertilizers the soil health can be maintained. Thus, savings were made in chemical fertilizers and also PPC. The awareness about precision farming practices and its benefit are lacking among non-participants. Hence there is a need to encourage and popularize this technology with the support of line departments, SAU's and other extension agencies.REFERENCESAhmad, S., Raymond, J.S., & Miller, W. (1997, July 27-30).

Precision farming for profits and environmental quality: Problems and opportunities. Paper presented in Annual

Meeting of Agricultural Economics Association. Toronto, Canada.

Koch, B., Khosla, R., Frasier, W.M., Westfall, D.G., & Inman, D. (2004). Economic feasibility of variable-rate nitrogen application utilizing site-specific management zones. Agronomy Journal, 96, 1572-1580.

Maheswari, R., Ashok, K.R. and Prahadeeswaran, M. (2008). Precision farming technology in resource-poor environments. Agricultural Economics Research Review, 21, 415-424.

Patil, M.B., Shanwad, U.K.,Veeresh,H., Mastan, R.B.G., Pandit R.R, Rajesh, N.L., ……….. Patil, B.V. (2013). Precision agriculture initiative for Karnataka: A new direction for strengthening farming community.Scholarly Journal of Agricultural Science, 3(10), 445-452.

Robert, P.C., Rust, R.H., & Larson, W.E. (1995). Site-specific management for Agriculture system. Proceedings of Site-Specific Management for Agriculture System, 27-30, March 1994, Minneaports.

Robertson, M., Peter, C., & Lisa, B., (2009). The economic benefits of precision agriculture: Case studies from Australian grain farms. Crop and Pasture Science, 60(9), 799-807. https://doi.org/10.1071/CP08342.

Singh, A.K. (2010). Precision farming. New Delhi: Water Technology Centre, IARI.

Snyder, C.J. (1996). An economic analysis of variable-rate nitrogen management using precision farming methods (Doctoral dissertation). Department of Agricultural Economics, Kansas State University, Manhattan, KS 66506, USA.

279


Swinton, S.M., & Lowenberg-DeBoer, J. (1998). Profitability of site-specific farming. Journal of Production Agriculture, 11(4), 439-446.

Thomas, J.L. (2006). A feasibility study of opening and operating a precision farming firm in Kentucky (Master's thesis). University of Kentucky, Lexington, Kentucky.

280


ABSTRACTAn attempt has been made to study the marketing of Khasi mandarin in Sohra market of East Khasi Hills and Mawkyrwat market of West Khasi Hills districts of Meghalaya during the year 2015-16. Primary data was collected from selected mandarin growers constituting eighty (80) growers and five (5) intermediaries operating at each level of marketing channels. Three distribution

channels were identified viz., Channel-I (Producer → Wholesaler → Retailer → Consumer), Channel-II (Producer→ Retailer→

Consumer) and Channel-III (Producer → Consumer). Channel-I was the most popular channel where the farmer disposed maximum of their produce. The price spread was found to be highest under Channel-I (41.26 percent) of consumer's rupee in Sohra market and 46.64 percent of consumer's rupee in the Mawkyrwat market, due to more marketing costs incurred by agencies involved and more marketing margins earned by them. Producer's share in consumer's rupee under Channel-III was highest (94.51 percent) in Sohra market and 92.34 percent in the Mawkyrwat market, due to absent of intermediaries as the produce was sold directly to the consumer. Channel-III was found to be more efficient in both markets with the efficiency of 17.20 in Sohra market and 12.50 in the Mawkyrwat market, as it involved direct marketing of the produce to the consumers.

KeywordsConsumer's rupee, mandarin, market, marketing channel.

JEL Codes G14, M31, M38, Q13.

1* 2Sukheimon Passah and A.K. Tripathi

1Department of Agricultural Economics, College of Post Graduate Studies, Central Agricultural University,2Umiam-793103 (Meghalaya), and Division of Agricultural Economics, ICAR Research Complex for NER,

Umiam-793103 (Meghalaya)


Received: August 04, 2017 Revision Accepted: April 28, 2018

Marketing Pattern and Efficiency of Khasi Mandarin in Meghalaya


DOI: 10.5958/2322-0430.2018.00131.2


www.soed.in


UGC Approved


281

INTRODUCTION India ranks second both in area and production

among fruits and vegetable producing countries of the world after China. There is an impressive growth in the exports of fresh fruits from `489.08 crores in 2003-2004 to `3524.50 crores during 2015-16 (APEDA, 2016). Among fruits, citrus occupies a place of importance in the horticultural wealth and economy of India and occupies an area of about 987 thousand hectares with a production of 12181 thousand tonnes which accounts for 13.71 percent of total fruit production. The most important commercial citrus species in India are sweet orange (Citrus sinensis), mandarin orange (Citrus reticulata) and acid lime (Citrus aurantifolia) sharing 37, 30.4 and 24.7 percent respectively. Of all citrus fruits produced in the

ndcountry, mandarin orange is the 2 most cultivated citrus fruits crop grown in India. It occupied nearly 31.4 percent of the total area under citrus cultivation in India (The

Government of India, 2015). Citrus fruits such as Khasi mandarin have been adjudged as an important variety, widely known throughout North Eastern Region (NER) as well as outside and having good acceptance among the consumers. The fruit is grown abundantly in Meghalaya and has now earned a spot at the Geographical Indication (GI) tagging category. This will pave the way for better branding and marketing of these products both in the domestic and international market (APEDA, 2015). The total area of Khasi mandarin in Meghalaya is 8.96 thousand hectare and production is 43.69 thousand tonnes. It is cultivated in all the eleven districts of Meghalaya with East Khasi Hills and west khasi hills districts contribute about 59.56 percent of the total area and 66.23 percent of the total production of mandarin in the state (The Government of Meghalaya, 2016).

Meghalaya being a difficult hilly terrain has only about 10 percent of the total land available for cultivation.

The production of Khasi mandarin being seasonal and localized to favoured agro-climatic conditions coupled with the perishability of the produce pose several problems on marketing. Several constraints such as lack of transport, communication, weak cooperatives organizations, and storage facility in the rural areas (Mahanta & Konwar, 2014), distress sale and volatile behaviour of price were other problem faced fruit growers of hilly areas who were also exploited by middlemen resulting in the low share of farmer in the consumer's rupee. Similarly, the continued adoption of unorganized marketing practices (Sain et al., 2013) also leads to high marketing costs, marketing margins and price spread of Khasi mandarin in the study area. In the backdrop of above situation, the studies can be very helpful in identifying alternative solutions that may be adopted by farmers, marketers and policy makers. The specific objectives were to identify the marketing channels involved in the marketing of Khasi mandarin and to compute marketing costs, marketing margins, price spread, producer's share in consumer's rupee and marketing efficiency of different channels.METHODOLOGY

A multi stage sampling was adopted for the selection of districts, blocks, and villages. The study was conducted in 4 selected villages of Shella and Ranikor blocks (two villages from each block) of East Khasi Hills and West Khasi Hills districts of Meghalaya. The selection was based on highest area and production of Khasi mandarin, compound annual growth rate and percentage change of area and production. Total of eighty (80) respondent farmers from the four villages have been drawn by using probability proportionate to size sampling method. Two major markets namely Sohra market from East Khasi Hills district and Mawkyrwat market from West Khasi Hills were taken with five intermediaries at each stage. The required information was collected through personal interview method, using well-designed and pre-tested schedules. This paper was based on primary data collected from a survey of mandarin producers, market intermediaries and fruit markets in mandarin production and consumption areas. Wholesalers and the retailers dealing in the marketing of mandarin in the selected market were also interviewed. Analytical Tools

The data collected were tabulated and analysed for examined the marketing cost and marketing margin, price spread, producer's share in consumer's rupee and marketing efficiency.Marketing cost

C = C +C +C +C +…+CF m1 m2 m3 mi

C = C + ÓCF mi

Where,C = Total cost of marketing of the commodityC = Cost paid by the producer at the time the F

producer leaves the farm till he sells it, and thC = Cost incurred by the i middleman in the process mi

of buying and selling the productMarketing margin of the middlemen

A = P – (P + C )mi ri pi mi

Where,th A = Absolute marketing margin of i middlemenmi

P = Total value of receipts per unit (sale price)ri

P = Purchased value per unit (purchased price)pi

C = cost incurred on marketing per unitmi

Producer's share in consumer's rupee

Where,P = Producer's share in the consumer rupee. s

P = Price received by the farmer per unit of output f

P = Retail price per unit of outputr

Price spreadPrice spread = P – Pc f

Where, P = Price paid by consumerc

P = Price received by the producerf

Marketing efficiency Marketing efficiency was calculated using Acharya's

modified marketing efficiency (MME) approach (Acharya &Agarwal, 2011)

Where,MME = Modified measure of marketing efficiencyFP = Price received by farmersMC = Marketing costMM = Marketing margins.

RESULTS AND DISCUSSIONMarketing Cost and Marketing Margin of Khasi Mandarin

In general marketing costs constitute the expenses on the items like transportation charges (labour and vehicle), loading and unloading charges, packaging charges, wastage/ spoilage, market fee charges and other charges. These costs are the actual expenditure incurred for the smooth running of the business as well as for efficient marketing of particular farm commodity. The study of marketing costs and margins is important as they reveal the nature, extent, genuineness of various marketing charges and the efficiency of the system. The findings can be utilized to introduce appropriate marketing and price policy that aims to provide reasonable price to producer and to ensure them for adue share in consumer's rupee. The result of finding can also be utilized to identify the reason of high marketing costs and a possible way to reduce them. It can also help in development and evaluation of the marketing policies like the regulation of the market charges for different market functionaries and functions. Thus, this section deal with the estimation of marketing cost, margins and price spread of each

100́÷÷

ø

öççè

æ=

r

f

sP

PP

MMMC

FPMME

+=

282


identified marketing channels for Sohra market and Mawkyrwat market.

According to market norms, market fee and other charges which have to be paid by retailers and wholesalers. The following three channels were identified in the marketing of Khasi mandarin:

Channel-I: Producer → Wholesaler/ Traders→

Retailer → Consumer

Channel-II: Producer → Retailer → Consumer

Channel-III: Producer → Consumer.In Channel-I (Table 1), the marketing cost incurred

by Khasi mandarin producer in Sohra market amounted to be `2.49 per kg (3.76 percent of the consumer's price). Among the cost component, transportation cost by labour (2.43 percent) was found to be the highest cost incurred by the producer. These results are in conformity with the finding of Gunwant et al. (2013), which stated that marketing costs was higher mainly due to unavailability of transport facilities. Further, the wholesaler incurred `0.71 per kg as marketing cost in which loading and unloading (0.51 percent) contributed a higher share of the consumer's price and market margin earned by them was `15.91 per kg (24.05 percent). The marketing cost incurred by retailer estimated to be ̀ 1.84 per. The cost due to wastage/ spoilage (1.12 percent) was highest among the costs incurred by retailer due to lack of proper storage facility in the study area. The marketing margin earned by retailer accounted to be `6.34 per kg. Similarly, in the Mawkyrwat market (Table 2) the marketing cost incurred by Khasi mandarin producer accounted to be ̀ 2.95 per kg (4.69 percent of the consumer's price) and transportation cost by labour (2.70 percent) was found to be the highest cost incurred by the producer. Further, the wholesaler incurred `1.20 per kg as marketing cost in which loading and unloading (0.89 percent) contributed the highest share of the consumer's price which was similar with the finding of Gowri & Shanmugam (2015) and themarket margin was accounted to be `13.98 per kg (22.22 percent). The marketing cost incurred by retailer estimated to be `2.36 per kg. The cost due to wastage/ spoilage was highest (1.49 percent) among the costs incurred by retailer due to lack of proper storage facility in the study area. The marketing margin earned by retailer accounted to be ̀ 8.85 per kg.

In Channel-II (Table 1), the marketing cost incurred by mandarin producer was `2.49 per kg in which transportation cost by labour (2.49 percent) was found to be the highest cost incurred by the producer. The marketing cost incurred by retailer was estimated to be `2.07 per kg of Khasi mandarin in which cost due to wastage/ spoilage was highest (1.40 percent) of the cost incurred. The result is in conformity with the finding of Yeware et al. (2010) and marketing margin earned by them accounted to be`18.08 per kg. Similarly, in the Mawkyrwat market (Table2) the marketing cost incurred

by mandarin producer was `2.95 per kg in which transportation cost by labour (2.85 percent) was found to be the highest cost incurred by the producer. The marketing cost incurred by retailer was estimated to be ? 2.18 per kg in which cost due to wastage/ spoilage (1.13 percent) was highest among the cost incurred and marketing margin earned by them accounted as `19.10 per kg.

In Channel-III (direct marketing), the marketing cost incurred by Khasi mandarin producer was `2.49 per kg (5.50 percent) in Sohra market and in the Mawkyrwat market it was `2.95 per kg (7.66 percent). Bhat et al. (2015) revealed the same result that the total marketing cost was less in channel-III mainly due to absent of intermediaries and the net price received by the producers was high as it involved direct marketing to the consumers.Price Spread of Khasi Mandarin

The price spread of Khasi mandarin in Sohra market of East Khasi Hills under respective Channel is presented in Table 1. The study revealed that net price received by mandarin producer was observed to be highest in Channel-III, which was amounted to `42.83 per kg and share about 94.51 percent of the consumer's rupee. It was followed by Channel-II, amounted to `42.09 per kg (65.02 percent) and Channel-I amounted to ̀ 38.85 (58.74 percent). It was evident from the study that Channel-III was more efficient in which marketing cost of ̀ 2.49 (5.49 percent) was found to be least, followed by Channel-II, `4.56 (7.04 percent) and Channel-I `5.04 (7.62 percent). Hence, increase in the marketing cost reduced the share of the margin of the mandarin producer in consumer's rupee. Consequently, mandarin producer received higher share under Channel-III (94.51 percent). It may be due to the absence of intermediaries. It is very clear when we look at price spread, it was found to be more on Channel-I (41.26 percent), followed by Channel-II (34.98 percent) and Channel-III (5.49 percent). The study also found that Channel-I had the highest share of market margin accounting for 33.64 percent of the consumer's price which was followed by Channel-II (27.93percent). The consumer's price was observed to be highest in Channel-I (`66.14), followed by Channel-II (`64.73) and Channel-II (`45.32). The results are similar with the finding of Namasivayam & Jaffar (2005).

Similarly, in the Mawkyrwat market of west Khasi Hills (Table 2), the net price received by mandarin producer was observed to be highest in Channel-III, which was amounted to be ̀ 35.55 per kg and share about 92.34 percent of the consumer's rupeeand similar finding by Pandey et al. (2011). It was followed by Channel-II, amounted to ̀ 35.32 per kg (59.31 percent) and Channel-I amounted to be `33.57 (53.36 percent). It was evident from the results that Channel-III was more efficient in which marketing cost of `2.95 (7.66 percent) was found to be least and followed by Channel-II (`5.13, 8.61 percent) and Channel-I (`6.51, 10.34 percent). Hence,

283

Passah and Tripathi: Marketing pattern and efficiency of Khasi Mandarin in Meghalaya

increase in the marketing cost reduced the share of the margin of the mandarin producer in consumer's rupee. Consequently, mandarin producer received higher share under Channel-III (92.34 percent) of consumer's price. It may be due to the absence of intermediaries. It is very clear when we look at price spread in which it was found to be more on Channel-I (46.64 percent), followed by Channel-II (40.69 percent) and Channel-III (7.66 percent). The study also found that Channel-I had the

highest share of market margin accounting for 36.29 percent of the consumer's price which was followed by Channel-II (32.07 percent). The consumer's price was observed to be highest in Channel-I (`62.91), followed by Channel-II (` 59.55) and Channel-III (` 38.50). These results are in comparison with the finding of Bhat et al. (2015) which revealed that direct sale from producer to ultimate consumer was beneficial for both producer and consumer i.e. price spread was less in Channel-III.

Particulars Channel-I Channel-II Channel-III-1`kg Percent -1`kg Percent -1`kg Percent

Producer's sale price 41.34 62.50 44.58 68.87 45.32 100.00

Cost incurred by the growers

i. Transportation charges (human labour) 1.61 2.43 1.61 2.49 1.61 3.55

ii. Transportation charges (Vehicle) 0.51 0.77 0.51 0.79 0.51 1.13

iii. loading and unloading charges 0.27 0.41 0.27 0.42 0.27 0.60

iv. Packaging charges — — —

v. wastage/ spoilage — — —

vi. Market fee charges 0.10 0.15 0.10 0.15 0.10 0.22

Total (i to vi) 2.49 3.76 2.49 3.85 2.49 5.50

Net price received by the growers 38.85 58.74 42.09 65.02 42.83 94.51

Price received by the wholesaler 57.96 87.63 — —

Cost incurred by the wholesaler

i. Transportation charges (human labour) — — —

ii. Transportation charges (Vehicle) — — —

iii. loading and unloading charges 0.34 0.51 — —


v. wastage/ spoilage 0.28 0.42 — —

vi. Market fee charges 0.09 0.14 — —

Total (i to vi) 0.71 1.07 — —

Wholesaler margin 15.91 24.05 — —

Price received by the retailer 66.14 100 64.73 100 —

Cost incurred by the retailer


ii. Transportation charges (Vehicle) 0.21 0.32 — —

iii. loading and unloading charges 0.29 0.43 0.30 0.46 —

iv. Packaging charges (@ 0.35/ Kg) 0.35 0.53 0.35 0.54 —

v. wastage/ spoilage 0.74 1.12 0.91 1.40 —

vi. Market fee charges 0.25 0.38 0.51 0.78 —

Total (i to vi) 1.84 2.78 2.07 3.19 —

Retailer margin 6.34 9.59 18.08 27.93 —

Price paid by the consumer 66.14 100 64.73 100 45.32 100

Marketing cost 5.04 7.62 4.56 7.04 2.49 5.49

Net marketing margin 22.25 33.64 18.08 27.93 —

Price spread 27.29 41.26 22.64 34.98 2.49 5.49

Producer's share in consumer's rupees (percent) 58.74 65.02 94.51

Marketing efficiency 1.42 1.86 17.20

Table 1. Marketing cost, marketing margin, price spread and marketing efficiency of Khasi Mandarin in Sohra market of East Khasi Hills district, 2015-16

284


Particulars Channel-I Channel-II Channel-III-1`kg Percent -1`kg Percent -1`kg Percent

Producer's sale price 36.52 58.05 38.27 64.27 38.50 100.00

Cost incurred by the growers

i. Transportation charges (human labour) 1.70 2.70 1.70 2.85 1.70 4.42

ii. Transportation charges (Vehicle) 0.63 1.00 0.63 1.06 0.63 1.64

iii. loading and unloading charges 0.52 0.83 0.52 0.87 0.52 1.35


v. wastage/ spoilage — — —

vi. Market fee charges 0.10 0.16 0.10 0.17 0.10 0.26

Total (i to vi) 2.95 4.69 2.95 4.95 2.95 7.66

Net price received by the growers 33.57 53.36 35.32 59.31 35.55 92.34

Price received by the wholesaler 51.70 82.18 — —

Cost incurred by the wholesaler


ii. Transportation charges (Vehicle) — — —

iii. loading and unloading charges 0.56 0.89 — —


v. wastage/ spoilage 0.48 0.76 — —

vi. Market fee charges 0.16 0.25 — —

Total (i to vi) 1.20 1.91 — —

Wholesaler margin 13.98 22.22 — —

Price received by the retailer 62.91 100 59.55 100 —

Cost incurred by the retailer


ii. Transportation charges (Vehicle) 0.36 0.57 — —

iii. loading and unloading charges 0.47 0.75 0.50 0.83 —

iv. Packaging charges (@ 0.35/ kg) 0.35 0.56 0.35 0.59 —

v. wastage/ spoilage 0.94 1.49 0.67 1.13 —

vi. Market fee charges 0.24 0.38 0.66 1.11 —

Total (i to vi) 2.36 3.75 2.18 3.66 —

Retailer margin 8.85 14.07 19.10 32.07 —

Price paid by the consumer 62.91 100 59.55 100 38.50 100

Marketing cost 6.51 10.34 5.13 8.61 2.95 7.66

Net marketing margin 22.83 36.29 19.10 32.07 —

Price spread 29.34 46.64 24.23 40.69 2.95 7.66

Producer's share in consumer's rupees (percent) 53.36 59.31 92.34

Marketing efficiency 1.14 1.46 12.05

Table 2. Marketing cost, marketing margin, price spread and marketing efficiency of Khasi Mandarin in the Mawkyrwat market of West Khasi Hills district, 2015-16

Marketing Efficiency of Khasi MandarinMarketing efficiency is the degree of market

performance. Comparative higher the degree indicates the Channel is more efficient. A change that reduces the cost of accomplishing a particular function without reducing consumer's satisfaction indicates an improvement in the efficiency. Table 1 depicts that the marketing efficiency was found to be highest in Channel-III with marketing efficiency of 17.20, and ranked as first. It may be due to lesser price spread in the Channel-III,

which was followed by Channel-II with marketing efficiency of 1.86 and Channel-I (1.42). Similarly, in the Mawkyrwat market of West Khasi Hills (Table 2), the marketing efficiency was found to be highest in Channel-III with marketing efficiency of 12.05 and ranked as first. It may be due to lesser price spread in the Channel-III, which was followed by Channel-II with marketing efficiency of 1.46 and Channel-I (1.14). The Channel-I showed the lowest efficiency among all the Channels, it may be due to highest price spread (marketing cost and

285


marketing margin). This indicates that marketing efficiency decreased with the increase in market intermediaries, that is, more the marketing functionaries, less the marketing efficiency. These results are in conformity with the finding of Bhat et al. (2011); Verma et al. (2015); Kwasi & Sharma (2017).

Hence, study suggests standardizing the different marketing costs prevailed in the market to enhance the efficiency of existing channels in the market. Especially the marketing costs which had been incurred by producers found to be highest among all. Alternative steps should be taken to reduce such costs so that profits can be maximised.CONCLUSIONS·Channel-I was the most popular channel in both

markets where the farmer disposed maximum of their produced and disposing of more quantity through this channel may be due to wholesaler purchase in large quantities or to avoid losses due to spoilage by the producers.

·The price spread was found to be higher under channel-I in both markets, due to more marketing costs incurred by agencies involved and more marketing margins earned by them.

·Producer's share in consumer's rupee was highest under Channel-III as the produce was sold directly to the consumer. So, they earned maximum benefit under this channel. Similarly the consumer earns benefit under this Channel as price paid by them was less as compare to others Channel.

·Channel-III was found to be most efficient in both the market with the market efficiency of 17.20 in Sohra market and 12.05 in the Mawkyrwat market. This was mainly due to the absent of intermediary and the net price received by them also was high.It can be concludes from the finding that for farmers

to gain more profits they should sell their produce directly to the consumers, but in reality it cannot be practise so, as the fruits is perishability in nature it cannot be stored for a longer period and moreover proper facilities for storage was not availability. So, bulk quantity of produce was sold mainly to wholesalers/traders with lesser price in a local (weekly) market to avoid losses due to spoilage. Also due to unavailability of transport facilities in the area, especially those who live and farm in deep valleys had to carry sack full of oranges weighing up to 100 Kgs on their back or as head load and climb steep hills of more than 2000 feet with slope of 60 to 70 degrees making their arduous climb extremely hard. Some time they have to hired human labour for transportation especially during peak season of harvesting and due to unavailability of labour during such period the farmers was charged with higher price for transportation. With the perishability of the produce, the options available to them were limited and therefore they were prone to high level of exploitation and distress sale. Even where road accessibility was available the transportation costs cut deep into the final

sale price the farmers. For distances of upto 15-20 km, the farmers pay up to ̀ 30 per basket of 50 kilograms.

To bring small farmers in the market as main players, necessary legal set up is required so that small farmers can get access to market, avail free and fair opportunity and not exploited by other stakeholders (mostly wholesaler/ traders) of the supply chain. Effort should be taken up by the state government and concern authority to provide immediate support for development of better road facility and marketing infrastructure such as cold storage or small processing unit in those areas so that they can increase their productivity on large scale and better marketing of their produce. The policy implications suggested, if properly implemented may result in increased revenue of the farmers in particular and the state in general. Thus it enhance the livelihood and income opportunities of the farmers.REFERENCESAcharya, S.S., & Agarwal, N.L. (2011). Agriculture marketing

thin India (5 ed.). New Delhi: Oxford and IBH publishing Co. Pvt. Ltd..

APEDA. (2015). Geographical Indication (GI) registration of khasi mandarin. Retrieved from http:/ipindiaservices. gov.in/GI_DOC/465/465percent20percent20percentreplypercent20FCRpercent20bypercent20Applicantpercent20percent2008-05-2014.pdf.

APEDA. (2016). Reports on vegetables and fruits production. The Governmentof India, Ministry of Commerce and Industry, New Delhi.

Bhat, A., Kachroo, J., & Kachroo, D. (2011). Economic appraisal of kinnow production and its marketing under North-Western Himalayan region of Jammu. Agriculture Economic Research Review, 24, 283-290.

Bhat, A., Kachroo, J., Singh, S.P., & Sharma, R. (2015). Marketing costs and price spread analysis for citrus in Samba district of Jammu region. International Journal of Agricultural Economics, 2(1), 41-46.

Gowri, M.U., & Shanmugam, T.R. (2015). An economic analysis of production and marketing of banana in India. American International Journal of Research in Humanity, Arts and Social sciences, 9(3), 234-240.

Gunwant, V., Raturi, M., Hussain, M., Khan, S. Md. F.A., & Rana, D. (2013). Marketing of sweet orange (malta) in India. International Journal of Emerging Research in Management and Technology, 2(3), 45-49.

Kwasi, R.B., & Sharma, M. (2017).Determinants of the choice of marketing outlet among kinnow farmers in Rajasthan state of India. Indian Journal of Economics and Development, 13(1), 11-22.

Mahanta, D.K., & Konwar, A. (2014). Production and marketing of orange in Assam – A study on Doomdooma region of Tinsukia district. Journal of Agriculture and Life Science, 1(1), 82-90.

Namasivayam, N., & Jaffar, A.M. (2005). Marketing cost of banana in Theni district. Indian Journal of Marketing, 35(5): 29-31.

Pandey, D., Kumar, A., & Singh, R. (2011). Marketing of sweet orange (malta) in Kumaon region of Uttarakhand. Journal of Recent Advances in Applied Sciences, 26, 6-11.

Sain, V., Luhach, V.P., Singh, H., & Jyoti, V. (2013). Constraints faced by guava growers in production and marketing of

286


districts of Haryana state. IOSR Journal of Agriculture and Veterinary Science, 5(5), 17-20.

The Government of India. (2015). Horticulture statistics at a glance. Ministry of Agriculture and Farmer Welfare, Department of Agriculture, Cooperation and Farmer Welfare, New Delhi.

The Government of Meghalaya. (2016). District-wise area, production and productivity of khasi mandarin in Meghalaya. Directorate of Economics and Statistics,

Shillong.Verma, G., Mahajan, P.K., & Bharati. (2015). Economic

appraisal of kinnow production and its marketing in lower hills of Himachal Pradesh. International Journal of Farm Science, 5(1), 177-187.

Yeware, P.P., Pawar, B.R., Deshmukh, D.S., & Landge, V.V. (2010). Marketable surplus and price spread in marketing channels of ambebahar sweet orange. International Journal of Commerce and Business Management, 3(1), 25-28.

287


ABSTRACTAgricultural insurance is one product by which farmers can stabilize farm income and guard against disastrous effect of losses due to natural hazards. The farming community in India consists of about 121 million farmers of which only about 20 percent avail crop loans from financial institutions out of which three-fourths of those are insured. In this context this study is focus on estimation of crop insurance across the crops, magnitude and reasons for crop losses along with determinants of crop insurance and constraints faced by the farmers in crop insurance. The findings of study point out that the share in crop insurance was highest in cotton (24.14percent) crop followed by paddy (21.11percent) and groundnut (19.13percent). The value of loss in crops value was higher in cotton (57.78percent) and groundnut (21.09percent) as compared to other crops. The study pointed out that most of the marginal landholding farmers did not opt for crop insurance. The social forwardness (other than ST, SC and OBC) directly affects the use of crop insurance. The irrigated land holding size negatively influenced the crop insurance use by the farmers. The farmers' whose primary occupation was crop production opt for more crop insurance as compared to farmers with wages, salaries and off-farm business. The higher crop value loss realization positively linked with the use of crop insurance at the farmer level. To achieve the desired results from new Pradhan Mantri Fasal Bima Yojana (PMFBY)there is a need for crop insurance campaign to focus on sensitizing the farmers about the realized crop losses in crop production, which are covered in PMFBY.

KeywordsCrop, insurance, Logit model.

JEL CodesC25, G22, O13, O17.

*Shiv Raj Singh , K.P. Thakar, and C. Soumya

Assistant Professors, Department of Agricultural Economics, C.P. College of AgricultureS.D. Agricultural University, Sardarkrushinagar-385506 (Gujarat)



Status and Determinants of Crop Insurance in Gujarat


DOI: 10.5958/2322-0430.2018.00132.4


www.soed.in


UGC Approved


288

INTRODUCTIONAgricultural insurance is one product by which

farmers can stabilize farm income but also helps the farmers to initiate production activity after a bad agricultural year and guard against disastrous effect of losses due to natural calamities. It cushions the shock of crop losses by providing farmers with some amount of protection. Insurance comes towards the end of risk management process and is redistribution of cost of losses of few among many, and cannot prevent economic loss. The farming community in India consists of about 121 million farmers of which only about 20 percent avail crop loans from financial institutions out of which only three-fourths of those are insured (Raju & Chand, 2008). The growth rate at country level in terms of farmers insured and area insured showed 10.03 and 8.01 percent annual growth over a period fifteen years (2000 to 2014),

whereas in Gujarat state, double-digit agriculture growth was realized over the decade but farmers insured and area insured growth showed negative growth (-2.12 and -1.63 percent respectively) over the fifteen years (2000 to 2014).Despite the higher mechanization in agriculture, large-scale rural electrification, better irrigation infrastructure, good extension programmes, sustainable diversification, adoption of quality seed, large-scale adoption of micro irrigation system and good market infrastructure along with better road connectivity have driven the higher agriculture growth in the Gujarat state (Shah et al., 2009). Most of these factors supported the high agriculture growth in the state simultaneously

thovercoming the production risk. As in 70 NSSO survey round namely Situation Assessment Survey of Agricultural Households collected most of the information related to farming and farmers. The unit level

data of this round allowed the researchers to study the various dimensions of crop insurance at the state level. This paper focuses on estimation of crop insurance across the crops, magnitude and reasons for crop losses along with determinants of crop insurance and constraints faced by the farmers in crop insurance. METHODOLOGYSampling Plan

The study was mainly based on the secondary data available from National Sample Survey Organization (NSSO) on Situation Assessment Survey of Agricultural Households in the year 2012-13 (visit-1). The survey was conducted (rural sector) in two rounds, i.e. visit-1 (January–July 2013) and visit-2 (August-December 2013). This comprised detailed information on socio-economic conditions, crop cultivation, animal husbandry, access to basic and modern farming resources, consumption expenditure and crop insurance.

This survey focussed on rural areas and the unit of observation was rural farm households. The sampling design used in the NSSO data is stratified multi-stage random sampling with districts as strata, villages as first-stage units and farm households as the second-stage units. In this round 35,200 and 34,907 farm households were surveyed at the all-India level in Visit-I and II respectively. The survey covered 1,317 and 1,303 farm households in Visit-I and II at Gujarat state level. Analytical Tools

Both descriptive statistics and econometric methods were employed for data analysis. The logit model was used for binary response analysis. Given the binary response of the variable under consideration (availed crop insurance in the Kharif season), the econometric specification followed a logistic regression. The logistic regression a useful statistical modelling technique in which the probability of a binary outcome is related to a set of potential explanatory variables. The binary logistic regression has been widely used to address decisions involving binary choice in farm technology adoption studies (Mburu et al., 2007; Kumar 2010; Suresh et al. 2007). For calculation of contribution of different factors for crop insurance, it is assumed that the use of crop insurance by farmers is a random phenomenon affected by a set of factors that could explain the outcome. This binary variable (availed crop insurance) was then regressed onto a set of explanatory variables. Since the dependent variable is binary, we cannot use least square method to estimate the coefficients. Instead, one can use maximum likelihood estimation technique to calculate the coefficients. This study used logit model that allowed us to calculate marginal contributions of different factors on the use of crop insurance. To predict the dependent variable, the farmers were classified into two groups, those who use crop insurance (dependent variable as 1) or not (dependent variable as 0).

The logit model used in this study is discussed as following:

Where P is the probability that a farmer used the crop n

insurance,

Where is 1- P the probability that a farmer did not use n

the crop insurance. The Odd's ratio

Taking logarithm on both sides;

Where, á is the intercept, â the vector of response coefficient, e the vector of random disturbance and X is n

the set of explanatory variables.The estimable equation is:Crop insurance = â0 + â1 family size+ â2 socially

forward+ â3 land holding + â4 irrigated land holding + â5 income from wages and salary + â6 income from crop production + â7 income from off-farm business + â8 income from livestock farming + â9 access to extension agent + â10 access to Agricultural Science Centre + â11 total loss in Kharif crop value +en, where, crop insurance is the crop insurance used by the farmers in the Kharif season, while â1………… â11 are coefficients associated with each explanatory variable and en is the error term. Several factors are hypothesized to influence the farmer's decision to use the crop insurance. The choice of these explanatory variables is mainly based on the general working hypothesis and partly on empirical findings from the literature. RESULTS AND DISCUSSION Status of Crop Insurance across the Different Crops

The share of different crops in the crop insurance availed by the farmers are listed in Table 1. In the cereals, the highest share was occupied by the paddy (21.11percent) followed by maize (14.37 percent) and jowar (4.46 percent), respectively and lowest by bajra (0.56 percent). Tur held a highest share of 9.27 percent among the pulses. In case of oilseeds, groundnut contributed 19.13 percent and sesamum contributed 0.34 percent share in crop insurance, respectively. In cash crops, the share was highest in cotton (24.14 percent) crop while in castor it was only 0.98 percent. Assessment of Crop Losses across the Different Crops

As per study different crops observed ̀ 31670 million

1

1 nn zP

e-=+

1n

n

nzP

Pe

æ ö= =ç ÷

-è ø

1n

n n n n

n

Pln Z X e

Pab

æ ö==+ +ç ÷

-è ø

1

1-1 nn zP

e-=

+1-

289

Singh et al.: Status and determinants of crop insurance in Gujarat

Crop Percentage share of different crops in crop insurance

Paddy 21.11Jowar 4.46Bajra 0.56Maize 14.37Wheat 1.14Chillies 0.31Tur (Arhar) 9.27Other pulses 0.35Groundnut 19.13Sesamum 0.34Castor seed 0.98Cotton 24.14Other fodder crops 3.84

Table 1. Share of different crops in crop insurance

Source: The researchers' estimates based on NSSO unit level data (Visit-1) onSituation Assessment Survey of Agricultural Households, 2012-13.

loss in value of insured crops and `66105 million loss in value of not insured crops. Overall, there was `97775 million loss of crops value. At all India level loss of crop value was `921851 million. In Gujarat state, it was 10.61 percent at all India level. In Rabi season loss in crop value experienced by the farmers at Gujarat and India was `6420 and 285335 million. Loss in crop value at Gujarat state level was 2.25 percent of all India figure. This shows that in Kharif season farmers experienced higher losses (15 times) as compared to Rabi season. Therefore, crop insurance of Kharif season crops is more important to minimize the monetary losses.

The value of crop losses across the different crops in Kharif season is presented in Table 2. In cereals, the loss in value of insured crops was highest in jowar (5.12 percent) followed by bajra (0.14 percent) and wheat (0.04 percent). The loss in value of not insured crops was highest in jowar (5.34 percent) followed by paddy (3.94 percent) and bajra (2.51 percent). In wheat and maize, loss in value of insured crops was 1.51 and 0.31 percent. Other cereals recorded 0.01 percent loss in value of insured crops. Loss in crops value was also highest in jowar (5.27 percent) followed by paddy (2.66 percent) and bajra (1.74 percent). In wheat, maize and other crops there was loss of 1.03, 0.21 and 0.01 percent, respectively. In horticultural crops, only chillies (0.10 percent) experienced the loss in value of insured crops. Loss in value of not insured crops was highest in cumin seed (0.73 percent) followed by sugarcane (0.41 percent), mangoes (0.20 percent) and chillies (0.15 percent). Loss in crops value was highest in cumin seed (0.49 percent) and lowest in chillies(0.14 percent), and mango (0.14 percent). In sugarcane, the loss in crops value was 0.28 percent. In case of pulses, only in urad, there was loss in value of insured crops. In gram, tur, moong and other pulses there was no loss in value of

insured crops. In pulses, loss in value of not insured crops was highest in gram (0.32 percent), followed by tur (0.29 percent), moong (0.22 percent) and urad (0.15 percent). The loss in value of not insured crops was 0.01 percent in the case of other pulses. In case of pulses, the loss in crops value was highest in gram (0.22 percent). The loss in crops value was 0.19, 0.15, and 0.15 percent in the case of tur, urad and moong. In oilseeds, the loss in value of insured crops was highest in groundnut (40.97 percent), followed by castorseed (2.15 percent) and sesamum (0.41 percent). In case of oilseeds, the loss in value of not insured crops was highest in groundnut (11.57 percent) and lowest in castorseed (1.05 percent). In sesamum, the loss in value of not insured crops was 1.22 percent and in other oilseeds, it was 1.29 percent. In oilseeds, the loss in crops value was highest in groundnut (21.09 percent) followed by castorseed (1.41 percent), sesamum (0.96 percent) and other oilseeds (0.87 percent). In cash crops, that is, cotton the loss in value of insured crops was 49.67 percent. The loss in value of not insured crops was 61.67 percent in cotton and 1.07 percent in guar. In cotton, the loss in crops value was 57.78 and 0.72 percent in guar. In all other crops, the loss in value of insured crops was 1.27 percent and loss in value of not insured crops was 6.03 percent. The loss in crops value was 4.49 percent in all other crops. Major Reasons for Crop Losses

The major reasons for crop losses are listed in Table 3. Study pointed out that 79.26 percent of the crop losses were due to inadequate rainfall or drought while 14.18 percent of the crop losses were due to disease or insect or animal attack. Other natural causes like fire, lightning, storm, cyclone, flood, earthquake etc accounted for the 5.12 percent of crop losses whereas 1.44 percent of the crop losses were due to other reasons. Major Constraints Faced by the Farmers for Using Crop Insurance

The constraints faced by the farmers in the crop insurance are listed in Table 4. The important constraint faced by the farmers in the crop insurance is that majority of the farmers were not aware of the crop insurance scheme (36.15 percent). In crop insurance, 14.51 percent of farmers were not aware about facility available and in the case of 14.14 percent farmers, insurance facility was not available. It was observed that 13.37 percent of the farmers were not interested in crop insurance and 13.23 percent of the farmers did not need the crop insurance. Similarly, 2.65 percent of the farmers didn't go for crop insurance because of the complex procedures while 2.37 percent of the farmers were not satisfied with the terms and conditions of the crop insurance. Moreover, 2.06 percent of the farmers did not opt for the crop insurance due to lack of resources for premium payment and 1.04 percent of the farmers did not want to do the crop insurance because of the delay in claim payment.

290


Causes of crop loss Percentage

Inadequate rainfall/drought 79.26

Disease/insect/animal 14.18

Other natural causes (Fire, lighting, storm, cyclone, flood, earthquake, etc.) 5.12

Others 1.44

Table 3. Major reasons for crop losses (Kharif season)

Source: The researchers' estimates based on NSSO unit level data (visit-1) on Situation Assessment Survey of Agricultural Households, 2012-13.

Crops Loss in value of insured crops

Loss in value of not insured crops

Loss in crops value

Cereals 5.30 13.62 10.92Paddy 0.00 3.94 2.66Jowar 5.12 5.34 5.27Bajra 0.14 2.51 1.74Maize 0.00 0.31 0.21Wheat 0.04 1.51 1.03Other cereals 0.00 0.01 0.01Horticultural crops 0.10 1.49 1.05Sugarcane 0.00 0.41 0.28Chillies 0.10 0.15 0.14Cumin seed 0.00 0.73 0.49Mangoes 0.00 0.20 0.14Pulses 0.13 0.99 0.71Gram 0.00 0.32 0.22Tur (Arhar) 0.00 0.29 0.19Urad 0.13 0.15 0.15Moong 0.00 0.22 0.15Other pulses 0.00 0.01 0.00Oilseeds 43.53 15.13 24.33Groundnut 40.97 11.57 21.09Castor seed 2.15 1.05 1.41Sesamum 0.41 1.22 0.96Other oilseed 0.00 1.29 0.87Cash crops 49.67 62.74 58.50Cotton 49.67 61.67 57.78Guar 0.00 1.07 0.72Other crops 1.27 6.03 4.49Total value (`Million) 31670 66105 97775

Table 2. Value of crop losses across the different crops (Kharif season)(Percent)

Source: The researchers' estimates based on NSSO unit level data (visit-1) on Situation Assessment Survey of Agricultural Households, 2012-13.Note: At all India level total loss (due to climatic factor) in value term was ̀ 921851 million. At Gujarat level, it was 10.61 percent of all India.

Characteristics of Farmers using and Not-using Crop Insurance

Selected institutional and socio-economic characteristics of farmers availed crop insurance and did not avail crop insurance are presented in Table 5. Out of the 3.93 million farmers engaged in farming, 30.28 percent farmers availed crop insurance during the study period. Those farmers having higher landholding (2.38 ha) availed the insurance scheme, whereas farmers who owned lower landholding (0.79 ha) did not avail crop

insurance. The social forwardness (other than ST, SC & OBC) also directly affected the use of crop insurance. Out of the 1.19 million farmers, 40.34 percent farmers availed crop insurance scheme belonged to the socially forward group. Only 11.68 percentage of the farmers belonged to the socially forward group out of the 2.74 million farmers who did not avail the crop insurance. Therefore, social status of farmers helped them to avail crop insurance facility. The average irrigated land holding in the farmers who availed crop insurance was about 2.7 times more than

291


Characteristics Crop insurance availed Crop insurance not availed

Household characteristic

Farm household (million) 1.19 2.74

Family size (1 to 5) (million) 0.74(62.18)

1.58(57.66)

Socially forward (million) 0.48(40.34)

0.32(11.68)

Land characteristic

Average land holding (ha) 2.38 0.79

Average irrigated land holding (ha) 1.04 0.39

Livelihood characteristic(`per month)

Average income from crop production 12851 3806

Average income from wages and salaries 1382 2475

Average income from off-farm business 7582 5992

Average income from livestock farming 4345 3615

Loss in crop value of Kharif season (`) 61979 6378

Cosmopolitan characteristic

Accessed extension workers (No.) 88155 (7.41)

125281(4.57)

Accessed Agriculture Science Centre (No.) 40830 (3.43)

91843 (3.35)

Table 5. Selected institutional and socio-economic characteristics of farmers availing crop insurance

Source: The researchers' estimates based on NSSO unit level data (visit-1) on Situation Assessment Survey of Agricultural Households, 2012-13.Figures in parentheses are percentages of farm household of respective category.

Reason for not insuring crops Percentage

Not aware 36.15

Not aware about availability of facility 14.51

Not interested 13.37

No need 13.23

Insurance facility not available 14.14

Lack of resources for premium payment 2.06

Not satisfied with terms and conditions 2.37

Complex procedures 2.65

Delay in claim payment 1.04

Others 0.48

Table 4. Constraints faced by the farmers in the crop insurance

Source: The researchers' estimates based on NSSO unit level data (visit-1) on Situation Assessment Survey of Agricultural Households, 2012-13.

the farmers who did not avail crop insurance. These farmers' possess 1.04 ha irrigated land holding while it was only 0.39 ha in case of farmers who did not avail crop insurance. The farmers who used crop insurance had about 3.4 times higher average income from crop production (`12851 per month) than the farmers who did not avail crop insurance. The farmers who did not avail the crop insurance have `3806 per month income from crop production. The income from wages and salaries was

also an important characteristic that determined the use of crop insurance. The farmers who had not availed crop insurance earned about 1.8 times more income from wages and salaries than the farmers who availed crop insurance. The income from wages and salaries of farmers who didn't use crop insurance was ̀ 2475 per month while it was only `1382 per month in case of farmers who used crop insurance. It shows that the farmers with higher income from wages and salaries for their livelihood security do not opt for crop insurance. In the case of farmers who used crop insurance, the income from off-farm business was about 1.26 times higher than the farmers who have not taken crop insurance (`7582 per month). The farmers who did not avail crop insurance got `5992 per month income from off-farm business. The farmers who used crop insurance got `4345 per month income from livestock while the farmers who didn't use crop insurance got `3615 per month. The loss in crop value of Kharif season was an important characteristic that affects the option for the crop insurance by farmers. The farmers whose loss in crop value of Kharif season was higher used the crop insurance. The farmers who availed crop insurance lost `61979 as compared to the farmers who did not avail crop insurance lost `6378 in crop value in the Kharif season. The access with the extension workers plays an important role in the usage of crop insurance by the farmers. Out of the 1.19 million

292


farmers who availed crop insurance, 7.41 percent farmers accessed the extension workers and 4.57 percent farmers accessed the extension workers out of the 2.74 million farmers who did not avail crop insurance. Whereas 3.43 percent farmers accessed Agriculture Science Centre out of the 1.19 billion farmers who availed crop insurance while 3.35 percent farmers accessed the Agriculture Science Centre out of the 2.74 billion farmers who did not avail crop insurance.Factors Influencing the Use of Crop Insurance

It was noticed that the majority of the characteristics helped in opting for the crop insurance were found to be significant statistically (Table 6). There is significant proportional relationship between both the socially forward group and total land holding with the usage of crop insurance. Both the social forwardness of the farmer and total land holding positively influenced the usage of crop insurance by the farmers. It implies that these two variables significantly affect the decision of farmer to use the crop insurance. A significant (at 10 percent level) negative relationship was observed between the income from wages and salaries and the usage of crop insurance by the farmers. The irrigated land holding size negatively influenced the usage of crop insurance by the farmers which indicated that increase in the irrigated land holding with decrease in the number of farmers who avail the crop insurance since they will be less affected by the inadequate rainfall or drought which is the major reason for the crop losses (Table 3). The significant positive

aCharacteristics Coefficients SE Z-value p>Z-value


Family size 0.0148 0.0472 0.31 0.754

Socially forward group 0.8880 0.3463 2.56 ***0.010

Land characteristic

Total land holding 0.5106 0.1525 3.35***0.001

Irrigated land holding -0.5540 0.2120 -2.61 ***0.009

Livelihood characteristic

Income from crop production 8.27e-06 2.84e-06 2.91 ***0.004

Income from wages and salaries -9.70e-06 5.91e-06 -1.64*0.101

Income from livestock farming 2.71e-07 2.26e-07 1.20 0.229

Income from off farm business 1.32e-06 2.38e-06 0.56 0.578

Total loss in Kharif crop value 8.70e-06 3.19e-06 2.72 ***0.006


Access to extension workers 0.1565 0.4510 0.35 0.729

Access to agriculture science centre 0.4932 0.6434 0.77 0.443

Constant -1.9743 0.3316 -5.95 ***0.000

Table 6. Binary logit model coefficient estimates for determining of farmers using crop insurance

Source: The researchers' estimates based on NSSO unit level data (visit-1) on Situation Assessment Survey of Agricultural Households, 2012-13.2 2 2Number of observation = 1317, Wald ÷ (11) = 71.21, p>÷ = 0.0000, Pseudo R = 0.2256, Log pseudo likelihood = -1867419.2

Note: 0 not used crop insurance and 1 is used crop insurance. ***, and * Significant at 1, and 10 percent level.

relationship between the income from crop production and usage of crop insurance by the farmers was observed. The farmers who derived a high income from crop production were more likely to use the crop insurance. The positive relationship was observed between the total loss in crop value and the crop insurance being availed by the farmers, which was significant. If there was high loss in crop value, farmers will avail crop insurance more to cushion the shock of crop losses, hence, stabilizing their farm income.

The perusal of Table 7 showed that there was a marginal effect for the variables, presented elsewhere with significant coefficients for availing the crop insurance. These probabilities showed how the changes in specific variables affected the probabilities of farmers responding towards the crop insurance. The prediction probability of model was 0.30 for availing the crop insurance. The results indicated that the most influential variable was the social status of the farmers. The second influential variable was the irrigated land holding. The results of marginal effect showed that with unit increase in the land size, one could increase the crop insurance being availed by 10.7 percent. The option for the crop insurance was very much influenced by the total land holding. A decrease in availing the crop insurance by 11.61 percent was observed with a unit increase in the irrigated land holding. If there was a unit increase in income from crop production, then it would increase (dy/dx = 1.73e-06) the crop insurance use by the farmers. The results indicated

293


Characteristics *Marginal effect SE Z-value p>Z-value


Family size 0.0031 0.0099 0.31 0.754

Socially forward group 0.2014 0.0822 2.45 ***0.014

Land characteristic

Total land holding 0.1070 0.0323 3.32***0.001

Irrigated land holding -0.1161 0.0444 -2.62 ***0.009

Livelihood characteristic

Income from crop production 1.73e-06 5.92e-07 2.92***0.003

Income from wages and salaries -2.03e-06 1.25e-06 -1.63 *0.103

Income from livestock farming 5.68e-06 4.81e-06 1.18 0.239

Income from off farm business 2.77e-07 4.95e-07 0.56 0.576

Total loss in Kharif crop value 1.82e-06 6.84e-07 2.66 ***0.008


Access to extension workers 0.0337 0.0991 0.34 0.734

Access to Agriculture Science Centre 0.1117 0.1545 0.72 0.470

Table 7. Marginal effects of explanatory variables on the probability of crop insurance used

Source: The researchers' estimates based on NSSO unit level data (visit-1) on Situation Assessment Survey of Agricultural Households, 2012-13.Note: (*) dy/dx is for discrete change of dummy variable from 0 to 1Marginal effects after logit y = Prob.(whether used crop insurance yes=1) (predict) = 0.30***, and * Significant at 1, and 10 percent level.

that an increase (dy/dx = 1.82e-06) in crop insurance availed by farmers would happen if there was a unit increase in total loss in crop value. From the policy point of view, the social status barriers could be overcome by more awareness and accessibility of crop insurance as around 50 percent farmers were not aware about crop insurance or inaccessibility. The income from the crop production should be increased and loss realization by the farmers in crop production needs to be informed. CONCLUSIONS

The share in crop insurance was highest in cotton (24.14 percent) crop followed by paddy (21.11 percent) and groundnut (19.13 percent).The value of loss in crops value was higher in cotton (57.78 percent) and groundnut (21.09 percent).The study pointed out that most of the marginal landholding farmers did not opt for crop insurance. The social forwardness (other than ST, SC and OBC) directly affected the option for crop insurance. The irrigated land holding size negatively influenced the crop insurance use by the farmers. The farmers whose primary occupation was crop production opted for crop insurance. The average total income of those farmers was `26,160 per month in which the average income from crop production was `12,851 per month. The farmers who derive more income from wages and salaries and off-farm business did not opt for crop insurance. The average income of those farmers was ̀ 15,888 per month in which the highest contribution (53.29 percent) from off-farm

income (wages and salaries and off-farm business) was `8,467 per month. The higher crop value loss realization positively linked with the use of crop insurance at the farmer level. To achieve the desired results from new Pradhan Mantri Fasal Bima Yojana (PMFBY) there is a need for crop insurance campaign to focus on sensitizing the farmers about the realized crop losses in crop production, which are covered in PMFBY.REFERENCESKumar, A. (2010). Milk marketing chains in Bihar: Implications

for dairy farmers and traders. Agricultural Economics Research Review, 23, 469-477.

Mburu, L.M., Wakhungu, J.W., & Gitu, K.W. (2007). Determinants of smallholder dairy farmers' adoption of various milk marketing channels in Kenya highlands. Livestock Research for Rural Development, 19(9), Retrieved from http://www.lrrd.org/ lrrd19/9/ mbur19134.htm

Raju, S.S., & Chand, R. (2008). Agricultural insurance in India: Problems and prospects. Working Paper No.8. ICAR-National Centre for Agricultural Economics and Policy Research, New Delhi.

Shah, T., Gulati, A., Hemant, P., Shreedhar, G., & Jain, R.C. (2009). Secret of Gujarat's agrarian miracle after 2000. Economic and Political Weekly, 44(52), 45-55.

Suresh, A., Gupta, D.C., Solanki, M.R., & Mann, J.S. (2007). Reducing the risk in livestock production: Factors influencing the adoption of vaccination against bovine diseases. Indian Journal of Agriculture Economics, 62(3), 483-491.

294


ABSTRACTThe nature and extent of industrial employment, being enjoyed by the labourer, at work places, enables him to take decision about his stay at the work station or to move somewhere else. It depends upon the working hours and nature of work which enables the migrant labour to do hard work and help increase the production. The income earned by the employee is the outcome of working atmosphere. Keeping this in view, the present study focuses on nature and extent of employment in industrial units where migrant labourers are employed. Using primary data from migrant labourers employed in industrial units in Punjab, the study concludes that a majority of the migrant industrial labourers working on casual basis, working overtime also as well as getting paid for that. A substantial number of migrant industrial labourers, although working as per their own choice and skill, however, without any proper training of work, do not get wages equal to what are being given to the local labourers.

KeywordsEmployment, income, industrial labourers, migration.

JEL CodesA13, C40, C83, J31, J70, J81, Y10, Z10.

1 2*Jasdeep Singh Toor and Ketanpreet Kaur

1 2Assistant Professor, and Ph.D. Scholar, Department of Economics, Punjabi University, Patiala-147002

*Corresponding author's Email: [email protected]

Received: December 21, 2017 Revision Accepted: May 15, 2018

Nature and Extent of Industrial Employment of Migrant Labourers in Punjab


DOI: 10.5958/2322-0430.2018.00133.6


www.soed.in


UGC Approved


295

INTRODUCTIONA better employment opportunity is the most

significant factor, which motivates the workers to migrate. Better job opportunities, agricultural and industrial development and comparatively higher wages in Punjab attract the workers. People migrate to rural agricultural areas during the sowing and harvesting seasons, and return back home during the non-sowing and non-harvesting seasons. People, who are in search for work, come to Punjab during the sowing and harvesting seasons of the two major crops, wheat and paddy, then return back with their income looking forward towards the next season (Sharma & Jaswal, 2006). The territory of Punjab is being crowded by population from Uttar Pradesh, Bihar, Rajasthan and a few more states as both the farm and non-farm sectors have developed manifolds. A majority of the migrant population has been from Bihar, followed by Uttar Pradesh, the rest of Indian states contributing a little lesser. As much as 83 percent of geographical area of Punjab is under cultivation which leaves very little chance for further expansion of

agriculture and hence, the additional unemployed population has shifted to the urban areas. Mostly, they work in urban areas as rickshaw pullers, construction workers, skilled and unskilled workers in the factories, small shopkeepers, cooks, waiters, etc. The majority of migrants engaged in different industrial activities (Bhagat, 2006).

Migration decisions are made collectively by household members and not separately by individuals. Migration decisions in rural, less developed areas are made not only in order to maximize income, but also to minimize risks such as drought, flood, or drop in prices by sending some of their members to work away from home as migrant workers. According to a National Commission on Rural Labour Report (1991), there were about 6 million Indians that left their homes seeking employment somewhere else in India in 1981. Most of the migrants belong to the lower classes, namely Scheduled Tribes and Castes; tend to be relatively young with poor education. The highest proportion of migrants left their home at 10 to 20 years age group. As high as 61.88 percent of the

migrants reported that the friends and relatives were instrumental in their migration.

Due to inability of the native place to meet the needs of the people, they are forced to either accept a life of low standard of living or to migrate to areas with better opportunities, usually the urban or developed rural areas. Most of the labourers migrating from the rural areas of different states comprise of landless agricultural labourers, casual labourers and very small proportion of cultivators who earn their livelihood through wage employment. One-third of the rural households is constituted of agricultural labour households who are the most exploited and depressed class in the country (Mehra, 2012). The number of migrants that migrated from one town to another during the last decade is 14.4 million (14.7 percent). It was also noticed that rural migrants being significantly better educated than rural non-migrants, get married at a later age and have smaller size of their families. The proportion of migrants in the productive sector like manufacturing and services is higher than that of locals (Mehta, 1996). The migrants who are unskilled with less educational and technical qualifications become a part of the informal sector. Migrant labourers in this sector earn lesser than their local counterparts (Duraisamy & Narasimhan, 1997).

Haberfeld et al. (1999) and the Youth and Labour Migration (2013) concluded that during migration, migrants face the problem of open unemployment, cost of travel, and cost of information also. Sometimes young people were forced to take jobs below their skills. Mostly, they were employed in construction, hotels, tea shops and factories, etc. Dupont (1992); Majumdar (1969); Singh et al. (2007); Kaur et al. (1994) focussed on in-migration. The studies showed that immigrants do find better employment opportunities and often reach a higher standard of living in the in- migrating state. The nature and extent of employment, being enjoyed by the labourer enables him to take decision about his stay at the workstation or to move somewhere else. Apart from this, the society in which he lives and the working environment, affects his contentment level. Also, it depends upon the working hours and work nature which enables the migrant labour to do hard work and help to increase the production. Keeping that in view, this paper focuses to analyze the nature and extent of industrial employment of migrant labourers in Punjab.METHODOLOGY

To fulfill its objective, the study uses a primary data of 350 migrant industrial labourers, taken from 50 industrial units relating to tractor and tractor parts and food and beverages units in Mohali city, through a survey of migrants, which was facilitated by structured schedules and observations. Tabulation and percentages were used for analyzing the sampled data.RESULTS AND DISCUSSIONNature of Employment

The perusal of Table 1 showed the distribution of

migrant industrial labourers on the basis of nature of employment. From the sampled industrial units in Mohali city, highest percent (42.86 percent) of migrant industrial labourers were employed on temporary basis, followed by 29.71 percent labourers working on contract basis and 27.43 percent labourers employed on regular basis. The results were not inconsonance with the findings of Mehra (2012). From the tractor and tractor parts industrial units, majority of (45.71 percent) of migrant industrial labourers were working on temporary/casual basis, followed by 28.57 percent labourers working on contractual basis and 25.71 percent of them on regular basis.

From the food and beverages industrial units, a majority of (34.26 percent) each of industrial migrant labourers were working on regular and contractual basis. 31.43 percent migrant industrial labourers were working on temporary basis.Working Hours

The perusal of Table 2 exhibits the distribution of industrial migrant labourers on the basis of working hours. From the total, a large proportion of (68.57 percent) of the migrant industrial labourers were working for 8 hours only. Only 31.43 percent migrant industrial labourers are working for more than 8 hours. Our results are in same line as that of Mehra (2012) found that a majority of the migrant industrial labourers working for more than 8 hours. Among tractor and tractor parts industrial units, 67.86 percent migrant industrial labourers work for 8 hours, whereas 32.14 percent migrant industrial labourers work for more than 8 hours. From food and beverages industrial units, about 71.43

Nature of employment Total

Number Percent

Tractor and tractor parts

Regular 72 25.71

Contractual 80 28.57

Temporary 128 45.71

Total 280 100.00

Food and beverages

Regular 24 34.29


Temporary 22 31.43

Total 70 100.00

Total

Regular 96 27.43


Temporary 150 42.86

Grand Total 350 100.00

Table 1. Distribution of migrant industrial labourers on the basis of nature of employment

Source: Field Survey, 2014-15.

296


percent migrant industrial labourers were working for 8 hours. On the other hand, 28.57 percent migrant industrial labourers work for more than 8 hours. Overtime Work

The perusal of Table 3 showed the distribution of migrant industrial labourers on the basis of overtime work. Among the industrial units in Mohali city, around 82.86 percent of the migrant industrial labourers were working overtime. Only 17.14 percent migrant industrial labourers were not working overtime. Mehra (2012), found a majority of the migrant industrial labourersnot working for overtime. From tractor andtractor parts, and food and beverages industrial units, 83.93 and 78.57 percent migrant industrial labourers, respectively were working overtime and rest 16.07 and 21.43 percent migrant industrial labourers, respectively are not working overtime.Hours of Overtime Work

From the total, the highest percent (42.07 percent) of migrant industrial labourers work overtime for 2-4 hours, followed by 38.97 percent working for less than 2 hours and 18.97 percent working for more than 4 hours (Table 4). Among the tractor and tractor parts units, a large number (43.83 percent) of the migrant industrial labourers work overtime for 2-4 hours, followed by 38.72 percent labourers work for less than 2 hours, 17.45 percent of them working for more than 4 hours. Among the food and beverages industrial units, a large percent (40 percent) of the migrant industrial labourers work for less than 2 hours of overtime, followed by 34.55 percent labourers working for 2-4 hours and 25.45 percent labourers were working for more than 4 hours.Wages for Overtime Work

The perusal of Table 5 showed that a majority (90 percent) of migrant industrial labourers get wages for overtime work and the remaining 10 percent were deprived of this. The results were similar to the findings of

Working hours Total

Number Percent

Tractor and tractor parts 8 hours 190 67.86More than 8 hours 90 32.14Total 280 100.00Food and beverages 8 hours 50 71.43More than 8 hours 20 28.57Total 70 100.00Total 8 hours 240 68.57More than 8 hours 110 31.43Grand Total 350 100.00

Table 2. Distribution of migrant industrial labourers on the basis of working hours

Source: Field Survey, 2014-15. Work overtime Total

Number Percent

Tractor and tractor parts Yes 235 83.93No 45 16.07Total 280 100.00Food and beverages Yes 55 78.57No 15 21.43Total 70 100.00Total Yes 290 82.86No 60 17.14Grand total 350 100.00

Table 3. Distribution of migrant industrial labourers on the basis of overtime work


Hours of overtime work Total

Number Percent

Tractor and tractor partsLess than 2 hours 91 38.722-4 hours 103 43.83More than 4 hours 41 17.45Total 235 100.00Food and beverages Less than 2 hours 22 40.002-4 hours 19 34.55More than 4 hours 14 25.45Total 55 100.00Total Less than 2 hours 113 38.972-4 hours 122 42.07More than 4 hours 55 18.97Grand total 290 100.00

Table 4. Distribution of migrant industrial labourers on the basis of hours of overtime work


Mehra (2012). From both type of industrial units such as tractor and tractor parts and food & beverages industrial units, a majorityof the migrant industrial labourers were getting wages for their overtime.Equality of Wages for Overtime Work

The perusal of Table 6 showed that a bulk of migrant industrial labourers (87.61 percent) were not getting the same wages for overtime work as daily routine work, whereas rest of labourers (12.26 percent) were getting the same. Among the tractor and tractor parts industrial units, 88.68 percent migrant industrial labourers were not getting the same wages for overtime work and other 11.32 percent were getting wages for the same. From the food and beverages industrial units, a majority (83.67 percent)

297

Toor and Kaur: Nature and extent of industrial employment of migrant labourers in Punjab

of the migrant industrial labourers were not getting the same wages for overtime work and other 16.33 percent industrial migrant labourers were getting equal wages for their routine as well as overtime work.Training Received for their Work Pursued

The perusal of Table 7 showed the distribution of migrant industrial labourers on the basis of training received for their work pursued. From the total industrial units, 82.00 percent were not trained for their work.Only18.00 percent migrant industrial labourers were trained in some kind of trade. From the tractor and tractor parts industrial units, a majority of the migrant industrial labourers (80.71 percent) have not taken any training while 19.29 percent have taken some kind of training.

The same trend was observed in food and beverages industrial units where in 87.14 percent of migrant industrial labourers did not have any training while 12.86 percent have taken the training. Work According to Choice

Table 8 showed the distribution of industrial migrant labourers on the basis of work according to their own choice. From the total units of the industries, a big majority (88.57 percent) of migrant industrial labourers work according to their own choice and rest 11.43 percent migrant industrial labourers were not working according to their choice. Majority of the migrant industrial labourers were working according to their choice.

In the case of tractor and tractor parts industrial units, almost same proportion (88.21 percent) migrant industrial labourers work according to their choice and others 11.79 percent migrant industrial labourers did not get work according to their choice. From the food and beverages industrial units, 90 percent migrant industrial labourers work according to their choice and remaining10 percent of them did not get work according to their own choice.Work According to Skills

The perusal of Table 9 showed the distribution of industrial migrant labourers on the basis of work according to skill. From the total, a major proportion (86.86 percent) of the migrant industrial labourers work according to their skills and rest 13.14 percent did not get the same opportunity. These results are in confirmity with Mehra (2012). Among the tractor and tractor parts industrial units, majority of (87.14 percent) migrant industrial labourers work according to their skills and rest 12.86 percent did not get opportunity.

The same trend was noticed in food and beverages industrial units where 85.71 percent of the migrant industrial labourers work according to their skills and rest 14.29 percent labourers did not get the opportunity.Shift Time of Work

The results presented in Table 10 revealed the total more than half (54.29 percent) of migrant industrial labourers were working in both the shifts, followed by 35.71 and 10 percent opting for morning and evening shifts.

Wages for overtime work Total

Number Percent

Tractor and tractor partsYes 212 90.21No 23 9.79Total 235 100.00Food and beverages Yes 49 89.09No 6 10.91Total 55 100.00Total Yes 261 90.00No 29 10.00Grand total 290 100.00

Table 5. Distribution of migrant industrial labourers on the basis of wages for overtime work


Same wages for overtime Total

Number Percent

Tractor and tractor partsYes 24 11.32No 188 88.68Total 212 100.00Food and beverages Yes 8 16.33No 41 83.67Total 49 100.00TotalYes 32 12.26No 229 87.74Grand total 261 100.00

Table 6. Distribution of migrant industrial labourers on the basis of equality of wages for routine and overtime work


Training Total

Number Percent

Tractor and tractor partsYes 54 19.29No 226 80.71Total 280 100.00Food and beveragesYes 9 12.86No 61 87.14Total 70 100.00TotalYes 63 18.00No 287 82.00Grand total 350 100.00

Table 7. Distribution of the migrant industrial labourers on the basis of training


298


Work according to choice Total

Number Percent


Yes 247 88.21

No 33 11.79

Total 280 100.00

Food and beverages

Yes 63 90.00

No 7 10.00

Total 70 100.00

Total

Yes 310 88.57

No 40 11.43

Grand Total 350 100.00

Table 8. Distribution of migrant industrial labourers on the basis of work according to choice


Work according to skills Total

Number Percent


Yes 244 87.14

No 36 12.86

Total 280 100.00

Food and beverages

Yes 60 85.71

No 10 14.29

Total 70 100.00

Total

Yes 304 86.86

No 46 13.14

Grand total 350 100.00

Table 9. Distribution of migrant industrial labourers on the basis of work according to skill


Work shift Total

Number Percent


Morning 100 35.71

Evening 30 10.71

Both 150 53.57

Total 280 100.00

Food and beverages

Morning 25 35.71

Evening 5 7.14

Both 40 57.14

Total 70 100.00

Total

Morning 125 35.71

Evening 35 10.00

Both 190 54.29


Table 10. Distribution of migrant industrial labourers on the basis of work shift


In the case of tractor and tractor parts industrial units, a large proportion (52.63 percent) of the migrant industrial labourers were working in both shifts, followed by 36.47 percent of migrant industrial labourers choosing for morning shift and rest 10.90 percent of migrant industrial labourers preferring for evening shift. The same trend was noticed in food and beverages industrial units.Work Shift According to Choice

The perusal of Table 11 showed that a majority (82.00 percent) of the migrant industrial labourers were not getting the work shift according to their choice, whereas the remaining 18.00 percent labourers were enjoying this option. Among the tractor and tractor parts industrial units, majority (83.21 percent) of the migrant industrial

Work shift according to choice Total

Number Percent

Tractor and tractor partsYes 47 16.79No 233 83.21Total 280 100.00Food and beveragesYes 16 22.86No 54 77.14Total 70 100.00TotalYes 63 18.00No 287 82.00Grand total 350 100.00

Table 11. Distribution of migrant industrial labourers on the basis of work shift according to choice


labourers were not getting work shift according to preference and others 16.79 percent migrant industrial labourers were getting work shift with their own choice. The similar results were encountered in the case food and beverages industrial units.Distance from Residence

It can be seen from Table 12 that half of the migrant industrial labourers were residing within the distance of 1-5 km from their workplace, followed by 30.86 percent residing within the distance of 5-10 km and 8.29 percent in factory premises itself. It was noticed 5.71 percent migrant industrial labourers were living at the distance of more than

299


Distance from residence Total

Number Percent

Tractor and tractor partsFactory premises 22 7.861-5 km 153 54.645-10 km 88 31.43More than 10 17 6.07Total 280 100.00Food and beverages Factory premises 7 10.001-5 km 40 57.145-10 km 20 28.57More than 10 3 4.29Total 70 100.00TotalFactory premises 29 8.291-5 km 193 55.145-10 km 108 30.86More than 10 20 5.71Grand Total 350 100.00

Table 12. Distribution of migrant industrial labourers on the basis of distance from residence


Mode of travelling Total

Number Percent


Factory bus 23 8.21

By foot 92 32.86

Bicycle 129 46.07

Scooter/ motorcycle 36 12.86

total 280 100.00

Food and beverages

Factory bus 1 1.43

By foot 18 25.71

Bicycle 41 58.57


Total 70 100.00

Total

Factory bus 24 6.86

By foot 110 31.43

Bicycle 170 48.57



Table 13. Distribution of migrant industrial labourers on the basis of mode of travelling


10 km. Majority of the migrant industrial labourers were living with the distance of less than 5 km followed by residing in factory provided premises, within 5-10 km and more than 10 km. Around half of migrant industrial labourers were residing within the distance of 1-5 km, followed by 31.43 percent within the distance of 5-10 km, 7.52 percent migrant industrial labourers were living in factory provided rooms and 5.26 percent at the distance of more than 10 km. On the other hand, from the food and Beverages industrial units, maximum number (57.14 percent)of migrant industrial labourers were residing within the distance of 1-5 km followed by 28.57 percent within the distance of 5-10 km, 10.00 percent on the factory site, and 4.29 percent at the distance of more than 10 km.Mode of Travelling

Table 13 exhibited the distribution of migrant industrial labourers on the basis of mode of travelling to their workplace from their place of residence. The results revealed that 48.57 percent of migrant industrial labourers travelled using bicycle, followed by 31.43, 13.14 and 6.86 percent going by foot, scooter/motorcycle, and factory bus. In the case of tractor and tractor parts industrial units, a large number (46.07 percent) of the migrant industrial labourers travelling on bicycle, followed by 32.86 12.86 and 8.21 percent travelling by foot, scooter/motorcycle, and factory bus, respectively. In the case of food and beverages industrial units, a large number of the migrant industrial labourers were found to be travelling on bicycle (58.57 percent), followed by foot (25.71 percent), scooter/motorcycle (14.29 percent) and factory bus (1.79 percent).

300


Reason for Choosing Current JobThe perusal of Table 14 showed that a large

proportion (41.43 percent) of the migrant industrial labourers preferred this job for shifting from agriculture to industrial sector. It was reported that 26.57 percent joined the current job on suggestion of their friends already working there, 16.57 percent according to their skills and 15.43 percent due to their family members already employed therein. In the case of the tractor and tractor parts industrial units highest number (41.43 percent) of the migrant industrial labourers joined their job for the shifting from agriculture to industrial sector, followed by 26.43 percent who joined the present job on the suggestion of their friends, 16.79 percent according to their skills and 15.36 percent because of some of their families members already working therein.

In the case of food and beverages industrial units, majority of (41.43 percent) of the migrant industrial labourers opted for this job shifting from agriculture to industrial sector. This was followed by 27.14, 15.71, and 15.43 percent on the suggestion of their friends, according to their skills and due to their family members already working there.

CONCLUSIONSA majority of the migrant industrial labourers were

working on casual basis. About two-thirds of the migrant industrial labourers are working for 8 hours only. More than four-fifths of the migrant industrial labourers are working overtime, with a major proportion of them working for 2-4 hours. A large number of the migrant industrial labourers are getting wages for their overtime work, however, not equal to what they get for their routine work. About four-fifths of the migrant industrial labourers have not taken any proper training pertaining to their work. Majority of the migrant industrial labourers have chosen the work according to their choice and skill. More than half of the migrant industrial labourers prefer to work in both shifts. It was found that they choose work shift

301


Reason of choosing job Total

Number Percent


Job according to skill 47 16.79

Shifting from agriculture to industry 116 41.43

Suggestion by friend 74 26.43

Suggestion by family member 43 15.36

Total 280 100.00

Food and beverages

Job according to skill 11 15.71




Total 70 100.00

Total

Job according to Skill 58 16.57




Total 350 100.00

Table 14. Distribution of migrant industrial labourers on the basis of reason of choosing job


according to their choice. Near about one-third of the migrant industrial labourers was residing within the distance of 5-10 km from their work place. Nearly half of the migrant industrial labourers use bicycle to reach their workplace. More than two-fifths of the migrant industrial labourers have chosen this job to shift from agriculture sector to industrial sector. REFERENCESBhagat, V. (2006). A study of migrant vegetable sellers in

Ludhiana (Master's Thesis). Punjab Agricultural University, Ludhiana.

Youth and Labour Migration. (2013). Youth and labour migration: Summary of 5-week online discussion. R e t r i e v e d f r o m t r a c t o r a n d t r a c t o r parts://www.ilo.org/wcmsp5/groups/public/ed_emp/documents/genericdocument/wcms_209613.pdf

Dupont, V. (1992). Impact of in-migration on industrial development: Case study of Jetpur in Gujarat. Economic and Political Weekly, 27(45), 2423-2436.

Duraisamy, P., & Narasimhan, S. (1997). Wage differentials between migrants and non-migrants and discrimination in urban informal sector in India. The Indian Journal of Labour Economics, 40(2), 223- 235.

Haberfeld, Y., Menaria R.K., Sahoo B.B., & Vyas R. N. (1999). Seasonal migration of rural labour in India. Population Research and Policy Review, 18(5), 473-489.

Kaur, P. (1994). An analysis of socio-economic conditions of industrial workers of Rajpura in Punjab (Doctoral dissertation). Punjabi University, Patiala.

Majumdar, P. (1969). Internal migration in India: Some socio-economic implications. The Indian Journal of Statistics, 31(3/4), 509-522.

Mehra, S. (2012). Socio-economic Implications of labour migration: A case study of industries in Ludhiana city (Doctoral dissertation). Punjabi University, Patiala.

Mehta, G.S. (1996). Employment structure and earnings of migrant workers in urban economy. Manpower Journal, 31(4), 29- 41.

Sharma, S., & Jaswal, S. (2006).Migration and magnitude of psychological distress. Journal of Social Science, 12(3), 225-229.

Singh, L., Singh, I., & Ghuman, R.S. (2007). Changing character of rural economy and migrant labour in Punjab. Munich Personal RePEc Archive, Paper No. 6420. Retr ieved f rom ht tps : / / ideas . repec.org/p/pra/ mprapa/6420.html.

ABSTRACTThe present paper is an attempt to examine indebtedness among the marginal and small farmers in rural areas of Punjab. The study revealed that85.46 percent of the marginal and small farm households in the rural areas of Punjab were under debt. The average per sampled household debt was `336272.39. The average per sampled farm household debt is found to be more in the case of small farm-size category as compared to the marginal farm-size category. The debt per acre was inversely related to farm size. An average sampled farm household incurred 66.29 percent of the total debt from the institutional agencies. The share of institutional agencies in the total debt increased with the increase in farm size. Due to the application of New Agricultural Technology, the marginal and small farmers borrowed funds mainly for growing crops. These farmers were unable to meet their consumption expenditure with their income. To fill this gap these farmers also borrowed for family maintenance. The analysis clearly brought out the fact that the marginal and small farmers were partly dependent upon the commission agents and money-lenders who charged exorbitant rates of interest.

KeywordsIndebtedness, marginal and small farmers, purposes, sources.

JEL CodesE43, G 21, G23, Q12, Q18.

1* 4 2 3 2Rupinder Kaur , Sukhvir Kaur , Anupama , Gurinder Kaur and Gian Singh

1 2 3Assistant Professor and Professors, Department of Economics, Professor, Department of Geography4Punjabi University, Patiala-147002 and Assistant Professor, Dashmesh Khalsa College, Zirakpur-140603 (Mohali)


Received: July 25, 2017 Revision Accepted: May 21, 2018

#Indebtedness among Marginal and Small Farmers in Rural Punjab


DOI: 10.5958/2322-0430.2018.00134.8


www.soed.in


UGC Approved


302

INTRODUCTIONpulation of India is increasing alarmingly and

according to 2011 Census, the population level reached 121.02 crores. Increase in population caused sub-division of landholdings, which further increased the number of marginal and small farmers. Introduction of modern technology in the field of agriculture increased the use of capital both in terms of building farm infrastructure and meeting operational costs. However, the Indian farmers, particularly small and marginal farmers are suffering from stagnation owing to low productivity arising from inadequate investment (Kaur, 2011). Farmers, in general, cannot afford investment from their own savings to transform traditional agriculture into the modern one. The loans obtained for investment in seeds, fertilizers, agro-chemicals and so on are partly spent for their bare subsistence and for the fulfilment of their social obligations (Kumari, 2005).

Borrowings become even indispensable in adverse circumstances like floods, droughts, pest attacks and

The poother natural calamities when even well-off segments of farmers are compelled to borrow not only to smooth their consumption requirements but also to invest on next crops and to purchase productive assets lost during such circumstances. Even in modern agriculture, farmers borrow to mechanize and modernize their farm operations for diversifying into more rewarding farming and non-farming activities. The mechanisation of agriculture brought the rural people closer to those in the cities, resulting in a mad race of having good standard of living. In this way, the farmers are caught in the vicious cycle of indebtedness (Kaur, 2011).

To bridge the expenditure-income gap, the farmers have no other option to avail loans from the various institutional and non-institutional agencies. For providing loans, institutional arrangements have not yielded expected results. As a consequence, the farmers are forced to approach the non-institutional agencies for credit to meet their needs. Despite the tremendous expansion of banking network and the growth of institutional credit for

agriculture, the severity of agricultural indebtedness persists (Sidhu &Rampal, 2016).

The most agonized are the smaller farmers who have been hit hard by the financial crisis arising out of rising cost of production, declining productivity and reducing returns. As farming turns out to be viable for these farmers, they are involuntarily manoeuvred towards borrowing the loan and hence, fall prey to the debt trap (Singh & Bhogal, 2014). There has been a distinct slowdown in agricultural growth during the past decades. The two faces of the emerging distress are the manifestation of agrarian crisis that threatens the livelihood of farmers, particularly small and marginal ones, and the agricultural development crisis of reduction in its overall growth rate accompanied by declining profitability (Kaur, 2016).

The Government of India fixed the Minimum Support Prices in such a way that remained remunerative for farmers from 1965 to 1969 and resulted in increasing the income of farmers. But the way the prices are being fixed and manipulated from 1970 onwards can be stated as un-remunerative and due to this agriculture has become a loss-making profession (Singh, 2014). The Minimum Support Prices no longer cover the cost of cultivation, they only accommodate the paid-out costs, with no profit when the cost of family labour, the value of interest on own capital and rent of land are taken into account forgetting about profit. Thus, the prevailing market price which depends on the Minimum Support Prices set out by the Central Government gives only subsistence to the self-exploiting farmer, not any re-investible surplus (Murthy, 2013). Agricultural indebtedness increases mainly because of a sharp deceleration in the growth of prices of many agricultural commodities and increases in the cost of cultivation after the introduction of economic reforms (Rao & Suri, 2006). The recent agrarian crisis, rural indebtedness and large-scale farmers' suicides in many parts the country, presents an extreme form of such a scenario (Kaur, 2012). The gravity of the problem as well as its causes pointed out that most of the suicide victims were cultivators belonging to the small and marginal category of farmers. Suicides were attributed to a number of reasons, ranging from poverty to crop failure, indebtedness, marital discord and alcoholism. It was mainly due to the economic crisis that the peasantry, in Punjab, in general, is facing and which led them to heavy borrowings. The heat has been felt more by the small and marginal farmers (Gill & Singh, 2006).

The present paper is an attempt to examine indebtedness among marginal and small farmers in the rural areas of Punjab. More specifically objectives were:

i. to analyse the extent and distribution of indebtedness among marginal and small farmers,

ii. to examine the various sources of debt,iii. to analyse per household debt according to

various purposes, and

iv. to compare and contrast the variations in rate of interest paid by the marginal and small farmers.

METHODOLOGYFor the purpose of the present study, data were

collected from the three districts of Punjab state representing the three different regions, that is, the South-West Region, the Central Plains Region and the Shivalik Foothills Region. The South-West Region comprised of Bathinda, Mansa, Ferozepur, Fazilka, Faridkot, Muktsar and Moga districts. The Central Plains Region constituted Patiala, Fatehgarh Sahib, Sangrur, Amritsar, Kapurthala, Jalandhar, Nawanshahr, Tarn Taran, and Ludhiana districts. The Shivalik Foothills Region comprises of Hoshiarpur, Pathankot, Gurdaspur and Ropar districts. Keeping in view the differences in agro-climatic conditions and to avoid the geographical contiguity of the sampled districts, it was deemed fit to select one district from each region on a random basis. Mansa district from the South-West Region; Ludhiana district from the Central Plains Region; and Hoshiarpur district from the Shivalik Foothills Region were selected.

On the basis of random sample method, one village from each development block of the selected districts was chosen. There are twenty-seven development blocks in the selected three districts. Thus, in all, twenty-seven villages were selected from the three districts under study. A representative proportional sample of households comprising marginal farmers, small farmers, medium farmers, large farmers and agricultural labourers were taken up for the survey. Out of these 27 villages, 681 households were selected from the three districts for the purpose of the survey. Out of total selected 681 households, 408 households belonged to marginal farm-size category and 273 households belonged to the small farm-size category. Out of which, 88 marginal farm households and 62 small farm households from Mansa district, 161 marginal farm households and 149 small farm households from Ludhiana district and 159 marginal farm households and 62 small farm households from Hoshiarpur district were selected. The data were analyzed using descriptive statistical tools such as averages, percentages, etc. The present study pertains to the agricultural year 2014-15.RESULTS AND DISCUSSIONExtent and Distribution of Indebtedness

The extent and distribution of indebtedness among the marginal and small farm households can be observed from Table 1. The results showed that 85.46 percent of the marginal and small farm households in the rural areas of Punjab were under debt. The percentage was 83.33 and 88.64 percent for the marginal and small farm-size categories. The average amount of loan per sampled household and per indebted household was `336272.39 and 393473.37, respectively.

The average amount of debt per sampled farm household was found to be more in case of the small farm-size category as compared to the marginal farm-size

303

Kaur et al.: Indebtedness among marginal and small farmers in rural Punjab

category. The average amount of debt per sampled farm household was `494051.28 and 230699.75 for the small and marginal farm-size categories, respectively. The average amount of debt per indebted household was `557338.85 for the small farm-size category and the corresponding figure for the marginal farm-size category is ̀ 276839.70.Indebtedness

The amount of debt per operated acre and the pre-owned acre is given in Table 2. The results revealed that for an average sampled farm household the amount of debt per owned acre and per operated acre was `129834.90 and 60372.06, respectively. The amount of debt per acre was inversely related to farm size. The category-wise amount of debt per owned acre and per operated acre was `140670.58 and 65169.42 for the marginal farm-size category. The corresponding figures for the small farm-size category were `120794.93 and 55573.82, respectively. These figures confirmed the findings of Kaur (2011) that the burden of debt was greater on the marginal farm-size category as compared to the small farm-size category. Indebtedness according to Sources of Debt

The average amount of debt availed by the small and marginal farm-size categories from the various institutional and non-institutional credit agencies is enlisted in Table 3. The results showed that an average sampled farm household availed `222909.69 from institutional agencies, while `113362.70 from the non-institutional agencies. Amongst the institutional agencies, commercial banks were providing the highest amount (`163163) to an average sampled farm household followed by co-operative credit societies/banks, land development banks and regional rural banks. On the other hand, in the case of non-institutional agencies, the commission agents were providing the highest amount (`67381.79) to an average sampled farm household followed by the money-lenders, relatives and friends, large farmers and traders.

The results further showed that the marginal farmers were under the debt of `230699.75, out of which `91019.61 availed from the non-institutional agencies and the remaining `139680.14 from the institutional agencies. The small farmers were indebted to the extent of

Per Acre

`146754.58 to the non- institutional agencies and `47712.64 to the institutional agencies. The institutional agencies were playing a greater role in providing a loan to the marginal and small farm households as compared to the non-institutional agencies.

The perusal of Table 4 revealed that institutional agencies were the major source of debt. An average sampled farm household incurred 66.29 percent of the total debt from the institutional agencies. The share of institutional agencies in the total debt increased with the increase in farm size. The remaining 33.71 percent of the total debt incurred was from the non-institutional agencies. The proportional share was 39.45 and 29.70 percent for the marginal and small farm-size categories.

Amongst the different agencies, commercial banks were the most important source of debt for an average sampled farm household contributing 48.52 percent to the total debt. This proportional share was the highest for the small farm-size category followed by the marginal farm-size category. These figures confirm the findings of Sekhon and Saini (2008) that commercial banks played an important role in the agricultural production by providing credit facilities to farmers.

As much as 20.04 percent of the total debt was incurred from the commission agents. The marginal farmers borrowed about 21 percent of their total debt from the commission agents, whereas the corresponding figure for the small farmers was 19.46 percent. The third important source of credit for an average sampled farm household was the co-operative societies/banks from which an average sampled farm household incurred 16.04 percent of the total debt. This proportion increased with increase in farm size. This was in clear contrast to the study conducted by Pal and Singh (2012), which stated that among institutional sources, cooperative

Farm categories No. of households Indebted households as percentage of

sampled households

Average amount of debt (`)

Sampled Indebted Per sampled household

Per indebted household

Marginal farmers 408 340 83.33 230699.75 276839.70

Small farmers 273 242 88.64 494051.28 557338.85

All sampled farmers 681 582 85.46 336272.39 393473.37

Table 1. Extent of debt among marginal and small farmers: Category-wise


Farm-size categories Debt per owned acre

Debt per operated acre

Marginal farmers 140670.58 65169.42

Small farmers 120794.93 55573.82

All sampled farmers 129834.90 60372.06

Table 2. Category-wise debt(Mean values in `)


304


societies/co-operative banks were an important source of debt which accounted for 30.10 percent of the total debt for the marginal and small farmers. The money-lenders with rank four were contributing 7.35 percent to the total debt of an average sampled farm household, whereas the corresponding shares for the marginal and the small farm-size categories were 9.35 and 5.95 percent, respectively.

Next in order of magnitude were relatives and friends

from which an average sampled farm household obtained 4.39 percent of the total debt and this proportional share was inversely related with farm size. The large farmers provided 1.12 percent of the total debt to average sampled farm households. The share of land development banks was 0.96 percent for an average sampled household. This proportional share was negatively associated with farm size. About one percent of the total debt of an average

Source of debt Marginal farmers

Small farmers

All sampledfarmers

A. Institutional sources

Primary agricultural cooperative societies/co-operative banks 32628.68 85805.87 53946.40

Commercial banks 102517.16 253798.53 163163.00

Land development banks 2696.08 4029.30 3230.54

Regional rural banks 1838.22 3663.00 2569.75

Sub-total 139680.14 347296.70 222909.69

B. Non-institutional sources

Commission agents 48117.65 96172.16 67381.79

Money-lenders 21575.98 29432.24 24725.40

Traders/shopkeepers 2700.98 2798.53 2740.09

Large farmers 3894.61 3553.12 3757.71

Relatives and friends 14730.39 14798.53 14757.71

Sub-total 91019.61 146754.58 113362.70

C. Total (A+B) 230699.75 494051.28 336272.39

Table 3. Debt incurred from different credit agencies: Category-wise (Mean values in `)

Source: Field survey, 2014-15.

Source of debt Marginal farmers

Small farmers

All sampledfarmers

A. Institutional sources

Primary agricultural cooperative societies/ co-operative banks 14.14 17.37 16.04

Commercial banks 44.44 51.37 48.52

Land development banks 1.17 0.82 0.96

Regional rural banks 0.80 0.74 0.76

Sub-total 60.55 70.30 66.29

B. Non-institutional sources

Commission agents 20.86 19.46 20.04

Money-lenders 9.35 5.95 7.35

Traders & shopkeepers 1.17 0.57 0.81

Large farmers 1.69 0.72 1.12

Relatives and friends 6.39 3.00 4.39

Sub-total 39.45 29.70 33.71

C. Total (A+B) 100.00 100.00 100.00

Table 4. Debt incurred from different credit agencies: Category-wise (Percentage of total debt)

Source: Based on Table 3.

305


sampled farm household incurred from traders and shopkeepers. This proportion was 1.17 and 0.57 percent for the marginal and small farm-size categories, respectively.

The above analysis showed that the institutional agencies were the major source of agricultural debt. The non-institutional agencies appear at the second rank. This has an important implication that with the passage of time the role played by the institutional agencies in providing agricultural credit is becoming important.Indebtedness according to the Purpose of Loans

The various purposes of granting loan for which the farm households are incurring debt are presented in Table 5. The results showed that the farm inputs were the major item for incurring loan. An average sampled farm household availed `215461.82 for this purpose and the amount increased with the increase in farm size. The family maintenance expenditure appears as the next important purpose for incurring debt. An average sampled farm household incurred ̀ 35582.97 for this purpose. This amount increased as farm increased. The third rank goes to house construction, an addition of rooms and major repairs. An average sampled farm household is under a debt of `27716.59 for this purpose. An average sampled farm household incurred `20132.16 and 12004.41 for paying the rent of land and marriages and other socio-religious ceremonies. About `10280 were incurred for education. A small amount was used for healthcare, the redemption of old debt, purchase of livestock, etc.

The proportionate share of debt incurred for the different purposes is presented in Table 6. The results indicated that among the different purposes the highest share goes to the purchase of farm inputs. As much as 64.07 percent of the total debt incurred for the purchase of farm inputs by an average sampled farm household. This share increased as farm size goes up. Next in order of magnitude was family maintenance expenditure which

accounted for 10.58 percent share in the total debt. This proportional share was negatively correlated with farm size. This proportional share was as high as 14.40 and 7.91 percent for the marginal and small farm-size categories. This finding confirmed the results of the study conducted by Singh (2010), which showed that the annual income of marginal and small farmers falls short of their expenditure and their frequent borrowing was mainly for consumption purposes.

Slightly more than 8 percent of the total loan incurred for house construction, the addition of rooms, and major repairs which were highest for the small farm-size category (9.60 percent) followed by the marginal farm-size category (6.29 percent). About 4 percent of total debt incurred for marriages and other socio-religious ceremonies. The higher debt for marriages and other socio-religious ceremonies was the result of conservative approach towards maintaining a fake social status which is far from reality. As much as 3.06 and 2.09 percent of total debt incurred for education and healthcare, respectively. Purchase of livestock and redemption of old debt has a meagre share in total debt.

The above analysis showed that due to the application of New Agricultural Technology the marginal and small farmers have borrowed funds mainly for growing crops. These farmers were unable to meet their consumption expenditure with their income. To fill this gap these farmers borrowed mainly for family maintenance.Indebtedness and Rate of Interest

The farm households incurred debt from the different agencies at different rates of interest. The perusal of the Table 7 showed the category-wise distribution of indebtedness with respect to the various levels of rate of interest. The results depicted that the highest amount of debt for an average sampled farm household incurred at the rate of interest ranging between 1 to 7 percent per annum. An average sampled farm household incurred

Purpose Marginal farmers

Small farmers All sampled farmers

Purchase of farm inputs and machinery 141991.42 325263.74 215461.82Pay rent of land 12083.33 32161.17 20132.16Marriages and other socio-religious ceremonies 10600.49 14102.56 12004.41House construction, addition of rooms and major repairs 14522.06 47435.90 27716.59Domestic needs 33230.39 39098.90 35582.97Healthcare 7291.67 6593.41 7011.75Purchase livestock 2720.59 3754.58 3135.10Education 3921.57 19780.22 10279.00Purchase land 0.00 0.00 0.00Repayment of loan 3602.94 5860.81 4508.08Small business 735.29 0.00 440.53Total 230699.75 494051.28 336272.39

Table 5. Debt incurred for different purposes: Category-wise(Mean values in `)


306


Farm size categories Rate of interest (Percent)

0 1 to 7 8 to 14 15 to 21 22 to 28 Above 28 Total

Marginal farmers 10276.96 101530.64 42009.80 45112.75 30078.43 1691.18 230699.75

Small farmers 1868.13 176014.65 177967.03 93234.43 43135.53 1831.50 494051.28

All sampled farmers 6906.02 131389.87 96512.48 64403.82 35312.78 1747.43 336272.39

Table 7. Debt according to rate of interest: Category-wise(Mean value in `)


Purpose Marginal farmers Small farmers All sampledfarmers

To purchase of farm inputs and machinery 61.55 65.84 64.07

To pay rent of land 5.24 6.51 5.99Marriages and other socio-religious ceremonies 4.59 2.85 3.57

House construction, addition of rooms and major repairs 6.29 9.60 8.24

Domestic needs 14.40 7.91 10.58Healthcare 3.16 1.33 2.09To purchase livestock 1.18 0.76 0.93Education 1.70 4.00 3.06To purchase land 0.00 0.00 0.00Repayment of loan 1.56 1.19 1.34Small business 0.32 0.00 0.13Total 100.00 100.00 100.00

Table 6. Pattern of debt incurred for different purposes: Category-wise (Percentage of total debt)


`131389.87 at the rate of interest ranging between 1 to 7 percent per annum and this amount increases as farm size goes up. An average sampled farm household availed `96512.48 at 8-14 percent range of rate of interest. The marginal and small farm-size categories have taken `42009.80 and 177967.03, respectively at 8-14 percent range of rates of interest, and ̀ 64403.82 availed at the rate of interest ranging between 15 to 21 percent. It was noticed that as much as `35312.78 and 1747.43 was incurred at a very high rate of interest ranging from22 to 28 percent and above 28 percent per annum, respectively.

The relative shares of different ranges of rate of interest in total debt are given in Table 8. An average sampled farm household incurred 39.07 percent of the

total debt at 8 to 14 percent range of interest rate. Both the farm categories incurred the major share of total debt in this range. An average sampled farm household incurred 28.70 percent of its total debt at the rate of interest ranging between 8 to 14 percent per annum. Another substantial proportion of (19.15 percent) of the total debt of an average sampled farm household comes in the range of 15-21 percent interest rate. This proportion decreased as the farm size increased.

About 11 percent of total debt incurred at the rate of interest ranging between 22-28 percent for an average sampled farm household. This proportion was negatively correlated with farm size. Slightly more than 2 percent of total debt has been incurred at zero rate of interest. An

Farm size categories Rate of interest

0 1 to 7 8 to 14 15 to 21 22 to 28 Above 29 Total

Marginal farmers 4.45 44.01 18.21 19.55 13.04 0.73 100.00Small farmers 0.38 35.63 36.02 18.87 8.73 0.37 100.00All sampled farmers 2.05 39.07 28.70 19.15 10.50 0.52 100.00

Table 8. Pattern of debt according to the rate of interest: Category-wise (Percentage of total debt)


307


average sampled farm household incurred 0.52 percent of the total debt at above 28 percent range of rate of interest. The above analysis clearly brought out the fact that the marginal and small farmers were partly dependent upon the commission agents and money-lenders who charged exorbitant rates of interest. These findings were in consonance with Kaur et al. (2016) that even though interest rates charged by the non-institutional were quite high, but even then the marginal and small farmers' remained dependent on them.CONCLUSIONS

The above analysis clearly showed that the institutional agencies were the most important source of debt in the case of marginal and small farm-size categories. This may be attributed to some extent awareness about the institutional facilities, easy availability of loans and greater approachability of banks in the rural areas. These farmers incurred debt mainly for the purchase of farm inputs and meet their family expenditure. Majority of farmers were unable to meet their consumption expenditure with their income. The average propensity to consume was greater than unity for both the categories. This expenditure-income gap compels these farmers to use a major proportion of the loans to meet their family requirements.

In spite of the fact that the institutional agencies were the most important source of agricultural credit, it appeared that the burden of indebtedness is likely to continue in the coming years on account of low income of the marginal and the small farm-size categories and their outstanding loans. Indebtedness will continue to grow in the case of marginal and small farm households if income remained static and if no efforts were made to improve their economic condition.

To overcome the problem of indebtedness, effective measures should be taken to increase the income of the marginal and small farmers. The government should introduce necessary land reforms by lowering the ceiling of land holdings, acquiring the surplus land and distributing this land among the marginal and small farmers. It is essential to provide crop insurance at a reasonable premium to overcome the losses caused by the natural calamities. However, in the case of marginal and small farmers, the insurance premium must be paid by the government or the agricultural marketing boards. Another solution to the existing problems of the farmers in Punjab lies in the establishment of producer's co-operatives in the case of agro-based industries in the rural areas. It will provide gainful employment opportunities at village level and the benefits of value addition would go to the farmers. The remunerative prices of the different crops grown should be fixed at the reasonable level on the basis of the

cost of production and consumer price indices in a manner that these farmers are able to meet their basic needs of food, clothing, shelter, and education, healthcare, clean environment and social security in a respectable manner. The enforcement of the already existing special programmes for the rural development, increase in the plan allocation and enlarging the scope of rural-specific schemes to cover the larger proportion of the population can go a long way in improving the economic conditions of the marginal and small farmers in the State.REFERENCESGill, A., & Singh, L. (2006). Farmers' suicides and response of

public policy: Evidence, diagnosis and alternatives from Punjab. Economic and Political Weekly, 41(22), 2767.

Kaur, K. (2012). Indebtedness and its determinants in Indian agriculture. Indian Journal of Economics, 88(367), 673.

Kaur, P. (2016). Extent of rural indebtedness with special reference to Punjab. Indian Journal of Economics and Development, 12, 311-316.

Kaur, P., Singh, G., & Singh, S. (2016). Magnitude and determinants of indebtedness among farmers in rural Punjab. Indian Journal of Economics and Development, 12, 241- 250.

Kaur, R. (2011). Indebtedness among farmers. Patiala: Twenty-First Century Publications.

Kaur, S. (2011). Poverty and indebtedness among marginal and small farmers in rural Punjab. (Doctoral Thesis). Punjabi University, Patiala.

Kumari, R. (2005). An economic analysis of rural indebtedness in Telangana zone of Andhra Pradesh. Indian Journal of Agricultural Economics, 60(3), 302.

Murthy, R.V.R. (2013). Political economy of agrarian crisis and subsistence under Neoliberalism in India. NEHU Journal, 11(1), 19-33.

Pal, D., & Singh, G. (2012). Magnitude and determinants of indebtedness among marginal and small farmers: A case study of Patiala district of Punjab. Agricultural Situation in India, 59,143-151.

Rao, N. P., & Suri, K.C. (2006).Dimensions of agrarian distress in Andhra Pradesh. Economic and Political Weekly, 41(16), 1546-1552.

Sekhon, A., & Saini, S.K. (2008).Awareness of farmers regarding agricultural credit facilities provided by land development bank of Ludhiana. Indian Journal of Social Research, 49(2), 152-154.

Sidhu, J., & Rampal, V.K. (2016). Causes and consequences of indebtedness: A brief review. Indian Journal of Economics and Development, 12(1a), 209-212.

Singh, G. (2014). Agrarian crisis in India and possible solution: An overview. Indian Journal of Economics and Development, 10(1a), 74-81.

Singh, N. 2010.Rural healthcare and indebtedness in Punjab. Economic and Political Weekly, 45(11), 22-25.

Singh, S., & Bhogal, S. (2014). Depeasantization in Punjab: Status of farmers who left farming. Current Science, 106(10), 1364-1368.

308


ABSTRACTIn the recent times, the emerging markets have been playing a crucial role because they have greater potential in terms of economic growth and investment opportunities. Brazil, Russia, India, China and South Africa (BRICS) are the five major emerging markets in the world economy. These economies have their own characteristics in the development of their economies. The process of convergence is essential to understand the growth of an economy. This paper attempts to examine whether or not any convergence is seen in BRICS nations. The empirical results of this study showed that there exists a â-convergence in BRICS nations during the period 1990-2015 at a rate of 0.32percent. However, the rates of convergence vary before 2001 (1.63 percent) and after 2001(0.29 percent). The sigma convergence was tested to know the dispersion among the BRICS economies. It was observed that the existence of sigma convergence in BRICS economies for the period 1990-2000 and 2008-2015 with a divergence during the period 2000-2008. This study shows that the BRICS economies are eventually converging at varying rates.

KeywordsBRICS economies, coefficient of variation, convergence, GDP per capita, panel data.

JEL CodesO11, O47, O57.

1 2*Sayel Basel and R. Prabhakara Rao

1 2Doctoral Research Scholar, Professor and Head, Department of EconomicsSri Sathya Sai Institute of Higher Learning, Prasanthi Nilayam- 515134 (AP)



â-Convergence of Real Per Capita GDP in BRICS Economies


DOI: 10.5958/2322-0430.2018.00135.X


www.soed.in


UGC Approved


309

INTRODUCTIONFrom the beginning of this century, the world has

witnessed a major shift in economic growth and development. The growth rate of developed countries slowed dramatically, whereas the economies of emerging countries have started to develop rapidly. The most representative economies from this group are Brazil, Russia, China, India, and South Africa (known as the BRICS). Despite being diverse, the emerging economies (EEs) have some common economic features.

In the year 2001 economist 'Jim O' Neil” of the Goldman Sach's was coined the term BRIC and later in 2010 by including another emerging country South Africa to this group, it emerged as a BRICS and became another important economic group in the world economy. BRICS countries are among the 10 largest countries in the world, both by the land mass and the population, and indeed they account for more than 40percent of the world's population. A huge domestic market is a driving force for the investor to invest in BRICS nations. This huge

domestic market has helped to differentiate them from other emerging economies because of successful business models these countries adopted. These Emerging economies playing an important role in the world economy in particular, their relatively faster development since the eruption of the global financial crisis in 2008 has drawn widespread attention from the world. However, Emerging economies are struggling with a set of common challenges that could prompt their interest in supporting a universal agenda.

The neoclassical growth model of Solow (1956) envisages that, by sharing the technological advancement among the advanced nations and identical preferences, cross-country differences in per capita real income decreases as each economy approaches its balanced growth path in the long run, and overall convergence holds among the developed economies. From a theoretical perspective, several modifications to the original neoclassical model have been proposed. See, for example, Parente and Prescott (1994), Barro and Sala-i-

Martin (1992); Basu and Weil (1998); Perez-Sebastian (2000); Howitt and Mayer-Foulkes (2005) replace the assumption of homogeneous technological progress in the neoclassical production function with cross-country technological heterogeneity. Also, Azariadis (1996); Galor (1996) showed that the neoclassical growth model can actually generate multiple equilibria; countries with identical economic structures need not converge to the same equilibrium growth path, instead some countries may converge to a high steady-state income level while others may face a poverty trap, giving rise to the club convergence hypothesis.

The researchers have studied this phenomenon of convergence by adopting different methodologies among these Baumol (1986), Barro and Sala-I Martin (1992); Mankiw et al. (1992), are those of â-convergence, observed as a tendency of poorer economies to grow faster than rich ones. The ó-convergence, which refers to a reduction of income dispersion between rich and poor countries; typically after controlling for a country's saving and population growth in which case we are talking about conditional convergence as opposed to unconditional convergence. These approaches also differ in their focus on whether economies grow at the same rate in the steady state (relative convergence) vs. whether they converge to the same steady-state income level (absolute convergence).

There exists some studies in the literature on the convergence among the group of countries as well as within the countries (Cashin & Sahay, 1996; Marques & Soukiazis, 1998; Farreira, 2000). Recently, Chowdhury (2005) examined the convergence of per capita GDP across seven South Asian economies during the period 1960–2000 and the empirical result suggested that there exist no evidence of sigma-convergence, absolute and conditional â-convergence in South Asia. McCoskey (2002) studied the convergence properties of 37 sub-Saharan African countries and did not found any evidence to support the convergence across African economies. Kaitila (2003) studied Convergence of real GDP per capita in EU 15. He examined sigma and â-convergences of income level in Europe and also examined the development in light of EU integration, trade, fixed investment and foreign direct investment for period 1960-2001. He found considerable convergence in GDP per capita levels occurring in two periods, in 1960-73 and 1986-2001 with an interim period of stagnation. Mathur (2005) examined the conditional convergence and measure the speed of conditional convergence across South and East Asian and European Union region. He observed that there is a conditional convergence among European Union, East Asian, South Asian and speed of convergence ranges from 0.26 to 1.82percent.In another study by Ismail (2008), The issue of convergence and economic growth in ASEAN taking data from 1960 to 2004 has been investigated and found no evidence of â and ó convergence in ASEAN 5 throughout the period

1960-2004. Similar studies on ASEAN countries was done by Chowdhary et al. (2010) where they studied convergence of GDP per capita in ASEAN countries during the period of 1990-2008. They concluded that ASEAN countries are converging towards common GDP per capita steadily but slowly. Vojinovic et al. (2009) analyzed sigma and â-convergence of per capita GDP among the ten European countries which joined the European Union in 2004. The existence of both sigma and â-convergences during 1990s and 2000s was reported. Charles et al. (2012) aimed to examine the absolute and conditional convergence of real GDP per capita in the Common Market for Eastern and Southern Africa (COMESA) during the period 1950 to 2003. Their result shows no evidence of stochastic absolute and conditional convergence but highlighted strong support for absolute income convergence for two groups (HMHDL, LDC) belonging to economic development. Dey and Neogi (2015) examined whether SAARC (7) economies are converging over a specified period or not and they also tried to see whether inclusion of China in SAARC ensures greater economic growth. The results of their study showed that the convergence of GDP per capita in SAARC in the period 1970-2011. They also concluded that rate of convergence was quite high in the post cooperation period (after 1985), than in pre cooperation period. Tsanana and Katrakilidis (2016) examined empirically the convergence for the selected Central Eastern Europe (CEE) economies and concluded that economic integration is a prerequisite for catching up.

Some researchers have examined the country specific convergence in emerging economies examined both the tendency and the speed towards income convergence among provinces in China (Yao & Week, 2000). Using cross-sectional approach they found no evidence of â-convergence among China's provinces whereas with the panel data approach they found an evidence of income convergence for both the period 1953-1997 and 1978-1997. Adabar (2004) analyzed regional dimension of economic growth in India within the convergence implication of neoclassical growth model, using dynamic panel model. He showed absolute divergence consistent with conditional convergence in India. Lima and Resende (2007) examined empirically convergence of per capita GDP in Brazil during 1985 to 1999 and observed that there exists a strong persistence of regional inequality pattern in Brazilian state. Agarwalla and Pangotra (2011) examined regional disparities in India over a period of 26 years (1980 to 2006). Their results suggested that the convergence trend in regional income, conditioned upon growth rates of inputs and rate of technological progress. It has been also found that estimated rate of convergence was faster during the 1992-2006. Barro (2016) aim to assess economic growth of China within the context of growth experienced by large number of countries over long period. He concluded that China will soon experience “Iron law of convergence”, where China's per

310


capita growth rate is likely to decline soon from around 8 percent per year to a range of 3.4 percent.

However, very few studies were carried out to evaluate the process of convergence among BRICS nations. So, at present this necessitates the study on convergence among BRICS nations as they are potential nations to grow at higher rate. This study is undertaken with an objective of testing the convergence among BRICS economies. The study also aims at examining the rate of convergence before and after the year 2001.METHODOLOGY

In accordance with the scope and objective of this study, the annual data during the period1990-2015 were obtained for BRICS economies from the World Bank (2017). We have considered growth rate of GDP per capita at purchasing power parity (PPP) as dependent variable (GRGDP) and initial level of log of GDP per capita as independent variable. The present study considered the testing of absolute beta (â) convergence and sigma (ó) convergence. The absolute â-convergence is said to exist if the poorer region tend to grow faster than the richer ones, so that the poor country tend to catch up to the rich in terms of levels of GDP per capita. However, the steady-state may depend on features specific to each economy, in which case convergence will still take place, but not necessarily at the same long-run levels. This will be the case when GDP per capita is supposed to depend on a series of determinants such as factor endowment or institutions, which may differ from one economy to the other even in the long-run. The â-convergence is then said to be conditional. If the study considers the relationship between growth rate of GDP per capita and initial per capita GDP level then it is refer to as absolute â-convergence. If the study concern with growth rate of per capita GDP on initial per capita GDP level and set of additional explanatory variables that define the steady state growth, the convergence is said to be conditional â-convergence. ó convergence refers to the reduction of disparities of levels of income across economies. Though â-convergence and sigma convergence are closely related with each other, â-Convergence tends to generate the convergence of sigma but this process is offset by new disturbances that tend to increase dispersion (Barro & Sala-I-Martin, 1992). Sigma convergence is measured by considering the levels and trends in the coefficient of variation of regional per capita GDP and standard deviation.

Here, we have tested sigma convergence through coefficient of variation and absolute â-convergence is estimated by undertaking cross-sectional regression analysis taking annual average growth rate of GDP per capita as the dependent variable and the initial level of GDP per capita as the independent variable.â-Convergence: The methodology used to measure â-convergence generally involves estimating a growth equation in the following form:

ln (? y ) = á + â ln (y ) +ãZ + uit i,t-1 it it

Where, ? y and (y ) are the level and the growth rate of GDP it i,t-1

per capita in region i at time t and t – 1 respectively. Z includes all other factors supposedly affecting the it

growth rate ; ( Control variables)u is the standard error term; andit

á, â and ã are the parameters to be estimated.If â > 0, it indicates divergence in GDP per capita that

is rich countries are growing at faster rate as compared to poor countries.

If â is significantly negative, that is, â<0, means negative relation between growth rate (? y ) and initial it

level of GDP per capita (y ), implies convergence in i,t-1

GDP per capita.If ã is restricted to 0, absolute convergence is

assumed. Otherwise conditional convergence is assumed.RESULTS AND DISCUSSION

The results revealed that the growth rates are showing an increasing trend with a dip in all countries in the years 1998 and 2009 except in India (Figure 1). However, the decline in India was observed during the years 1997 and 2008. This can be attributed to the effect of the East Asian crisis of 1997 and the global financial crisis during the period 2008-09. However, after 2010 all countries showing a declining trend and India shows some recovery

Year Brazil Russia India China South Africa

1991 -0.163 -5.259 -0.980 7.787 -3.0381992 -2.044 -14.568 3.394 12.885 -4.1581993 3.051 -8.565 2.710 12.635 -0.8821994 3.725 -12.461 4.608 11.807 1.0181995 2.816 -4.169 5.529 9.794 0.8951996 0.632 -3.460 5.527 8.779 2.0051997 1.800 1.568 2.118 8.115 0.2791998 -1.205 -5.143 4.243 6.824 -1.8351999 -1.061 6.730 6.892 6.690 -0.0422000 2.553 10.464 2.017 7.578 1.6512001 0.158 5.539 3.024 7.515 0.6202002 1.558 5.227 2.061 8.362 2.4722003 -0.282 7.784 6.091 9.337 1.6392004 4.346 7.608 6.194 9.424 3.1942005 1.908 6.783 7.575 10.699 3.8762006 2.751 8.508 7.594 12.061 4.1492007 4.924 8.721 6.988 13.600 3.8972008 4.023 5.294 2.382 9.063 1.7272009 -1.107 -7.849 6.951 8.692 -2.9652010 6.493 4.457 8.755 10.099 1.5162011 2.924 4.183 5.231 8.961 1.6542012 0.967 3.344 4.269 7.227 0.6452013 2.078 1.064 5.313 7.153 0.6062014 -0.780 -1.075 5.936 6.727 -0.0792015 -4.666 -3.934 6.277 6.358 -0.372

Table 1. Growth rates of Real Per Capita GDP (PPP) of BRICS

311

Basel and Rao: Convergence of real per capita GDP in BRICS economies

-20

-15

-10

-5

0

5

10

15

20

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

Re

al p

er

cap

ita

GD

P(P

PP

)

Year

1.Brazil

2.Russia

3.India

4.China

5.South Africa

42

3

1 5

Figure 1. Growth Rates of Real Per Capita GDP in BRICS during 1990-2015

in their path of growth. Brazil has recorded a maximum growth rate (6.493) in the year 2010 with minimum growth rate (- 4.666) in the year 2015. The average growth rate of Brazil is 1.416 with a standard deviation of 2.505. The Russian economy has registered a negative growth during 1990's later it is improving with maximum growth rate (10.464) in the year 2000 and minimum (-14.568) in the year 1992. The average growth rate is 0.832 with a standard deviation of 7.045. This indicates that the Russian economy had huge fluctuations in their growth. During the period 1990 -2015 India had a steady growth at an average growth 4.828 with a standard deviation of 2.285. India has registered its maximum growth (8.755) in the year 2010 and minimum (-0.980) in the year 1991. China has achieved an extraordinary growth rate with an average of 9.127 with standard deviation 2.113. The maximum growth (13.6) was in2007 and minimum (6.358) in 2015. South African economy has grown at an average of 0.73 8 with a standard deviation of 2.142 during this study period. It has registered a maximum growth (4.149) in 2006 and minimum (-4.158) in the year 1998. Among all the BRICS economies China and India had an impressive growth during the years 1990-2015.

Absolute â-ConvergenceIn this study we divided the total time period into two

sub-periods, viz., 1990-2000 (Sub-period 1) and 2001-2015(Sub-period2) according to before and after the formulation of the word BRICS.Absolute â-Convergence (1990-2015)

For this analysis, we have considered two different Panel data regression models- fixed effect model and random effect model. By performing the Hausman test we have chosen the random effect model as a suitable model for this data and the result is given in Table 2. It is observed that from the table 2 â-coefficient of initial GDP per capita is negative which signifies the existence of absolute â-convergence. The rate of convergence is 0.32

2 percent. However, a very low value of R implies that besides the initial level of GDP per capita income, there are additional variables that explain the growth of GDP per capita.Absolute â-Convergence (1990-2000)

The Hausman test suggests that fixed effect panel approach is more appropriate for this period. The fixed effect regression model is provided in Table 2. The coefficient of the initial level of log GDP per capita is

312


Time period Intercept â (Slope) 2R 2Adjusted R F-statistic

1990-2015 3.298(3.680)

-0.325(-3.295)

0.08 0.07 10.858

1990-2000 14.438(5.016)

-1.633(-4.627)

0.81 0.73 10.677

2001-2015 3.135(2.468)

-0.291(-2.114)

0.06 0.04 4.46

Table 2. Regression result for â-convergence of BRICS countries for different periods

Figures in the parenthesis are t values.

y = -0.011x + 0.772

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

coef

fici

ent

of

vari

ati

on

of

GD

P

cap

ita a

t P

PP

per

Years

Figure 2. Sigma convergence of GDP per capita in the BRICS countries, 1990-2015

negative and which signifies inverse relation between the growth rate of per capita GDP and initial level of GDP per capita. Here t-values for variables are also significant. The rate of convergence is quite high (- 1.633) and significant

2(high R value) before the formulation of word BRICs. The result suggested absolute â-convergence at the rate of 1.63 percent among BRICS region before 2001.Absolute â-Convergence (2001-2015)

For this analysis, we choose random effect panel model as per Hausman test. The random effect regression model is given in Table 2. The negative coefficient of the initial level of the log of GDP per capita implies absolute â-convergence at a rate of 0.29percent. However, a very

2low value of R implies that besides the initial level of GDP per capita income, there are additional variables that explain the growth of GDP per capita in BRICS after the formulation of word BRICs by “Jim O' Neil”. The results in table 2 shows that the convergence is taking place at a rate of 0.32 percent, however, the rate convergence for Sub-period-1 (1990-2000) is (1.63 percent) faster than

Sub-period-2 (2001-2015) (0.29 percent). These results support the theory of convergence of Barro and Sala-I-Martin (1992).Sigma Convergence

Sigma convergence is tested by using the coefficient of variation of GDP per capita for the BRICS economies. The results of sigma convergence for BRICS countries for the period 1990-2015 are presented in Table 3 and Figure 2.

From Table 3 and Figure 2 we can see that the coefficient of variation (CV) has not varied significantly between the period 1990 and 2015 and the changes are relatively small. However, the CV values are decreasing for the period before 2000 implying that there exists sigma convergence in BRICS nations. This is inconsistent with the convergence theory which suggests that â-convergence tends to generate sigma convergence Solow (1956). This also verified empirically by Dey and Neogi (2015) for SAARC economies. There may be some situations where this process is counter-balanced by random shocks that tend to increase inequality Barro &

313


Year Coefficient ofvariation

Year Coefficient of variation

1990 0.88 2003 0.611991 0.85 2004 0.611992 0.79 2005 0.611993 0.74 2006 0.611994 0.68 2007 0.611995 0.65 2008 0.621996 0.63 2009 0.561997 0.62 2010 0.541998 0.59 2011 0.541999 0.59 2012 0.542000 0.60 2013 0.522001 0.60 2014 0.502002 0.60 2015 0.47

Table 3. Coefficient of variation of GDP per capita (1990-2015)

Sala-I-Martin, (1992). This study finds that after 2000, the sigma divergence appears till 2008 and after 2008 the CVs of GDP per capita among BRIC countries are gradually declining which implies rising equalities and sigma convergence. The same phenomenon was also observed by Barro & Sala-I-Martin, (1992); Chasin & Sahay (1996); Young et al. (2008).CONCLUSIONS

In economics, the concept of convergence is a measure to test the growth process of an economy. The emerging economies such as BRICS nations play an important role in world economy. Here, we have undertaken a study to examine the convergence of BRICS economies by considering annual GDP per capita values of these economies from 1990 to 2015. To test the â-convergence we have employed panel data models and sigma convergence is tested by using the coefficient of variation. The empirical results of this study showed that there exists a â-convergence in BRICS nations during the period 1990-2015 at a rate of 0.32percent. However, the rates of convergence vary before 2001 (1.63 percent) and after 2001 (0.29 percent). The sigma convergence was tested to know the dispersion among the BRICS economies. It was observed that the existence of sigma convergence in BRICS economies for the period 1990-2000 and 2008-2015 with a divergence during the period 2000-2008. This study shows that the BRICS economies are eventually converging at varying rates.REFERENCESAdabar, K. (2004). Economic growth and convergence in India.

Working Paper, Institute for Social and Economic Change, Bengaluru.

Agarwalla, A., & Pangotra, P. (2011). Regional income disparities in India and test for convergence–1980 to 2006. India: Indian Institute of Management (W.P. No. 2011-01-04)

Azariadis, C. (1996). The economics of poverty traps part one: Complete markets. Journal of Economic Growth, 1, 449-

486.Barro, R.J., & Sala-i-Martin, X. (1992). Convergence. Journal

of Political Economy, 100(2), 223-251.Barro, R.J. (2016). Economic growth and convergence, applied

especially to China (No. w21872). National Bureau of Economic Research, 1050 Massachusetts Avenue C a m b r i d g e , M A 0 2 1 3 8 . R e t r i e v e d f r o m http://www.nber.org/papers/w21872.pdf

Basu, S., & Weil, D.N. (1998). Appropriate technology and growth. The Quarterly Journal of Economics, 113(4), 1025-1054.

Baumol, W.J. (1986). Productivity growth, convergence, and welfare: What the long run data show. American Economic Review, 76, 1072-1085.

Cashin, P., & Sahay, R. (1996). Regional economic growth and convergence in India. Finance and Development-English Edition, 33(1), 49-52.

Charles, A., Darné, O., & Hoarau, J.F. (2012). Convergence of real per capita GDP within COMESA countries: A panel unit root evidence. The Annals of Regional Science, 49(1), 53-71.

Chowdhary, R., Jore, S., Thakur, R., Agrawal, K., & Geete, V. (2010). Convergence of GDP per capita in ASEAN countries. Prestige International Journal of Management and Research, 3(2/1), 1-10.

Chowdhury, M.K. (2005). Convergence of per capita GDP in South Asia. International Journal of Applied Business and Economic Research, 3(2), 133-150.

Dey, S.P., & Neogi, D. (2015).Testing sigma and unconditional beta convergence of GDP for SAARC Countries: Can inclusion of China further consolidate the convergence? Global Business Review, 16(5), 845-855.

Ferreira. (2000). Convergence in Brazil: Recent trends and long run prospects. Applied Economics, 32, 479-489.

Galor, O. (1996). Convergence? Inferences from theoretical models. Economic Journal, 106(437), 1056-1069.

Howitt, P. and Mayer-Foulkes, D. (2005). R&D, implementation and stagnation: A Schumpeterian theory of convergence clubs. Journal of Money, Credit and Banking, 37, 147-177.

Ismail, N. W. (2008). Growth and convergence in ASEAN: A dynamic panel approach. International Journal of Economics and Management, 2(1), 127-140.

Kaitila, V. (2003). Convergence of real GDP per capita in the EU15 area: How do the accession countries fit it? (No. 865). ETLA Discussion Papers. Helsinki: The Research Institute of the Finnish Economy (ETLA).

Lima, M.A., & Resende, M. (2007). Convergence of per capita GDP in Brazil: An empirical note. Applied Economics Letters, 14(5), 333-335.

Mankiw, N.G., Romer, D., & Weil, D.N. (1992). A contribution to the empirics of economic growth. The Quarterly Journal of Economics, 107(2), 407-437.

Marques, A., & Soukiazis, E. (1998). Per capita income convergence across countries and across Regions in the European Union: Some new evidence. Paper presented at the International Meeting of European Economy. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/ download?doi=10.1.1.504.9426&rep=rep1&type=pdf

Mathur, S. K. (2005). Economic growth and conditional convergence: Its speed for selected regions for 1961-2001. Indian Economic Review, 40(2), 185-208.

McCoskey, S.K. (2002). Convergence in Sub-Saharan Africa: A

314


non stationary panel data approach. Applied Economics, 34(7), 819-829.

Parente, S., & E. Prescott (1994). Barriers to technology adoption and development. Journal of Political Economy, 102, 298-321.

Perez-Sebastian, F. (2000). Transitional dynamics in an R&D based growth model with imitation: Comparing its predictions to the data. Journal of Monetary Economics, 45 (2), 437-461.

Solow, R. (1956). A contribution to the theory of economic growth. Quarterly Journal of Economics, 70, 1, 65-94.

Tsanana, E., & Katrakilidis, C. (2016). The issue of convergence: New empirical evidence for the central Eastern Europe area. Applied Econometrics and

International Development, 16(1), 53-62.Vojinoviæ, B., Acharya, S., & Próchniak, M. (2009).

Convergence analysis among the ten European transition economies. Hitotsubashi Journal of Economics, 50, 123-141.

World Bank. (2017). World development indicators. Retrieved https://data.worldbank.org/

Yao, Y., & Week, M. (2000). Provincial income convergence in China, 1953-1997: A panel data approach (Mimeo.). Cambridge, U.K.: University of Cambridge.

Young, A.T., Higgins, M.J., & Levy, D. (2008). Sigma convergence versus beta convergence: Evidence from US county-level data. Journal of Money, Credit and Banking, 40(5), 1083-1093.

315


ABSTRACTThe study has understood the socio-economic status of the migrants in the Boudh District of Odisha. It has examined the determinants of seasonal migration in the study area and also explored suggestive measures to check migration. The primary data were collected from the seasonal migrants who have migrated for six months i.e. from January to June 2017. One hundred seasonal migrants were taken as samples from four villages of Harabhanga Gram Panchayat of Boudh district. The purposive sampling technique was used to select the sample. Among the hundred migrants in the sample, there were thirty-two respondents who migrated within the state and sixty-eight respondents migrated outside the state. Most of the people were going for migration because of poverty and misery. Unemployment, indebtedness, declining agricultural productivity and crop loss were found to be the major push factors propelling many people for migration. Similarly, better employment opportunity in the urban area, relative easiness in getting gainful employment, higher wages in comparison to the rural area were the pull factors for migration observed by this study. Although liberation from indebtedness, the rise in consumption and saving, skill development and asset accumulation in the short period are the positive outcomes of migration, separation from family, inattention to children's education, health hazards, psychological stress and exposure to substances are some of the grey areas which merit reasonable attention of all stakeholders.

KeywordsEconomic development, indebtedness, livelihood, marginalised communities, migration.

JEL CodesE21, E23, F63, O15.

1 2*Chittaranjan Nayak and Chinmaya Ranjan Kumar

Department of Economics, Ravenshaw University, Cuttack-753003 (Odisha)


Received: November 28, 2017 Revision Accepted: June 05, 2018

Seasonal Migration and Livelihood of Marginalised Communities-A Case Study in Boudh District of Odisha


DOI: 10.5958/2322-0430.2018.00136.1


www.soed.in


UGC Approved


316

INTRODUCTIONMigration is inevitable in the process of economic

development. In fact, there exists a two-way causation between migration and economic development. While migration induces economic development, economic development also stimulates further migration. It is well known that developmental disparities between rural and urban areas trigger rural-urban migration. People generally migrate from the less developed rural countryside to more developed urban centres. Migration is an indicator of changing socio-economic and political conditions at the national and international levels. It is also an indication of wide discrepancies in economic and social conditions

between the origin and the destination. It is a natural outcome of inequality in the distribution of resources. Therefore, 'migration and economic development' is a growing area of interest although, there has been much debate on the impact of migration on economic development and vice-versa. Contrary to the conventional wisdom that underdevelopment is a reason for migration, of late it is observed that prosperity also leads to migration. The history of migration is people's struggle to survive and prosper to escape insecurity and poverty and move in response to opportunity.

Migrations are usually caused by both push and pull factors. The push factors are elements that are

unfavourable to the area where one lives in and pull factors are elements that attract one to another area. Some researchers argue that the rural labour force is migrating because of the push factors like poverty, unemployment, low wage rate, etc., while for some others they are getting to urban life because of the pull factors like better standard of living, higher wages and incentives, children's education, etc. But, migration of workers has become a social, economic and universal phenomenon in modern times. Due to the expansion of transport and communication systems, it has become a part of the worldwide process of urbanization and industrialization. Industrialization widens the gap between rural and urban areas, including a shift of the workforce towards industrialized areas.

The narratives for the marginalised communities in a developing country like India, however, are different. It is believed that migration for them mostly takes place not due to the so-called pull forces of the destination place as usually happens in developed countries, but because of the push factors like poverty, unemployment, natural calamities and the under-development of the place of origin. The share of agriculture in Gross Domestic Product (GDP) has declined from around 40 percent in the 1990s to about 15 percent in 2017 as a large number of people along with their families are shifting to urban areas in pursuit of greater opportunities. The collapse of rural livelihood in many parts of India also forces the workers to migrate from their native places in search of employment. The migration of labourers from rural to urban areas is a reflection of India's misplaced development policies. Investment in economic growth has been biased toward the capital-intensive urban centres, despite the fact that majority of India resides in the rural areas.

In many parts of the country, seasonal migration is observed in which, a large chunk of rural labourers migrate to the city centre in off-seasons. When employment in agriculture decreases during the dry season, migrants seek additional income in the urban centres and return to the village during the rainy season. There can also be climatic reasons for this migration in seasonally flooded areas, especially by fishermen. Exploring the causes and assessing the effects of such migration especially for the marginalised communities have been a matter of serious research recently.

Singh and Kaur (2007) in their study have explored that economic and social causes were the prime factors which forced the respondents to migrate to Punjab. An empirical analysis was carried out by Devi et al. (2009) assessed the economic reasons as the main cause for migration. The study analysed the discrimination between the two groups such as those migrated for economic and non-economic reasons. The study

further demonstrated the significant gender gap in the literacy status of the male and female migrants. Literate female migrants were found to be less mobile than the illiterate females who are poverty- stricken.

The effect of seasonal migration on the household resource structure in the village economy was studied by Korra (2009). The study pointed out that the migration in distress condition is not good and is not a positive sign for the economic development of the country. Naik et al. (2009) reported that more work facilities and high wage in neighbouring places like Chickkamagalore, Mangalore, Hasan, Coorge, etc., attracted the migrants who migrated along with their children depriving children getting the education. The highest number of child labour, child trafficking and sale of kid were common. The study revealed that hard labour, very low wages and rampant corruption existed in the study area.

Angel et al. (2011) found that the people accruing in near-urban or nearly urban settlements have mostly come to be there as part of the net migration of people towards larger settlements or from the centre of urban settlements towards their peripheries. Nayak and Pattanaik (2011) have stated that the MGNREGA is successful in meeting its set objectives on this vital front. An empirical analysis on the effect of net migration on the economic growth of the developing countries was made by Dao (2010). The study concludes that an increase in net migration as a result of removing restrictions on labour mobility positively influences economic growth in developing countries.

Misra (2008) stated that the relatives and friends of the migrants were not only providing information about the employment opportunities in the destination area but also assisting in getting jobs and curtailing the cost of migration by arranging for the migrants' initial stay with them. A study conducted by Semyonov and Gorodzeisky (2008) found that the money that labour migrants send back home was mostly used by members of the households for consumption and education. Roy et al. (2006) studied the impact of rural-urban migration on female migrant fertility and the result showed that total fertility rate for rural-urban migrants was lower than rural non-migrants since they usually adopted smaller family size norm. This showed that there was both positive and negative social impact of migration on the migrants' households. The aim of the present study is not only to deal with all such multifarious aspects of migration like push and pull factors but also their different aspects of livelihood status of migrants. The main objectives were:

i. to understand the socio-economic status of the seasonal migrants of the study area,

ii. to explore the determinants of the seasonal migrations, and

317

Nayak and Kumar: Seasonal migration and livelihood of marginalised communities

iii. to assess the socio-economic impact of seasonal migration.

METHODOLOGY The present paper is based on primary data

collected by use of purposive sampling technique to select households. Data have been collected from four villages of Harabahanga gram panchayat of Boudh District of Odisha through a structured schedule. One hundred seasonal migrants were taken as respondent. The samples as such constituted by 45, 25, 20 and 10 households were taken from Bukeisingha, Harbhanga, Kudasinga and Diahaghata villages. This research has used both quantitative and qualitative methodologies. The schedule consisted of both open-ended and close-ended questions. In order to increase the accuracy of research work both quantitative and qualitative data scaling techniques were used. RESULTS AND DISCUSSIONSocio-economic Status of the Migrants

It was observed that all the migrants were male and no female member had migrated in the district during the study period. About 48 percent of migrants belonging to the scheduled caste which was highest among all the respondents followed by 40 percent of migrants belonging to scheduled tribe communities. Ten percent of migrants belonged to other backward castes and very few migrants were belonging to general caste (2 percent). Majority of the respondents (43 percent) of migrants were from the age group of 25 to 35 years, followed by 39 percent of migrants belonged to age group of below 25 years. The results showed that majority of un-married male workers were migrating in the off seasons (fifty-four percent of total migrated people). As regards the family status of the migrants, the results showed that most of the migrants (80 percent) were staying in joint-family and only 20 percent of people were in nuclear family. Similarly,

most of the households (58 percent) belonged to household size of above of 5 members followed by 32 percent comprising the family size of 4-5 members. For the rest of the households, the family size was below 4. As regards the level of educational status of the migrants, the results showed that most of the migrants (87 percent) were literate out of which 32 and 21 percent of them attended above secondary school and higher secondary and 10 percent migrants attended degree college (Table 1). Classification of Pre-migration Occupation

The pre-migration status of the migrants, the results showed that most of the migrants (89 percent) were engaged in both the agriculture and labour work within their locality.Beginning of Migration

In a response to the question that when did they migrate, and it was found that 69 percent of the respondents migrated during 2011-2015. This showed that in the rural-urban migration present decade people migrated more than before from rural to urban area. This is a quite surprising revelation that even after the enactment of Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA), 2005, migration actually increased. It was noticed that 20 percent of migrants started their migration during 2005-2010, and 5 percent of the migrants started their migration during 2000-2005. Only 6 percent of the migrants started migration after year 2015. This revealed that migration of rural people receded after 2015 due to unexplored reasons. Classification of Sources of Information

The study found that six percent migrants got information about the source of migration by self-enquiry whereas 17 percent migrants got information from the Agents. Only 2 percent migrants got information about the source of migration from

Variable Categories Migrants Variable Categories Migrants

Gender Male 100 Marital status Unmarried 46

Female 0 Married 54

Caste General 02 Family type Joint 80

SC 48 Nuclear 20

ST 40 Family size (No.) < 4 10

OBC 10 4-5 32

Age categorization(Years)

< 25 39 > 5 58

25-35 43 Educational qualifications

Illiterate 13

35-45 14 Primary 24

>45 03 Secondary 32

Higher secondary 21

Degree and above 10

Table 1. Socio-economic status of the migrants

Source: Field investigation.

318


established institutions. It was noticed that 75 percent of migrants got information about the source of migration from their friends and relatives. Misra (2008) reported that relatives and friends of the migrants were providing information about the employment opportunities as well as assisting in getting jobs and to reduce the cost of migration in the initial stage.Time Taken to Get Job

The study found that 94 percent migrants were getting a job immediately after going for migration and only a few migrants (6 percent) were not getting job immediately. Among these six respondents one migrant got within one week, two got within 2-3 weeks and 3 got a job after one month or so.Classification of Engagement of Migrants

The perusal of Table 3 exhibits a classification of jobs as well as the places where the migrants are engaged. It was observed that most of the people (i.e., 68 per cent of the total migrants) migrated to other states, and the rest have migrated within Odisha (32 per cent) of the total. Among the other states, people migrated mainly to Andhra Pradesh (33 per cent of total migration), followed by Tamil Nadu (15 percent of total). Most of the migrants (41 per cent) were engaged with most hazardous explosive work. In Andhra Pradesh, the migrants were mainly engaged in explosive work, hotel keeping and granite work. It was noticed that people were also migrating to Surat of

Variable Categories Migrants (Per cent)

Pre-migration occupation Only agriculture 10

Only labour 1

Both agriculture and labour 89

Beginning of migration 2000-2005 5

2005-2010 20

2010-2015 69

After 2015 6

Source of information for migration Self-enquiry 6

Agents 17

Stable owner 2

Friends and relatives 75

Getting job immediately?

Yes

No

If No, the time period of waiting for the Job

Up to 1 week

1-2 weeks

2-3 weeks

More than a month

94

6

1

0

2

3

Table 2. Village-wise classification of engagement of migrants in work and place

Source: Field investigation.

Gujarat for spinning millwork, Goa for drilling and labour work, Kerala for plywood work while few of them migrated to places like Chhattisgarh, Telangana and Delhi. In 'within state migration', people migrated to industrially based places like Angul, Jharsuguda, and Paradeep to be engaged as steel plant worker, priming worker and driver. Some of them have migrated to major cities of the state like Bhubaneswar and Cuttack to be engaged as the construction worker and in-wall painting work.Determinants of MigrationPush factors

In order to explore the various push factors of seasonal migration, which signify the dissatisfaction of the people and propel them to migrate for their livelihood, the paper attempted to unearth the reasons from the migrants' point of view. The results presented in Table 4 revealed that 77 percent of migrants either strongly agree or agree that poverty and misery propel them to go for migration. So 'poverty and misery' is an important factor responsible for migration. Similarly, unemployment is the main factor of seasonal migration as 95 per cent of migrants agreed that the migration was due to unemployment in the off-season. The recovery of the loan or debt factor also propelled them for migration because as 80 percent of migrants agreed with this factor. Due to various problems faced in agriculture and declining productivity and crop loss, these people preferred for migration to sustain their

319


Place to migrate Work at industries Bukesinga Harabhanga Dihaghat Kudasinga Total

Andhra Pradesh Explosive 11 10 05 05 31

Hotelkeeper 01 01

Granite 01 01

Telangana Labour 01 01

Tamil Nadu Explosive 06 02 02 10

Civil work 01 01

Worker 01 01

Hotel waiter 01 01

Security guard 01 01

Driving 01 01

Chhattisgarh Driving 04 04

Labour 01 02 03

Kerala Plywood 01 01

Goa Drilling worker 02 02

Labour 02 02

Gujarat (Surat) Spinning mill 02 02 02 06

Delhi Cook 01 01

Outside State Total 30 15 09 14 68

Bhubaneswar Labour 03 04 01 02 10

Wall painting 02 01 03

Angul Still plant worker 04 01 05

Paradeep Priming work 02 02

Cuttack Wall painting 04 04

Labour 02 03 05

Jharsuguda Driving 03 03

Within State Total 15 10 01 06 32

Overall Total 45 25 10 20 100

Table 3. Village-wise classification of engagement of migrants in work and place(Percent)

Source: Field investigation.Period of Seasonal Migration: 6 Months (January to June 2017).Migration Area: (N=100): Within state: 32 and Outside state: 68.

livelihood and increase earning potential.Pull factors

In a stark revelation, the analysis finds that 90 per cent of the migrants agreed that they migrate due to the better scope of employment of opportunity in the urban area (Table 5). As many as 69 percent agreed that it is

Push factors (-) Strongly agreed Agreed Neutral Disagreed Strongly disagreed Total

Poverty and misery 45 32 20 3 0 100Unemployment 65 30 5 0 0 100

Repayment of debt 52 27 14 4 3 100

Low agricultural productivity 28 31 20 17 4 100

Failure of crop 18 21 32 22 7 100

Landlessness 12 17 29 31 11 100Lack of irrigation facilities 31 42 22 4 1 100Lack of credit facilities 55 32 12 1 0 100

Table 4. Push factors for migration(Percent)

Source: Authors' calculation.

easier to get a gainful job in urban areas than in rural areas, and all the migrants agreed that the wage differential is the main reason for their migration. They get the higher amount of wages in urban areas than in their native places. Also, the advance payment by the owner in many cases attracts them to be engaged in

320


Pull factors (+) Strongly Agreed Agreed Neutral Disagreed Strongly disagreed Total

Better employment opportunity 58 32 6 4 0 100

Easy to get job 32 37 11 15 5 100

Wage differentials 92 8 0 0 0 100

Advance paid by the owner 21 28 24 13 14 100

Skill development in short period 21 22 27 18 12 100

Better amenities 4 17 45 23 11 100

Bright city lights 17 18 28 23 14 100

Table 5. Pull factors for migration(Percent)


urban jobs. Some of the migrants also revealed that the opportunity to develop skill in a short period is also a reason behind their migration. However, the attraction of better urban amenities is not a major reason behind migration. Only 21 percent, mostly unmarried males, agreed that better amenities are an attraction for them to migrate. As regards city light, 35 percent agreed that it is a pull factor whereas 37 percent migrants disagreed. Even in the context of the frequent power cut, attractions like availability electricity were not a strong factor against separation from the family for rural labourers to migrate to urban areas. In a nutshell, economic factors like better employment opportunity, easy access to job and higher wages found to be the major drivers of seasonal migration in the study area.Impact of MigrationHousehold annual income of the pre and post-migration

The annual income of the households increased significantly due to migration. In total, the average household income of the people in pre-migration was only `32,267 per annum whereas the average household income of the respondents in post-migration was `97,533 per annum, which was a big jump by about 200 percent (Table 6). Household annual expenditure in pre and post-migration situation

The change in an annual expenditure of the migrants' households between pre-migration and post-migration stages revealed that the annual expenditure of the households was phenomenally higher in the post-migration stage in comparison to the pre-migration situation (Table 7). This revealed that after migration the migrants were getting quite higher household income, therefore, enabling them for more expenditure. In total, the average household expenditure of the people in Pre-Migration was only `33,301 per annum whereas the average household income of the households in post-migration stage increased to `70866 per annum. This showed that the

spending capacity of the rural households increased significantly after migration.Saving or debt of the pre-migration and post-migration

The analysis also attempted to examine the change in household saving due to migration. As estimated, the annual average saving of households was only ̀ 1046 in the pre-migration condition which witnessed a quantum jump to `40870 in the post-migration state. Similarly, their average debt was `4326 per annum, which declined to `3280 in the post-migration stage. The result shows that the average amount of debt was

Source Average household annual income

Pre-migration Post-migration

Agriculture 19600 34600

Daily labour 12667 62933

Total 32,267 97,533

Table 6. Household annual income in pre-migration and post-migration situations

(`)

Source: Computed from primary data.

Expenditure Items Expenditure of the household

Pre-migration Post-migration

Food 8633 19067

Electricity 967 1800

Transport 2500 3533

Medical 8167 17133

Education 7900 20533

Interest on loan 1667 3067

Others 3467 5733

Total 33301 70866

Table 7. Household annual expenditure in pre and post-migration

(`)

Source: Computed from primary data.

321


more in the pre-migration condition as compared to the post-migration state. This showed their miserable economic situations before migration but after migration, the income increased and the saving of the migrants increased significantly. Therefore, it can be concluded that although seasonal migration is distress driven, it recedes rural indebtedness significantly. There is the positive impact of migration on income generation, household expenditure and saving of the rural people. Perception towards Seasonal Migration

The results showed that the attitude of the migrants towards different socio-economic aspects of their life. Most of the migrants strongly agreed that migration changed their economic status positively. They were able to repay their debt after earning more income. They could allocate more expenditure to the education and health of their wards. Moreover, they could generate some saving. However, that change came at a huge social cost for the migrants' households. Their absence for six-months from their nativity cast a negative impact on their own and families' social life.

On its economic impact, an overwhelming percentage of migrants stated that migration helps in economic upliftment of households; it enhanced saving and the debt-servicing ability of the households, etc. On social and sociological impact, the majority of migrants agreed that migration increased the capacity of the household to spend more on children's education and family functions. Employers also offered some kind of non-monetary benefits. However, on the flip side, the migrants were equally divided on the question if they suffer from discrimination in workplace. Thirty-five percent agreed (or strongly agreed) that they did not face discrimination in their workplaces, whereas thirty-six percent of migrant revealed that they faced

Perception towards migration Strongly agreed

Agreed Neutral Disagreed Stronglydisagreed

Total

Economic upliftment of the family due to migration 21 58 14 1 6 100More saving due to migration 78 21 1 0 0 100

Repaying the debt due to migration 18 53 20 7 2 100

Better opportunity for the education of children 56 23 10 11 0 100

Can spend more for family and religious ceremonies due to migration

75 22 3 0 0 100

There is no discrimination/ exploitation at the workplace 21 24 19 23 13 100

More non- monetary benefits are given by the employer 18 36 23 10 13 100

Migration has increased social status in the native place 12 25 24 30 9 100

Migration has increased isolation from the family and relatives

12 44 24 14 6 100

Better work environment 10 47 18 18 7 100

Table 8. Perception towards seasonal migration(Percent)


some kind of discrimination/exploitation in workplaces (Table 8).

Similarly, when thirty-seven percent of respondents agreed that migration enhances their social status, thirty-nine percent disagreed with the fact. Although a majority (57 percent) of migrants stated that migration provided better work environment than the work milieu at a native place, an overwhelming 56 percent of them stated that migration increased social isolation from family and relatives. This revelation marred by another serious issue of the little said aspects of migration which call for a detailed cost-benefit analysis. CONCLUSIONS

The results revealed that most of the rural labour forces were going for migration because of poverty and misery. The study by Korra (2009) has also pointed out the same that rural-urban migration is distress driven. Unemployment is a major factor of seasonal migration, so also the pressure of debt repayment. These results are in consonance with Devi et al. (2009) in their study. Problems faced in declining agricultural productivity and crop loss pushed many people for migration to sustain their livelihood. Similarly, better standard of living or employment opportunity in the urban area, relative easiness in getting a gainful employment, the higher amount of wage in comparison to the rural area were the pull factors observed. Although liberation from indebtedness, the rise in consumption and saving, skill development and asset accumulation in the short period are the positive outcomes of migration, separation from family, children's education, health hazards, psychological stress and exposure to substances are some of the grey areas which merit the reasonable attention of all stakeholders.

The results showed that the annual household

322


income increased after migration which helped in increasing the spending capacity of the household. The saving of the migrants increased significantly. Most of the migrants strongly agreed that migration has changed their economic status. The economic life of the migrants has been increased positively. The study by Dao (2010) also concluded that an increase in labour migration positively influences economic growth in developing countries and findings of the present investigation this contention.

The migrants were able to repay their debt after earning more income, which helps them for more expenditure in their children's education and health expenses which increased their standard of living. But most of the migrants were not satisfied with their social life after migration as their absence of six months from their home resulted in some negative impact on their family, viz. neglect of children's education and health issues of family members, social functions and rituals. Roy and Azad (2006) and Naik et al. (2009) also highlighted the same findings in their studies.

As seasonal migration by far is mostly distress-driven, policy focus should be on the distress factors which are enlisted as push factors in the present analysis. MGNREGA may fetch magnetic changes in the rural life, that is, both economically and socially. But some studies point that MGNREGA has failed to control distress driven seasonal migration (Nayak & Pattnaik, 2011). Although it has started with best of intentions real benefits are not being shared by the target demography. There are a lot of loopholes at the stage of implementation and accounting process. The wage offered in MGNREGA was significantly lower as compared to the wage offered in urban centres. Even MGNREGA wage rate was lower than the rural wage rate in many parts of the state including the study area. So wage rate of MGNREGA should be raised continually.

Mainly people are migrating due to agrarian distress. Therefore, it can be suggested that increase in agricultural productivity may deter the migration process. The government should promote sustainable agricultural practices in areas where migration is dominant through special provisioning of irrigation, high yielding variety seeds, machines and implements,

etc. in subsidized rates. Provisioning and delivery of agricultural credit and crop insurance shall be positive steps in this direction. Non-farm activities and diversification from crops to non-crop agriculture can act as a secondary but effective avenue to avert seasonal migration. Steps like provisioning of better rural infrastructure comprising irrigation, electricity, road, communication and adequate education, health and banking facilities and improvement of rural industries may deliver a better livelihood in the rural area that can help to check the seasonal migration. These are believed to be the effective measures to arrest distress driven seasonal migration. Despite all these, if seasonal migration continues, then that will at least not be distress-driven as of now. REFERENCESAngel, S., Parent, J., Civco, D.L., Blei, A., & Potere, D. (2011).

The dimensions of global urban expansion: Estimates and projections for all countries 2000–2050. Progress in Planning, 75(2), 53–107.

Dao, M.Q. (2010). Factor mobility, net migration, growth and a lot of the poorest quintile in developing countries. Margin: The Journal of Applied Economic Research, 4(1), 127-137.

Devi, A.P., Geetha, K.T., & Gomathi, K.R. (2009). Rural out-migration: Two group discriminant analysis. Social Change, 39 (1), 85-101.

Korra, V. (2009). Can seasonal labour migration alter the household resource structure? A micro-level analysis in Andhra Pradesh. ASSI Quarterly, 28(1), 110-125

Misra, H. (2008). Determinants of remittances: A case study of migrant labourers in Alang ship- breaking yard. Artha Vijnana, 15 (2),151-168.

Naik, L.L., Huddar, M.G., & Tarachand, K.C. (2009). Socio-economic problems of migrated lambanis- An empirical study. Indian Journal of Social Development, 9(1),177-185.

Nayak, N.C., & Pattanaik, F. (2011). Employment intensity of growth in India and its structural determinants. Asian Economic Review, 53(1), 173-188

Roy, T.K., & Azad, K.K. (2006). Impact of rural-urban migration on female migrant fertility in Bangladesh. Man in India, 86(4), 241-254.

Semyonov, M., & Gorodzeisky, A. (2008). Labour migration, remittances and economic well- being of households in the Philippines. Population, Research and Policy Review, 27(5), 619- 637.

Singh, S., & Kaur, A. (2007). Causes and consequences of migrant labour in Ludhiana city: A case study. Social Action, 57(1), 56- 64.

323


ABSTRACTThis paper attempts to estimate the extent and magnitude of indebtedness among the different categories of weaker section households in south-west Punjab. The results indicated the average amount of debt per sampled households is found to be the highest in the case of the small farm household (`260938), followed by the marginal farm (`93585) the labour (`64865) and the rural artisan (`52051) households. The institutional agencies are the main source of credit, providing 54.78 percent of the total debt. An average weaker section household avails 38.17 percent of total loans for the agriculture and related activities. The highest amount of total loans has been taken at the rate of interest ranging from 0-10 percent per annum followed by the ranging of 20-30, 10-20 and 30-40 percent.

KeywordsDebt, indebtedness, loan, weaker section.

JEL CodesG21, H31, Q10.

1* 2 3Manvir Kaur , R.K. Mahajan and Rupinder Kaur

1 1Research Scholar, and Assistant Professor, Department of Economics, Punjabi University, Patiala2 Former Professor, Department of Economics, Punjabi University Regional Centre, Bathinda-151001


Received: May 15, 2018 Revision Accepted: June 18, 2018

Indebtedness among Weaker Sections in Rural Areas of South-West Punjab


DOI: 10.5958/2322-0430.2018.00137.3


www.soed.in


UGC Approved


324

INTRODUCTIONThe landless labourers and marginal farmers pay

relatively much higher rates of interest than the rest. The landless labourer, marginal and small farmers devoted the major part of their borrowings to non-productive purposes (Singh and Mehrotra, 1973). The most prominent source of credit was commission agents because they provided loans for unproductive requirements with little formalities and procedures. The larger proportion of debt was reported on unproductive purposes like marriages, social ceremonies, family maintenance and health care (Singh & Toor, 2005). On small farms, farm business income was insufficient to meet the household consumption expenditure. Hence, small farms and on-adopter medium farmers were non-viable. These farmers were compelled to deplete their resources that is either selling out assets or incurring fresh debts (Vyas et al., 1969). The reasons for increased indebtedness were high production costs of agriculture and extravagant spending on social ceremonies. The agricultural economy was also characterized by stagnation, high mortality rate, inequality, gender discrimination, illegal activities like drugs and

emigration(Satish, 2006). The inability of farmer to pay-off loans was the major cause of suicides (Jeromi, 2007).The maximum credit was acquired by the marginal and small farmers for production purpose followed by consumption purpose, health purpose, social ceremonies, etc. The healthcare credit had major share in the total borrowing by marginal and small farmers as cheap medical facilities through government health services were inadequate and not available at time. Around 11 percent marginal and 9.4 percent small farmers were suffering from serious ailments requiring immediate medical assistance; due to lack of funds they were unable to avail of these services. The burden of private health of case services was responsible for large proportion of total borrowing by small and marginal farmers (Singh, 2010).The small and marginal farm households used a major portion of loans for productive purposes and very little portion for unproductive purposes. The magnitude of debt was from the institutional sources, more expenditure on unproductive purposes and larger the farm- size households were under more burden of indebtedness (Pal & Singh, 2013). The present paper is an attempt to analyze the debt position of the different

categories of weaker section households in south-westPunjab, and estimate the extent and magnitude of indebtedness.METHODOLOGY

South-western Punjab region covers the tehsils of Fazilka, Shri Muktsar Sahib, Faridkot, Bhatinda, Mansa and parts of Ferozepur which border Haryana and Rajasthan states in the south-west. For the consideration of empirical study, four districts such as Faridkot, Bhatinda, Muktsar and Mansa were taken. In the first stage two blocks were randomly selected from each of the districts. One village was selected from each block randomly. In the last stage of multi-stage random sampling, households of the weaker sections were selected. It was decided to select 40 households from each village. The weaker section mainly belonged to small farmers, marginal farmers, rural artisans and labourers. For the selection of households, first of all, the categories of these householders were listed. The number of households in each category was calculated proportionately. After deciding the number of the households, the selection was made randomly. The data were collected with the help of a schedule especially prepared and pre-tested for the purpose. The data so collected were analyzed by using descriptive statistical tools.RESULTS AND DISCUSSIONSocio-economic Profile

The socio-economic profile of sampled weaker section households is given in Table 1. The perusal of Table 1 showed that the average family size is 4.58 and it is the highest in the case of the labourers (4.91) and the least in the case of the small farmers (4.28).Overall sex ratio was found to be 893 females per 1000 males in

labour households. There were 859 females per 1000 males in small farm households, 832 females per 1000 males in the marginal farm households and 798 females per 1000 males in the rural artisan households. The sex ratio goes in favour of males in all different categories. For an average weaker section household the literacy rate was 68.47 percent, which showed that major chunk of sampled population was able to read and write. It was the highest among the small farm households (84.23 percent) followed by the marginal farm households (83.96 percent), the rural artisan households (75.80 percent)and the labour households (54.61percent).On an average dependency ratio was 57.33 percent which shows the ratio of dependents on the earners of the family. For an average weaker section household APC was 1.16. The APC was the highest for the rural artisan households (1.28), followed by the labour households (1.27), the marginal farm households (1.21) and the small farm households (1.05). The APC was more than one for all categories of the weaker section households. It showed that they all are maintaining their consumption levels whether it could afford it or not.Extent of Debt

The extent and distribution of debt among the different categories of south-west Punjab is shown in Table 2. The perusal of Table 2 clearly showed that 98.44 present of sampled households were under debt. However, there were certain variations across the different categories of weaker sections. The percentage of indebted households the small farm households was 98.75 percent, followed by the marginal farm (96.23 percent) and the rural artisan (94.87 percent) households. The cent percent labour households were under debt.

Particulars Marginal farmers Small farmers Labourers Rural artisans South-west Punjab

Family size 4.32 4.28 4.91 4.33 4.58

Sex-ratio 832 859 893 798 864

Literacy rate 83.96 84.23 54.61 75.80 68.67

Dependency ratio 60.70 57.89 55.02 61.54 57.33

APC 1.21 1.05 1.27 1.28 1.16

Table 1. Socio-economic profile of different categories of south-west Punjab

Source: Field Survey, 2015-16.APC=Average propensity to consume.

Particulars Marginal farmers

Small farmers

Rural artisans

Labourers South-westPunjab

No. of sampled households 53 80 39 148 320

Indebted households 51 79 37 148 315

Indebted households as percentage of sampled households 96.23 98.75 94.87 100.00 98.44

Amount of debt ( ) per sampled households` 93585 260938 52051 64865 117078

Amount of debt ( ) per indebted households` 97255 264241 54865 64865 118937

Average income of sampled Households 123462 199808 85385 74902 115449

Table 2. Extent of debt among different categories of south-westPunjab


325

Kaur et al.: Indebtedness among weaker sections in rural areas of south-west Punjab

The average amount of per indebted sampled weaker section was `118937, while the average amount of loan was `117078. The average amount of debt per sampled households was found to be the highest in the case of the small farm household (`260938) followed by the marginal farm (`93585), labourer (`64865) and the rural artisan (`52051) households. The average amount of debt per indebted household was also the highest in case of the small farm households (`264241), whereas it was ̀ 97255 for the marginal farm households, `64865 for the labour households and `54865 for the rural artisan households. The amount of debt increased with the increase in household income except in the labour households; whose households' income was lower than the rural artisan households but the amount of debt was higher than the rural artisan households. It is evident that the debt amount increases with the increase in the farm-size. It is mainly because of the fact that the inputs of the farming are costly and without them even the minimum income cannot be generated.

Indebtedness according to Source of CreditIndebtedness among different categories in south-

west Punjab according to source of credit is presented in Table 3. The results show that an average weaker section sampled households in south-west Punjab was taken `117078 of total debt, out of which the debt from institutional sources was ̀ 64141, while that from the non-institutional sources, it was `52938. It is clear from the total that institutional agencies are the main source of credit, providing, 54.78 percent of the total debt. This proportion was higher in the farming categories 73.39 percent for the marginal farm and 77.82 percent for the small farm households. The remaining 45.22 percent of the total loan was advanced by the non-institutional agencies. This proportion is low for the farming categories whereas it is high for the labour households (98.07 percent) and the rural artisan households (77.59 percent). The results further depicted that the institutional agencies were playing a greater role in loans to the weaker section households as compared to the non-institutional

Source of Loan Marginal farmers

Small farmers

Rural artisans

Labourers South-west Punjab

Institutional

Co-operative bank/societya35377(1) 92313(2) 0(5.5) 0(5) 28938

(3)b(37.80) (35.38) (0.00) (0.00) (24.72)

Commercial bank 33302(2) 110750(1) 11667(2) 1250(3)

35203(1)

(35.58) (42.44) (22.41) (1.93) (30.07)

Sub-total of institutional sources

68396 202438 11667 1250 64141

(73.08) (77.82) (22.41) (1.93) (54.78)

Non-institutional

Commission agents 17547(3)

46875(3)

0.00(5.5)

0.00(5)

14625(4)

(18.75) (17.96) (0.00) (0.00) (12.49)

Money lenders 3962(4)

6125(4)

5897(3)

2230(2)

3938(5)

(4.23) (2.35) (11.33) (3.44) (3.36)

Large farmers 0(6)

0(6)

34231(1)

61385(1)

32563(2)

(0.00) (0.00) (65.76) (94.64) (27.81)Shopkeepers 3396

(5)4875(5)

256(4)

0(5)

1812(6)

(3.63) (1.87) (0.49) (0.00) (1.55)

Sub-total of non-institutional sources

24906 57688 40385 63615 52937

(26.61) (22.11) (77.59) (98.07) (45.22)

Total 93585 260938 52051 64865 117078

(100.00) (100.00) (100.00) (100.00) (100.00)

Table 3. Debt according to different credit agencies in different categories of south-west Punjab

Source: Field Survey, 2015-16.a, and b: Ranks of source of credit agencies and percentages of the total.

326


agencies. Amongst the institutional agencies, commercial banks are providing more loans amount (`35203) whereas the corresponding figure for the co-operative societies/ banks was `28938. On an average weaker section household, on the other hand, in the case of the non-institutional agenesis, the commission agents are advancing the highest loan amount of `14625 to an average weaker section household followed by the large farmers (`32563), the money lenders (`3938) and the shopkeepers (`1812).

The small farm households were under debt of `260938; out of which `203063 was taken from institutional agencies and the remaining `57875 are from the non-institutional agencies. The marginal farm households were indebted to the extent of `68679 to the institutional agencies and `24906 to the non-institutional agencies. The rural artisan households have taken `40385 and 11667 from the non-institutional and institutional agencies, respectively. The debt of labour households was `63615 and 1250 of the non-institutional and institutional agencies, respectively. The farmers raised more loans from institutional agencies whereas the other two categories from the non-institutional agencies.

An average sampled weaker section household takes about 30 percent of the total loan from the commercial banks. This proportional share was the highest for the small farm households (42.44 percent) and the lowest for the labour households (1.93 percent). The large farmers were the second important source of debt for an average sampled weaker section household contributing 27.81 percent to the total debt and this proportion was 94.64 percent for the labour and 65.76 percent for the rural artisan households. The third rank was occupied by the co-operative bank/ societies from which an average

weaker section household raised 24.72 percent of total debt. This proportion was negatively associated with the farming categories for the marginal farm households value of debt percentage was 37.80 percent and the corresponding figure for the small farm households was 35.38 percent. The commission agents appear at the fourth rank providing 12.49 percent the total debt to the average weaker section households.

The money lender and the shopkeeper also add 3.36 and 1.55 percent respectively to the total debt. The above analysis showed that as the income increases the percentage of debt of institutional agencies also increases. The institutional agencies have provided 77.82 percent of the loan to the small farm households and 1.93 percent to the labour households.Purpose of Debt

It is very crucial to understand the purpose for which the loan was raised while analyzing the indebtedness by the weaker section households. The weaker section households availed loans for both productive and unproductive purposes. The loans for unproductive purposes, refers to the loans which were not directly utilized in the production process.

The amount of loans used for the different purposes among the different categories of south-west Punjab is shown in Table 4. The results indicate that an average weaker section household availed 38.17 percent of total loans for the agriculture and related activities. This figure was higher among the small farm households (`141584), followed by the marginal farm household (`56136). This showed the positive relationship between the farm-size and the amount of loans for agriculture and related activities.

The next main purpose of the loan was for the house construction, addition of rooms, and major repairs, which accounted for 25.78 percent for an average weaker section household, the labour households accounts the major

Purpose of Loan Marginal farmers

Small farmers

Labourers Rural artisans

South-west Punjab

1. Agriculture &related activities 56136(59.98)

141584(54.26)

0.00(0.00)

0.00(0.00)

44693(38.17)

2. House construction, addition of rooms and major repairs

11958(12.78)

47413(18.17)

29330(45.22)

22822(43.85)

30181(25.78)

3. Marriages and other socio-religious ceremonies

14321(15.30)

43668(16.74)

23953(36.93)

17599(33.81)

26512(22.64)

4. Family maintenance expenditure 5226(5.58)

8646(3.31)

7102(10.95)

5320(10.22)

6960(5.95)

5. Health Care 2844(3.04)

4063(1.56)

2021(3.12)

1841(3.54)

2646(2.26)

6. Education 3099(3.31)

15564(5.96)

2457(3.79)

4469(8.59)

6085(5.20)

Total 93585(100.00)

260937(100.00)

64865(100.00)

52051(100.00)

117078(100.00)

Table 4. Debt according to purpose of loan in different categories of south-west Punjab

Source: Field Survey, 2015-16, values in brackets are percentages.

327


proportion of total loan for this purpose. An average sampled weaker section household has availed about 23 percent of the loans for the marriages and other social ceremonies. Slightly less than 6 percent of the total loans were used for family maintenance expenditure; it showed negative relationship between the income and amount of loan. Education and the healthcare also added to their share to the total amount of loan around 5.20 and 2.26 percent, respectively. More debt for non-productive purposes such as house construction, addition of rooms and major repairs and marriages and other socio-religious ceremonies added to the burden to the weaker section households.Rate of Interest

The mean values of loan according to the rate of interest among different categories of south-west Punjab are exhibited in Table 5. The results revealed that average weaker section households raised highest amount of total loans at the rate of interest ranging from 0-10 percent per annum followed by the ranges 20-30, 10-20 and 30-40 percent. The results further showed the proportion of debt at different rates of interest among the different categories of south-west Punjab. On an average, 54.78 percent of the total loans availed at the rate of interest from 0-10 percent. This proportion increases as the income increases. Another substantial proportion 27.81 percent of the total loans of an average weaker section household appear in the range of 20-30 percent. This proportion was higher for the labour households (94.64 percent) than the rural artisan households (65.76 percent). Another proportion (14.04 percent) of the total loans of an average weaker section household falls in the range of 10-20 percent. The least proportional share (3.44 percent) falls in the range of 30-40 percent.CONCLUSIONS

The farming categories have borrowed plenty of funds to meet their requirements for inputs. The rural artisan and the labour households were also unable to meet their consumption expenditure needs, with their

Rate of interest Percent Total

0-10 10-20 20-30 30-40

Marginal farmers 68396(73.08)

20943(22.38)

0(0.00)

3962(4.23)

93585(100.00)

Small farmers 202438(77.58)

51563(19.76)

0(0.00)

6125(2.35)

260938(100.00)

Rural artisans 11667(22.41)

256(0.49)

34231(65.76)

5897(11.33)

52051(100.00)

Labourers 1250(1.93)

0(0.00)

61385(94.64)

2230(3.44)

64865(100.00)

South-west Punjab 63938(54.61)

16391(14.00)

32563(27.81)

3938(3.36)

117078(100.00)

Table 5. Amount of debt according to different Rate of Interest in different of South-west Punjabcategories

Source: Field Survey, 2015-16.Figures in parentheses are percentages.

income. To bridge the gap between income and consumption, the weaker section households borrow mainly for their day to day life. The average amount of debt per indebted household was the highest in the case of the small farm households, followed by the marginal farm households the labour households and the rural artisan households. The amount of debt increases with the increase in household income except in the labour households; whose households' income was lower than the rural artisan households but the amount of debt was higher than the rural artisan households. It was evident that the needs of farmer's category increase with an increases in the farm-size because without investing in operational as well as fixed costs, the major share of income cannot be generated. The farmer categories raised more loans from institutional agencies whereas the other two categories have taken more loans from the non-institutional agencies. More debt for non-productive purposes such as house construction, addition of rooms and major repairs and marriages and other socio-religious ceremonies added to the burden to the weaker section households and helped them to maintain false social status. The small farm households raised the maximum amount of loan at the rate of interest less than 10 percent and ranging from 10 to 20 percent. The labour households acquired the maximum amount of loan at the rate of interest ranging from 20 to 30 percent.REFERENCESJeromi, P.D. (2007). Farmers' indebtedness and suicides:

Impact of agricultural trade liberalization in Kerala. Economic and Political Weekly, 42(31), 3241-3247.

Pal, D., & Singh, G. (2013). Magnitude and determinants of indebtedness among small and marginal farmers: A case study of Patiala district in Punjab. Agricultural Situation in India, 69(3), 143-151.

Satish, P. (2006). Institutional credit, indebtedness and suicides in Punjab. Economic and Political Weekly, 41(26), 2754-2761.

Singh, N.D. (2010). Rural healthcare and indebtedness in Punjab. Economic and Political Weekly, 45(11), 22-25.

328


Singh, R. D., & Mehrotra, M. K. (1973). Problem of credit and indebtedness with small and marginal farmers and agricultural labourers. Indian Co-operative Review, 10(2), 205-222.

Singh, S., & Toor, M.S. (2005). Agrarian crisis with special

reference to indebtedness among Punjab farmers. Indian Journal of Agricultural Economics, 60(3), 335-346.

Vyas, V.S., Tyagi, D.S., & Misra, V.N. (1969). New agricultural strategy and small farmers: A case study in Gujarat. Economic and Political Weekly, 4(13), A49-53.

329


ABSTRACTVarious statistical/econometrical analytical techniques were used for the analysis of price integration of cauliflower among the major markets of Punjab and New Delhi. The stationarity property of time series of prices was checked through Dickey-Fuller (ADF) unit root test. The correlation coefficients in monthly prices of cauliflower between all the selected market pairs were positive and significantly different from zero indicating strong market integration. Johansen multiple co-integration procedure implied that there was long-run price association among the sample markets. Granger causality test explored the causality of relationship between the sample market pairs. Vector Error Correction Method (VECM) revealed that in the selected markets the flow of market information had occurred both in short run and long run. Despite of fair level integration, the presence of only unidirectional causality among some market pairs points towards the need to further strengthen the integration between markets. This could be achieved by establishment of sound market information and intelligence system along with strengthening of the cold storage and transportation facilities.

KeywordsCauliflower, cointegration, correlation, granger causality, price transmission.

JEL CodesQ02, Q12, M 31, P22, Q18.

*Shruti Mohapatra , Jasdev Singh and Sanjay Kumar




Cointegration among Major Cauliflower Markets in Punjab


DOI: 10.5958/2322-0430.2018.00138.5


www.soed.in


UGC Approved


330

INTRODUCTIONThe Punjab state showcases the classic instance of

fast agricultural development. However, growth in the state being based on only two-grain crops viz., wheat and paddy is culminating to agrarian crisis of stagnating productivity, growing indebtedness, falling income and farmers' suicides. Thus diversification from grain crops to vegetable crops has emerged as an important strategy for agricultural growth to provide gainful employment, improve income and save natural resources from further degradation.

However, there are many hindrances found in the efficient functioning of the markets in a developing economy like India (Beag & Singla, 2014). In the case of vegetables there are considerable seasonal price variations as well as regional price variations. The long-term perspective shows that changes of supply and demand in vegetables will provide the driving and force for price rise and the capitalization trend will result in the existence of frequent price fluctuation in vegetables. The

longer the distance between the vegetable markets, the weaker is the integration, and vice versa. Market imperfections, improvement of vegetable production risk management system, innovations in control measures of vegetables market risk, strengthening the monitoring system of vegetables, appropriate marketing policies, government intervention and determinants of marketing efficiency had always been remained major debatable issues. However, market integration is one of the ways to highlight the issue for analyzing the market performance (Mukhtar & Javed, 2008). Market efficiency and competitiveness is positively related with co-integration of markets because co-integrated markets are always competitive and efficient. In co-integrated markets, price differences between two markets are equal to the transaction costs between those two markets, provided that trade occurs. When there is a change in prices in one market, it will be transmitted to another market on a “one-to-one” basis either instantly or over a number of lags (Sanogo &Amadou, 2010). Integration is taken to denote

a state of affairs or a process involving attempts to combine separate national economies into larger economic regions. The degree, to which consumers and producers would get benefited, is mainly dependent on integration of domestic markets with world markets and that of regional markets with each other (Varela et al., 2012).Therefore, in the present study, an attempt has been made to investigate the spatial market cointegration of prices of cauliflower, one of the important vegetables being produced in Punjab state.METHODOLOGY

To achieve the specific objectives of the present study, three major markets of cauliflower in Punjab viz. Amritsar, Jalandhar and Ludhiana were selected on the basis of the highest annual arrivals of cauliflower and the data availability in the respective markets (Singh, 2014). To look into the integration of state markets with national markets, New Delhi market was also taken as sample market. The study is based on the secondary data. The time series data on monthly arrival and price of cauliflower required for the study were collected from the registers maintained in the respective APMCs as well as from web site of AGMARKNET. The reference period of study encompasses period from year 2007 to 2016.Analytical of Data

To study the market integration various statistical/econometric analytical techniques, namely correlation coefficient, ADF unit root test, cointegration, Granger causality test and vector error correction method were used.Correlation Analysis

The correlation considers magnitude of association between price series in selected markets. The bi-variate correlation coefficients between the price changes in different markets is the most commonly used technique for testing market integration (Lele, 1967; Sendhil et al., 2014). Correlation coefficient (r) between two market price series U and V was worked as following:

To test the significance of correlation coefficient (r) t-test was used with null hypothesis (H ): þ = 0 and alternate 0

hypothesis (H ): þ 0. The significance of correlation was 1

tested using the following formula:

degrees of freedom

Stationarity of the Time SeriesMarkets with long-term equilibrium between them

are considered to be integrated markets. There is a need to check the presence of stationarity in price series for establishing such relationship as its absence makes the relationship spurious as well as not having any significant meaning. When the stationarity in price series are established at the same level of differences, the price relationship is anticipated to be a good one. In the present

study, the stationarity in price series was estimated by employing Augmented Dickey-Fuller (ADF) test. The presence of unit root in the test implies non-stationarity of price series. Hence the presence of unit root at level in the data generating process directs the data to be transformed into first differences and the unit root test is repeated to check stationarity for the further analysis. The test was applied after running regression of the following form:

Where,Y = Price of cauliflower in a given market at time tt

ÄY = Y – Yt t t-1

å = Pure white noise error termm = Optimal lag value which is selected on the basis

of Schwartz Information Criterion (SIC) and Akaike Information Criterion (AIC)

To test for a unit root in the price series accepting the null hypothesis:

â = 0 indicates that time series is non-stationary. Rejection of null hypothesis and acceptance of

alternative hypothesis â< 0 indicates that the time series is stationary.

Co-integration TestThe degree of deviation from the long run

equilibrium relationship is explicated by cointegration test. It is the yoke between integrated processes and steady-state equilibrium and hence furnishes the relevant theoretical framework for examining kinetics of instantaneous changes in a pair of series along with their valuable long-run information. After confirmation of stationarity in the entire price series at same order of differences, the co-integration of markets were tested by Johansen maximum-likelihood techniques. In present study, the long run price relationships among the markets were investigated by conducting Johansen cointegration test (Johansen and Juselius 1990). This test is generally based on the maximum likelihood ratio test statistics which is used to examine the number of cointegrating vectors present. The trace statistic along with the maximum-eigen value-statistic is primarily used with a null hypothesis (H ): at most 'r' cointegrating vectors 0

present and an alternative hypothesis (H ): at most 'r+1' 1

cointegrating vectors present. The calculated values from the tests will denote the number of integrating vectors actually present and thereby the extent of co-movement of prices can be easily measured. The cointegration test implies that when the number of cointegrating vectors is increased it will give rise to increase strength and stability of price linkages.Granger Causality Test

Granger's causality technique was used to reveal the causal relationship between the cauliflower prices series in the sample markets. The presence as well as causality direction of long-run market price relationship can be evaluated by using the Granger causality test directed within vector autoregressive (VAR) model. It is a

)(),(

),(),(

vVaruVar

VUCovVU =

)2(~

1

22

--

-= nt

r

nrt

ebb +D+̧=D -=åit

m

i it YatY121

331

Mohapatra et al.: Cointegration among major cauliflower markets in Punjab

probabilistic account of causality using empirical data sets to find the patterns of causality. An autoregressive distributed lag (ADL) model for the Granger- causality test had been specified as below:

Where, 't' is the time periodu and u are the error terms and 10 20

X and Y are the prices series of different markets.To test the pattern of causality between two variables,

F-test was used with null hypothesis (H ): Lagged X does 0 0

not Granger cause Y and alternative hypothesis (H ): 0 1

Lagged X granger cause Y . The significance of F 0 0

statistic as tested with the p-value.Error Correction Method (ECM)

The Vector Error Correction Model (VECM) is a type of multiple time series model. It is mostly used when a long- run stochastic trend/ co-integration is present among the study variables. Short term as well as long-term effects of one time series on another can be estimated by using this method. Hence, the ECM approach, which can be used to estimate the short run as well as long-run price transmission of cauliflower in sample markets, was adopted to analyze the market price cointegration. The autoregressive distributed lag equation considered is explained as following:

Y = á X + a X + a Y + åt 01 t 11 t-1 12 t-1 t

The generalized pattern of the above equation for k lags along with the intercept term is given below.

Where,

Here, m indicates the adjustment rate of the short-run 0

deviations towards the long run equilibrium. The parameter (m ) can carry a range of values from 0 to 1, 0

where '0' refers that there is no adjustment and 1 denotes that there is an instantaneous adjustment.

In the present study, Vector Error Correction Model (VECM), which is a strong approach for the analysis of rate of price adjustment process, has been used. It contains many advantages like it allows the cointegration testing in a form of equations in only one

step. Likewise there is another advantage that the error term does not need to be carried over from one step to the others. In addition to these, there is no requirement of taking the prior assumption regarding the endogeneity of variables.RESULTS AND DISCUSSION

In order to ascertain the integration of markets with respect to prices of cauliflower in the selected markets, bivariate correlation matrix was constructed and the same has been presented in Table 1. It shows that the correlation coefficients of cauliflower prices between different pairs of markets were highly significant at one percent level and ranged from 0.77 to 0.81, thus, indicating that prices of cauliflower in markets of Punjab as well as in New Delhi were integrated with each other. Among the Punjab markets, the r-value was highest of the order of 0.81 between Ludhiana and Amritsar markets, followed by 0.80 between Amritsar and Jalandhar and 0.77 between Jalandhar and Ludhiana markets. In the case of correlation between Punjab markets and national market the r-value was highest of the order of 0.80 between Jalandhar and New Delhi market followed by 0.79 in Ludhiana and New Delhi market and 0.77 in Amritsar and New Delhi market. Overall the result of correlation analysis showed that the prices in cauliflower markets moved together and were well integrated in the selected markets.

As the correlation matrices had only shown the short-run integration between the selected markets, the long run integration between markets was also analyzed using cointegration test. To conduct the cointegration relationship test between different market prices of cauliflower, it is mandatory to check the stationarity property of time series data and for this purpose Augmented Dickey-Fuller (ADF) unit root test was employed. The results of ADF test are given in Table 2.

The Augmented Dickey-Fuller test values for cauliflower price series at their level were more than the critical value (1%) given by MacKinnon statistical tables except for Ludhiana market (Table 2). This imply that price series of Jalandhar, Amritsar and New Delhi market were free from consequences of unit root at their level and were stationary. However, at their first difference all of the price series (including Ludhiana market) were found to be stationary which is the pre-condition to run the cointegration tests. The results of Johansen multiple co-integration procedure on the integration among the selected cauliflower markets viz. Amritsar, Jalandhar, Ludhiana and New Delhi are presented in the Table 3. Unrestricted co-integration rank tests (Trace and maximum Eigen value) indicated the presence of at least 4 co-integrating equations at 5 percent level of significance, thus revealing that all sample cauliflower markets were having long-run equilibrium relationship.

The Granger causality was estimated between the selected pairs of cauliflower markets. The Granger causality shows the direction of price formation between

1001010 uXYaX j

n

j ji

n

i I ++= -=-= åå b

2001010 uXYY j

n

j ji

n

i I ++= -=-= åå dl

111

12

1101120 )1(

)()1( e+ú

û

ùêë

é-

-

+-+D=D -- tttt YX

a

aaaXaY

tktkt XXm e+--- ][ 1

it

k

i iit

k

i it mYaXaaY +D+D+=D -

-

=-

-

= åå 0

1

1 2

1

0 100

)1(0 20 å=

-=k

iaim

00 11 / maim

k

iå==

332


Markets Amritsar Jalandhar Ludhiana New Delhi

Amritsar ***1.00

Jalandhar***0.80 ***1.00

Ludhiana ***0.81 ***0.77 ***1.00

New Delhi ***0.77 ***0.80 *0.79 ***1.00

Table 1. Correlation coefficients of cauliflower prices in different market pairs

***, * and Significant at 1 and 10 percent level.

Markets At level Stationarity Critical values *(at 1% level)

At first difference

Stationarity Critical values* (at 1% level)

Amritsar -8.03 Stationary -3.49 -8.68 Stationary -3.49

Jalandhar -8.35 Stationary -9.37 Stationary

Ludhiana -3.13 Non-stationary -5.75 Stationary

New Delhi -8.13 Stationary -9.06 Stationary

Table 2. Results of Augmented Dickey-Fuller Test (ADF)

*MacKinnon (1996) one-sided p-values.

Hypothesized no. of CE(s) Eigen value Trace 0.05**p-values

Statistic Critical value*None 0.46 159.43 63.88 0.00

At most 1* 0.26 88.18 42.92 0.00

At most 2* 0.24 52.71 25.87 0.00

At most 3* 0.17 21.43 12.52 0.00

Table 3. Unrestricted Cointegration Rank Test (Trace) of selected cauliflower markets

Trace test indicates 4 co-integrating equations(s) at the 0.05 level.* denotes rejection of the null hypothesis at the 0.05 level.**MacKinnon-Haug-Michelis (1999) p-values.

two markets, that is, movement of the prices to adjust the prices difference (Ghafoor et al., 2009). The results of causal relationship between the price series in major cauliflower markets in Punjab and New Delhi market approached through Granger Causality technique are presented in Table 4. It was revealed that the cauliflower price of Amritsar market had shown unidirectional causality in price transmission with cauliflower price of Jalandhar and Ludhiana market. In other words, while prices of Amritsar market influenced the prices of Jalandhar and Ludhiana markets but itself these were not being affected by the prices of later. The cauliflower price in Ludhiana market showed bidirectional causality in price transmission with cauliflower price of Jalandhar. Hence the cauliflower prices of these markets were influencing each other simultaneously. The New Delhi market showed unidirectional causality through transmission of cauliflower price to Ludhiana and Jalandhar markets whereas it had bidirectional causality in price transmission with cauliflower price of Amritsar market. Overall, the selected cauliflower markets seem to be integrated with each other to fair extent.

Null Hypothesis F-Statistic p- *value

JDLR does not Granger Cause ASR 1.07 0.38

ASR does not Granger Cause JDLR 3.07 0.02

LDH does not Granger Cause ASR 1.83 0.13

ASR does not Granger Cause LDH 4.04 0.00

LDH does not Granger Cause JDLR 2.96 0.02

JDLR does not Granger Cause LDH 3.42 0.01

ASR does not Granger Cause NDLS 2.16 0.04

NDLS does not Granger Cause ASR 3.72 0.01

JDLR does not Granger Cause NDLS 2.01 0.10

NDLS does not Granger Cause JDLR 2.67 0.04

LDH does not Granger Cause NDLS 1.15 0.34

NDLS does not Granger Cause LDH 6.29 0.00

Table 4. Results of Granger Causality Test(n=116)

*Denotes MacKinnon-Haug-Michelis (1999) one-sided p-value.Price series: ASR- Amritsar; JDLR- Jalandhar; LDH – Ludhiana; NDLS- New Delhi.

333


The Granger causality defines the direction of price transmission, however it do not reveal the time required for attainment of short-run and long-run equilibrium across the selected markets. The same can be estimated through the error-correction mechanism. The results on Vector Error Correction Model applied for this purpose are presented in the Table 5. The Akaike Information Criterion (AIC) was lowest at the order lag 4 in the system the selected cauliflower markets. Thus, keeping the above in view, the number of lags in the VECM was assumed to be four.

The results of the co-integrating equations for cauliflower price transmission revealed that the coefficients of the long-run cointegrating equations for each of the selected markets were statistically significant (Table 5). This has confirmed that there was an existence of a long-run equilibrium relationship between the prices of cauliflower in these markets.

From the results of the error correction models it has been observed that the sign of the first co-integrating vector of normalized co-integrating coefficients for all of the markets were negative. This implied that the short-run

Particulars D (ASR) D (JDLR) D (LDH) D (NDLS)

Co-integrating Equation

Coefficient S.E. Coefficient S.E. Coefficient S.E. Coefficient S.E.

ASR (-1) 1**25.65 -12.45

***69.43 -15.22**-9.88 -4.82

JDLR (-1) **33.53 -12.52 1 *0.72 -0.31 **-9.61 -3.89

LDH (-1) ***46.63 -17.22 **1.39 -0.59 1 ***-13.36 -5.01

NDLS (-1) **-3.49 -1.54 ***-1.20 -0.36 **-1.06 -0.25 1

C -79187.8 -2362.34 -1698.51 22694.91

Error Correction

Coint Eq1 -0.0084 -0.001 -0.0110 -0.001 -0.0079 -0.001 -0.0107 -0.0017

D (ASR (-1)) -0.1967 -0.116 0.1489 -0.135 0.2258 -0.099 0.1978 -0.1221

D (ASR(-2)) 0.0488 -0.116 0.0558 -0.136 0.1586 -0.099 -0.0641 -0.1223

D (ASR(-3)) -0.1933 -0.117 0.2504 0.1365 0.0642 -0.099 -0.0702 -0.1226

D (ASR(-4)) -0.1592 -0.117 0.1807 -0.137 0.0352 -0.103 0.0628 -0.1231

D (JDLR(-1)) 0.1841 -0.111 -0.1306 -0.129 0.2660 -0.091 0.1945 -0.1164D (JDLR(-2)) 0.1179 -0.111 0.0876 -0.129 0.2598 -0.094 0.1480 -0.1166D (JDLR(-3)) 0.0711 -0.110 -0.0790 -0.128 0.2324 -0.093 0.1885 -0.1154

D (JDLR(-4)) 0.1899 -0.102 0.0671 -0.118 0.1079 -0.086 0.1588 -0.1067

D (LDH(-1)) 0.2192 -0.131 0.6194 -0.153 -0.2930 -0.112 0.4158 -0.4090

D (LDH(-2)) 0.2045 -0.148 0.1782 -0.173 0.0313 -0.127 0.2395 -0.1556D (LDH(-3)) 0.3407 -0.267 0.0343 -0.174 -0.0249 -0.127 0.2998 -0.1567

D (LDH(-4)) 0.0759 -0.130 0.1587 -0.152 -0.0551 -0.112 0.2633 -0.1369

D (NDLS(-1)) 0.2339 -0.112 0.2883 -0.133 0.1660 -0.097 -0.3593 -0.1199

D (NDLS(-2)) -0.1357 -0.125 0.2132 -0.146 -0.0385 -0.107 -0.2524 -0.1316

D (NDLS(-3)) 0.2379 -0.123 0.1719 -0.143 0.1299 -0.105 0.0648 -0.1292

D (NDLS(-4)) 0.0085 -0.119 -0.0442 -0.139 0.2718 -0.102 -0.3479 -0.1254

C -16.14 -31.22 -12.27 -36.35 -10.51 -26.58 -21.37 -32.652R 0.4536 0.4744 0.5438 0.4669

2Adjusted R 0.3577 0.3823 0.4638 0.3734

SSR 10764115 14592632 7802937 11768403

S.E. equation 333.122 387.87 283.62 348.32

F-statistic 4.74 5.15 6.80 4.99

Log-likelihood -821.37 -838.86 -802.87 -826.49

Akaike AIC 14.60 14.91 14.27 14.69

Schwarz SC 15.0273 15.33 14.71 15.12

Mean dependent -2.93 -2.65 -0.35 -8.16

S.D. dependent 415.67 493.52 387.33 440.06

Table 5. Results of Vector Error Correction Model

Price series: ASR-Amritsar; JDLR-Jalandhar; LDH-Ludhiana; NDLS-New Delhi.

334


New Delhi

Jalandhar Amritsar Ludhiana

Figure 1. Market integration among selected cauliflower markets

price movements in all of the study markets were stable. The Wald test for both one and two lag variables of selected markets had shown that Amritsar market price was influenced by the lagged prices of New Delhi market. The market prices in Jalandhar market were influenced by lagged prices of Amritsar, Ludhiana and New Delhi markets. Prices in Ludhiana market were influenced by its own lagged price as well as by the lagged prices of Amritsar, Jalandhar and New Delhi markets. New Delhi market prices were influenced by its own lagged price as well as by the lagged prices of Amritsar market.CONCLUSIONS

The present study analyzed correlation, stationarity, cointegration, causality and price transmission in major cauliflower markets of Punjab and New Delhi market. The overall results indicated that different selected cauliflower markets were fairly integrated with long-run price association across them. Despite of this certain markets showed only unidirectional integration. Among the market pairs of New Delhi-Jalandhar, New Delhi-Ludhiana, Amritsar-Jalandhar and Amritsar-Ludhiana, while the former market of each pair influenced the price formation in the second market of pair, it was not being affected by the prices of later. The policy intervention calls for faster movement of market information through strengthening market intelligence along with the establishing of online marketing system through computerization and networking. The market price information should be made available to the stakeholders through electronic and print media immediately. Development/strengthening of market infrastructure including transportation, storage and communication

facilities are the need of time in order to fully integrate the market prices.REFERENCESBeag, F.A., & Singla, N. (2014). Cointegration, causality and

impulse response analysis in major apple markets of India. Agricultural Economic Research Review, 27, 289-298.

Ghafoor, A., Mustafa, K.,Mushtaq, K., & Abedulla. (2009). Co-integration and causality: An application to major mango markets in Pakistan. Lahore Journal of Economics,14, 85-113.

Johansen, S., & Juselius. (1990). Maximum likelihood estimation and inference on cointegration with application to demand for money.Oxford Bulletin of Economics and Statistics, 52, 169-210.

Lele, U.J. (1967). Market integration: A study of sorghum prices in western India, Journal of Farm Economics, 49, 147-159. https://doi.org/10.2307/1237074

Mukhtar, T., & Javed, M.T. (2008). Market integration in wholesale maize markets in Pakistan. Regional and Sectoral Economic Studies,8, 85-98.

Sanogo, I., & Amadou, M.M. (2010). Rice market integration and food security in Nepal: The role of cross-border trade with India.Food Policy,35,312-322.

Sendhil, R., Sundaramoorthy, C., Venkatesh, P., & Thomas, L. (2014). Testing market integration and convergence to the law of one price in Indian onions. African Journal of Agricultural Research, 9, 2975-2984.

Singh, N. (2014). A study of integration of markets for onion and potato in South Gujarat. International Research Journal of Agricultural Economics and Statistics, 5, 241-244.

Varela, G., Carroll, E.A., & Iacovone, L. (2012). Determinants of market integration and price transmission in Indonesia. Policy Research (Working Paper 6098) Poverty Reduction and Economic Management Unit, World Bank.

335


ABSTRACTWorking capital is the fund invested in current assets required for meeting day to day expenses includes trade credit, bank credit, factoring and commercial paper. Of these, the present paper deals with only bank credit which represents the most important source for financing of current assets. From time to time, Reserve Bank of India has been issuing guidelines and directives to the banks to strengthen the procedure and norms for working capital financing. The present study has been designed to study the customers' as well as the bankers' perspective on working capital funding products offered by the Kotak Mahindra Bank along with the analysis of risk management practices adopted by the bank and reasons thereof to know the most preferred option of working capital funding and its management. To accomplish the objectives, the study has been broadly based on the primary data/information collected through direct communication with the customers, employees, senior managers etc. The required data/information was collected from randomly selected 60 customers and 20 bank employees – bankers' representatives. The study highlighted that about 75 percent customers were found to be more or less satisfied with the service delivery mechanism of the bank. The bank needs to improve further to satisfy the remaining 25 percent of its customers who showed their dissatisfaction in terms of delay in task as well as lengthy procedure followed at the end of the bank. The study further showed working capital as the major determining factor in devising strategies with preferred method- portfolio review for effective screening in making funds available to the customer. The policies pertaining to the working capital funding were mostly found to be reviewed as per the need and contingencies. Risk analysis has major role to play while funding to develop a cushion for the organization and the appraisal clearly brought out the preferred practices in terms of risk focused and suitable method adhering to the needs of the organization. Credit assessment of the borrower forms a major part of the whole process involved and it is essential that such an assessment is carried out without any biasness and discrepancy. The credit appraisal mechanism at KMBL was exhaustive and effective in appraising customer's applicant for credit. The organization should also strive to provide training to staff with regard to upgraded policies stated by RBI.

KeywordsCredit, creditability, finance, risk management, working capital.

JEL CodesC81, C83, D14, D53, G11.

Isha Chawla

Process Associate,Global Corporate Trust, Bank of New York Mellon, Pune, Maharashtra, India


Received: April 13, 2018 Revision Accepted: June 18, 2018

Performance Appraisal of Working Capital Funding Products and Risk Management Practices in Private Banking Sector-A Case Study of Kotak Mahindra Bank


DOI: 10.5958/2322-0430.2018.00139.7


www.soed.in


UGC Approved


336

INTRODUCTIONAccording to the Reserve Bank of India (RBI), the

banking sector in India is sound, adequately capitalized and well-regulated. Indian financial and economic conditions are much better than in many other countries of the world. Credit, market and liquidity risk studies show that Indian banks are generally resilient and have withstood the global downturn well. The optimism stems from factors such as the Government working hard to

revitalize the industrial growth in the country and the RBI initiating a number of measures that would go a long way in helping the banks to restructure. The Indian banking sector is fragmented, with 46 commercial banks jostling for business with dozens of foreign banks as well as rural and co-operative lenders. State banks control 80 percent of the market, leaving relatively small shares for private rivals. Recently, about 13.7 crore accounts have been opened under Pradhanmantri Jan Dhan Yojna (PMJDY)

and 12.2 crore RuPay debit were issued (SCRIBD 2015). Credit off-take has been surging over the past decade, aided by strong economic growth, rising disposable incomes, increasing consumerism and easier access to credit. Rising incomes are expected to enhance the need for banking services in rural areas and therefore drive the growth of the sector; programs like MNREGA have helped in increasing rural income aided by the recent Jan Dhan Yojana. All this translates into a strong growth for the banking sector too, as rapidly growing business turn to banks for their credit needs, thus helping them grow.

Kotak Mahindra Bank is the fourth largest Indian private sector bank by market capitalization. As on September 30, 2014, Kotak Mahindra Bank has over 641 branches and over 1,159 ATMs spread across 363 locations in the country (SQUARE CAPITAL 2014). Presently it is engaged in commercial banking, stock broking, mutual funds, life insurance and investment banking. It caters to the financial needs of individuals and corporate. The bank has an international presence through its subsidiaries with offices in London, New York, Dubai, Mauritius, San Francisco and Singapore that specialize in providing services to overseas investors seeking to invest in India. The bank offers complete financial solutions for infinite needs of all individual and non-individual customers depending on the customer's need-delivered through a state of the art technology platform. The Depository services offered by the bank allows the customers to hold equity shares, government securities, bonds and other securities in electronic or Demat forms. The bank's wholesale banking products offer business banking solutions for long-term investments and working capital needs, advice on mergers and acquisitions and equipment financing. To meet special needs of the rural market, the bank has dedicated business offerings for agricultural financing and infrastructure. Its Agriculture Finance Division delivers customized products for capital financing and equipment financing needs of our rural customers.Risk Management Practices: A risk can be defined as an unplanned event with financial consequences resulting in loss or reduced earnings and hence as the volatility of the potential outcome. There could be many types of financial risks involved while lending money. Credit risk (or counterparty risk) is increasingly faced by banks in their product assortment (not only lending) and can be considered as the oldest and largest risk in banking. Important in a bank relationship is know your client principle, by becoming familiar with the borrower and/or credit base. It is important that banks deal with customers with sound reputation and creditworthiness. Therefore banks need not only manage the credit risk in their credit portfolio but also that in any individual credit or transaction. The relationship between credit risk and other risks should also be considered by banks. The effective management of credit risk is a critical component of a comprehensive approach to risk management and

important to the long-term success of any banking organization. Effective credit risk management process is a way to manage portfolio of credit facilities. Credit risk management encompasses identification, measurement, monitoring and control of the credit risk exposures. The effective management of credit risk is a critical component of comprehensive risk management and essential for the long term success of a banking organization.

Bank has three lines of defense model towards risk management. Responsibilities for risk management at each line of defense are defined, thereby providing clarity in the roles and responsibilities towards risk management function. At the first line of defense are the various business lines that the Bank operates. The Bank believes that businesses understand their risks best and assume full ownership and accountability for managing them. The business lines assume risk taking positions on a day to day basis within the approved framework and boundaries put in place by the second line of defense. The second line of defense is made up of risk management, finance and compliance functions. This line sets the boundaries for risk taking by the first line, within the Board approved mandate. They also provide oversight over business and provide periodic reporting to the Board. The third line of defense is the internal audit function that provides independent assessment of the first and second line of defense and reports to the audit committee of the Board.

Davies and Merin (2014) viewed that the Companies that improve the performance of their working capital can generate cash and see benefits far beyond the finance department. Since, the onset of the economic crisis that engulfed most of the world beginning in mid-2007, liquidity has become more important than earnings growth to creditors and investors alike. As a result, treasurers and CFOs have heightened their focus on working capital as an effective lever of liquidity. According to Beard (2007) Working capital is used to fund day-to-day operations at companies, so in addition to receivables, it could cover expenses such as payroll and the costs of procuring, storing and managing inventory. Pergler and Rasmussen (2014) observed that a handful of pragmatic tools can help managers decide which projects best fit their portfolio and risk tolerance. Sultan and Roberts (2017) observed that over the past decade, many corporate have adjusted their financing strategies as the availability and cost of capital have changed due to a number of factors, including market, regulatory and economic transformations. The present study has been designed to study the customers' as well as the bankers' perspective on working capital funding products offered by the Kotak Mahindra Bank along with the analysis of risk management practices adopted by the bank and reasons thereof to know the most preferred option of working capital funding and its management. METHODOLOGY

The scope of study basically deals with the appraisal

337

Chawla: Performance Appraisal of working capital funding products and risk management practices in private banking sector

of working capital products and risk management practices with specific focus on Kotak Mahindra Bank Limited (KMBL) being the fourth largest Indian private sector bank by market capitalization. The study population comprised of finite number of business person dealing in day to day working capital funding of Kotak Mahindra Bank Limited. The first step in order to accomplish the task was to draw a representative sample. To serve this purpose the sampling technique adopted was random sampling. This technique was adopted with the assumption that the samples drawn would have the same composition and characteristics as of the population.Data Collection: To accomplish the objectives, the study has been broadly based on the primary data/information collected through direct communication with the customers, employees, senior managers, etc. The required data/information was collected from randomly selected 60 customers and 20 bank employees – bankers' representatives. A personal survey was conducted and 60 customer respondents and 20 employee respondents were approached personally and the questions were asked with the help of questionnaire. In-depth interviews were conducted to get deep insights to the problems. Being the research descriptive in nature, the information so collected was tabulated and classified for analysis using simple statistical techniques like averages and percentage etc to discover the potentiality of the products of working capital offered and risk management practices adopted in Kotak Mahindra Bank Limited.

RESULTS AND DISCUSSIONResults have been discussed under the following

heads:Customers' Perspective on Working Capital Funding ProductsBankers' Perspective on Working Capital Funding ProductsBankers' Risk Management Practices

Customers' Perspective on Working Capital Funding Products

Understanding the customers' perspective on the various working capital funding products offered by the bank is of paramount importance to pinpoint the funds requirements and product preferences at the end of the small – medium enterprises. The customers' perspective entailing the various parameters determining their decision – making process, classification to have better understanding as to what respondents prefer to opt and by what percentage, etc has therefore been studied and the same has been presented in Table 1.

The first parameter Association with Bank is an indicator of the brand loyalty that customer has towards the company. About 60 percent of the sampled customer respondents have been found as associated with the bank only for a period of less than one year, whereas 25 percent were associated for 1-3 years while 15 percent were dealing with the bank for longer period of more than 3 years. On the whole, result showed the fair associability of the bank with 40 percent of the respondents using some

Parameters Classification Number ofRespondents

Percent

Association with bank Less than a year 36 60

1-3 Years 15 25

More than 3 years 9 15

Customer capital preference Cash credit 15 25

Bill discounting - -

Working capital 12 20

Bank guarantee 21 35

Letter of credit 12 20

Preference for fund based/non- fund based working capital

Fund based working capital 33 55

Non-fund based working capital 27 45

Customer value metrics for working capital management

Net working capital 12 20

Return on investment 9 15

Risk management 12 20

Net cash conversion cycle 9 15

Benchmark against competition 12 20

Weighted average capital cost 6 10

Customer satisfaction regarding employees dealings

Fair in operations 27 45

Quick and prompt 18 30

Delay in task 6 10

Lengthy procedure 9 15

Table 1. Customers' perspective on working capital funding products offered by Kotak Mahindra Bank, 2015

338


or other services in terms of working capital for more than 1-3 years. The second parameter Customer Capital Preference focus on the product that customer prefer to choose. Cash credit, working capital, bank guarantee and letter of credit were found to be the most preferable working capital products preferred by 25, 20, 35 and 20 percent customers respectively. Bank Guarantee has been found to be the most preferred product. It was the most affective and flexible method wherein customer was provided high degree of liquidity to carry out its operations smoothly. Cash Credit also a method which was known and highly used by the customer to induce liquidity. The perusal of Table 1 further revealed the customers' preference for fund based/non-fund based working capital. Customer preferred type of working capital was all about the type preferred while choosing working capital funding. Fund based working capital was most preferable option as compared to non-fund based as it exhibited better understanding with less documentation involved in process to have funds in hand. Fund based products such as Cash Credit, Overdraft were really popular and highly used products. The results highlighted fund-based working capital preference of the 55 percent sampled customers, as depicted in Table1. The parameter, Customer value metrics for working capital management comprises the various determining factors in choosing suitable product. The net working capital, risk management and benchmark against competition were some of the factors that respondents found the most important while making an effective decision pertaining to working capital. It was found that net working capital, risk management and benchmark against competition were the common determinants preferred by 20 percent respondents each. The last but not the least, customer Satisfaction regarding employees dealings is all about measuring the satisfaction level derived when the customer were being dealt. It is always crucial to have a measure for the effectiveness in service provided by the banking sector. The service delivering mechanism has crucial role to play; therefore requires the feedback regarding the various prevailing practices. Kotak Mahindra Bank Limited was found fair in conducting all its operations while delivering the funding and other facilities pertaining to it. On the whole about 75 percent customers were found to be more or less satisfied with the service delivery mechanism of the bank. The bank needs to improve further to satisfy the remaining 25 percent of its customers who showed their dissatisfaction in terms of delay in task as well as lengthy procedure followed at the end of the bank. Bankers' Perspective on Working Capital Funding Products

Gaining insights into Bankers' perspective on the various working capital funding products offered by the bank is very crucial to have a fair understanding as to what are the practices preferred by the bank. Bankers has pivotal role in influencing the policy that bank adopt.

Therefore the perspective help making the organization well aligned with the customers' expectations. The parameters discussing preferred policy, methods and its review were studied and presented in Table 2.

The foremost parameter preferred bankers working capital practices state that the policy making practices depends on the various factors such as organization structure, performance drivers, and industry targets. About 35 percent respondents preferred to choose working capital as major deciding factor in devising any policy followed by 25 percent each for the favour of efficient governance and change management, explaining the working capital as most crucial aspect as compared to the other factors. It is the preliminary requirement of any business to run smoothly. Keeping in view these factors, the bank adopted suitable measures while delivering services to the customer and making them avail best product in market to have their business running at its better pace. Bankers' working capital review policy explains itself as the time duration when policy is reviewed in terms of how the funding to be dispensed and the like. It was found that banks kept their policy review flexible catering to the need of the hour and suitability of the organization with 35 percent of the employees agreeing to it. And the parameter bankers' working capital management method is about the methods prevalent to compute working capital funding. The results revealed that 45 percent of the employee respondents preferred going ahead computing with the help of portfolio review method and suggested bankers' choice. It involved the portfolio screening to assess the working capital requirement of the customer followed by outsourcing method preferred by 25 percent respondents. On the whole, the study showed working capital as the major determining factor in devising strategies with preferred method-portfolio review for effective screening in making funds available to the customer. The policies pertaining to the working capital funding were mostly found to be reviewed as per the need and contingencies. Bankers' Risk Management Practices

Risk management practices cover some of the various measures to mitigate the credit risk that prevail while providing funds to the customers. Bankers' perspective while adapting to the various risk is of great importance and has long term impact on the organization sustainability. So studying the preferred practices becomes vital to have a clear picture what risk appraisal is all about. The discussion of the preferred risk focus, method and analysis were presented in Table 3.

The risk focus of bankers entails the various risks that prevail in the organization. It was noticed that 25 percent of the respondents opted each for credit and liquidity risk focus. The credit and liquidity risk was found to be most focused upon while making any policy framework as it has potential impact of the basic functioning of organization. The other parameter credit risk method preferred is all about the method preferred by the bankers

339


Parameters Classification Number ofrespondents

Percent

Preferred bankers working capital practices

Importance of working capital within the organization. 7 35

Efficient governance/dedicated resources. 5 25

Design performance drivers 1 5

Outperform industry average targets 2 10

Embed with change management 5 25

Working capital review policy Monthly 2 10

Quarterly 3 15

Annually 4 20

Whenever necessary 7 35

Bankers working capital management method

Roll over agreements 2 10

Term Sheets 3 15

Collection agency - -

Securitization - -

Outsourcing 4 20

Portfolio review 9 45

Table 2. Bankers' perspective on working capital funding products offered by Kotak Mahindra Bank, 2015

Parameters Classification Number of respondents Percent

Risk focus of bankers Cash and liquidity risk 5 25

Interest risk 4 20

Credit risk 5 25

Operational risk 2 10

Foreign exchange risk 2 10

Political risk 2 10

Credit risk method preferred

Standardized approach 5 25

Credit modeling 6 30

Internal rating based approach 9 45

Bankers credit risk analysis Good 10 50

Satisfactory 5 25

Average 4 20

Poor 1 5

Table 3. Bankers' risk management practices, Kotak Mahindra Bank, 2015

keeping in view the best possible result and its understanding. The internal rating based approach of risk analysis was the one most opted by the bank to assess the credibility of the customers while providing funding, with 45 percent employees preferring it. The rates and weightage calculated in this method provided an effective tool to compute the risk associated. The bankers' credit risk analysis stated above seeks to analyze the effectiveness of the risk analysis carried. It was well implied from the study above that the 50 percent of the respondent found it good and easy to implement. The other shortcomings can be overcome by its consistent policy review that was contingent to the needs of the

organization. Hence, the risk analysis played a major role while funding to develop a cushion for the organization and the appraisal clearly brought out the preferred practices in terms of risk focused and suitable method adhering to the needs of the organization.Qualitative Determinants of Risk Management: The borrower's business was deeply analyzed along with its profitability. Bankers studied whether the business was capable enough of generating adequate profits so as to be ensured the timely repayments of interest and the principle. The bank officials also looked into the past records of the borrowers including the profits and losses, costs incurred, investments (short term and long term), all other

340


borrowings for the business etc. before sanctioning loan to the customers. The bank officials seriously glanced through whether the business which a borrower runs was successful in the industry or not. It might be the case wherein the borrower was a sole proprietor and was running a business which generated heavy profits but the same business was not recognized in the industry. Since, this profiteering phase was temporary for the proprietor and hence bank did not grant loan to such borrower. This was also an important aspect before sanctioning any loan. The management of the borrowers company was read and understood deeply by the credit team. In case borrower was a joint stock company, the quality of its promoters and board of directors was studied in depth. Here the quality would mean the integrity, number of borrowings in the past, quality of repayment of such borrowings, relationship with the stakeholders on part of such promoters and board of directors in question. Financials was also the most important aspect considered before issuing any kind of finance to a borrower. If the financials were not meeting the required standards at the borrower's end, even the subsequent credit installments of the sanctioned loan stood cancelled. Hence, legitimate financials of the borrower was thoroughly gauged while sanctioning any loan.CONCLUSIONS

While carrying out the research it could be inferred that the Bank was striving towards best to commit resources in optimum utilization. The net working capital, risk management and benchmark against competition were some of the factors that respondents found the most important while making an effective decision pertaining to working capital. It was always crucial to measure for the effectiveness in service provided by the banking sector. The service delivering mechanism has crucial role to play Kotak Mahindra Bank Limited was found fair in conducting all its operations while delivering the funding and other facilities pertaining to it. Around three-fourth customers were found to be more or less satisfied with the service delivery mechanism of the bank. The bank needs to improve further to satisfy the remaining customers who showed their dissatisfaction in terms of delay in task as well as lengthy procedure followed at the end of the bank. Working capital was the major determining factor in devising strategies with preferred method- portfolio review for effective screening in making funds available to the customer. The policies pertaining to the working capital funding were mostly found to be reviewed as per

the need and contingencies. The risk analysis played an important role while funding to develop a cushion for the organization and the appraisal clearly brought out the preferred practices in terms of risk focused and suitable method adhering to the needs of the organization. The credit risk management system was carrying out at its pace. The professionalism was still lagging in operations in terms credit rating. The credit appraisal mechanism at KMBL was exhaustive and effective in appraising customer's applicant for credit. Customers past records, current financial position and future aspects of business and earnings seemed to constitute the major factors guiding the credit appraisal process of the bank. The credit appraisal mechanism was found to be meticulous, endowed with an eye for details. It involved optimum application of analytical and computation skills. Effective monitoring resulted in lower non-performing accounts for KMBL. The competition in market should be kept well emphasized. The organization should also strive to provide training to staff with regard to upgraded policies stated by RBI. The operation carried by the bank was in strict conformance with RBI Basel Structure.REFERENCESBeard, M. (2007). Citibank online investment services.

Treasury and trade solutions. Retrieved from http://www.citigroup.com/transactionservices/home/oli/0921_2007.jsp

Davies, R., & Merin, D. (2014). Uncovering cash and insights from working capital. Corporate Finance Practice. Retrieved from https://www.mckinsey.com/business-funct ions /s t ra tegy-and-corpora te- f inance/our-insights/uncovering-cash-and-insights-from-working-capital.

Pergler, M., & Rasmussen, A. (2014). Making better decision about the risk of capital projects. Corporate finance practice. Mc Kinsey & Company. Retrieved from https://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/making-better-decisions-about-the-risks-of-capital-projects

Scribd. (2015). Banking sector analysis. Reserve Bank of India, Bank –Scribd. Retrieved from http://es.scribd.com

Square Capital. (2014). Kotak Mahindra Bank. Personalized banking services and information. Retrieved from www.squarecapital.co.in

Sultan, N., & Roberts, M. (2017). Working capital and other challenges faced by the corporate in today's complex environment. Treasury and trade solutions. Retrieved f rom h t t p s : / /www.c i t i bank . com/ t t s / i n s igh t s / assets/docs/articles/working-capital-other-challenges.pdf

341


ABSTRACTAgro based industry is regarded as the sunrise sector of the Indian economy in view of its large potential for growth and likely socio economic impact specifically on employment and income generation. To study the socio-economic profile of entrepreneurs engaged and to economics of attachaki as agro-based village industries. The study was conducted in Fatehgarh district of Punjab by selecting 40 villages falling in the district with 33 entrepreneurs of attachaki enterprise. These were categorised into different farm categories according to their agricultural land ownership. The study revealed thatall the entrepreneurs were male and with average age of 45.5 years. An average agricultural land ownership of 33 entrepreneurs was 10.54 acres and average limit of credit were 1.70 lakh. Capital investment was highest for entrepreneurs who were landless (`234625). Annual net returns were found highest in case of medium farm entrepreneurs (`67247). By investing one rupee on fixed and variable resources of attachaki enterprise, an amount of ? 1.23 on an average in Punjab as is indicated by output-capital ratio in attachaki. This showed that there were net earnings of `0.23 which came to be 18.69 percent of total earning and remaining 81.31 percent was the total cost on an average in Punjab. Similarly benefit cost ratio at both variable and fixed costs were 0.47 and 0.23 respectively. Average payback period of attachaki enterprise were 6.08 years

KeywordsCredit, enterpreneur, enterprise, investment, payback.

JEL CodesH81, L16, L26, L53, P25.

*Pardeep Kaur and Mini Goyal




Economics of Atta Chaki Enterprise in Punjab: A study of an Agro-based Industry


DOI: 10.5958/2322-0430.2018.00140.3


www.soed.in


UGC Approved


342

INTRODUCTIONAgriculture plays an important role in Indian

economy. About 55 percent of the population is engaged in agriculture and allied activities and contributes 17.4 percent to India's gross domestic product. As per the First Advance Estimates (AE) by Ministry of Agriculture and Farmers Welfare (2016), the production of Kharif food-grain for the period of 2016-17 is predictable at 135.0 million tons as compared to 124.1 million tonnes in 2015-16 (The Government of India, 2016). Every year in India, food grains production has increased and the country is among the top producers of pulses, rice, wheat, cotton and sugarcane. India is the largest producer of milk and ranks second in the production of fruits and vegetables (Deshpande, 2017). The generation of the surplus from agriculture ultimately depends on the increasing the agricultural productivity considerably (Ghatak & Kin,

1984). The share of agricultural product in the total export earning is also substantial and is significant at 12.46 percent in 2015-16.

The poor performance of productivity growth has been associated by industrial stagnation. This can be enhanced by increasing productivity and production, specifically in precedence sectors and by encouraging small scale sector such as village industries, cottage industries, agro industries etc with a view to generating employment (Bhatt, 2013). An approach on a strong social, economic as well as political ideology of agro and village industries was provided by Mahatma Gandhi (Goyal, 1994).

The stress on agro based village industries was made in the 1920's by father of nation as a segment of independence movement of India. Gandhi Ji once said that India lives in villages. These words of the man of the

millionaire are sufficient enough to manifest the well established and accepted fact that India is basically an agrarian economy and despite tireless efforts for the last seven decades, agriculture continues to occupy a place of pride, being the largest industry in the country.

The village industry also called as cottage industry is a kind of simple work of hands or small machines carried on by villagers in their houses (Acharaya, 2015). By definition, village industry is any industry locating in rural area which produces goods or renders services with or without the use of power and in which the fixed capital asset per head of an artisan or a worker does not over pass one lakh rupees or such other sum as may, by notification in the official gazette, be precise from time to time by the central government (Sarkar & Karan, 2005).

For eradication of the problems of poverty, unemployment and inequality as well as building up of self reliant socialist economy, it is necessary to develop small-scale village industries, mainly agro-based industries. In India, agro based industries are provided with high precedence, considering their potential contribution to rural development by using local agro based raw material. The growth of agro based industries is considered a central part of the national growing strategy because of their significance in generating value-addition to output of agriculture, rising employment and rural incomes and alleviating poverty in the countryside (The Government of Punjab, 2008).

The flour mills, popularly known as attachaki in Punjab are set up mainly for production of grinding mainly of wheat to convert it into wheat flour called atta. These chakkis clean the wheat, store and conditioned wheat and finally mill or grind the wheat to sell it in retail in the local market. The main product form the attachaki plant is wheat flour (Atta) and wheat bran (Chokhar), which is used as cattle feed (Rajan, 2014). These atta chaki is provide the milling services to the customers who bring their own stored wheat from time to time to get it floured. The wheat provided by the customers or entrepreneur's own raw material is poured in to the hopper of the machine powered by electricity and feeds the materials to the grinding chamber. The space between the rotating grinding plates are adjusted to get the consistency (Coarse/Fine) as desired by the customer. The wheat grounded thus as wheat flour is collected in bags/drums. Keeping in view the above facts the present study was undertaken to workout economics of attachaki - an agro-based village industry in Punjab. It also highlights the socio-economic profile of entrepreneurs who were engaged in attachaki agro-processing village industries in Punjab. METHODOLOGY

In order to achieve the objectives, the study was conducted in Punjab through the collection of primary data. The state of Punjab has been divided into three agro-climatic zones, namely sub-mountainous zone, central plain zone, and south west zone. Multistage sampling

technique was adopted for the research. Central zone having highest number of small scale units (Government of India, 2014) was selected in the first stage. One district namely, Fatehgarh sahib falling in the central zone, was randomly taken, in the second stage. A complete list of all the 444 villages falling under the district was prepared. From the list, forty villages were randomly taken for the study in the third stage. Each village was visited personally to find out the status of attachaki - an agro-based industry. In each village, the sarpanch or ckownkidar of the village was contacted and guidance about the location of such agro based industry present in that village was taken. In all villages, total 33 agro-based industrial units (attachaki) were found in 40 such villages. The information was collected from the entrepreneurs engaged in these agro based village industries taken under study by personal interview method. Fixed costs as well as variable costs were calculated. The variable costs included raw material costs, labour costs, electricity /diesel costs and interest on variable costs. The net returns were estimated by deducting total cost from gross returns. The output capital ratio, share of fixed and variable in the output capital ratio, benefit -cost ratio, benefit -cost ratio at variable and total cost and payback period were calculated by the following formulae:

RESULTS AND DISCUSSIONSocio-economic Profile of Atta Chaki Entrepreneurs

As discussed earlier, among forty villages, total 33 attachakies found under study (Table 1). Socio-economic profile represented the economic and social status of the

tTotal

ratioCapitalOutputratiocapitalOutput

cos

-=-

tTotal

Capital ratioOutputVariable cost

cos´

-

ratiocapitaloutputintfixedofShare cos

tTotal

ratiocapitalOutput

cos´

-

=

returnsnetAnnual

investmentfixedInitialperiodPayback =

Benefit cost ratio (at variable cost)=

343

Kaur and Goyal: Economics of atta chaki enterprise in Punjab: A study of an agro-based industry

Fixed cost

Return over variable costVariable cost

Benefit cost ratio (at fixed cost)=

Return over total costTotal cost

Share of variable cost in output capital ratio=

selected entrepreneurs of agro based village industries such as attachaki, ghani oil, gurkhandsari and carding industries taken. The various socio-economic parameters like gender, age, caste, religion, education level, family size, income of family and debt position, etc. of the entrpreneurs have been analyzed. All the entrepreneurs were male and their average age was 45.5 years. Majority of the entrepreneur of attachaki enterprise were married (93.94 percent) belonged to general category of caste (84.84 percent) having sikh religion (69.70 percent). About 78.19 percent of the entrepreneurs had nuclear family. Their average numbers of family members were 4.71 and their average years of schooling were 8.21 year. Average agricultural land ownership of 33 entrepreneurs was 10.54 acres and average limit of credit were 1.70 lakh. About 78.78 percent of the attachaki enterprises

were located at residential complex.Economics of Atta Chaki Enterprise

Table 2 gives the information about the economics of attachaki industry of the among different category of land farms of the entrepreneur. The various components like total capital investment, total fixed cost and total variable cost of attachaki had been computed in this table. Overall, the total investment in terms of working land, building and various machines was estimated at ̀ 222489. The total fixed cost of had been worked out as `27471 which included interest on capital investment (`24473), depreciation on building (`1197) and depreciation on machines (`1800). The total variable cost was `167829 which included the cost of raw material, charges of labour, cost of electricity and interest on variable cost of the amount of `28420, 78436, 49993, and 10979

Particulars Categories Respondents Particulars Categories Respondents

Gender Male 33(100.00)

Caste General 28(84.84)

Female - SC 4(12.12)

Age (Year) 20-35 1(3.04)

BC 1(3.04)

36-45 16(48.48)

Religion Sikh 23(69.70)

46-55 13(39.39)

Hindu 10(30.30)

56-65 3(9.09)

Agricultural land ownership

Landless 10(30.30)

Education level Illiterate 2(6.25)

Marginal (1-2.5)

5(15.15)

Up to primary 5(15.62)

Small (2.5-5)

7(21.21)

Middle 10(30.25)

Semi-medium(5-10)

9(27.27)

Matric 13(40.62)

Medium(10-25)

2(6.07)

Senior Secondary 3(8.06)

Large(> 25)

-

Nuclear 25(78.19)

Credit Nil 10(30.30)

Joint 7(21.81)

0.50 – 1.00 4(12.13)

Number of family members

1-2 - 1.00-2.50 9(27.27)

3-4 20(60.60)

2.50-5.00 10(30.30)

5-6 10(30.30)

> 5.00 -

More than 6 3(9.10)

Table 1. Socio economic profile of attachaki entrepreneurs

Figures in the parentheses indicate the percentage to total.

344


respectively per annum. The gross returns included the value of crushing grains per quintal (`300), price of flour (`22 per kg), and price of bran which was `1200 per quintal. The gross returns of attachaki entrepreneur were worked out to be ̀ 2466069 per annum. The net returns of entrepreneur were ̀ 51307.

The total fixed cost of entrepreneurs those were landless had been worked out as `28931 which was highest among the all farm categories as against capital investment `234625. This included interest on capital investment (`25809), depreciation on building (`1299) and depreciation on machines (`1823). The total variable cost was `166370 which included the cost of raw material, charges of labour, cost of electricity and interest on variable cost amount was`25010, 78480, 51996 and 10884, respectively per annum. The gross returns of landless farmers, per annum were `247946. The net returns of landless entrepreneurs were ̀ 52645.

Total fixed cost in case of attachaki industry for entrepreneurs having marginal farms was `27787 while total capital investment was `225550 per annum. The items of fixed cost in the descending order of importance were interest on capital investment (`24811), depreciation on building (`1192) and machines (`1785). Out of the total variable cost, the total cost of raw material and labour were the most important which were as high as `22570 and 78000 respectively. The cost of electricity/diesel/kerosene was `48720 and interest on

variable cost was `10450. The total variable cost of the attachakki entrepreneurs those were the marginal farmers was ̀ 159740 per annum. The gross returns and net returns were to the tune of `222712 and 351184 per annum respectively.

The results of the study indicated that total fixed cost of attachaki of small farms entrepreneurs was `26583. The order of various items of fixed cost was similar to those of landless and marginal farmers. The total variable cost per annum per unit was ̀ 170145 which were second highest among all farm categories. The cost of raw material (`28539) was the next most important item of variable cost. Human labour (`78514) was another important factor after raw material which was followed by electricity charges (`51960) and interest on variable cost (`11131). The gross return per unit per annum was `249573. Whereas, the net returns were `52846 which were second highest among entrepreneurs of all farm categories.

The table portrayed that the entrepreneur who had semi medium farm, the fixed cost per industry per annum were `26094. The component of total fixed cost in the descending order of their magnitude was interest on capital investment (`23186), depreciation on working building (`1135) and working land (`1774). Total variable cost per industry per annum was `169435. The labour charges were as high as `78666. Electricity charges and interest on variable cost were other items of

Particulars Landless Marginal Small Semi medium

Medium Overall

A. Capital investment

1. Land 87575 85250 79714 74055 85250 81727

2. Building 66300 60800 56285 57888 67000 61090

3.Various machines 80750 79500 79285 78833 82000 79667

Total 234625 225550 215285 210777 234250 222489

B. Fixed Cost

1.Interst on capital investment @ 11 percent P.A. 25809 24811 23681 23186 25768 24473

2.Depriceation on building @2 percent P.A. 1299 1192 110.20 1135 1313 1197

3.Depreciation on machinery @ 10 percent P.A 1823 1785 1798 1774 1844 1800

Total fixed cost 28931 27787 26583 26094 28924 27471

C. Variable cost

1.Raw material 25010 22570 28539 32364 41937 28420

2.Labour Charges 78480 78000 78514 78667 78000 78436

3.Electricity/Diesel cost 51996 48720 51960 47320 61740 49993

4.Interst on variable cost @7percent P.A 10884 10450 11131 11085 11777 10979

Total variable cost 166370 159740 170144 169435 180014 167829

Total cost (B+C) 195301 187527 196727 195529 208938 195299

D. Gross return 247946 222712 249573 249805 276185 246607

E. Net return (D-B-C) 52645 35185 52846 54277 67247 51307

Table 2. Economics of attachaki enterprise of various farm size categories of entrepreneurs in Punjab (`)

345


variable cost in the descending order of importance. These costs were ̀ 47320 and 11085 respectively. For the entrepreneurs those were having attachaki, the gross returns and net returns were to the tune of `249806 and 54277 respectively.

For the entrepreneurs with medium size farms, having attachaki enterprises, the total fixed cost was `28924. The two components, that is, depreciation on building and machinary were `1313 and 1844 respectively. Total variable costs per annum per industry were found to be `180014. The main items of variable cost were raw material, charges of labour, electricity and interest on variable cost which were `41937, 78000, 61740 and 11777 respectively. The gross returns were accounted to the tune of ̀ 276185 and were highest among all the five land farm categories of attachaki entrepreneur.

The perusal of Table 3 revealed that the overall, returns over variable cost were `74394 and returns over fixed cost were `5107. The attachaki entrepreneurs those had no agricultural land ( landless) had highest fixed cost (`28931) and medium farmers had highest variable costs (`180014) as compared to the other costs. Total cost for entrepreneurs those had medium size land worked out to be ̀ 208938 per unit per annum, which was highest among the other farm categories. The returns over variable cost were `96171and returns over total cost were `67247 which was highest entrepreneurs with for medium farms. For entrepreneur of attachaki with semi medium farms the gross return were as high as ̀ 249806 per annum per unit, yet net returns were found ̀ 54277 which were the lowest as compared to entrepreneur with small and landless farm categories. This was because higher total cost (`195529) latter resulted in lower returns. Again, returns over

Particulars Landless Marginal Small Semi medium Medium Overall

Gross return 247946 222712 249573 249805 276185 246607Fixed cost 28931 27787 26583 26094 28924 27471Variable cost 166370 159740 170144 169435 180014 167829Total cost 166370 159740 170144 169435 180014 167829Return over variable cost 66846 62972 79428 80370 96171 74394Return over total cost 52645 35185 52846 54277 67247 51307

Table 3. Returns and costs incurred on all the farm categories of entrepreneurs (`)


Actual quantity 484.00 392.00 505.00 573.44 458.50 497.42

Break even quantity 394.50 351.47 400.00 323.53 436.50 372.33

BEQ as percent of actual quantity 81.50 89.66 79.20 56.41 95.20 74.85

Quantity above BEQ 89.50 40.53 105.00 249.91 22.00 125.09

Payback period (years) 6.61 6.95 5.59 5.90 3.78 6.08

Table 4. Economic Evaluation of attachaki enterprise of various farm size categories of entrepreneur in Punjab (q)

variable cost and returns over total cost were found lowest in case of entrepreneurs with marginal farm were ̀ 62972 and `35185 respectively. It was `66846, 62972, 79428, 80370 and 96171 per annum per industry for entrepreneurs with landless, marginal, small, semi medium and medium farm categories respectively.Economic Evaluation of Atta Chaki Enterprise

The fixed and variable costs incurred and net revenue earned by the entrepreneurs of attachaki enterprises were considered for economic analysis. The economic analysis was further discussed by analyzing information on income and expenditure pattern of various units, viability of units and economic rationale of investment made by the attachakki entrepreneurs in different farm size categories.

To test the worthiness if the investment on attachaki enterprise, two indicators namely, Break-even quantity (BEQ) and Payback period were studied. It can be seen from the Table 4 that BEQ was found highest in case of entrepreneurs with small farm (400 kg per annum per unit) of attachaki enterprise and was found lowest in the case of semi medium farm which was 323.53 kg per annum per of attachakki enterprise. On an average, BEQ as percentage of actual quantity crushed and milled was 74.85 percent. It was found highest in case of entrepreneurs with medium farms i.e. 95.20 percent and lowest in the case of that of semi medium farms (56.41 percent). It was concluded that the present value of wheat grain crushed and grinded was more than the quantity which was required for the survival of attachaki industry.

The payback period refers to the period of time required to recoup the funds expended in an investment, or to reach the break-even point. Initial capital investment in attachaki enterprise was `234625, 225550, 215286,

346



Output-capital ratio 1.24 1.14 1.23 1.25 1.29 1.23Benefit cost ratio (at variable cost) 0.50 0.58 0.46 0.49 0.53 0.47Benefit cost ratio(at total cost)

0.24(19.35)

0.14(12.28)

0.23(18.69)

0.25(20.00)

0.29(22.48)

0.23(18.69)

Share of fixed cost on output capital ratio 0.20(16.12)

0.20(17.54)

0.19(15.44)

0.20(16.00)

0.21(16.27)

0.20(16.26)

Share of variable cost in output capital ratio 1.02(82.25)

0.95(83.33)

1.03(83.73)

1.05(84.00)

1.08(83.72)

1.02(82.92)

Table 5. Input-output relationship of attachaki enterprises in Punjab

210778 and 234250 for entrepreneurs landless, marginal, small, semi medium and medium farm categories respectively. It was observed that payback period on an average was 6.08 years for the investment of the enterprise. However, it was 6.61 years for landless, 6.95 years for marginal, 5.59 years for small, 5.90 years for semi medium and 3.78 years for medium farmers to cover their investment. Increase in investment with increase in the size of attachaki enterprise might be responsible for the longer payback period of the enterprise.Input-Output Relationship of Atta Chaki Enterprise

Income is the outcome investment. The rate of income in relation to investment may vary. An attempt has been made in Table5 to present the different rates of output/benefit in relation to different types of cost concepts. It was found from the study that by investing one rupee on fixed and variable resources, an amount of ? 1.23 on an average in Punjab as is indicated by output-capital ratio in attachaki. This shows that there was net earnings of rupee 0.23 which came to be 18.69 percent of total earning and remaining 81.31 percent was the total cost on an average in Punjab. The output- capital ratio was worked out to be 1.24, 1.14, 1.23, 1.25 and 1.29 of attachaki for the entrepreneurs' different farm categories in Punjab. Out of their gross returns per rupee invested 19.35, 12.28, 18.69, 20 and 22.48 percent were the net return per rupee while the remaining 80.65, 87.72, 81.31, 80 and 77.52 percent came to be total cost attachaki in the different farm categories in Punjab. On average, the share of fixed cost in capital output ratio was lower (16.26 percent) as compared to the share of variable cost (82.92 percent) in attachaki enterprise.CONCLUSIONS

From this study it is concluded that attachaki enterprise is an important mean to earn daily livelihood to the entrepreneur. It study revealed that entrepreneurs who had medium farms had highest annual net returns (`67247). The payback periods of atta chaki enterprise

were 6.08 years of the investment of the enterprise. Therefore, attachaki enterprise is an enterprise which makes profit in long run. The average break even quantity was estimated to be 372.33 kg per annum of wheat crushed and grinded was more than the quantity which was required for the survival of the attachaki enterprise. Benefit cost ratio over variable cost was 0.47 and benefit cost ratio over total cost was 0.23.On average, the share of fixed cost in capital output ratio was lower (16.26 percent) as compared to the share of variable cost (82.92 percent) in attachaki enterprise.REFERENCESAcharaya, R. (2015). Importance of village industries and its

problems. Retrieved from http://www.wisefide.com/ 2015/11/importance-of-village-industries-and.html.

Bhatt, T.P. (2013). Growth and structural changes in Indian institutes. Working Paper, 1-65, New Delhi: ISID.

Deshpande, T. (2017). State of agriculture in India. Retrieved from http://www.fao.org/3/a-i7658e.pdf.

Ghatak, S., & Ken (1984). Agriculture and economic development. Pp: 69. Baltimore MD: Johns Hopkins University Press.

Goyal, S.K. (1994). Policies towards development of agro-industries in India. In G.S. Bhalla (Ed.) Economic Liberalization and Indian Agriculture. Pp. 241–286. Institute for Studies in Industrial Development, New Delhi.

Rajan, R. (2014). Mini wheat flour mills. Ministry of Small and Medium Enterprises pp: 83-87.

Sarkar, S., & Karan, A.K. (2005). Status and potentials of village agro-processing unit/industries. Occasional Paper -37. Mumbai: National Bank for Agriculture and Rural Development.

The Government of India. (2014). Annual report: 2014-15. Development Commission, Ministry of Micro, Small and Medium Enterprises, Government of India.

The Government of India. (2016). Economic survey, 2016-17 New Delhi: Government of India. Retrieved from https://www. indiabudget.gov.in/es2016-17/echapter.pdf

The Government of Punjab. (2008). Economic survey of Punjab: 2007-08. Economic Advisor to Government of Punjab, Chandigarh.

347


ABSTRACTOver the decades, the livestock sector has grown tremendously in India owing to the country's predominant livestock population and changes in the composition of livestock population. Livestock population has undergone changes continuously over time owing to numerous economic, technological, and environmental factors. It was, therefore, necessary to study the changes in the livestock compositions in order to gain an understanding of the dynamics of the changes over a different period of time. The findings of the study showed that the share of cattle and sheep population have declined while the share of buffalo has increased in total livestock population. Interestingly, goat population was found stagnant in Gujarat. The results of Transitional Probability Matrix (TPM) revealed that buffalo milch and indigenous in milk population retained higher retention probability. Furthermore, the projected data of bovine population revealed that crossbred and indigenous cow population is likely to set increased. On the contrary, buffalo in milk and milch population is anticipated to show a downward trend in projected period.

KeywordsLivestock population, retention probability, structural change, TPM..JEL CodesQ01, Q10, Q18, Q56.

*Mahammadhusen Khorajiya , R.L. Shiyani, N.J. Ardeshna, M.G. Dhandhalya and B. Swaminathan

Department of Agricultural Economics, Junagadh Agricultural University,Junagadh-362001 (Gujarat)

*Corresponding Author's e-mail: [email protected]

Received: August 01, 2017 Revision Accepted: May 21, 2018

#Compositional Shift Analysis of Gujarat Livestock Population


DOI: 10.5958/2322-0430.2018.00141.5


www.soed.in


UGC Approved


348

#Paper is the part of Ph.D. research programme.

INTRODUCTIONIndia is being fortunate with one of the largest

livestock and poultry populations in the world. It ranks first in buffalo, second in cattle and goat, third in sheep

thand fifth in poultry population. During 19 livestock census, the total national livestock population was 512.06 million numbers in 2012 which has decreased by about 3.33 percent over the previous census in 2007 (The Government of India, 2015). The livestock sector is substantially zooming up in Gujarat. The state has capitalized on growing several high yielding breeds of livestock species indifferent regions of the state. Several dairy developments, poultry and livestock development programmes have promoted livestock sector in the state notably. Gujarat constitutes 5.29 percent of total livestock population and 2.06 percent of total poultry population in India during 2012. In case of Gujarat, livestock population has increased from 23.51 million in 2007 to 27.12 million in 2012 (excluding 0.29 million stray cattle) registering a positive growth of 15.36 percent over the

previous census (The Government of India, 2013a). Augmenting the stock of population is not sustainable unless to abridge the gap between requirement and availability of feed and fodder. This gap is mainly due to declining area under fodder cultivation and reduced availability of crop residues as fodder although livestock population is increasing (The Government of India, 2013b). Thereby, a study on livestock population was crucial to examine the changes the overall composition and shifting over the decade.METHODOLOGY

Data pertaining to the state level livestock population (including crossbred cows in milk and milch, indigenous cows in milk and milch, buffalos in milk and milch) were compiled for the period between 1985-86 and 2014-15. This period was further classified into four sub-periods; 1985-86 to 1994-95 (Period-I), 1995-96 to 2004-05 (Period-II), 2005-06 to 2014-15 (Period-III) and 1985-86 to 2014-15 (Period IV).The livestock population data of Gujarat were collected from various issues of integrated

sample survey published by the Directorate of Animal Husbandry, Government of Gujarat while livestock census data were compiled from Basic Animal Husbandry and Fisheries Statistics published by Department of Animal Husbandry, Dairying and Fisheries, Government of India, New Delhi.Markov Chain Analysis

It is an application of linear programming to the solution of a stochastic decision process that can be described by a finite number of states. A Markov chain named after Andrey Markov is a mathematical system that undergoes transitions from one state to another on a state space. In this study, first-order Markov process as that of Dent (1967) was used to study the structural change in export commodity and thereby gain an understanding of the dynamics of the changes. The probabilities associated with various state changes are called transition probabilities and Matrix Table is called Transitional Probability Matrix (TPM).

Central to Markov chain analysis is the estimation of the transitional probability matrix P. The element P of ij

this matrix indicates the probability that population was moving from population i to population j with the passage of time. The diagonal P measures the probability that the ij

population share of the population was retained. In plain words, the diagonal element of TPM indicates the retention probability. The rows of the matrix showed the loss to the other population category whereas columns indicate population gained by the respective group. Hence, an examination of the diagonal element indicated the retention capacity of a particular livestock population category. The average population to a particular category was considered to be a random variable which depends only on its past population to that category and which could be denoted algebraically as:

Where,thE = Population during the year t to j category,jt

th E = Population to i category during the year t-1,it-1 thP = The probability that population will shift from i ij

th category to j category,e = The error-term which is statistically independent jt

of E , andit-1

r = The number of population categories.The transitional probabilities P , which can be ij

arranged in a (c × r) matrix, have the following properties:

0 ≤ P≤ 1 -------------------- (2)ij

Thus, the expected population share of each category during period't' was obtained by multiplying the population to thiscategory in the previous period (t ) with –1

the transition probability matrix. A method to derive parameter estimates when equality or inequality restriction is present to make use of minimization of Mean

Absolute Deviation (MAD) estimator. The transitional probability matrix was estimated in the linear programming (LP) framework by a method referred to as MAD. The estimates of the transitional probabilities were obtained using the following LP formulation,

Min O' P* + Ie---------------------- (4)Subject to

X P* + v = y------------- (5)GP* = I------ (6)

P* ≥0--------- (7)Where, P* is a vector of the probabilities P , 0 is a ij

vector of zero, I is an appropriately dimensioned vector of category, e is the vector of absolute errors (|U|), y is the vector of population to each category, x is a block diagonal matrix of lagged values of y, and v is the vector of errors and G is a grouping matrix to add the row elements of P arranged in P* to unity.

Prediction for livestock population was made by using the TPM,

Bt = B * T------------------ (8)0

B = B * T----------- (9)t+1 t+i-1

Where, B is the population in the base year, Bt is the 0

population in next year (predicted), T represents TPM RESULTS AND DISCUSSIONPercentage Changes in the Composition of Livestock Population

Before proceeding to Markov chain analysis, percentage changes in various livestock species in total livestock population was examined in this section for various livestock census from 1951-2012. Percentage share was calculated for national level and for Gujarat state and the results are furnished in Table 1. The results revealed that from 1951 onwards, the share of the cattle population in national total livestock declined radically except in 1961. In 1951, cattle accounted for 53.03 percent share and it declined to 37.30 percent in 2012. Declining trend in cattle population was observed in Gujarat also. As the share was higher in 1956 and 1961 and gradually reduced to 32.18 percent in 1997. Again, the share of cattle showed improvement and occupied 36.45 percent share in 2012 in Gujarat. A gradual decline in the share of the cattle population in the state and nation level was governed by several factors. Cattle are the more important to sustainability as they account for the bulk of the livestock load on land. Replacement of the indigenous cattle by crossbreds and cows of developed Indian breeds population would reduce overall cattle numbers and improve sustainability. The crossbreeding of nondescript indigenous cattle was thus seen as a major solution to India's oversized cattle population (Kurup, 2002).

Such decline in cattle population at the national level is a challenging issue as this decline may also attribute to a shortage of good quality of fodder, the frequent occurrence of drought and uneven climatic situation, increasing industrialization demanding diversion of land towards non-agricultural use, deterioration of grazing land, and farm mechanization. Therefore, it is necessary

)1(11 ----------+å= -= jtijitrijt ePEE

)3...(.......................,111 for all iPiji =å=

349

Khorajiya et al.: Compositional shift analysis of Gujarat livestock population

to promote the selective crossbreeding technology, particularly for nondescript indigenous cattle to facilitate the future propagation. Buffalo population showed a similar trend at national as well as state level. The share of buffalo in total livestock population was augmented from 14.82 to 21.24 percent during 1951 to 2012. During the same period, the share of buffalos in state livestock population increased from around 21 to 38 percent. Therefore, it may be concluded that share of buffalo in the state total livestock population increased faster than at the national level. Increase in the buffalo population was also noted by Selvakumar (1996); Tysdell and Gali (2000).

The share of sheep population showed a mixed trend. At the national level, the share of sheep population declined from 1951 to 1987, except during 1966 and 1982. After that, the share increased in three consecutive censuses of 1997, 2003 and 2007 but it showed a declining trend in the recent period. In Gujarat, the share of sheep population reduced gradually after 1992.By and large, the share of sheep population in state total livestock population declined at a faster rate than at the national level. Khan et al. (2002) reported that majority of the sheep breed in India are nondescript due to indiscriminate breeding and intermixing of the breed and mostly they evolve through natural selection, migration, tropical diseases, poor nutrition, and a shortage of drinking water. The share of goat population increased from 16.12 to 26.41 percent during 1951 to 2012 at the national level. On the flip side, in Gujarat, the share of goat population almost remained stable and did not see much variation.

Although after 1992, percentage share was observed to be declining. The percentage share of pig population increased marginally and during the period 2003 to 2007, a declining trend was observed. The other livestock such as horses and ponies, mules and donkeys, camel, dog, and the rabbit shared a negligible contribution in the total livestock population of Gujarat while an increasing trend during 1982 to 2003 was observed. A myriad of problems including scarcity of water and food resources, the spread of infectious diseases and heat-related deaths, risks of vector-borne and other diseases followed by exacerbated by climate change were responsible for the same. The greatest impact of climate change was felt on grazing systems in arid and semi-arid areas, particularly at low latitudes (FAO, 2006; 2009). Higher temperatures reduced animal feed intake and lower feed conversion rates (FAO, 2006; Boko et al., 2007). For instance, Bluetongue in Europe and Rift Valley Fever in goats in East Africa are two documented examples of increased vector-borne disease risk with climate change. Therefore, the challenging task is how to make our livestock population sustainable in the changing climatic scenario. In this context, sustainable and economically viable livestock sector management is indispensable which should not only aid income generation but also provide a livelihood.Markov Chain Analysis for Changes Livestock Composition of Gujarat

The preceding section basically showed the share of various livestock species in total livestock population

Species 1951 1956 1961 1966 1972 1977 1982 1987 1992 1997 2003 2007 2012

All India level

Cattle 53.03 51.75 52.19 51.15 50.48 48.73 45.87 44.86 43.46 40.99 38.20 37.60 37.30

Buffaloes 14.82 14.64 15.22 15.38 16.25 16.78 16.63 17.07 17.89 18.53 20.20 19.90 21.24

Sheep 13.35 12.82 11.95 12.31 11.32 11.10 11.62 10.27 10.79 11.85 12.68 13.52 12.71

Goats 16.12 18.07 18.10 18.75 19.11 20.46 22.70 24.76 24.49 25.29 25.66 26.55 26.41

Horses and ponies

0.51 0.49 0.39 0.32 0.25 0.24 0.21 0.18 0.17 0.17 0.15 0.12 0.12

Camels 0.20 0.26 0.27 0.29 0.31 0.30 0.26 0.22 0.22 0.19 0.13 0.10 0.08

Pigs 1.50 1.60 1.55 1.45 1.95 2.06 2.40 2.39 2.72 2.74 2.79 2.10 2.01

Mules 0.02 0.01 0.01 0.02 0.02 0.02 0.03 0.04 0.04 0.05 0.04 0.03 0.04

Donkeys 0.44 0.36 0.33 0.32 0.28 0.27 0.24 0.22 0.21 0.18 0.13 0.08 0.06

Yaks NC NC 0.01 0.01 0.01 0.04 0.03 0.01 0.01 0.01 0.01 0.02 0.02

Gujarat

Cattle 44.63 45.50 48.73 45.64 42.77 41.69 37.93 35.98 34.58 32.18 32.49 33.52 36.45

Buffaloes 20.99 19.84 21.68 21.90 22.97 24.11 24.09 25.96 26.78 29.97 31.25 36.87 37.92

Sheep 13.14 13.11 11.01 11.52 11.41 11.05 12.78 8.99 10.30 10.29 9.03 8.41 6.24

Goats 19.42 19.54 16.52 19.33 21.26 21.41 17.90 20.67 21.56 20.91 19.88 19.50 18.11

Other livestock*

1.82 2.01 2.06 1.60 1.60 1.74 7.30 8.41 6.78 6.64 7.35 1.69 1.29

Table 1. Percentage changes in the composition of livestock species in total livestock population during inter census period from 1951to 2012

*Horses and ponies, mules and donkeys, camel, dog, pig and rabbit. NC: Not collected during the census, so not considered.

350


changed over time. In this part, Markov chain analysis was applied to study the composition change in the bovine population of the state. The rows show the loss to another category of population group and column shows the gain by respective population group. Crossbred cow in milk, crossbred cow milch, indigenous cow in milk, indigenous cow milch, buffalo in milk and buffalo milch were taken for the analysis. The results of Transitional Probability Matrix (TPM) are presented in Table 2.

The results of the TPM of Period-I revealed that farmers retained 40 percent of the previous period share of crossbred cow in milk population during the current period. Remaining 60 percent share of crossbred cow in milk population shifted to the crossbred milch population. Crossbred cows in milk population were neither shifted to indigenous nor buffalo in milk and milch population. At the same time, crossbred in milk population gained 50 percent of previous period's population of crossbred

milch cow. The crossbred cows have similar retention as that of

crossbred in milk population (40 percent) of its previous share was added in the current period. Crossbred milch cow population lost 10 percent of the previous share to indigenous in milk. Indigenous in milk occupied its 90 percent of the previous share in current period while lost 10 percent to buffalo in milk population. The category of indigenous milch cow population retained 60 percent of its previous share and lost 40 percent to buffalo in milk population. The buffalo in milk population retained 50 percent of the previous period population while remaining 50 and 10 percent of its previous period population was shifted to indigenous milch and buffalo milch population. Buffalo milch population emerged as one of the most stable categories as it retained 100 percent of its share in the current period.

In Period-II, retention power among all the categories

Period-I: TPM of livestock population of Gujarat during 1985-86 to 1994-95

CB in milk CB milch IND cow in milk IND cow milch Buffalo in milk Buffalo milch

CB in milk 0.4000 0.6000 0.0000 0.0000 0.0000 0.0000

CB milch 0.5000 0.4000 0.1000 0.0000 0.0000 0.0000

IND cow in milk 0.0000 0.0000 0.9000 0.0000 0.1000 0.0000

IND cow milch 0.0000 0.0000 0.0000 0.6000 0.4000 0.0000

Buffalo in milk 0.0000 0.0000 0.0000 0.4000 0.5000 0.1000

Buffalo milch 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000

Period-II: TPM of livestock population of Gujarat during 1995-96 to 2004-05


CB in milk 0.6000 0.4000 0.0000 0.0000 0.0000 0.0000

CB milch 0.3000 0.6000 0.1000 0.0000 0.0000 0.0000

IND cow in milk 0.0000 0.0000 0.9000 0.1000 0.0000 0.0000

IND cow milch 0.0000 0.0000 0.0000 0.7000 0.3000 0.0000

Buffalo in milk 0.0000 0.0000 0.0000 0.2000 0.7000 0.1000

Buffalo milch 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000

Period-III: TPM of livestock population of Gujarat during 2005-06 to 2014-15


CB in milk 0.4000 0.6000 0.0000 0.0000 0.0000 0.0000

CB milch 0.5000 0.4000 0.1000 0.0000 0.0000 0.0000

IND cow in milk 0.0000 0.0000 0.9000 0.1000 0.0000 0.0000

IND cow milch 0.0000 0.0000 0.0000 0.9000 0.1000 0.0000

Buffalo in milk 0.0000 0.0000 0.0000 0.0000 0.9000 0.1000

Buffalo milch 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000

Period-IV: Overall TPM of livestock population of Gujarat during 1985-86 to 2014-15


CB in milk 0.4333 0.5667 0.0000 0.0000 0.0000 0.0000

CB milch 0.5333 0.3667 0.1000 0.0000 0.0000 0.0000

IND cow in milk 0.0000 0.0667 0.8333 0.0333 0.0667 0.0000

IND cow milch 0.0000 0.0000 0.0000 0.6333 0.3000 0.0667

Buffalo in milk 0.0000 0.0000 0.0667 0.2667 0.3333 0.3333

Buffalo milch 0.0000 0.0000 0.0000 0.0690 0.3103 0.6207

Table 2. Results of Markov chain analysis for changing in the composition of livestock population of Gujarat

CB: Crossbred, IND: Indigenous.

351


of livestock population showed a higher percentage than Period-I. This is a clear indication of stability in livestock population was more stable and the low probability of shifting to another category of population. During Period-II, milch buffalo population continued to emerge as one of the most stable categories. During a recent decade in Period-III, both crossbred in milk and milch retained 40 percent of their previous share in current time. Crossbred in milk lost 60 percent of the population to crossbred milch cow population and crossbred milch cow population lost its 50 percent share to crossbred in milk cow population. Apart from that, it lost 10 percent share to indigenous cow in milk population. Indigenous cow in milk and milch, as well as buffalo in milk population, retained 90 percent of their previous share in current time. Interestingly, buffalo milch population retained 100 percent in Period-I, II and III and became highly stable population category during the first three periods.

The results pertaining to the Period-IV showed that crossbred in milk population retained 43 percent of the previous period population in current time while lost around 57 percent to the crossbred milch population. Although it gained around 53 percent share from crossbred milch cow population, the crossbred milch retained 37 percent of its share. Indigenous cow in milk population was a most stable category as it retained around 83 percent of their previous share in current time.

But, it lost around 3 and 7 percent to indigenous cow milch and buffalo in milk population, respectively. Meanwhile, it gained 10 and 7 percent from crossbred cow milch and buffalo milch population.

Year Crossbredin milk

Crossbredmilch

Indigenous in milk

Indigenous milch

Buffalo in milk

Buffalo milch

2015-16 8739(5.77)

9529(6.30)

17713(11.70)

33266(21.98)

37759(24.95)

44363(29.31)

2016-17 8868(5.86)

9628(6.36)

18232(12.04)

34789(22.98)

37512(24.78)

42340(27.97)

2017-18 8977(5.93)

9772(6.46)

18658(12.33)

35565(23.50)

37294(24.64)

41104(27.15)

2018-19 9102(6.01)

9916(6.55)

19012(12.56)

35927(23.73)

37098(24.51)

40315(26.63)

2019-20 9232(6.10)

10062(6.65)

19309(12.76)

36062(23.82)

36921(24.39)

39785(26.28)

2020-21 9366(6.19)

10209(6.74)

19559(12.92)

36073(23.83)

36757(24.28)

39406(26.03)

2021-22 9503(6.28)

10356(6.84)

19771(13.06)

36018(23.79)

36605(24.18)

39116(25.84)

2022-23 9641(6.37)

10502(6.94)

19952(13.18)

35930(23.74)

36463(24.09)

38882(25.69)

Table 3. Projection of Gujarat livestock population(00' No.)

Figures in the parentheses are percentage to the total bovine population.

Indigenous cow milch population retained around 63 percent in current time while it lost around 30 and 7 percent to buffalo in milk and milch population. The category of indigenous cow milch population gained 3, 27 and 7 percent from indigenous cow milch, buffalo in milk and milch population, respectively. One of the unstable categories in Period-IV was buffalo in milk as it retained only 33 percent of its share of the previous period. It gained around 7, 30 and 31 percent from indigenous cow in milk, indigenous cow milch and buffalo milch population, respectively. It was worth mentioning that buffalo milch category retained around 62 percent of its share in sharp contrast to Period-I, II, and III. This time buffalo milch gained from 7 and 33 percent from indigenous cow milch and buffalo in milk population, respectively. In general, buffalo milch and indigenous in milk population retained higher share specifically buffalo milch because of special characteristics like higher milk yield and fat content, lactating efficiency, capable to digest ordinary feeds, wide preference for household consumption over cow milk, etc.Projection of Livestock Population of Gujarat

The projection of state bovine livestock population into different categories was computed using the TPM and the results of actual and estimated data are presented in Table 3. The projection of different categories and its share was examined upto 2022. The results revealed that there is a thorough increase estimated population of crossbred cow in milk and crossbred cow milch and further increased their share in the bovine population. The projected value suggested that its share would be

352


increased continuously up to 2022.From 2015-16 to 2022-23, the percentage share of a projected crossbred cow in milk and milch population increased to 6.37 and 6.94percent, respectively. The estimated population is expected to increase to 9641and 10502 thousand in 2022-23, respectively.

As far as the indigenous cow in milk population is concerned, its projected population will augment in a future. Regarding the indigenous cow milch population, the pattern was almost similar to that of indigenous in milk cow population. The future share increased from 11.70 and 21.98 percent in 2014-15 to 13.18 and 23.74 percent in 2022-23. The absolute population anticipated showing an upward trend and it would be 19952 and 35930 thousand by the end of 2022-23, respectively.

The projected data of future buffalo in milk population was found to decrease from 37759 (25.05 percent) to 36463 thousand (24.09 percent) in 2022-23 over 2015-16. In addition, buffalo milch population was gradually declined both in absolute and percentage term during estimation period. Projected data on buffalo milch population is likely to be decline from 47836 thousand (29.31 percent) to 38882 thousand (25.69 percent) in 2022-23 over 2014-15. From the foregoing discussion, by and large, crossbred and indigenous cow population is anticipated to increase and buffalo in milk and milch population will show a declining trend in future.CONCLUSIONS

Agro-ecologic variability across the regions, growing demand and technological advancement are the major drivers of changing livestock composition in India. The share of the cattle population in national and state livestock population was found to decline over the years whereas the share of buffalo in total livestock population at the state and national level was found to increase. Goat population was found to be almost stable. The TPM in the overall study period (1985-86 to 2014-15) showed that crossbred in milk population retained 43 percent in current time. Buffalo milch category retained around 62 percent share in Period-IV while its retention share was 100 in all the other three periods. In this day and age, the most panic threat that the planet is confronted with climate change and its implication which is not only limited to agriculture and food production but also over livestock production.REFERENCESBoko, M., Niang, I., Nyong, A., Vogel. C., Githeko, A., Medany,

M., & Yanda, P. (2007). Climate change 2007: Impacts, adaptation and vulnerability (pp. 433-467). Contribution of Working Group-II to the Fourth Assessment

Report of the Intergovernmental Panel on Climate Change, M. L. Parry, O.F. Canziani, J.P. Palutikof, P.J. Van Der Linden and C.E. Hanson (Eds.). Cambridge, UK: Cambridge University Press.

Dent, W.T. (1967). Application of Markov analysis to international wool flows. Review of Economics and Statistics, 49(2), 613-616.

FAO. (2006). Livestock's long shadow: Environmental issues and options. Retrieved from ftp://ftp.fao.org/ docrep/fao/010/a0701e/a0701e00.pdf

FAO. (2009). The state of food and agriculture: Livestock in the b a l a n c e . R e t r i e v e d f r o m h t t p : / / w w w. f a o . org/docrep/012/i0680e/i0680e.pdf

Khan, B.U., Arora, A.L., & Sharma, R.C. (2002). Enhancing productivity of sheep: Te c h n o l o g y d i m e n s i o n s . Technology Options for Sustainable Livestock Production in India, Proceedings of the workshop on documentation, adoption, and impact of livestock technologies in India, ICRISAT- Patancheru, India 18-19 January, 2001. National Centre for Agricultural Economics and Policy Research and International Livestock Research Institute: 113-124.

Kurup, M.P.G. (2002). Cross breeding of indigenous Indian cattle with exotic breeds to increase milk production: A critical analysis. Technology Options for Sustainable Livestock Production in India, Proceedings of the workshop on documentation, adoption and impact of livestock technologies in India, ICRISAT-Patancheru, India,18-19 January, 2001. National Centre for Agricultural Economics and Policy Research and International Livestock Research Institute, 41-57.

Selvakumar, K.N. (1996). Growth dimensions of livestock sector in Tamil Nadu: An econometric analysis (Doctoral Thesis). University of Agricultural Sciences, Bangalore.

thThe Government of India. (2013a). 19 livestock census, Volume-I. Department of Animal Husbandry, Dairying and Fisheries (DADF), Ministry of Agriculture, Government of India, New Delhi. Retrieved from http://dahd.nic.in/

The Government of India. (2013b). National livestock policy, 2013. Department of Animal Husbandry, Dairying and Fisheries (DADF), Ministry of Agriculture, Government o f I n d i a , N e w D e l h i . R e t r i e v e d f r o m http://dahd.nic.in/sites/default/filess/NLP%202013%20Final11.pdf

The Government of India. (2015). Basic animal husbandry and fisheries statistics. Ministry of Agriculture and Farmers welfare, Department of Animal Husbandry, Dairying and Fisheries (DADF), Government of India, New Delhi. Retrieved from http://dahd.nic.in/

Tysdell, C., & Gali, J. (2000). Trends and developments in India's livestock industry. Working Paper No. 43, Economics, Ecology and the Environment, University of Queensland, Australia.

353


ABSTRACTThe present study has been devised to have an in-depth knowledge about credit utilization of large farmers in Punjab. For this study, data was collected from large farm households having operational holding more than 10 hectares (25 acres) as per the standard classification of operational holding. Sampled farmers were found to be availing large amount of institutional credit in terms of short-term credit due to their high credit limit, which was diverted for other purposes like purchase of lan and tractors, construction of houses and marriage ceremonies. Source-wise, about 93.45 and 6.55 percent of the total loan availed was borrowed from institutional and non-institutional sources, respectively. The regression coefficients for the factors like farm investment, expenditure on dwelling house and expenditure on social ceremonies were found to be having positive and significant effect on diversion of credit in south-western Punjab. So, it was suggested that, basis of credit limit formulation needs to be modified to check the diversion of short-term credit, as diversion of it was found to be high on the sampled farms.

KeywordsDiversion, institutional credit, large farm households.

JEL CodesQ12, Q13, Q14, Q15.

1* 2 3Sukhdeep Singh , Arjinder Kaur , and Poonam Kataria

1 2 3M.Sc Student, Senior Agriculturist Economist, Professor of EconomicsDepartment of Economics and Sociology, Punjab Agricultural University, Ludhiana-141004


Received: November 17, 2017 Revision Accepted: April 21, 2018

Agricultural Credit Availed and its Utilization on Large Sampled Households in Punjab


DOI: 10.5958/2322-0430.2018.00142.7


www.soed.in


UGC Approved


354

INTRODUCTIONAgriculture forms the core sector of the Indian

economy. Agriculture and allied sectors accounted for 14.6 percent of the Gross Domestic Product (GDP) in 2010-11 (The Government of India, 2016). The economic contribution of agriculture to India's GDP has been steadily declining with the country's broad-based economic growth. Still, agriculture is demographically the broadest economic sector and plays a significant role in the overall socio-economic fabric of India. Punjab, the breadbasket of India, was historically considered to be one of the most fertile areas on earth. It has a total of 50.36 lakh hectares of geographical area and 82 percent of it was under cultivation and out of this 99.9 percent is irrigated (The Government of Punjab, 2016).

Utilization aspect of credit is as important or in a sense more important than availability of credit. If available credit was utilized for the productive purposes, it helped not only in increasing the returns to the farmer,

but also created its repaying capacity. On the other hand, if the available funds get diverted for unproductive purposes or misutilizated for other motives, the income did not increase to the desired extent and the very purpose of credit availability gets defied. But, the farmers live in social matrix, they were required to fulfill many personal and social obligations, but returns were lagged with respect to investment in agriculture along with income generation at two or three points of time in a year. So, when credit was made available to them for some specific purpose, tendency to divert the funds gets high. So, utilization aspect of agricultural credit needed to be analyzed to highlight the problem of diversion of borrowed funds. Thus, on one hand, there was need for the expansion of agricultural credit and on the other hand, it was equally urgent to ensure improved quality and efficiency of lending operations (Varde, 1993). The main source of credit in rural India was comprised of both institutional well as non-institutional agencies. Modern

agriculture being highly capital intensive and access to institutional credit at affordable rate of interest became central for adequate input use and productivity growth on all farms. The institutional sources meet only 51 percent of the credit requirement of the farm sector (Singh et al., 2009). Therefore, the non- institutional sources were also approached by the farmers frequently due to lack of their security assets, routine needs, inadequate supply of institutional credit, undue delay involved in formal procedure and malpractices adopted by institutional lending agencies, etc.

In India during 1960-61, there were 4.5 percent large holdings which decreased to 0.70 percent in 2010-11 and large farmers were operating 29 percent of total cultivated land during 1960-61 which has also come down to 10.6 percent in 2010-11 (The Government of India, 2016). On the other hand, in Punjab, large farmers comprised 6.01 percent of total holdings during 1990-91, which increased to 7.25 percent in 2000-01 and marginally came down to 6.62 percent in 2010-11. These farmers were operating on 10.29 lakh hectares land constituting 25.93 percent of total cultivated area of Punjab (The Government of Punjab, 2016). The large farm category was found to be the highest surplus generating category in the state. Still a large proportion of these farms were indebted and availing a higher proportion of institutional loan for productive as well as non-productive purposes (Singh et al., 2013; Kumar & Kaur, 2017).Thus, present study was undertaken to bring about the extent of credit availed and utilization/diversion of it.

METHODOLOGYThe study was based on primary data collected

during 2015-16 from large farm households having operational holding more than 10 hectares (25 acres) as per the standard classification of operational holdings. Multi-stage sampling technique has been followed for the selection of farm households. The South-Western area of Punjab is comprised of six districts namely Bathinda, Faridkot, Ferozpur, Sri Muktsar Sahib, Mansa and Fazilka, Out of these two districts with highest proportion of large operational holdings, namely, Ferozpur and Sri Muktsar Sahib in the region were selected purposively at first stage. At the second stage, two blocks from each selected district namely Malout and Gidderbaha from Sri Muktsar Sahib and Makhu and Zira from Ferozpur district were selected at random. At the third stage, a cluster of villages from each sampled block were selected. At the final stage, twenty large farmers from each selected cluster of villages have been selected for the present study. Thus total sample comprised of 80 large farm households. The collected data was analyzed with the help of various statistical techniques like averages, percentages etc. Regression analysis was undertaken to bring out the factors affecting diversion of agricultural credit on sampled households.Regression Analysis: After applying different functional

forms, Cobb-Douglas production function was found to be best fit to identify the factors affecting diversion of credit by the large farmers.

nLog Y = Log A + Ó b log x + u i=1 i i

Log Y = LogA+ b log x + b log x +…+ b log x + u1 1 2 2 10 10

WhereY = Diversion of loan (percent)A = Constant termx = Non-farm income (`per farm)1

x =Share of non-institutional loan in total loan 2

availed (percent)x = Literacy level3

x =Household general expenditure (` per farm 4

household)x = Farm investment (`per farm)5

x =Expenditure on dwelling house 6

(` per farm household)x =Expenditure on social ceremonies 7

(` per farm household)x = Major medical expenses (`per farm household)8

x = Expenditure on vehicles (`per farm household)9

x = Family size (number).10

U= Random term with usual properties.Also, the independent variables were tested for

stochastic independence.RESULTS AND DISCUSSIONBrief Socio-economic Profile of Sampled Households

It has been observed that among the overall sampled farmers, about 41 percent were in the age group of 40-50 years. Fifty one percent of these were having small families comprised of five or less number of members. Average family size was found to be of 6.06 members in large sampled farm households. Out of the total sample of 80 large farmers, majority of the farmers (30 percent) were found to be graduates. The proportion of owned land in size of operational holding was 94.88 percent in large sampled farms. Average size of owned land for the sampled farmers was worked out to be 13.38 hectares. It was found that crop farming was the major source of income on sampled farms and about 92 percent of the total income was generated from this source alone. Out of the total expenditure, the expenditure on food and vehicles was the major cost item heads which accounted for 20.33 and 19.53 percent to the total expenditure respectively. The average number of tractors owned per farm unit came out as more than one (1.35) on an overall farm situation. The current value of farm inventory was found to be `1021792 per farm and `72466 per hectare basis on the sampled farms in 2015-16.Distribution of Sampled Farmers according to Source of Credit Availed

The credit is one of the most important inputs for agricultural development and plays a pivotal role in the

å=

=n

i

bi

ixAY1

355

Singh et al.: Agricultural credit availed and its utilization on large sampled households in Punjab

stage of transformation of agriculture to a commercial enterprise. An important aspect that has emerged in last three decades is that the credit was not only obtained by the small and marginal farmers for survival but also by the large farmers for enhancing their income through adoption of new technologies in farming. On the other side, as indicated in Table 1, 38.75 percent of farmers have availed non-institutional credit which showed that 61.25 percent of farmers were not dependent on non-institutional credit in the sampled category. All the sampled farmers were found to be availing credit from institutional sources. It was also found that 100 percent of sampled farmers have availed credit from co-operatives both in cash and kind. About 93.75 percent of sampled farmers have availed credit from co-operatives and commercial banks. The results also revealed that 38.75 percent sampled farmers have borrowed from institutional as well as non-institutional sources of credit.Source-wise Extent of Credit Availed by Farmers

As indicated in Table 2, the average amount of credit availed by the sampled farmers was worked out to be `1269000 per farm on the sampled households during 2012-16. The source-wise, an amount of `1185875 and 83125 per farm household was borrowed from institutional and non-institutional sources which accounted for 93.45 and 6.55 percent of the total loan availed respectively. Nearly 81.87 percent of the total institutional credit availed by the sampled farmers was short-term loan, while rest about 18.13 percent was investment loan. All of the non-institutional credit was availed from only one source namely, commission agents by the sampled farmers. Hence, the sampled farmers have preferred institutional agencies over non-institutional sources in the study area for their various credit needs. The reason could be traced to better access to institutional agencies and large credit limits due to large size of holdings. So, their dependence on non-institutional sources of credit was found to be less.Purpose-wise Credit Availed by Large Sampled Farmers

Farmers tap both institutional and non-institutional sources of credit for various purposes. Farm investment can be directly productive in nature like purchase or development of land, irrigation structures, purchase of heavy machinery, cattle or construction of farm buildings etc. It can also be indirect productive investment sometimes called as non-productive like construction of dwelling house, higher education of children, heavy expenditure on medical treatment, and sometimes totally unproductive like social/religious ceremonies, etc. Though not adding to income stream directly but its importance cannot be negated as farmer live in socio-cultural environment and has to undertake this type of expenditure willingly or unwillingly. So, the pattern of investment undertaken by the farmers during last five years has been shown in Tables 3 and 4 respectively.

Direct Productive Investment In case of direct productive investment, data

furnished in Table 3 revealed that sampled farmers have not availed any credit for purchase/leveling of land or water channels in the last five years. It was noticed that `13125 per farm were availed as credit for construction of farm buildings and whole of it was sourced from non-institutional sources. It was also found that for purchase of tractors 79.38 per cent and 20.62 percent of credit, which came out to be ̀ 55550 and 14425 per farm was taken from institutional and non-institutional sources, respectively. Moreover, for purchasing trolleys and other farm machinery 100 percent of credit was availed from

Source of credit Number of farmers

Institutional sources 80(100.00)

Co-operatives 80(100.00)

Commercial banks 75(93.75)

Both (Co-operatives and commercial banks)

75(93.75)

Non-institutional sources 31(38.75)

Commission agents 31(38.75)

Both institutional and non-institutional

31(38.75)

Table 1. Distribution of sampled farmers according to source of credit availed

(N=80)

Figures in parentheses indicate percentage to the total.

Particulars Credit availed ( per farm)`

Percentage

Institutional SourcesShort-term credit (A)Co-operatives 108375 8.54Commercial banks 862500 67.96BothSub-total (A) 970875 76.50Investment credit (B)Commercial banks 215000 16.95Sub-total (B) 215000 16.95Total (A+B) 1185875 93.45Non-institutional credit Commission agents (Arthiyas) 83125 6.55Total 1269000 100.0

Table 2. Source wise extent of credit availed by sampled farmers

(N=80)

356


institutional sources (`59250 per farm). There was no amount borrowed from any source directly for the purchase of land purpose due to higher income of large farmers, but a large sum of crop loan was found to be diverted (`300000 per farm) for this by sampled farmers during study period. Similarly, short-term credit diverted for purchase of tractors was found to be `56250 per farm during the same period.Indirect Productive investments unproductive As shown in Table 4 for construction of dwelling expenditure 75.83 and 24.17percent of loan was availed from institutional and non-institutional sources of credit respectively. The amount was estimated to be ̀ 62750 and ` 20000 per farm household. For the marriage ceremonies 100 percent of credit (`35575 per farm household) was taken from non-institutional sources. It was also found that 100 percent loan (`37450 per farm household) was taken or diverted from institutional sources for general purposes (purchase of vehicles for personal use, clothing and transportation, etc.). No credit was availed by the sampled farmers for medical treatment and education of

their children from any source. A large diversion of crop loans was found for construction of houses by sampled farmers (`168750) per farm household and `263750 per farm household for the purpose of marriages ceremonies held in the household.Factors Affecting Diversion of Credit in South-western Punjab

The production function analysis was applied to ascertain the factors leading to diversion of agricultural credit for purposes other than stipulated ones by the sampled farmers in the study area. The regression analysis results of the production function fitted for the diversion of loan by the sampled farmers in the study area have been shown in Table 5. The coefficient of determination was found to be 0.67 for the Cobb-Douglas production function, the one found to be the most suitable.

The regression coefficient pertaining to x (share of 2

non-institutional credit) was found to be significant at five percent level of significance. It revealed that with an increase in the share of non-institutional credit, the diversion of credit will be decreased. Regression

Purpose Percentage of loan taken Total Short term divertedInstitutional Non-institutional

Purchase of land - - - 300000Leveling of land - - - -Reclamation of soil/water channel - - - -Farm buildings - 13125

(100.0)13125

(100.0)-

Electric motors/ diesel engines/ Submersible pump - - - -

Tractors 55550 (79.38)

14425 (20.62)

69975 (100.0)

56250

Trolleys and other farm machinery 59250 (100.0)

- 59250 (100.0)

-

Cattle - - - -

Table 3. Direct productive investment undertaken by sampled farmers during 2012-16( /farm)`


Purpose Percentage of loan taken Total Short term divertedInstitutional Non-institutional

House construction 62750 (75.83)

20000 (24.17)

82750 (100.0)

168750

Marriage ceremonies - 35575 (100.0)

35575 (100.0)

263750

Education of children - - - -Medical treatment - - - -General purpose 37450

(100.0)- 37450

(100.0)-

Table 4. Indirect productive investment/unproductive expenditure undertaken by sampled farmers during 2012-16( /farm household)`


357

Singh et al.: Agricultural credit availed and its utilization on large sampled households in Punjab

Factors Coefficients Calculated t-value

Intercept -11.7431 -3.06Non-farm income (x )1 -0.0190 -0.57

Share of non-institutional credit (x )2**-0.2809 -2.55

Literacy level (x )3NS0.0605 0.16

Household general expenditure (x )4 NS1.0562 1.51

Farm investment (x )5***1.0053 4.85

Expenditure on dwelling house (x )6***0.1749 5.89

Expenditure on social ceremonies (x )7***0.1630 5.79

Major medical expenditure (x )8 0.0421 1.35

Expenditure on vehicles (x )9 -0.0110 -0.34

Family size (x )10 0.7242 1.192R 0.67

Table 5. Factors affecting diversion of credit on sampled farm households

***, and ** Significant at 1 and 5 percent level.NS: Non-significant.

coefficient pertaining to x (extent of non-farm income), 1

x (household general expenditure), literacy level (x ) and 4 3

family size (x ) did not have significant role in diversion 10

of the loan undertaken by the sampled farmers. Whereas, regression coefficient pertaining to x (farm investment) 5

came to be significantly positive with a coefficient value of 1.0053, indicating that increase in farm investment would lead towards increase in diversion of credit.

Regression coefficient pertaining to x (expenditure 6

on dwelling house) and x (expenditure on social 7

ceremonies) was found to be significantly positive with coefficient values of 0.1749 and 0.1630 respectively. This indicated that increase in expenditure on dwelling house and expenditure on social ceremonies would lead towards increase in diversion of credit by the sampled farmers. However, the regression coefficients pertaining to x 9

(expenditure on vehicles) and x (major medical 8

expenditure) came to be non-significant.CONCLUSIONS

The analysis of study showed that the large farmers were availing more credit from institutional sources than non-institutional sources. Farm investment was found to be higher in purchase of land as well as for tractors. So far

as unproductive expenditure was concerned, construction of dwelling house and social ceremonies were found to be major purposes. The diversion of short-term credit limit was found to a large extent for all these purposes. REFERENCESKumar, D., & Kaur, A. (2017). Accessibility of marginal and

small farmers to institutional credit in southwestern Punjab. Indian Journal of Economics and Development, 13, 577-583.

Singh, S., Kaur, M., & Kingra, H.S. (2009). Inadequacies of institutional agricultural credit system in Punjab state. Agricultural Economic Research Review, 22, 309-318.

Singh, S., Sharma, V, K., & Kingra, H.S. (2013). A study into the economics of farming and the pattern of income and expenditure distribution in the Punjab agriculture. Research report. Pp 16-20. Department of Economics and Sociology, Punjab Agricultural University, Ludhiana, Punjab.

The Government of India. (2016). Agricultural statistics at a glance. Department of Agriculture, Cooperation and Farmers welfare, Government of India, New Delhi.

The Government of Punjab. (2016). Statistical abstract of Punjab. Government of Punjab, Chandigarh, India.

Varde, V. (1993). Agriculture credit situation in India. Financing Agriculture, 25, 19-24.

358


ABSTRACTThe mango, being the chief commercial fruit of Gujarat state occupies the highest area among all fruit crops. Present study estimates the growth rate and instability of area, production and productivity of Mango in Navsari and Valsad districts and Gujarat state during the years 2005-06 to 2015-16.To analyse the growth rate and instability of area, production and productivity of mango, the statistical tools like CGR, CV and decomposition of production were used. The Results of the study showed that the CGR of area and production in Navsari, Valsad and Gujarat were found positive and significant. Whereas, the CGR for productivity in the same mentioned areas were observed positive and non significant. The fluctuation in production was more in the Gujarat State as a whole compared to the selected districts.

KeywordsArea, compound growth rate, instability, production, productivity.

JEL CodesC01, D23, E23, K19, O47.

*Hamidullah Younisi and J.J. Makadia

Department of Agricultural Economics, Navsari Agricultural University, Navsari-396450



Growth and Instability in Area, Production and Productivity of Mango Crop in Gujarat


DOI: 10.5958/2322-0430.2018.00143.9


www.soed.in


UGC Approved


359

INTRODUCTIONMango (Mangifera indica L.), which belongs to the

family of Anacardiaceae, is one of the most important tropical and subtropical fruit of the world and is popular both in fresh and processed form. Mango has been under cultivation by man for over 4000 years. It occupied an important place in horticulture during the rule of the Mogul emperors in India, and Akbar the Great planted an orchard of one lakh mango trees. It is considered as king of fruits in the tropical areas of the world. Indian mangoes come in various shapes, sizes and colours with a wide variety of flavour, aroma and taste. The Indian mango is the special product that substantiates the high standards of quality and bountiful of nutrients packed in it.

Mango cultivation is carried in many countries of Southeast Asia-the Philippines, Indonesia, Java, Thailand, Burma, Malaysia and Sri Lanka. Introduction of the mango to East and West Africa and subsequently to Brazil occurred during the sixteenth century. The cultivated mango varieties are the result of continuous selection by man from original wild plants. Native to eastern India and Burma, several hundred varieties of

mango exist, but only a few are commercialized. Besides banana, the mango is the most consumed tropical fruit in the world. Mango is cultivated in the 2516 thousand ha with production of 18.43 million tonnes contributing 40.48 percent of the total world production of mango. The productivity of mango was 7.3 Mt/ha (Ministry of Agriculture, 2014)

Gujarat itself produces 1241.59 thousand Mt of mango which contributes 6.00 percent of total production in India. The most famous types of mango available in Gujarat are Alphonso, Kesar, Rajapuri, Totapuri, Dasehri and Langra. The area under mango in Gujarat increased from 0.529 lakh ha in 1996-97 to 1.53 lakh ha in 2015-16, and the production of mango also increased from 289 thousand Mt in 1996-97 to 1242 thousand Mt in 2015-16 (Director of Horticulture, 2016). The study was carried out to analyze the growth of area, production and productivity of Mango in Gujarat, and to study the instability of area, production and productivity of mango in Gujarat.METHODOLOGY

The secondary data in respect of area, production and

productivity of mango in Navsari, Valsad and Gujarat for the last 11 years (2005-06 to 2015-16) were collected from the Directorate of Horticulture at Gujarat and research reports etc.Growth Rates

Compound growth rate CGR is a key indicator to measure agricultural growth and can be used for forecasting area/production/productivity, etc. of various commodities. This plays a vital role in agricultural policy making. Therefore, predicted value of growth rate needs to be very precise so that suitable policies can be adopted accordingly. Accuracy of predicted value depends largely on proper statistical procedures followed to estimate it. The compound growth rates of area, production and productivity of mango were worked out using exponential function.Instability

The magnitude of instability in area, production and productivity of mango in Navsari and Valsad districts as well as Gujarat state measured by working out the coefficient of variation (CV)based on time series data. Area and yield of each district and Gujarat State were detrended by using following linear equation.

Z =a + bt +ut t

Where: Zt = Dependent variable (Area, Production

and Yield)a = Interceptb = Parameters to be estimatedt = Time variable (years), and u = Error term with usual assumptionst

After detrending, the residuals (u ) were centred t

on the mean area and mean yield (Z) for each district. The

detrended time series data (Z) for area and yield were calculated as:

Z = u +Ztt

The time series data of detrended production were calculated as the production of detrended area and yield. Finally, the coefficient of variation (CV) of mango production was estimated from the detrended series for the study period.

The variance of production was decomposed into its constituent sources, viz. area variance yield variance and area yield covariance to examine the source of instability.

2 2V (Q) = A V (Y) + Y V (A) + 2 A Y Cov (A, Y) – Cov 2(A, Y) + R

Where;V (Q) = Production varianceA = Mean areaY = Mean yieldV(Y) = Yield varianceV (A) = Area varianceCov (A, Y) = Area – yield covariance

2 Cov (A, Y) = Higher order covariance between area and yield

R = ResidualRESULTS AND DISCUSSION

The results and discussion in the link with the objectives enshrined in the study in respect of mango are presented and discussed under the following major heads area, production and productivity of mango in Navsari and Valsad districts and Gujarat State.Growth in Area of Mango

The results pertaining to area and CAGR of mango in Navsari and Valsad districts as well as Gujarat State are presented in Table 1. It is observed from the Table 1 that

Year Navsari Valsad Gujarat

Area Percent change over previous year



2005-06 14.43 20.00 96.032006-07 14.90 3.29 21.84 9.20 101.99 6.212007-08 16.67 11.88 23.14 5.95 109.61 7.472008-09 18.05 8.27 24.24 4.75 115.69 5.552009-10 19.06 5.61 25.14 3.71 121.52 5.042010-11 20.92 9.77 26.25 4.42 130.02 6.992011-12 22.80 8.98 28.00 6.67 136.18 4.742012-13 23.94 5.00 29.40 5.00 141.26 3.732013-14 24.19 1.04 29.57 0.57 142.69 1.012014-15 28.73 18.76 29.99 1.42 150.05 5.162015-16 29.25 1.83 31.48 4.96 153.18 2.09Total 232.93 74.41 289.04 46.65 1398.22 47.98Mean 21.18 6.76 26.28 4.24 127.11 4.36CAGR ***3.22 **1.90 **2.07

Table 1. CGR in area of mango in Navsari, Valsadand Gujarat, 2005-06 to 2015-16(000' ha)

Source: Director of horticulture, Gujarat 2016.*** and ** significant at 1and 5 percent level.

360


the area of mango in Navsari district was 14.43 thousand ha in the year 2005-06 which was steadily increased to 29.25 thousand ha in the year 2015-16. It showed positive and significant growth of area at rate of 3.22 percent. Likewise, the area of mango in Valsad district was 20.00 thousand ha in the year 2005-06 which was continuously increased to 31.48 thousand ha in the year 2015-16 which showed positive and significant CAGR at the rate of 1.90 percent. As far as the Gujarat State as a whole is concerned, the total area of mango was 96.03 thousand ha in the year 2005-06 which was gradually increased to 153.18 thousand ha in the year 2015-16 indicating positive and significant (5 percent) CAGR at the rate of 2.07 percent. The results obtained are in conformity with the results of Tirlapur et al. (2014) studied on compound growth rate of area, production and productivity of mango in India during years 2001-12.Growth in Production of Mango

The perusal of Table 2 revealed that the highest production of mango was found in Gujarat State 1241.59 thousand MT followed by Valsad (299.02 thousand MT) and Navsari (272.03 thousand MT) in the year 2015-16. While, the lowest production of mango was found in Navsari and Valsad districts as well as Gujarat State were 45.12, 48.48 and 299.82 thousand MT in the year 2008-09. Similar results were observed by Parmar et al. (2012) in which they stated that the reasons for low production of mango in South Gujarat in the year 2008-09 was due to the adverse climatic condition (high night temperature and less sunshine) during flowering time caused poor flowering resulting in to severe decrease in the yield of mango. It is also concluded that the positive and

significant growth of production of mango was found in Navsari (4.09 percent) followed by Valsad district (2.93 percent). Whereas, in Gujarat State was found (2.80 percent) during study period.Growth in Productivity of Mango

It is observed from the Table 3 that the highest productivity of mango were found in Valsad district (9.80 MT/ha) followed by Navsari district (9.50 MT/ha) in the year of 2005-06. The highest productivity of mango for Gujarat State was found to 8.49 MT/ha in the year 2007-08. Whereas, the lowest productivity of mango were found in Valsad (2.00 MT/ha) followed by Navsari district (2.50 MT/ha) and Gujarat State (2.59 MT/ha) in the year 2008-09. Similar results were observed by Parmar et al. (2012) in which they stated that the reasons for low productivity of mango in South Gujarat in the year 2008-09 was due to the adverse climatic condition (high night temperature and less sunshine) during flowering time caused poor flowering resulting in to severe decrease in the yield of mango. It is also inferred from the table that the positive and non significant growth rate of productivity of mango was found in Valsad district (1.01 percent) followed by Navsari district (0.85 percent). While, in Gujarat State was found (0.72 percent) during mentioned period. The results obtained are in conformity with the results of Tirlapur et al. (2014) studied on compound growth rate of area, production and productivity of mango in India during years 2001-12.Instability in Area, Production and Productivity of Mango

The coefficient of variation of production, area and productivity of mango crop was estimated from detrended


Production Percent change over previous year



2005-06 137.04 196.00 772.13

2006-07 140.06 2.21 203.11 3.63 834.29 8.05

2007-08 158.37 13.07 219.83 8.23 930.13 11.49

2008-09 45.12 -71.51 48.48 -77.95 299.82 -67.77

2009-10 152.48 237.94 150.84 211.14 856.74 185.75

2010-11 177.84 16.63 154.88 2.68 911.30 6.37

2011-12 201.60 13.36 168.00 8.47 965.95 6.00

2012-13 213.07 5.69 271.27 61.47 1003.71 3.91

2013-14 210.44 -1.23 251.32 -7.35 1125.61 12.14

2014-15 261.40 24.22 277.39 10.37 1219.71 8.36

2015-16 272.03 4.07 299.02 7.80 1241.59 1.79

Total 1969.42 244.44 2240.14 228.49 10160.98 176.10

Mean 179.04 22.22 203.65 20.77 923.73 16.01

CAGR***4.09 ***2.93 ***2.80

Table 2. CGR in production of mango in Navsari, Valsad and Gujarat, 2005-06 to 2015-16(000' Mt)

Source: Director of horticulture, Gujarat 2016.***Significant at onepercent level.

361

Younisi and Makadia: Growth and instability in area, production and productivity of mango crop in Gujarat

time series data for the last 11 years (2005-06 to 2015-16) for the Navsari and Valsad districts as well as Gujarat State. The results are presented in Table 4.

The perusal of Table 4 revealed that the coefficient of variation for production of mango in the Gujarat State was found to 202.14 percent for the period 2005-06 to 2015-16. The coefficient of variation in production was markedly high for Valsad district (63.25 percent). Instability was at moderate level in the district of Navsari (43.59 percent). The coefficient of variation for mango production was lower for the selected districts as compared to the state as a whole. Thereby, the fluctuation in production was more in the Gujarat State as a whole compared to the selected districts. The coefficient of variation for area under mango crop in Gujarat state was found to be 2.13 percent, whereas, in Valsad it was lowest (0.49 percent) followed by Navsari district (0.78 percent).

The perusal of Table 4 that revealed the coefficient of variation for mango productivity in Navsari and Valsad district was found to be 1.99 and 23.44 percent while in Gujarat it was lowest (1.62 percent). It can be safely concluded that the magnitude of instability for mango production was high in the State as well as in the selected districts. The destabilizing effect on production was more compared to area and productivity. The results obtained were in conformity with the results of Singh and Rani (2013) studied on growth rate of area, production and productivity of fruit crops in Jharkhand during 1990-91 to 2009-10.Sources of Variation in Mango Production

To analyze the variables, explaining the changes in


Productivity Percent change over

previous year

Productivity Percent change over previous year

Productivity Percent change over previous year

2005-06 9.50 9.80 8.04

2006-07 9.40 -1.05 9.30 -5.10 8.18 1.74

2007-08 9.50 1.06 9.50 2.15 8.49 3.74

2008-09 2.50 -73.68 2.00 -78.95 2.59 -69.46

2009-10 8.00 220.00 6.00 200.00 7.05 172.04

2010-11 8.50 6.25 5.90 -1.67 7.01 -0.59

2011-12 8.84 4.02 6.00 1.69 7.09 1.20

2012-13 8.90 0.65 9.23 53.78 7.11 0.17

2013-14 8.70 -2.25 8.50 -7.88 7.89 11.02

2014-15 9.10 4.60 9.25 8.82 8.13 3.04

2015-16 9.30 2.20 9.50 2.70 8.11 -0.29

Total 92.24 161.80 84.98 175.56 79.68 122.62

Mean 8.39 14.71 7.73 15.96 7.24 11.15

CAGR NS0.85 NS1.01 NS0.72

Table 3. CGR in productivity of mango in Navsari, Valsad and Gujara, 2005-06 to 2015-16(Mt/ha)

Source: Director of horticulture, Gujarat 2016.NS: Non-significant.

instability of mango production, production variance was decomposed into area variance, yield variance and area-yield covariance for the selected districts as well as Gujarat State using Hazell (1982) decomposition technique. The perusal of Table 5 showed that the yield variance accounted 104.03 percent of total variance in mango production for the State. The area variance was

Particulars Coefficient of variation

Area Production Productivity

Navsari 0.78 43.59 1.99

Valsad 0.49 63.25 2.44

Gujarat 2.13 202.14 1.62

Table 4. Coefficient of variation of area, production and productivity for mango crop in selected districts and Gujarat, 2005-06 to 2015-16

(Percent)

Particulars Area variance

Yield variance

Area-yield covariance

Navsari 2.24 93.20 4.56

Valsad 0.37 102.65 -3.02

Gujarat State 0.58 104.03 -4.61

Table 5. Sources of variation in mango production in selected districts and Gujarat, 2005-06 to 2015-16

(Percent)

Sum of variance = 100

362


next in line and accounted 0.58 percent. The area-yield covariance was(-)4.61 percent indicating thereby that area-yield covariance showed stabilizing effect on instability of mango production. The yield fluctuation was a dominant source in total variation in production of mango in Valsad district (102.65 percent) followed by Navsari (93.20 percent). Whereas, the area variance was found low in Navsari district (2.24 percent) in total variation in production of mango followed by Valsad (0.37 percent).

It was also observed that the area-yield covariance was negative in Valsad district (-3.02 percent) indicating thereby the stabilizing effect on the instability in mango production. While, the area-yield covariance was positive in Navsari district (4.56 percent) which suggested that the combined effect of area and yield have affected the output instability in the same direction across the time period. The results were inconsonance with Abhiram et al. (2017).CONCLUSIONS

The above results indicated the fact that the growth rates in area under mango cultivation were found positive trend in Navsari and Valsad districts and Gujarat. But the percentages of coefficient of variation were observed lowest in Valsad followed by Navsari and Gujarat State. Whereas, the compound growth rate of production and productivity of mango were found low but positive with high instability in above mentioned areas due to very low productivity of mango in the year of 2008-09 thus it

caused for low production of mango as well. So for increasing the productivity of mango is possible by developing some of the technologies like high yielding pest resistant verities which will increase the productivity and quality of the mango.REFERENCESAbhiram, D., Dhakre, D.S., & Bhattacharya, D. (2017). Fitting

of appropriate model to study growth rate and instability of mango production in India. Journal of Agricultural Science and Digestion,37(3), 191-196.

Director of Horticulture. (2016). Year wise area and production o f m a n g o i n G u j a r a t . R e t r i e v e d f r o m https://doh.gujarat.gov.in/horticulture-census.htm

Hazell, P.B.R. (1982). Instability in Indian foodgrains production (Research. Report No. 30). International Food Policy Research Institute, Washington, U.S.A

Ministry of Agriculture. (2014). Indian horticulture database. R e t r i e v e d f r o m h t t p : / / n h b . g o v . i n / a r e a -pro/NHB_Database_2015.pdf

Parmar, V.R., Shrivastava, P.K., & Patel, B. N. (2012). Study on weather parameters affecting the mango flowering in South Gujarat. Journal Agrometeorology,14(Special issue), 351-353

Singh, R.P., & Rani, N. (2013). To study growth rate of area, production and productivity of fruit crop in Jharkhand. Journal of Economics and Social Development, 9(1), 52-60.

Tirlapur, L.N., Navalur, N.P., & Patil, B.O. (2014). Status of Indian mangoes-A trend analysis. International Journal of Commerce and Business Management, 7(2), 396-399.

363

Younisi and Makadia: Growth and instability in area, production and productivity of mango crop in Gujarat

ABSTRACTIndia is the world's second-largest producer of food next to China and has the potential of being the biggest industry. It is necessary to study the consumption pattern under the changing situations of liberalization, privatization and globalization. The average monthly per capita consumer expenditure (MPCE) in 2004-05 was `599 and 1052 in rural and urban India. The share of non-vegetable items was found to be more in high monthly per capita expenditure groups (`433.49). The expenditure on cereals was found to be increasing with increase in total expenditure. Thus the consumption of cereals declined in Kolkata over the periods. The monthly per capita consumption of pulses was almost stable over the two periods in Kolkata. The overall per capita monthly expenditure was lowest for butter than curd and paneer. The percent share of per capita monthly expenditure found for curd was lower than ghee and ice-cream. There were positive association between the type of family and annual food expenditure revealed that the annual expenditure would be more for joint families. The food habit dummy exerted a positive influence on food expenditure for consumers. The positive relation implied that the food expenditure would be more for non-vegetarian consumers. The expenditure elasticity was almost near to one in the case of milk, vegetables. The expenditure elasticities for different food items varied between 0.72 and 1.014 in the case of cereals and non-veg items.

KeywordsCereals, food expenditure, liberalization, MPCE, privatization.

JEL CodesB21, D03, E21, P46, Q18.

1* 2Arnab Roy and Ravinder Malhotra

1Ph.D. Scholar, Department of Agricultural Economics, University of Agricultural Sciences, GKVK, Bengaluru-560065

2Principal Scientist, Dairy Economics Division, ICAR- National Dairy Research Institute, Karnal-132001


Received: November 19, 2017 Revision Accepted: April 15, 2018

An Economic Analysis of Food Consumption Pattern in West Bengal with Special Reference to Dairy Products


DOI: 10.5958/2322-0430.2018.00144.0


www.soed.in


UGC Approved


364

INTRODUCTIONThe understanding of consumption patterns of the

households of different income groups is essential for the development of successful food security policies. As food is basic necessity, the measurement of food consumption is also used as an alternative to income in assessing household well-being. This provides a benchmark for evaluating the welfare programmes. Further, the empirical information on temporal changing in consumption patterns provides an insight into the living conditions of the households and human resources of a country. Increasing number of working women, rise in per capita income, changing lifestyles and increasing level of affluence in the middle income group have also brought about changes in food habits. Kumar and Mittal (2002) noticed more than threefold increase in

consumption of fruits from a level of 2.3 kg in 1983 to 10.2 kg in 1999 among the rural consumer and from a level of 3.6 kg in 1983 to 14.9 kg in 1999 among urban consumers. Rapid urbanization and sociological changes like the desire on the part of the housewives to spend less time in the kitchen, the increased value for leisure, weakening of family ties, increased impact of television and its advertisement as well as changing lifestyles of the families, have brought about the changes in food consumption pattern. The study on food consumption pattern or expenditure pattern is very important as it is related to poverty and standard of living of our society. The analysis of changing food consumption pattern over time would help in designing appropriate policies related to food production and distribution. The food production pattern in India is

diversifying towards high value crops. The decline in cereal consumption was attributed to the diversification of food production, easy access of high value commodities, changed tastes and preferences. Randhawa and Chahal (2005); Shrivastava et al. (2006); Murthy and Subramanya (1974). Radhakrishna and Ravi (1992); Kumar (1979); Singh and Patel (1982), Chandha, (2007) reported that, a rise in per capita income, urbanization, changing tastes and preferences were the dominating factors for the change in the per capita consumption of cereals. The underlying objective of all development programmes is to improve the consumption levels of the population especially of those belonging to the poorer strata of the society (Kumar & Mathur, 1996). Since food is the most important item of the consumption basket, an analysis of the changes in food consumption pattern over time has a special significance which is the most important component for low and middle income groups. Food expenditure pattern is an excellent indicator of economic well-being of people. If the society is wealthy proportionately high expenditure will be made on secondary necessities, comfort, luxury products and conspicuous consumption. On the other, if the society is at subsistence level, people will spend proportionately more on food. Engel's law also states that the poorer the family, the greater is the proportion of its total income devoted to provision of food. With this background the present study was undertaken with the following objectives:

i. to study the changes in food consumption pattern in rural and urban areas of Kolkata, and

ii. to study the factors influencing food consumption in rural and urban areas

METHODOLOGYFor the present study, Kolkata was purposively

selected as this metropolitan due to massive growth of urban population and more concentration of population. Multistage random sampling technique was adopted for the selection of sample household to achieve the stipulated objectives of the present study. The primary data on various aspects relating to the consumption patterns of the sample households were collected through the personal interview method using suitably designed pre-tested schedule/ questionnaire for the year 2016-2017. Kolkata Municipality area is divided into 16 boroughs and each borough consists of several wards. Hence, 5 boroughs (namely II, VIII, IX, XI, and XIV) were selected randomly and from each borough 2 wards were selected randomly. Further, from each selected ward, two colonies were selected randomly and from each colony ten household were selected. Thus, a sample of 200 households from two wards was drawn. Based on food consumption pattern in urban, semi-urban, and rural areas of Kolkata district, the food items selected includes rice, wheat, ragi, maize and jowar, among cereals, peas, soybean and gram among pulses, fruits, vegetables, edible oil, milk and milk products, nuts/dry fruits, meat,

egg, sugar, salt, spices, beverages, etc.Analytical Tools

The data collected were tabulated and analyzed. The tools used for the analysis of the data are presented and discussed below. The tabular analysis was used to analyze the changes in the food consumption pattern and expenditure. The following functional relationship was estimated (Musebe & Kumar, 2006) to compute the expenditure elasticities of demand for different food items.

Log -inverse function: ln Y = a + a (1/X) + u0 1

Log-log-inverse function: ln Y = b + b ln X + b (1/X) 0 1 2

+ uWhere,

Y = Monthly per capita expenditure on a specific food item in rupees.

X = Monthly per capita total consumption expenditure in rupees

a , b , a , b and b are the regression coefficients0 0 1 1 2

u = Random error termThe expenditure elasticities (ex) for each commodity

were derived from the derivatives of each equation with respect to expenditure. Both functions were fitted to the data and log-log-inverse function proved to be superior on the basis of the high value of the Coefficient of Multiple Determination (R²) and low standard errors of the coefficients. To study the factors influencing the food consumption pattern in Kolkata district, multiple linear regression analysis was carried out. In the analysis, total annual expenditure on food is used as a dependent variable and the other independent variables used were family size, type of family dummy, food habit dummy and annual income. The functional form of regression equation used was

Y = a + a X + a X + b D + b D + uo 1 1 2 2 1 1 2 2

Where,Y= Annual food expenditure (`/family)X = Family size (No.)1

X = Annual income (`/family)2

D = Family type Dummy (value '1' for Joint and '0' 1

for Nuclear family)D = Food habit Dummy (value '1' for Non-Veg and '0' 2

for Veg family)RESULTS AND DISCUSSION

Overall the highest percent share of per capita monthly expenditure on milk and milk products was incurred on rasogolla followed by milk (26.61 percent) and other products. In non-salaried groups, the percent share on milk, sweet, curd, and paneer was found to be 25.16, 22.16, 3.53 and 4.96 percent, respectively (Table 1). In salaried groups, the percent share on milk, ghee, butter, paneer, rasogolla, and ice-cream was found to be 27.23, 6.1, 1.78, 1.49, 37.83 and 4 percent respectively. Among different occupational groups, per capita expenditure on rasogolla was found to be highest in salaried group followed by non-salaried groups. The total

365

Roy and Malhotra: An economic analysis of food consumption pattern in West Bengal with special reference to dairy products

Items Monthly per capita expenditure

Non-Salaried(n =31)1

Salaried(n =169)2

Overall(N=200)

Milk(Liquid and powder)

125.10(25.16)

111.52(27.23)

115.05(26.61)

Curd 17.54(3.53)

10.25(2.50)

12.15(2.81)

Ghee 24.45(4.92)

24.98(6.10)

24.84(5.75)

Butter 6.43(1.29)

7.27(1.78)

7.05(1.63)

Paneer 24.68(4.96)

6.12(1.49)

10.95(2.53)

Sweet 112.70(22.66)

77.05(18.82)

86.32(19.97)

Rasogolla 173.85(34.96)

154.91(37.83)

159.84(36.97)

Ice cream- 12.53(2.52)

16.40(4.00)

15.39(3.56)

Total 497.28(100.00)

409.51(100.00)

433.85(100.00)

Table 1. Monthly expenditure on milk and milk products in different occupational groups

(` Per capita)

Figures in the parentheses indicate percentage to the total expenditure.

per capita monthly expenditure on milk (`125.10) was found to be higher than the ghee (`24.45) and curd (`17.54) for non-salaried groups. The overall per capita

Consumption items

Low(<7500)(n =36)1

Lower middle(7500-10000)

(n =63)2

Upper middle(10000-12500)

(n =64)3

High(>12500)(n =37)4

Overall(N=200)

Milk and milk products

393.67(23.43)

469.62(22.60)

528.17(21.80)

569.95(20.00)

493.25(21.85)

Cereals 234.21(13.94)

274.27(13.20)

277.8811.47

305.9310.73

274.07(12.14)

Pulse and pulse products

124.94(7.43)

155.03(7.46)

158.53(6.54)

160.98(5.65)

151.84(6.73)

Fruits and vegetables

405.60(24.14)

412.79(19.86)

439.62(18.15)

495.61(17.39)

435.40(19.29)

Edible oil 120.18(7.15)

129.66(6.24)

133.56(5.51)

150.93(5.30)

133.13(5.90)

Non-vegetables 119.30(7.10)

303.53(14.61)

543.86(22.45)

769.55(27.00)

433.49(19.20)

Sugar and jaggery 48.69(2.90)

59.03(2.84)

60.45(2.50)

66.69(2.34)

59.04(2.62)

Other foods item 233.94(13.92)

274.10(13.19)

280.30(11.57)

330.3411.59

279.26(12.37)

Total 1680.54(100.00)

2078.03(100.00)

2422.37(100.00)

2849.97100.00

2257.70(100.00)

Table 2. Monthly expenditure on food products across different food expenditure groups(` per capita)

Figures in the parentheses indicate percentage to the total expenditure.

366


monthly expenditure was lowest for butter than curd and paneer. The percent share of per capita monthly expenditure found for curd was lower than ghee and ice-cream.

The per capita monthly expenditure on milk and milk products and others food items in different MPCE groups is given in Table 2, which showed that the average per capita monthly expenditure on milk and milk products was found maximum in high expenditure MPCE group (`569.95) which is more than that of the overall average per capita monthly expenditure on milk and milk products (` 493.25).The share of non-vegetable items was found to be more in high monthly per capita expenditure groups (`433.49). The expenditure on cereals was found to be increasing with increase in total expenditure. There was no such pattern of change in the consumption of cereals in urban areas.

The expenditure elasticity was almost near to one in the case of milk, vegetables in this area. The expenditure elasticities for cereals and non-veg items are 0.72 and 1.14 respectively in this area (Table 3). Radhakrishna et al. (1979) used NSSO data to study expenditure elasticity for rice varied between 0.35 and 1.32 in Punjab and Mysore (Karnataka). As far as cereals it varied from 0.39 and 0.70 for Punjab and Assam (Table 3). The estimated expenditure elasticity of demand for all food items was positive. The elasticities were less than one for all the food items, except egg fish and meat, and vegetable item.

The regression coefficients of family size and annual income were not only positive but also significant in all

Food items Expenditure elasticities

Cereals 0.72Pulses 0.78Milk and milk products 0.90Edible oil 0.82Egg, fish, and meat 1.13Vegetables 1.14

Table 3. Estimated expenditure elasticities of demand for different food items

Variables Unstandardized coefficients

b S.E.

Intercept 26436.40

Family size (X )1***11143.02 1421.81

Annual income (X )2***0.082 0.023

Family type dummy (D )1**4493.66 1998.53

Food habit dummy (D )2NS6619.19 3883.98

R² *** 0.85

Table 4. Factors influencing food consumption expenditure

*** and ** Significant at one and five percent level.NS: Non-significant.

the cases barring the annual income of consumers. The results thus, clearly showed that annual family expenditure on food was found to increase with an increase in family size. The increase in the annual family expenditure on food with every increase in the family member was to the extent of `11143.02 in the case of urban consumers (Table 4). The regression coefficients of family type dummy were found to be positive and significant only for family type.

The positive association between the type of family and annual food expenditure revealed that the annual expenditure was more for joint families. The food habit dummy exerted a positive influence on food expenditure for consumers. The positive relation implied that the food expenditure would be more for non-vegetarian consumers. However, this relationship was not statistically established. Wandel (1995) used multivariate analysis to study factors influencing the consumption of vegetables and fruits among Norwegian consumers. The factors, which determined consumption were sex, age, income and household structure.CONCLUSIONS

The consumption of cereals in physical quantities showed a declining trend across all the income groups in Kolkata over the two periods. The expenditure elasticity was highest for vegetables (1.14) and lowest (0.72) for cereals items in this city. The functional analysis carried out to study the factors influencing food expenditure revealed that there would be an increase in the annual family expenditure on food with every increase in the family size

to the extent of `11143 in consumers. Similarly, every rupee rise in the annual income would increase the annual expenditure on food by about `0.08. Almost all the respondents irrespective of their locations preferred to buy milk on a daily basis and milk products on a monthly basis.Policy Implications

The implications based on the findings of the current study are the quantities of cereals and pulses consumed declined over the two periods, but the monthly per capita expenditure on these two food items showed an increase of more than 60 percent. Cereals and pulses being an essential component of food, the price rise needs to be kept under control. Due importance to be accorded to cereal and pulses from the viewpoint of food and nutritional security until the level of per capita income is large enough to permit the purchase of adequate quantities of horticulture and livestock products. The per capita consumption of non-cereal based food and processed foods is increasing implying that there is a great demand for these products in the urban and semi-urban areas. The share of these products in the total expenditure is increasing over the years. Increase in income, education and easy availability of ready-to-eat foods may bring about enormous changes in the food consumption pattern in the near future. Therefore production, processing and distribution of processed foods should have priority in the policies of the state.REFERENCESChandha, G.K. (2007). Changing the structure of demand for

agricultural commodities: Preparing for the future. Indian Journal of Agricultural Marketing, 21(1), 1-42.

Kumar, P. & Mathur, V.C. (1996). Structural changes in the demand for food in India. Indian Journal of Agricultural Economics, 51(4), 664-673.

Kumar, P., & Mittal, S. (2002). Long-term changes in dietary patterns and food demand in Uttar Pradesh. Indian Journal of Agricultural Marketing, 16(2), 1-9.

Kumar, S. (1979). Changes in consumption experiment all India rural (1960-61 to 1973-74). Sarvekshama, 3(2), 9-14.

Murthy, G.V.S.N., & Subramanya, T. (1974). Some results on the power regressions with an application to Indian consumption patterns. Anvesak, 4(2), 179-188.

Musebe, O.R., & Kumar, P. (2006). Food expenditure pattern of rural household in Andhra Pradesh. Indian Journal of Agricultural Marketing, 20(1), 131-139.

Radhakrishna, R., Murthy, G.V.S., & Shah, N.C. (1979). Models of consumer behaviour for the Indian economy. Ahmedabad: Sardar Patel Institute of Economic and Social Research.

Radhakrishna, R., & Ravi, C. (1992). Effects of growth, relative price and preferences on food and nutrition. Indian Economic Review, 27, 303-323.

Randhawa, G.S., & Chahal, S.S. (2005). Consumption pattern of milk and milk products in rural Punjab. Indian Journal of Agricultural Economics, 61(3), 141.

Shrivastava, K.K., Saxena, P., & Sahai, V. (2006). Food security in India: Status and strategy analysis. Indian Journal of Agricultural Economics, 61(3), 426.

Singh, B. & Patel R.K. (1982). An analysis of consumption

367

Roy and Malhotra: An economic analysis of food consumption pattern in West Bengal with special reference to dairy products

pattern in rural and urban Muzaffarnagar (UP). The Asian Economic Review, 24(1), 1-16.

Wandel, M. (1995). Dietary intake of fruits and vegetables in

Norway: Influence of life phase and socio-economic factors. International Journal of Food Science and Nutrition, 8(4), 341-352.

368


ABSTRACTOrganic fruits and vegetables which are free from chemicals contributes many nutritional benefits to human health as well as to overcome disease hazards. In India, from last decade organic farming has gained more importance and consumers are willing to purchase organic produce. Double-Bounded Dichotomous Choice (DBDC) method of Contingent Valuation and Binomial Multiple Logistic Regression model has been used to estimate the factors affecting the WTP of the farmer. It was estimated that the mean willingness to pay for organic French bean was ̀ 89.6 per kg, for organic banana was 51 per kg and finally for organic grapes was `151.33 per kg. It is also found that the market potential for organic french bean in domestic market was estimated as ̀ 7.37 crore, for organic banana it was estimated as ̀ 4.61 crore and for organic grapes it was estimated as ̀ 10.41 crore. From the results, it can be concluded that there will be a huge demand and market potential for organic fruits and vegetables in future days.

KeywordsDBDC, multi-logit, organic produce, WTP.

JEL CodesC67, G13, M31, Q51, R15.

`

1* 2 3L.K. Adarsha , M. Mohan Kumar , and D. Jennie Samuelnavaraj

1Department of Agricultural Economics, Bidhan Chandra Krishi Viswavidyalaya, Mohanpur- 741252 (W.B)2Department of Agricultural Economics, University of Agricultural Sciences, Bengaluru-5600653Department of Agricultural Economics, Tamil Nadu Agricultural University, Coimbatore-641003



Consumers' Willingness to Pay for Organic Fruits and Vegetables and its Market Potential in Bengaluru District, Karnataka


DOI: 10.5958/2322-0430.2018.00145.2


www.soed.in


UGC Approved


369

INTRODUCTION Organic fruits and vegetables are the fruits and

vegetables are produced without the use of chemical fertilizers and pesticides. Due to food safety and environmental quality concerns, policymakers worldwide are attaching more importance to the production and consumption of such food products. The consumption of fresh organic food products could enhance the prevention of some of the health hazards associated with the consumption of conventional foods. Organic farming systems have attracted increasing attention over the last one decade because they are perceived to offer some solutions to the problems currently besetting the agricultural sector. Organic farming has the potential to provide benefits in terms of environmental protection, conservation of non-renewable resources and improved food quality (Hass et al., 2005; Worthington, 2001).

India's rank in terms of World's Organic Agricultural

land was 15 as per 2013 data. The total area under organic certification is 5.71million ha (2015-16). This includes 26 percent cultivable area with 1.49 million ha and rest 74 percent (4.22 million ha) forest and wild area for collection of minor forest produces (APEDA, 2015). Due to increase in literacy level and awareness about negative effect of chemical usage on fruits and vegetables cultivation, consumers are now so much cautious of their health and issues. Meanwhile, there is a huge scope for organic produce in cities where people are more educated and with high income. Also there is huge scope for market potential in domestic as well as in foreign markets. Hence the study has been taken to examine the consumer behaviour towards willingness to pay for organic fruits and vegetables and its market potential.METHODOLOGY

Data for estimation of market potential was collected from organic consumers and traders in Bengaluru city. The field survey was conducted for the year 2015-2016,

the primary data was collected through personal interview method using pre-tested and well-structured schedules designed for the purpose. To analyze the consumer preference to organic agricultural produce 45 consumers were selected randomly. To study the market potential for organic produce data was collected from 45 organic traders also.Double-Bounded Dichotomous Choice (DBDC) Method of Contingent Valuation

Recently, the most frequently used question formats for estimating willingness to pay (WTP) are single bound and double-bound dichotomous choice formats. In the single-bound format each respondent is asked once whether he/she would be willing to pay a specified bid amount, and in the double-bound format, after the single-bound question, he/she is asked once again whether he/she would be willing to pay another bid amount. The single-bound format is incentive compatible theoretically and has the advantage of making the responses easy since it is similar to our real purchase actions. Although the double bound format do not possess such advantages, it is more efficient in estimating willingness to pay (WTP) since it makes the respondent's WTP more restrictive.

There are three ways why Double-Bounded Dichotomous Choice (DBDC) is more efficient than single bounded. First, the yes-no and no-yes provides a clear bound of WTP, second the no-no and yes-yes estimate efficiency gains, and third the number of responses is substantially increased, especially for larger sample sizes. To avoid initial bid biases, the initial bids were randomly assigned to respondents.

The first question was a simple yes or no question “Are you willing to pay amount X for organic produce?” If the answer was yes (or no), another question followed to elicit a maximum (or minimum) value. Hence, the respondents identified two amounts that limited their maximum WTP (Misra et al., 1991; Wilner et al., 2012). Contingent Valuation Approach

To examine the factors affecting consumers 'willingness to pay for Organic produce Double Bounded Dichotomous Choice (DBDC) method of Contingent Valuation was employed. For this, a hypothetical situation of organic market in which the consumers have to pay a definite amount of money for purchasing organic produce and their willingness to pay (WTP) was elicited. Here, the initial bid was proposed to the consumers and depending upon the answer to the first bid, a second bid was proposed, which was higher than the first bid for “yes” response and lower for “no” response for the initial bid. It was denoted that the first bid with P*, and the second bid with PH if it was higher and with PL, if it was lower than P*. Accordingly, there were four possible response groups: (G1) respondents who said “yes” to both the bids,

so that WTP ≥ PH; (G2) those who said “yes” to the first

bid, but “no” to the second bid so that P* ≤ WTP < PH; (G3) those who said “no” to the first, but “yes” to the

second bid, so that PL ≤ WTP< P*; and (G4) those who said “no” to both bids, so that WTP < PL. The bids were distributed randomly in the survey schedules to get the desired variation (Owusu & Anifori, 2013).Model Used

Since the data was collected by using DBDC method, we had two discrete responses from every consumer for the first and second bids. Therefore, we used a Binomial Multiple Logistic Regression model to estimate the factors affecting the WTP of the farmer. It was estimated by Maximum likelihood (Elsa et al., 2007).

Y = b0 + b X + b X + b X + b X +b X +e 1 1 2 2 3 3 4 4 5 5

Where, Y = Average additional willingness to pay in rupees,

X = Household income in rupees, X = Age in years, X = 1 2 3

Years of education, X = Family size, b X = bid, e = Error 4 5 5

term and b = constant0

So, double bounded dichotomous choice was used in this study to elicit the WTPMarket Potential Estimation

The market potential of the organic products is estimated by multiplying the mean willingness to pay by the number of population depending on the sample unit used. Setting an upper boundary on the market size, the estimated market potential could be expressed in either units or sales. Estimating the market potential for a product requires specific information such as the number of potential buyers, an average selling price, and an estimate of the purchasing rate for a specific period of time. Once all this information are obtained, the empirical market potential is computed as,

M =N×P×A Where,

N= Number of potential buyers, P = average willingness to pay, A =Average annual purchasing.

Consumers' willingness to pay and market potential for organic agricultural produce

Consumers' willingness to pay and market potential for organic agricultural produce is estimated for French bean, Banana and Grapes which are commonly purchased by sample consumers in Bangalore. While evaluating the WTP, consumers have only two options, either to pay or not to pay premium price. This gives the dependent variable (AWTP) a special feature in that is it takes two outcomes. Therefore, OLS is not an appropriate method to analyze the WTP, because of the nature of the dependent variable. The WTP expressed by consumers can only be positive or nil. The appropriate technique in this case is applied such as Tobit estimation. RESULTS AND DISCUSSIONTobit Analysis for Estimation of Willingness to Pay for Organic French Bean

The willingness to pay for organic French bean was estimated by using Tobit model keeping market price of conventional French bean as base value. The results of the

370


maximum likelihood estimation (MLE) of the Tobit model presented in Table 1. The willingness to pay was influenced by several parameters viz., age, education, family income, family size, bid value and gender. The consumer's income, education and gender have positively influencing his/her WTP for organic French bean and significant at 0.10, at 0.05 and at 0.05 probability level, respectively. An increase in the income of consumer by one hundred rupee above mean level leads to increase in the consumer WTP by `56, with the increase in the education of the consumer by one level leads to increase in the consumer WTP by ̀ 277 and with the increase in the bid value of the consumes by one hundred rupee above mean level leads to decrease in the WTP by $65. Family size of the consumers had no significant impact on the WTP premium price for organic French bean.

Additional willingness to pay premium price for organic French bean was obtained by indicating the prevailing prices of conventional French bean in the market as the base value. On an average respondents were willing to pay `57.60 (Table 2) more per kg of organic French bean than conventional French bean. The actual average premium price paid was `38 more per kg. The mean willingness to pay for organic French bean was `89.60 per kg of organic chili than conventional French bean (`32 per kg). Tobit Analysis for Estimation of Willingness to Pay for Organic Banana

The willingness to pay for organic banana was estimated by using Tobit model keeping market price of conventional banana as base value. The results of the maximum likelihood estimation (MLE) of the Tobit model presented in Table 3. The willingness to pay was influenced by the several parameters viz., age, education, family income, family size, bid value and gender the consumer's income and education were positively influencing his/her WTP for organic banana and significant at 0.05 and at 0.01 probability level, respectively and family size have negative impact on the WTP for organic banana at 0.05 probability level. An increase in the income of consumer by one hundred rupee above mean level leads to increase in the consumer WTP by ̀ 42, with the increase in the education of the consumer by one year leads to increase in the consumer WTP by `140 per year and with the increase in the family size of the consumer by member from above level leads to decrease in the consumers WTP by `3 per person. Age of the consumers has no significant impact on the WTP premium price for organic banana.Additional willingness to pay premium price for organic banana was obtained by indicating the prevailing prices of conventional banana in the market as the base value. On an average respondents were willing to pay `26.0 more per kg of organic banana than conventional banana but, actually average premium price paid was `15 more per kg (Table 4). The mean

Parameters Coefficients Standard error

Z-test

Age 0.01 0.39 0.03

Bid value ***0.65 0.07 -8.74

Income *0.56 0.42 1.33

Family size - 4.22 3.66 -1.15

Education 2.77** 1.42 1.95

Gender 17.07** 11.26 1.52

Constant 45.72* 32.12 1.42

Mean additional price 57.60

Log likelihood -88.40

Table 1. Results of Tobit model for the willingness to pay (WTP) for organic French bean

***, **, and * Significant at 1, 5, and 10 percent level.

Particulars French bean

Prevailing prices of conventional French bean 32.00

WTP for organic French bean 89.60

Actually paid for organic French bean 70.00

Table 2. Summary of willingness to pay (WTP) for organic French bean

(`/kg)

Parameters Coefficients Standard error Z-value

Age 0.0013 0.16 -0.01

Bid value***-0.63 0.06 -10.54

Income **0.42 0.21 1.96

Family size **-3.03 1.65 -1.83

Education ***1.40 0.58 2.41

Gender NS-2.98 4.40 -0.68

Constant*16.99 13.21 1.29

Mean additional price 26.00

Log likelihood -60.29

Table 3. Results of Tobit model for the willingness to pay (WTP) for organic Banana

***, **, and * Significant at 1, 5, and 10 percent level.NS: Non-significant.

Particulars Banana

Prevailing prices of conventional Banana 23.00

WTP for organic Banana 51.60

Actually paid for organic Banana 40.00

Table 4. Summary of willingness to Pay (WTP) for organic banana

(`/kg)

371

Adarsha et al.: Consumers' willingness to pay for organic fruits and vegetables and its market potential

willingness to pay for organic banana was $51 per kg of organic banana than conventional banana ($25 per kg). Tobit Analysis to Estimate Willingness to Pay for Organic Grapes

The willingness to pay for organic grapes was

estimated by using Tobit model keeping market price of conventional grapes as base value. The results of the maximum likelihood estimation (MLE) of the Tobit model presented in Table 5. The willingness to pay is influenced by several parameters viz., age, education, family income, family size, bid value and gender the consumer's age, income and education have positively influenced his/her WTP for organic grapes and significant at 0.01, at 0.10 and at 0.05 probability level. An increase in the income of consumer by one hundred rupee above mean level leads to increase in the consumer WTP by ̀ 36, increase in the bid value of the consumes by one hundred rupee above mean level leads to decrease in the WTP by $ 71 and with the increase in the education of the consumer by one level leads to increase in the consumer WTP by `338 and increase in the age of the consumers by one year consumer WTP increases by `220. Gender had no significant effect on WTP for premium price for organic grapes. Additional willingness to pay value for organic grapes was obtained by indicating the prevailing prices of conventional grapes in the market as the base value. On an average respondents were willing to pay ̀ 81.33 (Table 6) more per kg of organic grapes than conventional grapes but, actually average premium price paid was $ 15 more per kg. The mean willingness to pay for organic banana was `151.33 per kg of organic grapes than the price of conventional grapes (`70 per kg). Estimation of Market Potential for Organic Produce

The market potential is estimated using average price of organic produce, number of potential buyers of the produce and purchasing frequency of the produce. In this study the mean willingness to pay price is calculated by using average willingness to pay for the organic produce by the sample consumers in the domestic market (Bengaluru). It is assumed that only 0.25 percent of the total population of Bengaluru is considered as potential buyers of organic produce. In importing countries, it is assumed that only 0.50 percent of total population is considered as potential buyers of the organic produce.

The market potential was calculated by using factors such as frequency of purchase of organic produce per year, number of potential buyers of the organic produce in the locality, price of the produce (In this study mean additional willingness to pay was taken as price of the organic produce. Finally, market potential was estimated in both domestic and export markets).Market Potential for Organic Produce in Domestic Market

The perusal of Table 7 indicates the market potential for organic agricultural produce in domestic market. The results indicated that market potential for organic French bean, Banana and Grapes are `7.99, `3.60 and `11.28 crores, respectively.Market Potential for Organic Produce in Foreign Market

The market potential for organic produce in export markets was estimated with some limitations regarding

Parameters Coefficients Standard error Z-test

Age ***2.20 1.05 2.09Bid value ***-0.71 0.11 -6.45Income

*0.36 0.24 1.50Family size

NS-6.56 9.46 -0.69Education 3.38** 1.65 2.05Gender NS20.64 28.50 0.72Constant -71.85 87.84 -0.82Mean additional price 81.33Log likelihood -112.31

Table 5. Results of Tobit model for the willingness to pay (WTP) for organic Grapes

***, **, and * Significant at 1, 5, and 10 percent level.NS: Non-significant.

Particulars Grapes

Prevailing prices of conventional Grapes 70.00

WTP for organic Grapes 151.33

Actually paid for organic Grapes 125.00

Table 6. Summary of willingness to Pay (WTP) for Grapes

(`/kg)

the data on prices of organic produce in foreign markets because it was not possible to collect data regarding prices of organic markets in the foreign markets. So unit export price (`30.78) of the organic produce in the overseas market is estimated by dividing the total volume of the produce exported by total value realized. The unit price calculated was `30.78 per kg. The estimated results of market potential for organic products in the overseas market are presented in Table 8. The results indicated that the market potential for organic produce in foreign markets is ̀ 2463.56 crore.CONCLUSIONS

The mean willingness to pay for organic French bean was `89.60 per kg. On an average respondent were willing to pay `57.60 more per kg of organic french bean than the price of conventional french bean. The market potential for organic French bean in domestic market was estimated as ̀ 7.37 crore. The mean willingness to pay for organic banana was `51 per kg. On average respondents were willing to pay `26 more per kg of organic banana than the price of conventional banana. The market potential for organic banana in domestic market was estimated as `4.61 crores. And the mean willingness to pay for organic Grapes was ̀ 151.33 per kg. On an average respondent were willing to pay ` 81.33 more per kg of organic Grapes than the price of conventional Grapes. The market potential for organic Grapes in domestic market was estimated as `10.41 crore. Hence it could be concluded that there is huge scope in future for organic

372


Variables French bean Banana Grapes Total

Frequency of purchase per month 4.26 4.26 4.26 4.26Frequency of purchase per year 51.2 51.2 51.2 51.2Potential buyers of the products 270993 270993 270993 270993Mean additional WTP (` ) 57.6 26 81.33 334.88Estimated market potential in crore (`) 7.99 3.60 11.28 46.46

Table 7. Estimation of market potential for organic products in the domestic market

It is assumed that out of total population in Bengaluru (10,839,725), 0.25 percent of the population is considered as potential consumers of organic produce (2015).Based on the source http://www.organic-food-for-everyone.com/organic-india.html.

Variables Organic produce

Frequency of purchase per month 4.0

Frequency of purchase per year 48

Potential buyers of the products(Million) 16.67

Mean price of organic produce (`) 30.78

Estimated market potential in crore (`) 2463.56

Table 8. Estimation of market potential of organic products in the export market

1. It is assumed that out of total population in the importing countries (3334.47 million) only 0.5 percent is of the population (16.67 million) is considered as potential consumers of organic produce in the foreign market.2. Frequency of purchase of organic produce is considered a weekly once means monthly four times.

farming so government should take proper policies to create awareness among framers as well as in consumers in its aesthetic values.REFERENCESAPEDA. (2015). Organic products. Retrieved from

h t t p : / / a p e d a . g o v. i n / a p e d a w e b s i t e / o r g a n i c /

Organic_Products. htm.Elsa, R., Victoria, L., & Beatriz, L. (2007). Willingness to pay

for organic food in Argentina: Evidence from a consumer survey. Paper presented at Seminar International Marketing and International Trade of Quality Food Products, Italy.

Hass, G, Geier U., Frieben, B., & Kopke, U. (2005). Estimation of environmental impact of conversion to organic agriculture in Hamburg using the Life-Cycle-Assessment method. Retrieved from http: / /orgprints .org/ 13935/1/HH_LCA_Publ05.pdf

Misra, S.K., Huang, C.L., & Ott, S.L (1991).Consumer willingness to pay pesticide-free fresh produce. Western Journal of Agricultural Economics, 16, 218–227.

Owusu, V., & Anifori, M.O. (2013). Consumer willingness to pay a premium for organic fruit and vegetable in Ghana. International Food and Agribusiness Management Review, 16 (1), 67-86.

Wilner, J. P., Tim, H., & Fred, H. (2012). Willingness to pay for biodiesel in diesel engines: A stochastic double bounded contingent valuation survey. Paper presented at the American Agricultural Economics Association Annual Meeting, Portland

Worthington, V. (2001). Nutritional quality of organic versus conventional fruits, vegetables and grains. The Journal of Alternative and Complementary Medicine, 7(2), 161-173.

373

Adarsha et al.: Consumers' willingness to pay for organic fruits and vegetables and its market potential

ABSTRACTThe present study was conducted with the objectives to obtain the information regarding the problem faced by borrowers as well as bankers while dealing with financial aspects. It was observed that highest incidence of the problem faced in the utilization of loan at the time of disbursement of bank loans by 100.00 percent respondents, followed by disbursement of loan/ installment release by 55.00 percent, other needs by 46.67 percent and the amount by 21.67 percent, respectively. The foremost problem faced as the Guarantor / Securities / Certificates by 100.00 percent, followed by guidance by the bank was 71.67 percent, bank process by 66.67 percent, forms issued by the bank by 60.00 percent, knowledge about type of loan by 51.67 percent, filling up of forms by 48.33 percent and then knowledge about banks by 38.33 percent, respectively. While the highest incidence faced by the respondents regarding the supervision and other agricultural & allied by 100.00 percent, followed by interest rates faced 81.67 percent, funds & capital faced by 80.00 percent, knowledge and skill problem faced by 76.67 percent and other problems viz; transportation, bank knowledge, repayment period, insect-pest and diseases and marketing. The foremost important problems that the banker in general faced were the existence of high overdue it was due to non-repayment of dues. Most of the borrowers were not sincere in repaying their due installment and this caused stagnation in lending for further developmental activities and others wanting to take loan. The co-operative bank in general gives financial assistance to people from all walk of life. Even financing to the remote areas and other localities, and this has caused supervision problems due to poor connectivity and the distance between the bank branch and the loanees. The bank related information that needs to be passed to farmers gets delayed. Also the distance has caused the problems in relation to training that needed to be imparted to them.

KeywordsBank, co-operatives, credits, finance, marketing, prices.

JEL CodesQ13, G21, M31, P42, P33, H81.

1 2*Keviu Shuya and Amod Sharma

1 1Research Scholar and Professor-cum-Head, Department of Agricultural EconomicsNagaland University, SASRD, Medziphema Campus, District Dimapur - 797 106 (Nagaland)


Received: January 27, 2018 Revision Accepted: May 21, 2018

Problems Faced by the Borrowers in Utilization and Acquiring of Co-operative Bank Loans in Nagaland


DOI: 10.5958/2322-0430.2018.00146.4


www.soed.in


UGC Approved


374

INTRODUCTIONAgriculture is the mainstay of Indian economy not

only in terms of contribution to the gross domestic product but also the people dependent upon it. A high level growth of agriculture is essential both for achieving the objective of food security at macro and micro levels and also to alleviate poverty in India. Approximately 15.70 percent (at current price) of the GDP is contributed by agriculture and allied sector, with about 52.10percent of the country's population dependent on this sector and accounts for about 12 percent share of the country's exports (Ministry of Finance, 2010).

Agriculture is far the most important sector of the Indian Economy and hence the need for rapid growth of production and productivity has been considered as crucial for economic development. In the last few years the farmers are experiencing a rapid change where increased use of capital and credit which has become a common phenomenon rather than an exception. These further increase the overall capital as well as credit requirement of the farmers where finance is being considered as the lifeblood of all economic activities. The increased pressure of population and the need to raise living standard has created the scopes for better financial

institutions and better bank networks. To meet the challenges ahead, greater emphasis and facilities to boost them up has been underlined by the Bankers and the policy makers (Sharma et al., 2002).

India having one of the largest networks of Co-operative Banks plays a key role in the development of the rural sector in general and agriculture in particular. They are engaged in several economic activities such as disbursement of credit, distribution of agricultural inputs and in arranging storage, processing and marketing of farm produce. Over the years, although there is massive expansion of financial infrastructure including agricultural financing in the country, the pace of development in the North-eastern India is however, not up to the mark (Reserve Bank of India, 2012).

Microfinance is a broad term that includes deposits, loans, payment services and insurances to poor. The concept of microfinance and microcredit are used interchangeably. But microcredit does not include savings; hence microfinance is more appropriate term (Vaibhav, 2012). The concept is understood as providing poor families with very small loans to help them engage in productive activities or grow their tiny businesses. A success indicator of microfinance lies in a 'credit-plus' approach, where the focus has not only been on providing credit, but to integrate it with other development activities. Today microfinance is very much in the agenda of public policy and it has been increasingly used as a vehicle for reaching the otherwise unreachable poor in the country (Sharma, 2002).

A co-operative bank is a financial entity which belongs to its members, who are at the same time the owners and the customers of their bank. Co-operative banks are often created by persons belonging to the same local or professional community or sharing a common interest. Co-operative banks generally provide their members with a wide range of banking and financial services like loans, deposits, banking accounts, etc. (Shuya & Sharma, 2014).

Keeping in view the present study was conducted with the following specific objectives:

i. to study the utilization of bank loan and problems faced by the borrowers, and

ii. to study the problems faced by borrowers in acquiring bank loans.

METHODOLOGYThe present study was conducted in Dimapur district

of Nagaland state during the year 2008-11, the borrower households were chosen as the unit of analysis for assessing the impact of microfinance. The research was conducted by using primary data collected during the field survey (Shuya, 2012). A stratified random sampling method was adopted for the present study, a total of 60 numbers of borrowers were selected and interviewed with the help of pre-tested structured schedule. In the first stage, out of total four blocks in Dimapur district; two

blocks viz; Dhansiripar and Medziphema were selected randomly, and a complete list of villages pertaining to each selected block were obtained from the block offices and three villages from each block were selected by random technique. Finally, six villages were selected in the second stage and lastly out of six villages, ten beneficiaries respondents from each villages were selected by stratified random sampling techniques; a total sample of 60 respondents.

The perusal of Table 1 revealed that the distribution of the respondents according to landholding were total of 60 respondents from borrowers then having 10 respondents (16.67 percent) with marginal land holdings, 40 respondents with small land holdings (66.67 percent) and 10 respondents with medium size land holding (16.67 percent), respectively. RESULTS AND DISCUSSION

The results presented in Table 2 revealed that problem faced by the respondents in utilizing the bank loan was highest (100.00 percent) at the time of disbursement of bank loans, followed by problem faced in disbursement of loan/instalment release by 55.00 percent, other needs by 46.67 percent and the amount by 21.67 percent, respectively.

The perusal of Table 3 revealed that problems faced by the borrowers in acquiring loans was highest (100.00 percent) for guarantor/securities/certificates, followed by guidance provided by the bank (71.67 percent), during bank process (66.67 percent), forms issued by the bank (60.00 percent), knowledge about type of loan (51.67 percent), filling up of forms (48.33 percent) and then by knowledge about banks (38.33 percent), respectively.

The other related problem faced by the respondents was highest (100.00 percent) with the supervision and other agricultural and allied problems, followed by interest rates faced by 81.67 percent, funds and capital faced by 80.00 percent, knowledge and skill problem faced by 76.67 percent and other problems viz; transportation, bank knowledge, repayment period, insect-pest and diseases, and marketing (Table 4).

Category Land holdings (ha)

Total borrowers

Sample borrowers

Group I (Marginal)

< 0.5 10(16.67)

10(16.67)

Group II (Small) 0.5 - 1.0 40(66.67)

40(66.67)

Group III (Medium)

1.0 - 2.0 10(16.67)

10(16.67)

Total 60(100.00)

60(100.00)

Table 1. Distribution of sample respondents according to landholding

Figure in the parentheses represents the percentage.

375

Shuya and Sharma: Problems faced by the borrowers in utilization and acquiring of Co-operative bank loans in Nagaland

The problems faced by the bankers in financing agricultural and allied activities were the existence of high overdue due to of non-repayment of dues. Most of the borrowers were not sincere in repaying their due installment causing stagnation in lending for further developmental activities and others wanting to take loan. These findings were inconsonance with Sangtam and Sharma (2015).

The co-operative bank in general gives financial assistance to people from all walks of life. Even financing to the remote areas and other localities caused supervision problems due to poor connectivity and the distance between the bank branch and the loanees. The bank related information needs to be passed to respondents got delayed. Also the distance caused the problems in relation to training to be imparted.

The bankers also face uneven distribution of borrowers leading to supervision problem. The idea of

group borrowing and area approach system for better supervision were not always solved as there were no respondents from the target area, while there are individuals who want loan and not from the target groups/area.

It was found that there were borrowers who were unfaithful towards supervision officials especially when the official/staff went for evaluation of the projects. The problem was such that the banker finds it difficult to evaluate the actual benefits or significance of bank loans to the borrowers from those diverted to other activities or consumption purposes. The misutilization of bank loan in the study may not be due to lack/inadequate funds, but the unfaithful nature of the borrowers itself. Misutilization of the funds allotted to them was one of the major factors in the problems of non-repayment of the dues on time for which the loan was taken; similar findings were in the line of findings with Sangtam and Sharma (2015);

Groups Sample Size

Amount Disbursement of loan Time of disbursement

Other Needs

Yes No Yes No Yes No Yes No

Group I 10(16.67)

1(7.69)

9(19.15)

6(18.18)

4(14.81)

10(16.67)

0(0.00)

3(10.71)

7(21.87)

Group II 40(66.66)

8(61.54)

32(68.08)

23(69.70)

17(62.97)

40(66.66)

0(0.00)

17(60.71)

23(71.88)

Group III 10(16.67)

4(30.77)

6(12.77)

4(12.12)

6(22.22)

10(16.67)

0(0.00)

8(28.57)

2(6.25)

Total 60(100.00)

13(21.67)

47(78.33)

33(55.00)

27(45.00)

60(100)

0(0.00)

28(46.67)

32(53.33)

Table 2. Problems faced by the borrower in utilization of loan

Figures in the parentheses represents the percentage.

Number

Problems Group I Group II Group III Total


Guarantor / Securities / Certificates 10(16.67)

0(0.00)

40(66.67)

0(0.00)

10(16.67)

0(0.00)

60(100)

0(0.00)

Guidance fromBank 6(10.00)

4(6.67)

29(48.33)

11(18.33)

8(13.33)

2(3.33)

43(71.67)

17(28.33)

Bank process 7(11.67)

3(5.00)

27(45.00)

13(21.67)

6(10.00)

4(6.67)

40(66.67)

20(33.33)

Form issued by the bank 7(11.67)

3(5.00)

22(36.67)

18(30.00)

7(11.67)

3(5.00)

36(60.00)

24(40.00)

Knowledge about type of loan 3(5.00)

7(11.67)

20(33.33)

20(33.33)

8(13.33)

2(3.33)

31(51.67)

29(48.33)

Filling up of forms 5(8.33)

5(8.33)

17(28.33)

23(38.33)

7(11.67)

3(5.00)

29(48.33)

31(51.67)

Knowledge about banks 4(6.67)

6(10.00)

13(21.67)

27(45.00)

6(10.00)

4(6.67)

23(38.33)

37(61.67)

Table 3. Problems faced in acquiring bank loan(Numbers)


376


Problems Group I Group II Group III Total


Supervision 10(16.67)

0(0.00)

40(66.67)

0(0.00)

10(16.67)

0(0.00)

60(100)

0(0.00)

Others 10(16.67)

0(0.00)

40(66.67)

0(0.00)

10(16.67)

0(0.00)

60(100)

0(0.00)

Interest Rates 7(11.67)

3(5.00)

33(55.00)

7(11.67)

9(15.00)

1(1.67)

49(81.67)

11(18.33)

Funds & Capital 8(13.33)

2(3.33)

32(53.33)

8(13.33)

8(13.33)

2(3.33)

48(80.00)

12(20.00)

Knowledge & Skill 7(11.67)

3(5.00)

34(56.67)

6(10.00)

5(8.33)

5(8.33)

46(76.67)

14(23.33)

Transportation 6(10.00)

4(6.67)

24(40.00)

16(26.67)

7(11.67)

3(5.00)

37(61.67)

23(38.33)

Bank Knowledge 3(5.00)

7(11.67)

20(33.33)

20(33.33)

8(13.33)

2(3.33)

31(51.67)

29(48.33)

Repayment Period 5(8.33)

5(8.33)

19(31.67)

21(35.00)

4(6.67)

6(10.00)

28(46.67)

32(53.33)

Insects-Pest & Diseases 2(3.33)

8(13.33)

19(31.67)

21(35.00)

4(6.67)

6(10.00)

25(41.67)

35(58.33)

Marketing 5(8.33)

5(8.33)

16(26.67)

24(40.00)

2(3.33)

8(13.33)

23(38.33)

37(61.67)

Table 4. Other problems faced by the borrowers(N=60)


Kulshrestha and Sharma (2004).CONCLUSIONS

The bankers also faces uneven distribution of borrowers, which also leads them to the supervision problem. Though they have come up with the idea of group borrowing and area approach system for better supervision yet, the problems were not always solved as there are no respondents from the target area while there were individuals who want loan and not from the target groups/area. It was noticed that there were borrowers who are unfaithful towards supervision officials especially when the official/staff went for evaluating the projects of the borrowers. The problem was such that the banker finds it difficult to evaluate the actual benefits or significance of bank loans to the borrowers from those diverted to other activities or consumption purposes. The misutilization of bank loan in the study may not be due to lack/inadequate funds but the unfaithful nature of the borrowers itself. Misutilization of the funds allotted to them was one of the major factors in the problems of non-repayment of the dues on time for which the loan was taken.

The bankers also observed that some of the borrowers submitted their loan proposal late. If the loan was sanctioned to these borrowers, there may be a possibility of either diversion of the loan. There was also a chance in repayment problem as the loan sanctioned may not be able to generate returns to repay the install in due date. Thus, the bankers face the problems of advancing the loans in odd times.

The main reason why the co-operative banks were not so successful in the state was due to the lack of human resources. The lack of manpower has hindrance on the bank expansion as well as supervision. As there were less bank branches more area has to be covered by a bank branch in that locality, this caused workload on the bankers especially on the limited field staff who cover more areas/villages. The limited bank staffs with overloaded work encountered with poor or lack of logistics supports and poor communication to undertake their works efficiently.

Apart from misutilization and diversion of loans for other uses, the use of modern technology in the farming system was also lacking which affected the productively of crops adversely. The farmers neither were reluctant to participate in trainings on modern means of farming nor are willing to use modern technology in the farming system.REFERENCESKulshrestha, R.K., & Sharma, A. (2004). Employment structure

through rural consumer credit in Agra region. The Andhra Agricultural Journal, 51(3 and 4), 486-489.

Ministry of Finance. (2010). Economic survey. Ministry of Finance, Government of India. Retrieved from h t t p : / / i n d i a b u d g e t . n i c . i n / e s 2 0 0 9 -

th10/chapt2010/chapter.zip [Accessed 11 Nov 2012].Reserve Bank of India (2012). Brief history of urban

cooperative banks in India . Retrieved from http://www.rbi.org.in/scripts/fun_urban.aspx.

Sangtam, L.L.T., & Sharma, A. (2015). Impact of bank finance

377

Shuya and Sharma: Problems faced by the borrowers in utilization and acquiring of Co-operative bank loans in Nagaland

on employment and income through piggery enterprise in Nagaland. EPRA International Journal of Economic and Business Review,3(11), 273-276.

Sharma, A. (2002). Source and knowledge on beneficiaries about the purpose of credit-A case study of Agra region of Uttar Pradesh. Journal of Interacademica, 6(3), 374-379.

Sharma, A., Tyagi, D.B., Singh, J., & Sharma, R. (2002). The regional rural bank's loan availability, utilization and problems in agriculture-A case study of Etah district of Uttar Pradesh. The Journal of Rural and Agricultural Research,2(1), 53-56.

Shuya, K. (2012). A study on co-operative bank in financing

agricultural and allied activities with special reference to Dimapur district of Nagaland (Doctoral dissertation). Nagaland University, SASRD, Medziphema Campus, District Dimapur, Nagaland.

Shuya, K., & Sharma. (2014). Impact and constraints faced by the borrowers of cooperative bank finance in Nagaland. Economic Affairs,59(4), 561-567.

Vaibhav. (2012). Understanding the concept and process of microfinance. Retrieved from http://www.smallenterprise india.com/index.php?option=com_content&view=article&id=995:understanding-the-concept-and-process-of-microfinance&catid=82:featureone.

378


ABSTRACTPolicy Analysis Matrix analysis was carried out to study whether the maize production system of India is favourable to farmers. The results revealed that maize system enjoyed a total positive transfer of `152 per quintal on its tradable-input costs. If Government had not intervened, the maize farmers would have to pay ̀ 408 per quintal, but the actual policies permitted this cost to be reduced to `256. EPC (0.55) was less than one, indicated that the producers were not protected through policy interventions. The average DRC value of 0.43 indicated positive social profit and showed that the maize production was economically efficient and the country had a comparative advantage in the maize production. The estimated values have shown that maize production system was protected domestically through domestic policies.

KeywordsDomestic resource cost, effective protection coefficient, private cost ratio, profitability coefficient, subsidy ratio to producers.

JEL CodesQ11, Q15, Q19.

M. Srikala

Department of Agricultural Economics, S.V. Agricultural College, Tirupati-517502


Received: September 14, 2017 Revision Accepted: May 03, 2018

The Policy Analysis Matrix of Maize Cultivation in Andhra Pradesh


DOI: 10.5958/2322-0430.2018.00147.6


www.soed.in


UGC Approved


379

INTRODUCTIONMaize (queen of cereals) the food of the Gods that

created the Earth is a cereal crop which is cultivated throughout the world and has the highest production of all cereals. It is an important staple food in many countries and also used in animal feed and many industrial applications. Maize is cultivated throughout the world, and greater weight of maize is produced each year than any other cereal. Global maize production has grown at 3.4 percent over the last ten years from 717 to 990 million tonnes (India Maize Summit, 2014). In 2015, global maize area reached a historical high of 177 million ha. Globally maize production has shown an increasing trend in the recent years following strong industrial demand.

Maize holds a prominent position in Indian agriculture. The increasing use of maize as animal feed, increasing interest of the consumers in nutritionally enriched products and rising demand for maize seed are the driving forces behind emerging importance of maize crop in India. Maize is grown throughout the year in India. It is predominantly a Kharif crop with 85 percent of the area under cultivation in this season. It accounts for 9 percent of total food grain production in the country.

Industrial uses of maize and maize products have steadily increased in importance. India produces around 15 million tonnes of maize annually. Production of maize in India has increased at 5.5 percent from 14 million tonnes in 2004-05 and 21 million tonnes in 2014-15. Among the cereal crops in India, maize with annual production of around 23 million tonnes from 9.4 million ha, ranks third in production and contributes to 2.3 percent of world's maize production with almost 5 percent share in world 's harvested area in 2013-14 (Indiastat.com). In India, Karnataka, Andhra Pradesh, Tamil Nadu, Rajasthan, Maharashtra, Bihar, Uttar Pradesh, Madhya Pradesh and Gujarat states contribute about 85 percent of maize production and 80 percent of the area under cultivation.

ndAndhra Pradesh with 10.06 lakh ha ranks 2 in the st national area and it ranks 1 in the production with 48.62

lakh tonnes. India maize exports per on an average ranged between 3-3.2 million tonnes.

The Policy Analysis Matrix (PAM) framework developed by Monke and Pearson (1989) was used for computation of input-use efficiency in production, comparative advantage and degree of divergence between social and private costs. The PAM is a product of two

accounting identities; one defining profitability which is the difference between revenues and costs and the other measuring the effects of divergences (distorting policies and market failures) as the difference between observed prices and social prices that would exist if the divergences were removed. The basic information needed for compiling a PAM are yields, input requirements, and the market prices of inputs and outputs. The data on transportation cost, processing cost, storage cost, port charges, production/ input subsidies, and export tariffs are also required to derive the social prices.

The present study is an application of a policy analysis matrix (PAM) to assess the competitiveness of maize, which is produced under a web of contradictory policies, including price supports, and various input subsidies, such as fertilizer, power, irrigation, and credit subsidies. With this background, the present study was carried out to study whether the maize production system of India is favourable to farmers. More specifically the objective of the paper is to examine the importance of trade policy towards maize production system in Andhra Pradesh.METHODOLOGY

In order to achieve the stipulated objective of the present study the data on the cost of cultivation for the maize crop were collected from Directorate of Economics and Statistics, Government of India, New Delhi. The study is based on secondary data covering a period of three years from 2012-13 to 2014-15. International reference prices of crop under study were obtained from Export Product Rice (n.d.). The basic steps in using the PAM method are, identifying the commodity system, assembling representative budgets for each activity in the system, calculating social values, aggregating the budgetary data into a matrix, analysing the matrix and simulating policy changes (Rani et al., 2014). The method rests upon familiar identity: Profit = Revenue-Costs. The differences between revenues and costs show the profitability of production system under current conditions and in a perfectly functioning market. The differences between parameters in private and social prices show an impact of policies and market failures.

Social values are the prices, which would prevail in the absence of any policy distortions (such as taxes or subsidies) or market failures (monopolies). The social costs have been calculated using Value Marginal Product approach, which uses factor shares (S ) of various inputs i

(X ) together with the mean values of inputs and outputs i

(Y) and price (P ). The computation of social cost of y

inputs is as follows: *P = [(S /X ) Y] P . ..... (1) xi i i y

An important thing to keep in mind is that for a given commodity system, the costs and profits represent as aggregates for all activities from the farm to wholesale. For revenues A is the wholesale price and E is the world price of the comparable product in the comparable location. From the Table 1 several useful values appear (Yao, 1997). The first row provides a measure of private profitability (D) defined as the difference between private revenue (A) and private costs (B+C). Private profit demonstrates the competitiveness of the agricultural system, given current technology, prices for inputs, outputs, and policy. The second row of the matrix calculates the social profit that reflects social opportunity costs. The third row of the matrix estimates the difference between the first and second rows. The difference between private and social values of revenues, costs, and profits can be explained by policy interventions. One of the main strengths of this approach is that it allows varying degrees of disaggregation. It also provides a straightforward analysis of policy-induced effects. RESULTS AND DISCUSSION

The primary objective of constructing a PAM is to derive a few important policy parameters for policy analysis. In this study, seven parameters are derived. Profitability Coefficient (PC), Effective Protection Coefficient (EPC), Domestic Resource Cost (DRC), Private Cost Ratio (PCR), Subsidy Ratio to Producers (SRP), Nominal Protection Coefficient of Output (NPCO) and Nominal Protection Coefficient of Tradable-input (NPCI) are the parameters used to computation of trade competitiveness of the maize. These indices are calculated under exportable hypothesis. Under exportable hypothesis, the domestic good would compete at a foreign port. The border price under this hypothesis is Free On Board (FOB) price, net transportation costs (both domestic and International), port clearance charges, marketing costs, trader's margin and processing costs are necessary to make the commodity tradable.

Table 2 presents the level of profitability and divergence in maize production for the study period. The last row in Table showed that maize system enjoyed a total positive transfer of ̀ 152 per quintal on its tradable-input costs. If Government had not intervened, the maize

Particulars Value of output (Revenues)

Value of input (Costs) Profit

Tradable inputs Non-tradable inputs

Private prices A B C D=A-B-C

Social prices E F G H=E-F-G

Effects of divergence I=A-E J=B-F K=C-G L=D-H=I-J-K

Table 1. Policy analysis matrix

Source: Monke and Pearson (1989).D = Private profits, J = Input transfers, H = Social profits, K = Factor transfers, I = Output transfers, and L = Net transfers.

380


Particulars Revenue (`/q) Costs (`/q) Profits (`/q)

Tradable inputs Domestic factors(Non-tradable inputs)

2012-13Private prices 1100 248 407 445Social prices 1964 404 676 884Effects of divergence -864 -156 -269 -439PC 0.50EPC 0.55DRC 0.43PCR 0.48SRP -0.22NPCO 0.56NPCI 0.612013-14Private prices 1045 264 434 347Social prices 1863 388 640 835Effects of divergence -818 -124 -206 -488PC 0.42EPC 0.53DRC 0.43PCR 0.56SRP -0.26NPCO 0.56NPCI 0.682014-15Private prices 1180 257 433 490Social prices 2088 432 719 937Effects of divergence -908 -175 -286 -447PC 0.52EPC 0.56DRC 0.43PCR 0.47SRP -0.21NPCO 0.57NPCI 0.59Overall periodPrivate prices 1108 256 425 427Social prices 1972 408 678 885Effects of divergence -863 -152 -254 -458PC 0.48EPC 0.55DRC 0.43PCR 0.50SRP -0.23NPCO 0.56NPCI 0.63

Table 2. Policy Analysis Matrix (PAM) of maize (2012-13 to 2014-15)

farmers would have had to pay `408 per quintal, but the actual policies permitted this cost to be reduced to ̀ 256. This total positive transfer of `152 resulted from the policy combination of subsidies on fertilizers and seeds.

Similarly, in respect of domestic factors, maize farmers had the advantage of total positive transfer of `863 per quintal of maize. This total positive transfer resulted from the subsidy extended for power used for irrigation.

381

Srikala: The policy analysis matrix of maize cultivation in Andhra Pradesh

Also, the policy of price determination caused a reduction of farm revenues from `1,972 to 1,108. The table reveals that production of maize was socially profitable. This implies that the system utilized scarce resources efficiently in the production of maize and the system can survive without government interventions. There was, however, a negative divergence between private and social profits thus suggesting that the net effect of policy intervention reduced the profitability of maize production at the farm level, which is detrimental to producers.

The findings of the PAM framework constructed for maize and the coefficients of PC, EPC, DRC, PCR, SRP, NPCO and NPCI for maize are presented in Table 2. The Profitability Co-efficient (PC-measures policy incentives as an estimation of net policy transfer) showed positive net transfers. The observed PC values suggest a positive net transfer payment of about 50, 42, 46, and 48 percent for the overall period, 2012-13, 2013-14 and 2014-15 respectively. The EPC which reveals the degree of protection accorded to the value added process also had values less than unity for maize and as such so indicating that producers were not protected through policy interventions on the value-added process, and that producer face anet tax of 45 percent. This result is in conformity with those of the studies conducted by Ogbe et al. (2011) for rice and maize ecologies and Yao (1997) for rice.

DRC is an indicator of social profit or efficiency. It is the ratio of the social opportunity cost of domestic factors of production relative to the value added in the world prices. The average DRC value of 0.43 indicated positive social profit and showed that the maize production was economically efficient and the country had a comparative advantage in maize production. This means that the value of domestic resources used in one tonne of maize was less than what it would cost to import.

The PCR indicates private profit or competitiveness. The average PCR value of 0.50 was obtained from the ratio of the private opportunity cost of domestic factors of production relative to the value added in domestic prices. This value indicated positive private profit which showed that the maize production was competitive given the actual prices in the product and factor markets.

SRP is said to indicate the level of transfers from divergence as a proportion of the undistorted value of the system revenue (Monke & Pearson, 1989). Hence, if market failures are not an important component of the divergence, then SRP shows the extent to which a system's revenue has been increased or decreased because of policy. SRP value of -0.23 indicated that the existing policies have reduced the gross revenue of maize farmers

in the region by 0.23 percent of that value.The NPCO was 0.56 inferring that existing policies

have reduced the private revenue to be 44 percent. On the input side, the NPCI was (0.63) less than one, which shows that the policy regime favours farmer and reduces the cost of tradable inputs to some extent. Similar results were found by Yao (1997) and Rani et al. (2014). CONCLUSIONS

The findings of PAM framework indicated that maize cultivation enjoyed a total positive transfer of `152 per quintal on its tradable input costs in the overall period (2012-13 to 2014-15). The NPCI ratio of 0.63 indicated that the policies of the government favoured farmers to reduce expenditure on tradable inputs by 37 percent. The NPCO was 0.56 inferring that existing policies have reduced the private revenue to be 44 percent less than without policy. The PC value which is less than one (0.48) indicated negative net transfers. The EPC of 0.55 showed that cultivation of maize was largely not protected by policies. The estimated DRC (0.43) was less than unity indicating that maize has long run comparative advantage in its cultivation as compared to other countries. The PCR which is 0.50 demonstrates the ability of production system to cover the cost of domestic factors and continue to be competitive. The value of SRP (-0.23) was negative indicating that the system's revenues decreased due to distorting policies. The estimated DRC for maize indicated that India enjoys a comparative advantage in the cultivation of the maize crop. The Government should look into tapping opportunities for expanding the exports of rice and maize.REFERENCESE x p o r t p r o d u c t r i c e . ( n . d . ) . R e t r i e v e d f r o m

https://www.eximpulse.com/export-product-Rice.htmIndia Maize Summit. (2014). Retrieved from https://www.

ficci.in/spdocument/20386/India-Maize-2014_v2.pdf.Indiastat.com. Retrieved from https://www.indiastat.com/

table/agriculture/2/harvestingseasonsofgraminindia/330564/897928/data.aspx.

Monke, E.A., & Pearson, S.R. (1989). The policy analysis matrix for agricultural development. Ithaca, and London: Cornell University Press.

Ogbe, A.O., Okuruwa, V.O., & Saka, O.J. (2011). Competitiveness of Nigeria rice and maize production ecologies: A policy analysis approach. Tropical and Subtropical Agroecosystems, 14, 493-500.

Rani, U., Reddy, G.P., Prasad, Y.E., & Reddy, A.A. (2014). Competitiveness of major crops in post-WTO period in Andhra Pradesh. Indian Journal of Agricultural Economics, 69(1), 125-141.

Yao, S. (1997). Comparative advantage and crop diversification: A policy analysis matrix for Thai agriculture. Journal of Agricultural Economics, 48, 211-222.

382


ABSTRACTSurat and Navsari are the major districts cultivating Gerbera under protected condition in South Gujarat region. The production of gerbera in Surat and Navsari districts was 612.5 metric tonnes during 2015.A major challenge before the Gerbera cultivators is to minimize the cost and increase the returns. Keeping in view the challenges the present study was conducted to estimate costs and returns of gerbera cultivation under protected condition. For the present study 50 polyhouses were selected from Surat and Navsari districts. The primary data collection for cost and return analysis was done with schedule by personal interview method for year 2015-16. An establishment cost and cost of cultivation per acre was worked out by using standard cost concepts. The results of study showed that establishment cost, cost of cultivation, gross return and net return for gerbera was ̀ 49.51, 17.61, 29.08, and 11.47 lakh per acre, respectively. The highest share was of cost of construction of polyhouse (64.05 percent) and charges of human labour (18.39 percent) in establishment cost and cost of cultivation, respectively. The output-input ratio was worked out to 1.65 indicated that the Gerbera cultivation was profitable under protected condition in the selected study region. Attack of pest and diseases, high cost of construction of polyhouse were the major constraints faced by the Gerbera growers in production.

KeywordsCost and return, establishment cost, Gerbera, protected cultivation.

JEL CodesO13, O33, Q10, Q12, Q16, Q19.

*Smita Kumari, Narendra Singh , D.J. Chaudhari and V.M. Thumar

ASPEE College of Horticulture & Forestry, Navsari Agricultural University, Navsari 396450, Gujarat (India)



Economic Analysis of Gerbera Cultivation in the Protected Condition in South Gujarat


DOI: 10.5958/2322-0430.2018.00148.8


www.soed.in


UGC Approved


383

INTRODUCTIONt is one of the major flower producing state in

India. The rose, marigold, mogra, lily, carnation, gerbera, orchid are the major flowers produced in Gujarat. During the year 2005-06 the production of different flowers in Gujarat was 0.42 lakh MT which increased to 1.84 lakh MT during the year 2015-16. Ahmadabad, Navsari, Vadodara, Surat, Bharuch, Kutchh are the potential region for flower production in the state. Since the last few years, most of the farmers have ventured into the floriculture business. Exotic flowers like a hybrid tea rose and gerbera has emerged as big money spinner for farmers of Gujarat.

Gerbera is the latest sensation to Indian floriculture, commercially grown throughout the world in a wide range of climatic conditions (Shridevi, 2014). Gerbera is a major greenhouse crop cultivated on large scale in Gujarat. In South Gujarat region, Surat and Navsari are

Gujarathe major districts cultivating gerbera under protected condition. The production of gerbera in Surat and Navsari districts was 612.5 metric tonnes during 2015-16. Gerbera has medicinal value and it is used in cosmetic industries and decoration in the vase. Most of the gerbera cultivators from Navsari and Surat districts exporting flowers to the countries like Japan, New Zealand, Germany and U.K. There are several varieties of gerbera like Dalma, Rosaline, Tarajuba, etc. Gerbera cultivation is profitable for the farmers of Gujarat due to good income in short period. It is high-value flower crop fetches a good price in marriage and festival season. Gerbera cultivation in polyhouse is very economical due to its lowest cost compared to other crops. Since the last few years more and more farmers have ventured into the floriculture business and today the turnover is `50 crores. Nowadays gerbera is a major greenhouse crop. Commercialization of gerbera production provides direct contact to farmers

with the market. So, commercialization of gerbera by using modern techniques will improve the condition of gerbera cultivation.

The fundamental problems faced by the gerbera cultivator are the environmental and technological changes, hike in input price and fluctuation in output price, disease-pest infestation, requires large numbers of labours, etc. Therefore a major challenge before the gerbera cultivators is to minimize the cost and increase the return. Keeping in view the challenges the present study was conducted to estimate establishment cost, cost and return of gerbera cultivation under the protected condition and to study constraints faced by growers in the production of Gerbera under the protected conditionMETHODOLOGY

The present study based on primary as well as secondary data. The secondary data pertaining to the status of greenhouse in South Gujarat was collected from the reports of Department of Horticulture, Gujarat and percentage were worked out to know the status of Greenhouse and net house in South Gujarat.

To study the cost and return of gerbera cultivation under protected condition primary data was collected from the respondent farmers. For this purpose, Surat and Navsari districts from the South Gujarat Region were selected purposively on the basis of highest area under the crop. Nine talukas from Surat and three talukas from Navsari district were selected. Based on availability of polyhouse from these 12 talukas, 50 polyhouses were selected for the present study. The data related to input use, cost and return from gerbera cultivation was collected from the respondent farmers by personal interview method with the help of specially designed schedule for the agricultural year 2015-16.

To estimate the cost and return from gerbera under protected cultivation the costs were classified into variable and fixed costs. The variable costs included the expenses incurred on fertilizers, pesticides, irrigation, electricity, transportation, marketing and labour. The fixed costs included amortized cost of establishment of structure and planting materials, interest on fixed capital and depreciation on fixed assets. The cost of cultivation of gerbera was worked out by using standard cost concepts, such as Cost A, Cost B, and Cost C.RESULTS AND DISCUSSIONStatus of Greenhouse in South Gujarat

The perusal of Table 1 showed that the number of functional greenhouses in Gujarat was 349 and the number of the net houses was 488 in 2012-13. The South Gujarat region has 169 functional greenhouses and 82 net houses. Among the districts of South Gujarat Surat has highest numbers of greenhouses (33.23 percent) and net houses (12.30 percent) of Gujarat. Navsari District has 19 functional Greenhouses.Establishment cost for Gerbera Cultivation

The establishment cost under the protected cultivation for gerbera includes the cost of construction of

polyhouse, cost of planting material and preparation of beds, land value, the cost for irrigation structure, etc. The cost of construction of polyhouse for gerbera cultivation depends on size and shape of polyhouse.

The perusal of Table 2 showed that the cost of establishment for gerbera cultivation in polyhouse was worked out to `49.51 lakh per acre. Among the different cost items, highest cost was accounted for construction of polyhouse (64.05 percent) followed by planting material cost (15.15 percent) and value of land (9.49 percent). This indicated that the construction of polyhouse is an expensive venture. It was observed that the cost of construction of polyhouse for gerbera cultivation depends on size and shape of polyhouse, area of polyhouse, quality of material used and other basic necessary infrastructure needed. The sample farmers using medium and high technology polyhouse and the establishment cost depends on the size of the polyhouse. The least cost of the total cost was observed for electric installation and storeroom (0.30 percent). The results are similar to the findings of Shrinivasa (2009). It was found that establishment cost for gerbera cultivation under greenhouse condition was `9.70 lakhs per acre, among which the highest cost was incurred for construction of greenhouse (49.49percent) followed by planting material cost (23.64 percent).Different Input Costs Involved in Gerbera Cultivation

From Table 3 it was observed that the total cost involved for different inputs used in gerbera cultivation was `17.61 lakhs in which the share of variable cost was worked out to 79.63 percent and the share of fixed cost was 20.37 percent. Among the different variable costs, the expenditure on human labour was worked out to be the highest (18.39 percent) followed by the cost of fungicide (14.99 percent), fertilizer cost (10.22 percent) and supervisory cost (10.22 percent). Gerbera requires more labour for picking, spraying, fumigating, and weeding throughout the year, which increases the labour cost. The higher cost of fungicide was due to the high incidence of diseases on gerbera under protected cultivation. The fixed costs the highest cost was on account of Amortized establishment cost (11.72 percent), followed by the rental value of land (8.18 percent). Similar results found by Shrinivasa (2009); Bhosale et al. (2011).Cost and Return of Gerbera Cultivation

The perusal of Table 4 showed that the total cost of cultivation of Gerbera under the protected condition was worked out to `17.61 lakh per acre. Cost A and Cost B 2

worked out to ̀ 14.15 and 15.65 lakh per acre. The cost of production for gerbera was estimated to be `2.27 per flower. The results were in conformity with Ghantage (2002); Potekar (2008); Shrinivasa (2009); Bhosale et al. (2011).

The gross return obtained from gerbera cultivation was estimated to `29.08 lakh per acre while net return worked out to `11.47 lakh. The output-input ratio was worked out to 1.65 indicated that the Gerbera cultivation is profitable under the protected condition in the selected

384


Districts No. of greenhouse No. of net house

Bharuch 5.00(1.43)

8.00(1.63)

Narmada 5.00(1.43)

0.00

Surat 116(33.23)

60.00 (12.30)

Valsad 9.00(2.58)

11.00 (2.25)

Tapi 15.00(4.30)

3.00 (0.62)

Navsari 19.00(5.45)

0.00(0.00)

South Gujarat 169(48.42)

82(16.80)

Gujarat state 349.00(100)

488.00(100)

Table 1. Status of functional greenhouse and net house in Gujarat during 2012-13

Source: Database 2014, Directorate of Horticulture, Gujarat.Figures in parentheses indicate percentage to total.

Particulars Cost (` lakh)

Percentage to total cost

Value of land 4.70 9.49

Irrigation structure 0.50 1.01

Poly-house construction 31.71 64.05

Electric installation 0.15 0.30

Store room 0.15 0.30

Planting material cost 7.50 15.15

Land development and bed preparation

4.50 9.09

G.P. room 0.30 0.61

Total cost 49.51 100

Table 2. Per acre cost of establishment for Gerbera under protected condition

Sr.

No.

Item Costs

(`in lakh)

Percentage

of total cost

A Variable Cost

1 Human labour 3.24 18.39

2 A) Hired labour 2.88 16.35

B) Family labour 0.36 2.04

2 Fertilizer 1.80 10.22

3 Pesticide 1.20 6.81

4 Fungicide 2.64 14.99

5 Irrigation 0.15 0.85

6 Electricity 0.22 1.28

7 Supervisory 1.80 10.22

8 Managerial 1.60 9.09

9 Interest on working capital 1.02 5.82

10 Miscellaneous 0.34 1.95

Total Variable Cost 14.01 79.63

B Fixed Cost

1 Depreciation 0.02 0.13

2 Interest on fixed capital 0.05 0.33

3 Land revenue 0.002 0.01

4 Rental value of owned land 1.44 8.18

5 Amortized cost 2.06 11.72

Total Fixed cost 3.57 20.37

Total Cost 17.61 100.00

Table 3. Per acre input costs involved in Gerbera cultivation

Particulars Amount (` lakh)

Percentages to Cost C3

Cost-A 14.15 80.36Cost-B1 14.21 80.69Cost-B2 15.65 88.86Cost-C1 14.57 82.73Cost-C2 16.01 90.90Cost-C3 17.61 100.00Yield per acre(Flower in Numbers)

775714

Cost of production(` per flower)

2.27

Harvest price (`/flower) 3.75Value of gross output 29.08Net return 11.47Input-output ratio 1:1.65

Table 4. Per acre cost and return of Gerbera cultivation under protected condition

study region. Bhosale et al. (2011) worked out the output-input ratio 1.14 for the gerbera cultivation in poly house. Similar results were also reported by Sudhanger (2013); Sharma et al. (2014).Constraints in Gerbera Production

A perusal of Table 5 showed the major constraints faced by the Gerbera grower under protected condition. The results revealed that attack of pest and disease infection was the major problem faced by 100 percent respondent Gerbera growers and ranked at first position followed by the constraints, high establishment cost of polyhouse (97.71 percent) and high labour cost (94.47 percent) and ranked at second and thirds position, respectively.

Moreover, it is also evident that the constraint of

non-availability of labour was least perceived by the respondent gerbera growers (68.57 percent) and ranked at the lowest position. Sudhangar (2013) reported that huge investment in high tech floriculture and pest and disease attack on crop were the major constraints in high tech floriculture in Tamil Nadu. The results of this study

385

Kumari et al: Economic analysis of gerbera cultivation in the protected condition in south Gujarat

Constraints Frequency Percent Rank

High labour cost 28 80.00 IV

High cost of planting material 33 94.71 III

Attack of pest and diseases 35 100 I

Non-availability of skilled labour in time 24 68.57 VI

Lack of knowledge about production technology 25 71.42 V

High establishment cost of polyhouse 34 97.14 II

Table 5. Constraints faced by Gerbera grower under protected condition

are in the line with Kumar et al. (2011); Singh et al. (2015).CONCLUSIONS

It can be inferred safely that establishment cost of gerbera under the polyhouse was `49.51 lakh per acre in which the highest share was of the cost of construction of polyhouse (64.05 percent). The cost of construction of polyhouse depends on shape and size of polyhouse. Among the major costs of inputs, cost of human labour was highest which accounted for 18.39 percent of the total cost because of more number of labours are required for various operations in polyhouse. The cost of cultivation, gross return and net return for gerbera was ̀ 17.61, 29.08, and 11.47 lakh, respectively. The output-input ratio worked out to 1.65. Attack of pest and disease, the high cost of establishment of polyhouse, and the high cost of planting material were the major constraints faced by the grower in the production of gerbera under protected cultivation. The results of the study indicated that the gerbera cultivation is profitable under the protected condition in the selected study region and helpful for increasing the income of the farming community. The number of polyhouses in South Gujarat region was 48.42 percent of the state indicated that there is high potential among the farmers of the region to cultivate the crops under protected condition. Hence, gerbera cultivation under protected condition should be encouraged to increase the income of the farmers.

REFERENCESBhosale, M.Y., Shelke, R. D., Aher, V.K., & Shenew, B.A. (2011).

Production and marketing of gerbera cut-flowers. International Research Journal of Agricultural Economics and Statistics, 2(2), 328-331.

Ghantage, L.N. (2002). Economics of greenhouse in Kolhapur region (Master's Thesis). Shivaji University, Kolhapur, Maharashtra.

Kumar, R., Singh, D., Yadav, R.N., Singh, V.K., & Kumar, M. (2011). A study of infrastructural profile and constraints of cut flower growers. Annals of Horticulture, 4(2),181-186.

Potekar, V.S. (2008). Polyhouse technology of gerbera crops. Retrieved from www.aaqua.persistant.co.in.

Sharma, M., Thakur, R., & Mehta, P. (2014). Economic feasibility analysis of major flower crops in Himachal Pradesh state of India. International Journal of Advanced Research, 3(9), 30-40.

Shridevi, K. (2014). Business analysis of gerbera cultivation under polyhouse-A case study in Ranga Reddy district of Andhra Pradesh. (MBA Thesis). Acharya N. G. Ranga Agricultural U n i v e r s i t y T e l a n g a n a ) . R e t r i e v e d f r o m http://krishikosh.egranth.ac.in/bitstream/1/76692/1/D9734.

Shrinivasa, G.M.V. (2009). Hi-tech floriculture in Karnataka. Department of Economic Analysis and Research. National Bank for Agriculture and Rural Development, Mumbai.

Singh, D.R., Sivaraman, N., & Anil, K. (2015). An economic analysis of traditional and hi-tech rose (Rosa spp.) cultivation. Indian Agricultural Statistics Research Institute, New Delhi.

Sudhangar, S. (2013). Production and marketing of cut flower (Rose and Gerbera) in Hosur Taluk. International Journal of Business and Management Invention, 2(5), 15-25.

386


ABSTRACTThe study has assessed the economic sustainability of SRI (system of rice intensification) and traditional methods of paddy cultivation in Gumla district of Jharkhand. Sustained yield levels will ensure stable production and in turn food security, while economic sustainability would encourage the farmers to adopt the technology over time and space. The present study has been reported that the yield levels of SRI farmers were higher than those of traditional farmers as evidenced from the positive regression coefficient (1.17) and the positive symmetry index (1040). Out of 30 farmers 14 farmers showed Di value significantly higher than Ds value and in the remaining cases they were lower than D , thus sustainability index for the yield of SRI method in Gumla district s

worked out to be 46.7 percent. It indicates that the SRI can thrive in the conditions of the study area and farmers can avail greater output and employment.

KeywordsEconomic sustainability, rice, SRI, sustained yield.

JEL CodesD01, Q10, Q12, Q16.

*Tulika Kumari , Binita Kumari, Priyanka Lal and Ritu Rathore

*Research Scholar, Dairy Economics, Statistics and Management Division,ICAR- National Dairy Research Institute, Karnal-132001

*Email of the corresponding author: [email protected]

Received: December 31, 2017 Revision Accepted: April 20, 2018

Economic Sustainability of Systems of Rice Intensification (SRI) in Gumla District of Jharkhand


DOI: 10.5958/2322-0430.2018.00149.X


www.soed.in


UGC Approved


387

INTRODUCTIONRice is an important food item of Indian household.

India is one of the leading rice-producing countries of the world with the cultivated area of 43.95 M ha and production of 104.40 Mt in 2015-16. The leading states in rice production are: West Bengal, Uttar Pradesh, Andhra Pradesh, Punjab, and Orissa. Jharkhand ranked fourteenth with cultivated area of 1.50 M ha and production of 2.88 Mt 2015-16 (Department of Agriculture, Cooperation and Farmers Welfare, 2017). Rice is the main crop of Jharkhand. Mainly Kharif rice is grown in the state whereas boro or summer rice is cultivated in some areas. Rice is cultivated as conventional transplanting, SRI (system of rice intensification) and broadcast under wetland condition (Hassan & Upasani, 2015). Due to shrinking operational holding-size and water scarcity, innovative production practices are required to meet the growing demand of rice. Under such conditions, system of rice intensification (SRI) is an important technology for rice production. System of rice intensification is a new

system of rice cultivation for increasing rice productivity with a comprehensive package of practices involving less seed, water, chemical fertilizers and pesticides (Devi and Ponnarasi, 2009; Devi et al., 2017). System of rice intensification was developed in 1983 by the French Jesuit Father Henri de Laulaniéin Madagascar, which was under the severe grip of hunger and malnutrition during 1980s. For this food crisis, SRI was discovered as solution (Barah, 2009). It is a bundle of agronomic practices aimed at increasing the yield of rice production. It is a low water, labour intensive, organic method that uses younger seedlings singly spaced and typically hand weeded with special tools. SRI works by integrating processes such as reduced plant population, transplanting single young seedling, wider square planting, mechanical weeding from 10 days after transplanting, and use of the Leaf Colour Chart (LCC) for better nitrogen management, converting these various practices synergistically into a higher yield production process (Anbarassan et al. 2013). The specificities of SRI are conservation ofland, water

and bio-diversity and utilization of biological power of plant and solar energy. Yield sustainability of SRI has economic importance; sustained yield levels among the community will ensure stable production and in turn food security, while economic sustainability would encourage the farmers to adopt the technology over time and space. On the other hand, the traditional method of paddy cultivation requires large quantities of inputs, particularly water, fertilizer and pesticides, contributing to high cost of cultivation. It leads to depletion of water levels, indiscriminate use of chemical fertilizers and pesticides, damaging the ecosystem equilibrium and reducing the quality of produce, leaving the residues behind (RamaRao, 2011). In Gumla district system of rice intensification (SRI) is being promoted among the farmers by the district agriculture department. With this background, this paper has focused on sustainability of system of rice intensification (SRI) in Gumla district of Jharkhand.METHODOLOGY

The present study is based on primary data collected from sixty farmers of Gumla district. From twelve developments block of district two blocks were randomly selected for the present study. From selected two blocks, a list of villages was prepared in which the farmers of villages were growing paddy using SRI method. Out of these villages, two villages from each block were randomly selected. From each selected village list of farmers were prepared for SRI adopters and non-adopters separately. A sample of 30 each SRI adopters and non-adopters were selected randomly and making a total sample size of 60. For computing the Sustainable Index (SI), the method by Kiresur et al. (1996) was employed. The sustainability of an improved technology could be reflected by its proportional response to the existing crop technology situation, which in turn could be quantified by a site-specific index. For a given crop growing situation, the site index can be computed for each selected field adopting local practices by taking the deviation of the

yield per unit area from overall mean of all such sample fields adopting local practices. The various steps involved in the computation of sustainability index (SI) are p[resented in Table 1.RESULTS AND DISCUSSION

The results on the computation of sustainability index for farmers adopting SRI method of paddy cultivation, along with the values of Di and Ds as well as yield levels of SRI and traditional methods presented in Table 2. The

E = (X -X )i i m E = Site indexithX = Yield level of traditional method for i fieldi

X = Mean yield level of traditional methodm

Y = a+bEi i

thY = yield level of SRI for i fieldi

a= Interceptb= Regression coefficient

S= (E +E )/2max min S= Symmetry of site indexE = Maximum value of Emax i

E = Minimum value of Emin i

D = Y +bSi i

thD = Desirable yield level of SRI for i fieldi

D = Y +Ss m D = Standard yield level of SRI for the growing situations

Y = Mean yield level of SRI for the growing situationm

S.I.= (D /D )*100i s S.I.= Sustainability index

Table 1. Steps involved in the computation of sustainability index

SRI yield (kg/ha)

Traditional yield

(kg/ha)

Desired yield) (Di)

Sustainability index (SI)

8000 5000 9224.58 *119.353000 2000 4224.58

NS54.707500 6500 8724.58 **112.883000 1500 4224.55 NS54.605000 4000 6224.50 NS80.5310000 7500 11224.57 *135.223000 2500 4224.44 NS54.656666 5000 7891.25 **102.106000 5000 7224.50

NS93.479000 6000 10224.85 ***132.295000 3750 6224.58

NS80.53

7500 3750 8724.42**112.88

7500 5000 8724.50 **112.7810000 7500 11224.58

***125.22

7000 6000 8704.51 **113.81

8000 6500 9224.58 **119.355500 3750 6724.51

NS87.00

6000 3000 7224.01 NS93.475000 3500 6212.58 NS80.535000 3500 6224.58 NS80.01

Table 2. Actual yield levels and sustainability index of SRI method of cultivation

D = 7728 kg/ha, b= 1.17.s

***,**, and * significant at 1, 51 and 10 per cent level.NS: Non-significant.

388


yield levels of SRI farmers were higher than those of traditional farmers as evidenced by the positive regression coefficient (1.17) and the positive symmetry index (1040).

The desired yield levels was higher than the actual level. The standard yield level (Ds) of the SRI method was estimated to be 7728 kg/ha. Out of 30 farmers 14 farmers showed Di value significantly higher than Ds value but in remaining cases, Di value was were lower than D values s

and sustainability index for the yield of SRI method in Gumla district was worked out to be 46.7 percent. Baruah et al. (2017) reported sustainability index value of SRI in Assam as 56 percent. The results of sustainability indices revealed that even though the yield of SRI-adopters were higher than of traditional method adopters, the sustainability indices of SRI method against traditional method varied from 54.60 to 135.22. Whenever the symmetry of the site indices was closer to zero, irrespective of the regression coefficients of SRI method yield on site index, the mean yield of SRI was closer to the corresponding standard yield level (Ds), indicating that in such cases, the mean yields of SRI could be used for assessing the sustainability of SRI.CONCLUSION

The present study revealed that the SRI was found to be sustainable with sustainability indices ranging from 54.60 to 135.22. It was estimated to be 46.7 percent which implies that SRI can thrive in the conditions of the study area where the farmers can avail greater output and employment, thereby, higher income by practising SRI method of rice cultivation.REFERENCESAnbarassan, A., Karthick, V. Swaminathan, B., & Arivelarasan,

T. (2013). System of rice intensification (SRI) and its

implications over food security and farmer sovereignty. Retrieved from file:///C:/Users/S.S/ Downloads/SSRN-id2239451.pdf.

Barah, B.C. (2009). Economic and ecological benefits of the system of rice intensification (SRI) in Tamil Nadu. Agricultural Economics Research Review, 22, 209-214.

Baruah, A., Singh, R., & Pardhi, R. (2017). An economic analysis of yield gap and sustainability of the system of rice intensification (SRI) in Assam. International Journal of Agriculture, Environment and Biotechnology, 10(3), 401-406.

Department of Agriculture, Cooperation and Farmers Welfare. (2017). Annual report, 2016-17. Department of Agriculture, Cooperation & Farmers Welfare, Government of Indian, New Delhi.

Devi, D.A.R., Kumari, V.R., Reddy, P.D., & Dinesh T.M. (2017). Study of economics of paddy cultivation under transplantation, system of rice intensification (SRI) and direct seeding in Warangal district of Andhra Pradesh. Indian Journal of Economics and Development, 13, 262-264

Devi, S.K., & Ponnarasi T. (2009). An economic analysis of modern rice production technology and its adoption behaviour in Tamil Nadu. Agricultural Economics Research Review.22, 341-347.

Hassan, D., & Upasani R.R. (2015).Effect of crop establishment and weed control methods on productivity of rice (Oryza sativa L.). Journal Crop and Weed, 11, 228-230.

Kiresur, V., Balakrishnan, R., & Prasad, M.V.R. (1996). A model for estimation of economic sustainability of improved oilseed crop production technologies. Indian Journal of Agricultural Economics,51(3), 328-341.

Rama Rao, I.V.Y. (2011). Estimation of efficiency, sustainability and constraints in SRI (System of Rice Intensification) vis-a-vistraditional methods of paddy cultivation in North Coastal Zone of Andhra Pradesh. Agricultural Economics Research Review, 24(2), 325-331.

389

Kumari et al.: Economic Sustainability of Systems of Rice Intensification (SRI) in Gumla district of Jharkhand

ABSTRACTNatural resource management at household level assumes significance in effective utilization of scant resources. Over-exploitation of the country's resources like land, water, fuel etc. has resulted in degradation of resources mainly due to industrial pollution, soil erosion, deforestation and urbanization. Women have direct contact with natural resources like fuel, food and fodder, forest, water and land especially in rural areas where 70 percent of Indian population resides and directly dependent upon natural resources. Because women are the primary users of natural resources, their role in the management of renewable natural resources cannot be overlooked. Women are responsible for using these resources to satisfy the basic needs of their families. The scarcity of natural resources of water, fuel wood and fodder causes social tension and disturbances in the social system. This paper discusses the challenges in the management of renewable natural resources and participation and utilization of renewable natural resources by women who live in close association with these resources.

KeywordsManagement, perception, renewable natural resources, rural women, utilization.

JEL CodesQ20, Q21, Q23, Q24, Q25, Q51.

*Jaspreet Kaur , Ritu Mittal, Varinder Randhawa

Department of Home Science Extension Education and Communication Management, Punjab Agricultural University, Ludhiana-141004 (Punjab)


Received: October 18, 2017 Revision Accepted: June 09, 2018

Perception, Utilization and Management of Renewable Natural Resources by Rural Women: A Brief Review


DOI: 10.5958/2322-0430.2018.00150.6


www.soed.in


UGC Approved


390

INTRODUCTION Renewable natural resources (RNRs) are crucial for

our existence on this planet. A renewable resource (RR) is a natural resource which can replenish with the passage of time, either through biological reproduction or through other naturally recurring processes. The very name renewable natural resource (RNR) suggests that these resources can be used again and again as they are renewable. In other words, it can be defined as an NR that renews itself at a rate that is faster, or equal to the rate of consumption. Renewable Resources are a part of earth's natural environment and the largest components of its ecosphere. They differ from resources that once depleted never return or take millions of years to set renewed such as fossil fuel.

The use and cultivation of renewable resources help to minimize the impact humanity has on the earth while ensuring continued survival. For example, wood is an RR which we get from forests. Forests are the lifeline of all development activities and sustenance of human life. So

(NR)

(RRs)

long as we take good care of the forests & use it judiciously, we will never run out of wood. This brings us to an important aspect of judicious use and proper management of NRs for sustainability, for which we need to ensure that the rate at which RR is being consumed should not exceed its renewal rate. Another reason in support of proper management of RNR is due to benefits it provides to all living things, such as trees help to clean the air we all breathe and the water quenches thrust of all creatures. Forests provide food and home for birds, fish, deer, squirrels, and all kinds of life. People use wood provided by forests in many ways, such as fuel, fibre, etc. So, different forms of life could not exist without RNRs.

However, increasing demand on NRs has led to resources degradation and exploitation. With each millennium, new advances in the sophistication of use and accumulation of material resources due to the ever-increasing population and development are resulting in the destruction of the ecosystem and natural habitat. All these have led to degraded landscapes, resources and loss

of potential RRs to the great detriment (loss) of the wildlife and rural communities. Thus, a sustainable practice for management of NRs to meet the need of survival for both the present and future generations is of crucial concern to all societies. The consequence of degradation of the resources is no longer localized as it affects everyone. For example, the ever-increasing population needs more resources, which results in intensification of agriculture for increasing production to meet market demand; all contribute to today's fast exploitation of NRs. In addition, clearing of forests for firewood, for new farmlands or to realize expensive furniture, lead to degraded landscapes and loss of potential RRs to the great detriment of the wildlife and rural communities concerned. Its impact and contribution to global warming are, however, far wider with greater ramifications. Climate change with the rise in temperatures is resulting in melting of polar ice, scarcity of water and degradation of NRs for livelihoods. There is a global consciousness on the degradation of NRs resulting from gradual depletion of ecological services and life support systems. However, the time available for beginning serious action to avoid severe global consequences is growing short.

Climate change poses a serious risk to poverty reduction and development, with adverse impacts expected on the environment, human health, food security, economic activity, natural resources, and infrastructure. Global warming will have profound effects on agriculture, forestry, grasslands, livestock, and fisheries and, thus, on food security (FAO, 2007a). Ghosh et al. (2009) have observed a varied trend in Indian summer monsoon rainfall which has not only affected by global warming but also may be affected by local changes due to rapid urbanization, industrialization and deforestation. Changes in precipitation and evaporation patterns are of great significance in the hydrological balance of a river basin. Major rivers worldwide have experienced dramatic changes in flow, reducing their natural ability to adjust to and absorb disturbances (Palmer, 2008).Such climate change impacts on various components of the ecosystem such as air, water, plants, animals and human beings, with special emphasis on the economy (Vijaya et al., 2012).

These recent anthropogenic challenges to the environment, which threaten our existence, show the need of new relationships with respect to the environment and control on the use of NRs. We have to turn to better management practices not only at macro levels but also at micro levels to moderate the impact and renew these resources for future use.

Rural women are closely associated with RR as she depends upon RR such as forests, rivers etc to fetch food, fuel, fodder & water for her family and domestic animals. The present paper is

The perception, use and management of natural resources by communities who live in close proximity and are a direct use of these natural resources impacts to a great extent.

based on a systematical review of existing literature in terms of perception, utilization and management of renewable natural resources by rural women.Management of Renewable Natural Resources: Challenges and Strategies

Natural resources have for centuries been an important part of people's diet, and also have economy, social, cultural and spiritual relevance (Achin & Gonzalo, 2004). In October of 1944, with the end of WW in sight, US president Franklin Delano Roosevelt proposed a meeting of the united and associated nations as the first step towards conservation and use of natural resources. In a memo to his secretary of state, Cordell Hull, Roosevelt wrote: “I am more and more convinced that conservation is a basis of permanent peace” (Holdgate, 1999). The utilization, conservation and management of natural resources (plants and animals) was done with respect and guided by conservation requirements of never using more than what is required (Abu & Millar, 2004). By the early twentieth century, the rise of independent nation-states throughout the world, combined with the growing centralized power of the national governments and the development of ecology as a scientific discipline, permitted the management of lands at national scales. It begun to make sense to speak of “ national forests” and “national grasslands”, among others, as resources managed by a national government for the good of its citizens (Guha, 2000), and to manage such lands according to strategies of “multiple use” and “sustained yield.” The term forest implies 'natural vegetation' of the area, existing from thousands of years and supporting a variety of biodiversity. More than half of the known terrestrial plant and animal species live in forests (Millenium Ecosystem Assessment, 2005). Lambrou and Piana (2006) observed that in future years to come, the natural resources needed to sustain the human population will exceed available resources at current consumption levels. Unsustainable and uneven consumption levels will result in an increasingly stressed environment, where natural disasters, desertification, and biodiversity loss endanger humans as well as plant and animal species. The challenge of reversing the degradation of natural resources while meeting increasing demands for them involves significant changes in policies, institutions, and practices (FAO, 2007b). The exploitation of natural resources was one of the main reasons for environmental degradation, which had a direct bearing on the lifestyle of people. Hence, it is important to manage the country's natural resources for its human resource development (Yadav, 1999). The natural resource degradation had caused concern at international, national and local levels. Therefore, management of natural resource was a global concern and its protection is one of the challenging tasks facing mankind today (Bimlesh, 1999). Many researchers studied and discussed the impact of degrading natural resources on women and their role in management of natural resources due to their intimate relationship.

391

Kaur et al.: Perception, utilization and management of renewable natural resources by rural women: A brief review

Participation and Utilization of Renewable Natural Resources by Rural Women

The close association between women and natural resources is valid primarily in rural context perhaps owing to rural women's intimate association with the environment and the nature of socio-economic role which over generations has required them to provide for food, water, fuel, fodder and income from the surrounding resource base (Saxena, 1991). A village history from Anhui Warns, “Every family must take care of the mountains and waters around. Plant trees and bamboo as shelters keep an eye on the environment and protect it from damage. This was a chore for people of one hundred generations to undertake” (Coggins, 2003). But degradation in biological resources would also lead to erosion in local knowledge which serves as a critical livelihood asset for poor rural women and men for securing food, shelter and medicines (FAO, 2005). Over one-third of the world's population, 2.4 billion people, rely on traditional biomass in the form of fuelwood, agricultural residues, and animal wastes for their primary energy needs (Sagar et al., 2007). The report of the Fuel Wood Committee in 1982, warned that with rapidly dwindling sources of non-commercial energy sources in rural areas, i.e., firewood, animal dung and crop waste, very soon fuel will become a major constraint rather than the availability of food-grains itself. For example, as forests are cleared, women must walk further to collect necessary resources such as fuelwood (Agarwal, 1992). During the 1970's, women in Nepal were able to collect fuelwood in 2 hours; however, just ten years later, fuelwood collection took an entire day and involved walking through difficult terrain (Loughran & Pritchett, 1997). It was not unusual for women in India to spend five hours daily collecting firewood when traditionally this chore had been done weekly (Buckingham-Hatfield, 2000). As the health and availability of natural resources declines, women's workloads increase. Women must travel further and increase personal risk in order to secure resources from new locations (Koda, 2004). As time burdens increase for women, girls are forced to leave school and assist with daily household chores (UN, 2008). Over the past 50 years, ecosystems had changed more rapidly than in any comparable period of time in human history, largely because of the need to meet rapidly growing demand for food, water, timber, fiber, and fuel (MEA, 2005). Women in women-headed households report water and fuelwood collection as their most time-consuming tasks FAO/IFAD (2003). They collected firewood for cooking, roots, tubers and wild fruits for human consumption. They collected not only firewood but also other minor forest produce, and travel 10-12 km from their villages to market these products. After spending hours in collecting fuelwood they travelled by trains or trucks to the nearest urban centers in the evening, spend the night either at the railway station or public places while railway staff and a police constable on duty

take their share of payment. They returned to their villages on the next day after buying essential items for their households. In addition to this collection and selling of fuelwood, honey tapping, charcoal burning and herbs among others forms have been an important source of income to these people (Sen & Grown, 1987). A study conducted by the NCAER (1986) on fuel use clearly indicates that 2/3rd of all non-commercial fuel for households was collected mostly by women and the cost of this labour is almost zero.

The use of natural resources such as wildlife, forest products, water resources, and land among others had been necessary for the wellbeing of the people. Their way of life had comprised mechanisms of conserving or ensuring sustainable utilization of such resources through systems of values and taboos (Chambers, 1991). However, the value of a natural resource might vary from one place to another, depending on how it was valued, who used it (included here are gender and generational interest), and for what purpose it was used for. Resource users place different values on the use of the same resource. To a farmer, trees might seem useless as they hinder cultivation, while to a pastoralist they can have value as important as forage for livestock (Birgegard, 1993). Hence, natural resource use was significant to livelihoods. For the poor, forest and wildlife resources were part of larger body of rural non-farm economic activities that act as a sponge absorbing those unable to obtain employment (Arnold & Townson, 1998). The contribution of forest and wildlife used to livelihoods was highest for the poorest users, but the heaviest use of forest was by wealthier users. In developing countries, about 50 percent of wood energy was used for cooking (Nonhebel, 2005). Cooking accounts for a significant 54 to 84 percent of the total energy used in rural households. Fuelwood, dung cake, coal/coke/charcoal, gas (LPG), biogas, kerosene and electricity were the energy sources typically used for cooking (Ravindranath & Hall, 1995).

Kaur et al. (2016). In developing countries, about 2 kcal of wood were utilized in cooking, kcal of food (Pimentel & Pimentel, 2008).Economic Role of Women in Management of Renewable Natural Resources

Birgegard (1993), in economic terms referred to natural resources as those things found in nature that have economic value e.g. land for construction, forest products (timber) and wildlife. Colin & Chauveau (2002) thus, natural resources provide both subsistence needs and cash incomes, particularly to poor rural households. The role of the world's rural women in natural resource management had long been overlooked in statistics and qualitative research alike. Statistics had mainly focused on the type of work that could be converted into economic output. As many of the tasks undertaken by women had

The use of natural fuel was 4kg/capita/day among low socio-economic status families as compared to 5 kg/capita/day for medium and 3kg/capita/day for high socioeconomic status categories of the respondents

392


been classified as reproductive or directed at consumption, women's economic contributions have largely been ignored (Shiva, 1992). Women's roles and knowledge are often overlooked or underestimated in natural resource management and related policies and programs (Howard, 2003). Rural people, especially rural women, were often isolated from economic opportunities, had less access to basic social services, and therefore rely heavily on goods and services derived from natural resources (OECD, 2001). Boserup's seminal study of women's role in economic development in 1970, women gained recognition as the main food-producers in third world countries, and thereby as important actors for rural development – a role that was extensively explored and used as a basis for adding components addressing women's practical needs to existing development programmes and policies. This assumption had been widely discussed in gender research. The classic yet still unresolved topic of women's relationship to nature entails cultural-specific understandings of nature, of gender division and of women's relationship with nature. In Nepal and Gujarat, forest cover was increased by 75 percent when women were included in the process of protecting forests (Agarwal, 2003). Vandana Shiva stressed the ethical point of “women as care-takers” and saw the linkage between nature and women as being due to a gendered cultural development that led to a deeper spiritual connection of women to nature than men have (Shiva, 1988). According to Mies and Shiva (1993), women and nature work in partnership with each other with a special relationship. If the land was degraded and eroded, both the land and women suffer a decline in productivity. Agricultural livelihoods of poor rural women and men depended on the condition of natural resources, particularly livelihoods of people living on fragile lands (World Bank, 2005). Moreover, among female-headed households, forest resources often contribute significantly more total household income than in male-headed households (Shackleton & Shackleton., 2004). Evidence from West Africa suggests that indigenous domesticated fruit trees can improve the livelihoods of poor households, especially women (Leakey et al., 2005). In one study, women perceived greater livelihood benefits from agroforestry tree products than men (Akpabio 2009).

In the Amazon, about a third of the work involved in rubber tapping is done by women and children (Abramovitz & Nichols., 1992). In the state of Maranhao, Brazil, women comprised 86 percent of the estimated 400,000 rural workers who earned income from the vegetable oil extracted from the babassu palm kernel, used for the manufacture of soap and other products. The babassu palm also provides poor women with important raw materials to produce both household and market goods such as baskets, fish traps, bird cages, animal feed, and oil (Hecht et al., 1988). Women in southern Africa rely upon wild plants for use in food, medicines,

& Ibok.,

construction, tool manufacturing, and income. Baskets made by women from the leaves of palms in Botswana, Zimbabwe and Zambia constitute an important craft export, while tubers of the grapple plant found in western Botswana are exported for use as arthritis medicine (Hunter et al., 1990). Drewes (1982) found that women in three small traditional fishing villages in Tamilnadu, India, played key roles in the small-scale marketing of fish. By virtue of their economic roles women, also played a role in making decisions about the purchase of fishing nets, boats, and other fishing equipment. Women of certain tribal communities in India, for example, know medicinal uses for 300 forest species (Abramovitz & Nichols., 1992). A survey in Sierra Leone demonstrated that women could name 31 products that they gathered or made from the nearby bush while men were able to name only eight (FAO/SIDA, 2000). Women also had information on the varieties of wild fruits and plants that are important supplements in the diets of poor rural people, especially during the hungry season, and on the medicinal uses of plants. In the Parana State of Brazil, the association of Small Fanners in Turvo discovered that local women collectively knew of and used more than 60 medicinal plants. Although not all women were familiar with all such plants, their interest in improving such knowledge prompted the association to set up educational meetings at which nearly 3,000 women exchanged information. As many of the plants were near extinction, the spread of information about their medicinal properties might have contributed to those species survival (UNEP, 1991).

(2014) studied that the gender roles in Kenya put women in direct contact with natural resources such as forests, water, land and wildlife. They utilized and conserved these resources to supply basic needs for their families. Therefore conservation of natural resources in rural areas could not be done without the involvement and training of women. They need to be educated on the values, management and sustainability of natural resources as alternative sources of livelihood. But to have success, they must not only be appreciated as invisible land managers but must benefit from relevant incentives in their cultural roles.Management of Renewable Natural Resources by Women

Researchers concluded that “even with increased patrolling effort or more severe penalties, law enforcement policies alone are unlikely to protect the woodlands because they fail to provide alternative supplies of fuelwood for resident households” (Abbot & Mace, 1999). Work, as the active, labour-based interaction of human beings with the material world, is important because it involved people putting their personal time and energy into the use and management of natural resources. Historically, this interaction had been intricately tied to the natural environments in which human populations survived. Women's work often still

Wasike

393


involved this kind of direct connection, for example through the collection of water and fuel; gathering plant and animal products and the management of agricultural, grazing and forest lands and also of fisheries. Any discussion of women and the environment must, therefore, account for the gendered division of labour and responsibility (Neefjes, 2000).

World Bank (1997) report revealed that in developing countries women provide 70 percent of agricultural labour, 60–80 percent labour for household food production, 100 percent labour for processing the basic foodstuffs, 80 percent for food storage and transport from farm to village, 90 percent for water and fuelwood collection for households. This, therefore, suggests that their role in natural resource exploitation and management cannot be undermined. Women are the primary collectors of wild plants for food. Such foods provide important micronutrients in the diet and are vital for the survival of the households during food shortages. Women possess the extensive and unrecognized knowledge, for example, knowledge of wild plants for achieving household food security and nutritional well-being. However, women's roles and knowledge are often overlooked or underestimated in natural resource management and related policies and programs (Howard, 2003).

Venkateswaran (1995) pointed out diversity between urban and rural women and among rural women of different classes. In this study on women's roles in activities relating to the environment, she discussed the impact of environmental degradation on women and their marginalization from environmental management policies, using case studies and empirical data from research in issues such as croplands, forest and water resources, and energy. Carolyn (1996) showed how environmental degradation results from economic and development practices that disadvantage rural women. In addition, she explored the strategies women use for resistance and survival in the face of these trends. Offering a range of examples from different countries, she discussed commonalities and differences in women's knowledge of and interactions with the natural environment. In order to adopt policies and prompt institutions to take appropriate actions, it was useful to distinguish women's practical needs, such as access to land and water, food security, health services and education, and their strategic needs, including political participation and decision-making. In 2005, there were over 40 women Ministers of Water or Environment, representing every region and level of development in the world (UN-Energy, 2007).

Women were also knowledgeable about and active participants in, conservation and environmental management and in the protection and promotion of biological diversity. In some African villages, women had found ways—against serious odds—to protect animal and plant species from extinction. Lacking legal access to

their own land, women kept alive as many as 120 plant and animal species by planting on the interstices among the men's cash crops (Abramovitz & Nichols, 1992). Efforts such as these, made by women to preserve biodiversity and conserve resources, had prompted noted environmentalist Diane Rocheleau to characterize rural women as holding "the threads to past knowledge of biodiversity and the skills needed to reweave the web of livelihoods and living things" (Abramovitz & Nichols., 1992). Uniyal et al. (2008) conducted a study on 'Empowering Rural Women for Sustainable Livelihoods through Natural Resource Management' in Garhwal Himalaya, Uttarakhand, India and concluded that indigenous uses of medicinal plants were time-tested system and used by people worldwide. This system had high potential medicine value and designated as safe and eco-friendly tradition for curing many diseases. The therapy had minimal side effects and cost-effective compared to other systems of medicine. The success of cultivation of medicinal plants as alternate income generation option for rural community mainly depends on awareness, interest and training programme for rural women in this region. Proper assured market, return of invested cost and appropriate agro-techniques may certainly help in improving economic condition of the people of this mountainous region. Upadhyay (2005) attempted to delineate women's role in natural resource management of water, agriculture, livestock, forestry and fishery a case study in rural areas of India and Nepal was done during 2003 and 2004. Taking women as primary respondents, empirical work used participatory techniques, such as in-depth surveys, focus group discussion and participant observation. Findings suggest that women clearly outdo men in terms of their involvement in use and management of all the studied sectors, i.e., water, agriculture, livestock, forestry and fishery. Yet, they faced categorical exclusion and denial of equal sharing of benefits from natural resources. In order to ensure sustainable use of these resources, policy makers, planners and development workers must have a better understanding of the relative and often shifting roles of men and women in natural resource management, including division of labour, access to resources, decision-making and traditional knowledge and practices. “There is urgent need for more research on how far men and women have different environmental knowledge, how far decisions that relate to the use of resources and particular geographical locations lie with women, whether women are more environmentally protective than men, how women and men experience environmental change, seasonality and family size, how women organize, preserve and transmit environmental knowledge, the kinds of experiences women associate with local landscapes, and the way greater gender awareness in the social differentiation of local knowledge is used in a strategic way to further women's interests” (Nuttall, 1998). Poor rural women are more severely

394


affected by environmental degradation than men, due to their role as main food-producers and collectors of natural resources such as water, fodder and fuel. Moreover it was concluded that due to their important role as users and managers of natural resources women had the potential to be strategic actors in sustainable development and environmental conservation (Dankelman, 2002).

Women's roles and responsibilities were pivotal not only to the management of natural resources but also to the management of the domestic economy. Studies had also shown that women work longer hours, pool more of their income to the household budgets, manage the day today consumption and cash flow needs (Boserup, 1970). Over much of the world, it was mainly women who were wild plant gatherers, and managers, home gardeners and plant domesticators, herbalist and healers, as well as seed custodians. In several regions and cultures, women were also principal farmers and informal plant breeders, particularly of indigenous crops (Howard, 2003).

Any discussion on women's relationship to the environment should incorporate these different factors. It should also draw from a participatory appraisal involving the various stakeholder groups and explicitly including women and their organizations (Dankelman, 2003).CONCLUSIONS

The paper has discusses the various ways women participate actively in environmental protection and natural resource management in order to ensures sustainable use of environmental resources. Drop by drop is the water pot filled. Only Government's programmes, projects and laws cannot save the natural resources. This is responsibility of each person in country to save the environment by proper management and judicious use of natural resources. Those who live in close proximity of forests, use natural resources to a food extent to fulfill their day to day needs should be the part of programmes of environment safe guarding. The life and economic status of these people have been affected by climate change. Moreover there is urgent need to make rural women aware of environment management policies and strategies, so that she may take care of renewable natural resources while earning livelihood from these natural resources for sustainable development of one and all. REFERENCESAbbot, J.I.O., & Mace, R. (1999). Managing protected

woodland: Fuel wood collection enforcement in Lake Malawi National Park. Conservations Biology, 13(2), 418-442.

Abramovitz, J.M., & Nichols, R. (1992). Women and biodiversity: Ancient reality, modern imperative Development,2, 85-90.

Abu, A., & Millar, D. (2004). Land, natural resources and spirituality: Policy implication on rural livelihood in Northern Ghana, CECIK, Bolgatanga, Ghana

Achin, S.,& Gonzalo, O. (2004).Indigenous knowledge and natural resource management: Local pathways to global development. Retrieved from http://documents. w o r l d b a n k . o rg / c u r a t e d / e n / 9 8 1 5 5 1 4 6 8 3 4 0 2

49344/pdf/307350ENGLISH0ik0local0pathways.pdfAgarwal, A., (2003). Communities and the environment:

Ethnicity, gender, and the state in community based conservation. Piscataway: Rutgers University Press.

Agarwal, B. (1992). The gender and environment debate: Lessons from India. Feminst Studies,,18(1), 119-157.

Akpabio, I., & Ibok, I. (2009). Agro-forestry practices among male and female farmers in South–South, Nigeria. Small Scale Forestry. 8, 63-76.

Arnold, M., & Townson, I. (1998). Assessing the potential of forest product activities to contribute to rural incomes in Africa.Overseas Development Institute Natural Resource Perspectives No 37. London, UK.

Bimlesh. (1999). Management of renewable natural resources by rural women in semi-arid zone(Doctoral dissertation). CCSHAU, Hisar.

Birgegard, L. (1993). Natural resource tenure: A review of issues and experiences with emphasis on Sub-Saharan Africa . Uppsala, Sweden: International Rural Development Centre.

Boserup, E. (1970).Women's role in economic development. Earthscan Publications: London.

Buckingham-Hatfield, S. (2000). Gender and environment. New Fetter Lane, London: Ruetledge.

Carolyn, E.S. (1996). Gendered fields: Rural women, agriculture, and environment. Boulder, Colorado: West view Press.

Chambers, R. (1991). In search of professionalism, bureaucracy stand sustainable livelihoods for the 21 century.IDS

Bulletin, 22(4), 1-15.Coggins, C. (2003). The tiger and the pangolin: Nature, culture

and conservation in China. Honolulu, HI: University of Hawaii Press,

Colin, T., & Chauveau, D. (2002). Negotiating access to land in West Africa: A synthesis of findings from research on derived rights to land. International Institute for Environment and Development, London.

Dankelman, D. (2003). Gender, environment and sustainable development: Theoretical trends, emerging issues and challenges. INSTRAW, Santo Domingo

Dankelman, I. (2002). Introduction: Gender, environment and sustainable development: Understanding the linkages. In Gender, Society and Development Editorial Team. Natural Resource Management and Gender: A global source book. Critical Review and annotated bibliography series. Royal Tropical Institute, Amsterdam.

Drewes, E. (1982). Three fishing villages in Tamil Nadu: A socio-economic study with special reference to the role and status of women. Madras, India: Food and Agriculture Organization of the United States (FAO).

FAO. (2005). Building on gender, agro-biodiversity and local knowledge. Food and Agriculture Organization, Rome.

FAO. (2007a). Adaptation to climate change in agriculture, forestry and fisheries: Perspectives, framework and priorities. Food and Agriculture Organization, Rome.

FAO. (2007b). Environment and agriculture. Committee on t hAgriculture, 20 Session.Food and Agriculture

Organization, Rome.FAO/IFAD. (2003). Rural women's access to land and property

in selected countries. Food and Agriculture Organization, International Fund for Agricultural Development and International Land Coalition, Rome.

FAO/SIDA. (2000). Restoring the balance: Women and forest

395


resources. Food and Agriculture Organization and Swedish International Development Authority. Rome and Stockholm.

Ghosh, S., Luniya, V., & Gupta, A. (2009). Trend analysis of Indian summer monsoon rainfall at different spatial scales. A t m o s p h e r i c S c i e n c e L e t t e r s , 1 0 , 2 8 5 -290.DOI:10.1002/asl.235.

Guha, R. (2000). Environmentalism: A global history. Longman, New York

Hecht, S.B., Anderson, A.B., & May, P. (1988). The subsidy from nature: Shifting cultivation, successional palm f o r e s t s , a n d r u r a l d e v e l o p m e n t . H u m a n Organization,47(1), 25-35.

Holdgate, M. (1999). The green web: A union for world conservation. Earthscan: London

Howard, P.L. (2003). Women and plants: Gender relations in biodiversity management and conservation. London and New York: Zed Books Ltd.

Hunter, M.L., Robert, K.H., & Barbara, W. (1990). Women and wildlife in Southern Africa Conservation Biology,4(4), 448-451

Kaur, J., Randhawa, V., & Mittal, R. (2016). Involvement of rural women in utilization and management of renewable natural resources in Kandi area of Punjab. International Journal of Research in Applied, Natural and Social Sciences, 4(12), 1-10

Koda, B. (2004). Women, deforestation, and the wood fuel crisis: The case study of Tanzania. Journal of Development Studies,5(2), 53-72.

Lambrou, Y., & Piana, G. (2006). Energy and gender issues in rural sustainable development. Rome: Food and Agriculture Organization.

Leakey, R., Tchoundjeu, Z., & Schreckenberg, K. (2005). Trees and markets for agro forestry tree products: Targeting poverty reduction and enhance livelihoods. International Journal of Agricultural Sustainability, 1(3), 1–23.

Loughran, D.L., & Pritchett. (1997). Environmental scarcity, resource collection, and the demand for children in Nepal. World Bank. Washington D.C.

MEA.(2005). Millennium ecosystem assessment. Washington DC: Island Press.

Mies, M., & Shiva, V. (1993). Ecofeminism.London: Zed Books.

Millenium Ecosystem Assessment. (2005). Ecosystem and human wellbeing: Current state and trends. Findings of the conditions and trends working group. In R. Hassanm R. Scholes, and N. Ash (Eds.). Millennium ecosystem assessment series. Washington, U.S.A.: Island Press.

NCAER. (1986). Domestic fuel consumption in rural India. New Delhi: National Council of Applied Economic Research.

Neefjes, K. (2000). Environments and livelihoods: Strategies for sustainability. Oxfam, UK: Oxford.

Nonhebel, S. (2005). Renewable energy and food supply: Will there be enough land? Renewable and Sustainable Energy Reviews, 9(2), 191-201.

Nuttall, M. (1998). Protecting the Arctic: Indigenous peoples and cultural survival. Harwood Academic Publishers, Amsterdam.

OECD. (2001). Guidelines for gender equality and women's empowerment in development cooperation. Paris: Organization for Economic Cooperation and Development.

Palmer, (2008). Climate change and the world's river basins: Anticipating management options. Frontiers in Ecology and the Environment,6(2), 81-89.

Pimentel, D., & Pimentel, M. (2008). Food, energy and society. Boca Raton, FL: CRC Press.

Ravindranath, N.H., & Hall, D.O. (1995). Biomass, energy, and environment: A developing country perspective from India. New Delhi: Oxford University Press.

Sagar, A., & Kartha, S. (2007). Bioenergy and sustainable development? Annual Review of Environmental Resources,32, 131-167.

Saxena, N.C. (1991). Women and wasteland development-Policy issues. New Delhi: International Labour Office.

Sen, D., & Grown, P. (1987). Development crises and alternative visions: Third world women perspectives. London: Earthscan Publication Ltd.

Shackleton, C.M., & Shackleton, S.E. (2004). The importance of non-timber forest products in rural livelihood security and as safety nets: A review of evidence from South Africa. South African Journal of Science,100, 658-664.

Shiva, V. (1988). Staying alive: Women, ecology and development. London, UK: Zed Books.

Shiva, V. (1992). Women's indigenous knowledge and biodiversity conservation. In GeetiSen (Ed.). Indigenous vision: Peoples of India attitudes to the environment. New Delhi: Sage Publications.

UN. (2008). The millennium development goals report. United Nat ions . Ret r ieved f rom ht tp : / /www.un.org/ millenniumgoals/2008highlevel/pdf/newsroom/mdg%20reports/MDG_Report_2008_ENGLISH.pdf.

UN-Energy. (2007). Sustainable bioenergy: A framework for decision-makers. New York, UN.

UNEP. (1991). Success stories of women and the environment. A preliminary presentation of the global assembly of women and the environment. United Nations Environment Programme and Worldwide Network. Washington, D.C.

Uniyal, B., Uniyal, V.P., & Bhatt, V.K. (2008). Empowering rural women for sustainable livelihoods through natural resource management in Garhwal Himalaya, Uttarakhand, India. Retrived from http://vpuniyal.com/ images/mountain_conf.pdf .

Upadhyay, B. (2005).Women and natural resource management: Illustrations from India and Nepal. Natural Resources Forum, 29, 224-232.

Venkateswaran, S. (1995). Environment, development and the gender gap. New Delhi: Sage Publications Inc.

Vijaya, V.R.S., Iniyan, S., & Ranko, G.R. (2012). A review of climate change, mitigation and adaptation.,16(1), 878-897.

Wasike, S.L. (2014). Challenges facing women in accessing and controlling natural resources in Tuuti Ward, Bungomacounty in Western part of Kenya (Project report). Kenya University, Nairobi.

World Bank. (1997). Taking action to reduce poverty in Sub-Saharan Africa. Retrieved fomhttp://documents. worldbank.org/curated/en/451961467990001471/pdf/multi0page.pdf

World Bank. (2005). Agriculture investment source book. World Bank, Washington, DC.

Yadav, S. (1999). Management of renewable natural resources by rural women in arid zone.(Doctoral dissertation). CCSHAU, Hisar.

396


Mehak Jain. (2018). A Sociological Study on Rural Youth in Sangrur District of Punjab. Department of Economics and Sociology, Punjab Agricultural University, Ludhiana

Major Subject: SociologyMajor Advisor: Dr. Simran Kang SidhuJEL Codes: Y40

India is one of the youngest nations in the world, with almost 65 per cent of the population under the age of 35 years. The youth in the age group of 15 to 29 years comprised 27.5 per cent of the population. Attributes of strong passion, motivation and will power make them the most valuable human resource for fostering economic, cultural and political development of a nation. However, the present situation characterized by lack of proper education and occupational opportunities on one side and their inability to adapt global culture but running down faith in local cultural practices on the other has brought Indian youth at the verge of exigency. To get youth out it and to channelize their creative energies, there is call for a comprehensive policy through systematic studies focusing the nature and state of contemporary youth. The proposed study is an effort in this direction with the objectives (i) to assess physical, social and economic profile of youth in the village.(ii) tostudy institutional pattern of conduct of youth in the village(iii) to highlight problems of youth and suggest remedial measures. The study was conducted in Balewal village of Sangrur district of Punjab. By door to door survey, all the young persons of the village having age between 15-35 years and residing in that village from the last ten years were taken as respondents. Sample size was 281respondents (136 males & 145 females). The findings of the study revealed that 12 percent males were overweight and 50 percent females were under weight. Except 15 percent of respondents, majority dropped studies at matric/senior secondary level and reasons for drop out given by majority were either disinterested in studies or inability to get admission in college due to poor academic performance. Most of the leisure time of youth in the village was either spent by watching television or by gossiping with friends or neighbors. Majority of the youth still consider caste and family background as main considerations for marriage. All females but only nine percent of males were teetotalers. Among the males who consumed intoxicants, half were used to consume alcohol only while others, along with alcohol were hooked to drugs as well. Among family problems, criticism and vigilance by parents were the main problems faced by young respondents while at social front, their low economic and educational status was the main problem in their life. The study concluded that high quality education leading to occupational opportunities may solve almost all the problems confronted by rural youth. It was suggested that at block level, two or three big schools,

397

Dissertation/Thesis Abstract

each catering to the needs of approximately five adjoining villages equipped with modern means and methods at affordable to all should be established. To deal with psychological problems of young, Government and Non-Government Organizations must come up with youth counseling cells and youth clubs so that youth energies can be channelized and utilized in constructive activities for social and economic development of the country.

Darshnaben P. Mahida. (2017). A Comparative Analysis of Technical Efficiency of Cooperative Member and Non-member Dairy Farms in Gujarat. Division of Dairy Economics, Statistics and Management, National Dairy Research Institute, Karnal.

Major Subject: Agricultural EconomicsMajor Advisor: Dr. Sendhil R. (Scientist, IIWBR,

Karnal)JEL codes: Q01, Q12, Q13

Crop and livestock farming are essential attributes of Indian agriculture and pursued to complement each other. Livestock farming began in the country as a saviour in paucity, a source of income in the situation of crop failure. It is the secondary and continuous source of income for farmers, especially for small and marginal landholders. Globally, India ranks first in milk production with an output of 155.49 million tonnes (2015-16). High production is attributed to large cattle population rather than their productivity. Gujarat has been a pioneer state in dairy cooperatives where dairy industry is more organised and efficient in comparison to others. Despite high dairy development, disparities do exist owing to the difference in natural resource endowment, dairying practices, adoption of technology, availability of veterinary facilities, irrigation facilities, feed availability and attitude of farmers across regions. In the context, the present study entitled ‘A comparative analysis of technical efficiency of cooperative member and non-member dairy farms in Gujarat’ has been undertaken with the specific objectives of computing dairy development index for the districts of Gujarat, estimating and comparing the technical efficiency of cooperative member and non-member dairy farms and finding out the determinants of technical efficiency of dairy farmers. A total sample size of 180 dairy farmers comprising 90 dairy cooperative members and 90 non-members were selected from three districts having different dairy development status and analysed with appropriate statistical tools and techniques like data envelopment analysis, multiple linear regression and regression tree. Principal component analysis has been employed to assign weights for dairy development indicators. The results showed that the index value for 26 districts was ranging between 0.19 to 0.58. Dahod district was found to be the least developed district while Mehsana (0.58) was having the highest development index value. Highest variability among the

districts was found in artificial insemination performed per 1000 adult female bovine followed by the cooperative development parameters and animal yields, respectively. The least variation was found in the indicators namely area under fodder per 1000 adult female bovine followed by surface road length. In the least developed district (south and south-eastern districts of Gujarat) veterinary infrastructure was found strong but the productivity of animals and cooperative structure was found to be very poor. Subsequently, Anand from high, Bharuch from medium and Tapi from low dairy development categories were randomly selected for further data collection and analysis. The district-wise analysis on technical efficiency of cooperative members in Anand, Bharuch and Tapi indicated that they were efficient by 91.20, 90.58 and 87.09 percent, respectively. In the case of non-members, the efficiency score was 75.91, 85.30 and 80.56 percent, respectively. The overall member and non-member comparison showed that members (83.27 percent) were more efficient than non-members (75.31 percent). The overall mean efficiency score was found to be 79 percent indicating the scope for additional output to the tune of four liters per farm by following the best practice in the region. Among herd size, medium (81.05

percent) category farmers were found to be least efficient in the case of member farms while farmers with small herd size were found to be most efficient in both the member (87.21 percent) and non-member (81.59 percent) category. Most of the medium herd size farmers were rearing buffaloes for their milk need which contributed to their inefficiency. The major determinants of technical efficiency were found to be distance from dairy cooperative society at village level and herd size with negative effect. Non-farm annual income, access to information, farming experience and membership in the dairy cooperative had positive impact on the technical efficiency. Coefficients of location dummy were found to be positive and significant as non-member farmers of Anand district were highly inefficient owing to buffaloes as the mainly reared animal for their own milk consumption. Policy focus should be on improving the animal productivity and intensifying cooperatives in the least dairy developed districts for additional output with the existing technology and resource endowments. Further, the farmers should use fodder resources optimally, especially green fodder supported with additional non-farm income and access to information for improving the overall efficiency.

398

Procedure to Upload/Re-upload Articles/Check Article Status

New Submission

Visit www.soed.in ? Click Register at Header Menu ? Fill the required information,

Your Email is Login Address and choose the password ? Click Upload Article at Header

Menu ? Click Submit Article Button ? Now you will be asked pay Processing Fee

(`500.00 per paper) and you may proceed to remit processing online by using internet

banking, debit card, credit card, etc. (Please ensure that Processing Fee is Non-

Refundable even if your article is not found fit for publication at any stage of processing)

? You will receive SMS having 5 digits Order No. which may be used OTP and you will

receive OTP on your registered Email. It may be noted both are the same ? Click Upload

Article ? Submit Article Button and now use Order No. Or OTP ? This will take you to

article submission area for uploading your article ? Fill up all the details and upload

article, save, and exit.

Have an Account

Visit www.soed.in ? Click Upload Article at Header Menu ? Click Submit Article Button

? Now you will be asked pay Processing Fee (`500.00 per paper) and you may proceed to

remit processing online by using internet banking, debit card, credit card, etc. (Please

ensure that Processing Fee is Non-Refundable even if your article is not found fit for

publication at any stage of processing) ? You will receive SMS having 5 digits Order No.

which may be used OTP and you will receive OTP on your registered Email. It may be

noted both are the same ? Click Upload Article ? Submit Article Button and now use

Order No. Or OTP ? This will take you to article submission area for uploading your

article ? Fill up all the details and upload article, save, and exit.

Re-upload Revised Articles

Log in your account by using your Log in address (Email) and Password and click Re-

upload Article Button ? Now attach the revised article, update and exit.

Check the Status of an Article

Click Check Article Status at Header Menu ? Log in your account by using your Log in

address (Email) and Password and see the current status of article.


LISTING THE REFERENCES: APA FORMATJournal Article in PrintShah, D. (1990). Implementation of MGNREGA and sustainable livelihood security in Maharashtra: A region-wise

analysis. Indian Journal of Economics and Development, 13(3), 461-468.Singh, P. & Datta, K.K. (2016). Economic analysis of traditional milk supply chain in Ranchi district of Jharkhand.

Indian Journal of Economics and Development, 13(3), 495-502.Kaur, N., Singh, J., & Kumar, S. (2016). Comparative economic analysis of basmati and non-basmati paddy cultivation

in Punjab.Indian Journal of Economics and Development, 13(3), 439-444.More than 7 authorsVissing, K., Brink, M., Lonbro, S., Sorensen, H., Overgaard, K., Danborg, K., ...Aagaard, P. (2008). Muscle adaptations

to plyometric vs. resistance training in untrained young men. Journal of Strength and Conditioning Research, 22(6), 1799-1810.

Journal Article Found OnlineAli, M.A. (2016). Industrial structure and firm's agglomeration: A study on Tunisian data. Indian Journal of Economics

and Development, 12(3), 405-412. doi: 10.5958/2322-0430.2016.00155.4 or retrieved from http://www.indianjournals.com

Paper in a Conference/SymposiumKaur, G., Gill, S.S., & Singh, N.P. (2010, February 7-9). Adoption gaps in paddy cultivation in Punjab-cost involved

thand returns thereof. Paper presented at 13 Punjab Science Congress. Punjab University, Chandigarh.Williams, J., &Seary, K. (2010).Bridging the divide: Scaffolding the learning experiences of the mature age student. In

J. Terrell (Ed.), Making the links: Learning, teaching and high quality student outcomes. Proceedings of the 9th Conference of the New Zealand Association of Bridging Educators (pp. 104-116). Wellington, New Zealand.

Conference paper (online) Cannan, J. (2008). Using practice based learning at a dual-sector tertiary institution: A discussion of current practice. In

R. K. Coll, & K. Hoskyn (Eds.), Working together: Putting the cooperative into cooperative education. Conference proceedings of the New Zealand Association for Cooperative Education, New Plymouth, New Zealand. Retrieved from http://www.nzace.ac.nz/conferences/papers/ Proceedings_2008.pdf

MacColl, F., Ker, I., Huband, A., Veith, G., & Taylor, J. (2009, November 12-13). Minimising pedestrian-cyclist conflict on paths. Paper presented at the Seventh New Zealand Cycling Conference, New Plymouth, New Zealand. Retrieved from http:/ /cyclingconf.org.nz/system/fi les/NZCyclingConf09_2A_ MacColl_PedCycleConflicts.pdf

Book in PrintFinney, J. (1970). Time and again. New York, NY: Simon and Schuster.

rdDhillon, P. (1970). Economics and Marketing(3 ed.). New Delhi, NY: S. Chand and Publishing Company.e-Book from an e-ReaderEggers, D. (2008).The circle [Kindle Version]. Retrieved from http://www.amazon.com/Book Found in a DatabaseNevin, A. and Chhina, S.S. and Gill, K.S. (2015). Investment in early childhood development: Review of the World

Bank's recent experience. doi: 10.1596/978-1-4648-0403-8Chapter in an Edited BookSharma, J.L. & Gill, S.S. (2009).Sustainability of agriculture development in Punjab. In Jain, P.K. Jain, B.S.Hansra,

K.S. Chakraborty, &J.M. Kurup (Eds.),Food Security and Sustainable Agriculture (278-290).U-Day Publishers and Advertisers, New Delhi

Haybron, D.M. (2008). Philosophy and the science of subjective well-being. In M. Eid & R.J. Larsen (Eds.), The science of subjective well-being (pp. 17-43). New York, NY: Guilford Press.

Magazine Article in PrintTumulty, K. (2006, April). Should they stay or should they go? Time, 167(15), 3-4.Magazine Article Found OnlineTumulty, K. (2006, April). Should they stay or should they go? Time, 167(15) Retrieved from

http://content.time.com/time/magazine/article/0,9171,1179361,00.htmlNewspaper Article in Print Toor, J.S. (1997, March 31). Electronic discovery proves an effective legal weapon. The Tribune, p. D5.Newspaper Article Found OnlineJohl, S.S. (1997, March 31). Electronic discovery proves an effective legal weapon. Indian Express, Retrieved from

http://www.tribuneindia.com

General Website Article with an AuthorSidhu, D.S. (2015, January 9). Gold monetization scheme. Retrieved: http://economictimes.indiatimes.com/

topic/Gold-Monetization-SchemeGeneral Website Article without an AuthorTeen posed as doctor at West Palm Beach hospital: Police. (2015, January 16). Retrieved from

http://www.nbcmiami.com/news/local/Teen-Posed-as-Doctor-at-West-Palm-Beach-Hospital-Police-288810831.html

Online Lecture Notes or Presentation SlidesDhawan, J.S. (2012). Technology and me: A personal timeline of educational technology [Powerpoint slides].

Retrieved from http://www.slideshare.net/Bclari25/educational-technology-pptEncyclopaedia Entry in PrintKammen, C., & Wilson, A.H. (2012).Monuments. In: Encyclopaedia of local history. (pp. 363-364) Lanham, MD:

AltaMira Press.Thesis/Dissertation PrintChahal, S.S. (1989). An economic analysis of milk marketing in Punjab(Doctoral Dissertation). Punjab Agricultural

University, Ludhiana.Chahal, S.S. (1981). An economic analysis of tomato marketing problems in Punjab (Master's thesis). Punjab

Agricultural University, Ludhiana.Online Thesis/DissertationSankhayan, P.L. (2013). Eastward voyages and the late medieval European worldview (Master's thesis, University of

Canterbury, Christchurch, New Zealand). Retrieved from http://hdl.handle.net/10092/9187Basic Reference List Format for a Print ReportGill, K.S. (1991).Marketing of mango in Punjab (Report No. 3). Ludhiana: Department of Economics and Sociology,

Punjab Agricultural University, 1-60.Basic Reference List Format for an Online ReportGill, K.S. (1991).Marketing of mango in Punjab (Report No. 3). Retrieved from [Punjab Agricultural University and

www.pau.edu]Internet: No author, No datePet therapy.(n.d.). Retrieved from http://www.holisticonline.com/stress/stress_pet-therapy.htmInternet-Organisation / Corporate authorMinistry of Health. (2014). Ebola: Information for the public. Retrieved from http://www.health.govt.nz/your-

health/conditions-and-treatments/diseases-and-illnesses/ebola-information-public Act (Statute / Legislation)Health and Safety in Employment Act 1992. (2013, December 16).Retrieved from http://www.legislation.govt.nzBrochure / Pamphlet Tamihana, B. (2007). Gambling health promotion: Mate petipetiwhakapikihauora[Brochure]. Palmerston North, New

Zealand: Best Care (Whakapai Hauora) Charitable trust.Brochure / pamphlet (no author)Ageing well: How to be the best you can be [Brochure]. (2009a). Wellington, New Zealand: Ministry of Health.Dictionary (print)

thWeller, B. F. (Ed.). (2009). Bailliere's nurses dictionary: For nurses and health care workers (25 ed.). Edinburgh, Scotland: Elsevier.

Dictionary (online) Cambridge dictionaries online. (2011). Retrieved from http://dictionary.cambridge.org/Software (including apps) UBM Medica.(2010). iMIMS (Version1.2.0) [Mobile application software]. Retrieved from http://itunes.apple.com

CITATION EXAMPLES APA FORMATSingle author

1. Use only the surname of the author followed by a comma and the year of publication:(Matthews, 1999).

2. Include page, chapter or section numbers if you need to be specific. The abbreviation for page is p. and the abbreviation for pages is pp.: Matthews discusses the role of drawings in the psychological evaluation of children (1979, pp. 34-35).

3. OR… in the psychological evaluation of children has been studied elsewhere (Matthews, 1979, pp. 34-35).Two authors

1. Cite both authors every time you cite within the text.2. Separate the authors' names in the citation with an “&”:(Lawson & Green, 1997, pp. 34-35).3. When the authors' names are incorporated into the text the “&” is replaced with “and”. Always cite both names

every time the reference occurs in the text:Lawson and Green (1997, pp. 34-35) were unable …Three or more authors

1. The first citation in the text of a work with three, four or five authors gives the surnames of all the authors:Wasserstein, Zappulla, Rosen, Gerstman and Rock (2004, p. 301) have found … OR… as has been found in a previous study (Wasserstein, Zappulla, Rosen, Gerstman & Rock, 2004, p. 301).

2. In subsequent citations in the text, only the surname of the first listed author is used, followed by the expression “et al.” which means “and others”:Wasserstein et al. (2004, p. 301) have found…

3. If there are six or more authors, only the surname of the first author is used, followed by et al.:Littlewood et al. (1997) have found … OR… as has been previously demonstrated (Littlewood et al., 1997).

Volume numbers included1. Include volume numbers within the citation between the year and the page numbers.2. If more than one volume is given separate with a “;”:This theory is dealt with in detail by Brysen (2003, vol. 2,

p. 23; vol. 3, pp. 17-36).Authors with the same surname

1. Make a distinction between authors with the same surname by including the author's initials.2. If the author's surname is incorporated in the text place the initials before the surname; if it is a citation within

brackets the initials follow the surname:The theory was propounded in 1970 (Larsen A.E., 2001) …M.K. Larsen (2003) is among those …

Multiple works by the same author in the same year1. A distinction is made by adding lower case letters, a, b, c, etc. to the date.2. These letters are also included in the full reference in the reference list to distinguish between the two

documents: Bursch (2005a) described how the yak made transport possible in the high mountains of Inner Asia, as did the llama in the Andes of South America (Bursch, 2005b).

Corporate author1. These are works without a personal author.2. Corporate authors may be associations, agencies like government departments, corporations or organisations.3. Names of organisations should be given in full the first time they are cited within the text.4. In subsequent citations, these names may be abbreviated in the text if the abbreviation is meaningful or well

known: (CSIRO, 1999) …5. As predicted by the Centre of Independent Studies (1997) ………………………………………………

More than one work cited1. List all sources of information either in the text or within the citation separated by a semicolon (;):

(Haddon, 1999; Larsen, 1991) …Haddon (1999) and Larsen (1991) demonstrated that …(Haddon, 1999, vol. 3, p. 734; Larsen, 1991, p. 11)

2. No author: When a work has no author or the author is anonymous, cite in the text the first few words of the reference list entry (usually the title) and the year.

3. Use italics for the title:This was apparently not the case in seventeenth-century England (On Travelling to London, 1683) … OROn Travelling to London (1683) reveals that this was not true.

4. No date of publication: Use the abbreviation n.d. to indicate that no date of publication is given: Carruthers (n.d.) has suggested … OR(Carruthers, n.d.)

Newspapers1. If the author of the article is named, cite in the normal way with the author and date. If there is no author

given, cite the newspaper title in italics.2. Include the specific date as well as year and page or section numbers if appropriate:

(Canberra Times, 24 Jan. 1997, p. B6) …3. The Weekend Australian (24-25 Jan. 1997, p. 19) reported …


Guidelines for Submission of Articles

style

Economics and Indian Journal of Development

Date of Publication: June 20, 2018 NAAS Score: 4.82Indexed in Clarivate Analytics (ESCI)UGC Approved (48981)

9 772277 541005

ISSN 2277-5412

Secretarial Office

Department of Economics and SociologyPunjab Agricultural UniversityLudhiana - 141004 (Punjab) INDIAEmail : [email protected]

Society of Economics and Development

indian journal of economics and development print issn

Documents