evaluation of irrigation inefficiencies in samrat ashok … is a historical place. samrat ash ok...

INTERNATIONAL JOURNAL OF CIVIL AND STRUCTURAL ENGINEERING Volume 1, No 2, 2010

© Copyright 2010 All rights reserved Integrated Publishing services Research article ISSN 0976 – 4399

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Evaluation of Irrigation Inefficiencies in Samrat Ashok Sagar Major Irrigation Project by Use of Data Envelopment Analysis

Sanjay Sitaram Phadnis 1 , Mukul Kulshrestha 2 1 Research Scholar and Executive Engineer, M.P. Water Resources Department, Bhopal,

Madhya Pradesh, India 2 Associate Professor, Department of Civil Engineering, National Institute of

TechnologyBhopal , Madhya Pradesh, India

ABSTRACT

Water is scare and it has several characteristics that make the role of its development and management more essential than other goods available for mankind. Water allocation to various stakeholders is a complex issue due to increasing demand. Due to population increase, it is difficult to meet the demand of food in resources available worldwide. Therefore, it is a primary objective to ensure optimal utilisation of water within an available resource. Major consumer of water is Irrigation. About 83% share is consumed by Irrigation. But it is evident from statistics that only 40% potential is utilised in India because of poor maintenance of canal, flooding methods adopted in larger part of country for application of water and inadequate facilities for irrigation services. In view of this Government of India is seriously making efforts to improve the existing systems performance and suggesting their State Government to adopt benchmarking of Irrigation and drainage sector to assess the performance. Benchmarking indicators are also suggested by a committee constituted by Central Water Commission, Government of India to compare internal and external benchmarking of Irrigation Projects. The simple comparison of indicators involves subjective judgmentmaking which provides little information about the global position of an organization with respect to others. In order to address this problem, a Data Envelopment Analysis (DEA) technique is used to evaluate inefficiencies in a Samrat Ashok Sagar Projact.

Keywords: Madhya Pradesh, India, Irrigation, efficiency Data envelopment analysis

1. Introduction

As per Census of India (2001), the population of India is 1.03 crores( Source: Office of the Registrar General, India) . The statistics claim that annual food grain production is 195.2 million metric tonnes per year (Economic Survey 200708). It means that the annual food grain need may be around 450 million metric tonnes. The total geographical area of India is 305 million hectares and cultivable area is 184 million hectares with net sown area is around 140.3 million hectares (Directorate of Economics & Statistics, Ministry of Agriculture, 200607). This area has limitation due to increasing demand for habitation of growing population, there is practically no scope for bringing any additional area under agriculture; rather availability of lands for agriculture may even go down, as more lands may be needed for habitation and development of infrastructural facilities for



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increasing population. Under such a scenario country is left with only one option and that is to increase yield of food grain. To overcome above situation out of which, high yielding variety of seeds, fertilizers, pesticides, improved technology and water for irrigation are usually considered as main ingredients for improved agriculture in the country. However despite all above measures being provided in correct quality and quantity, if adequate quantity of water at right time is not provided to the crops, all measures taken for improving productivity would not give desired results. Therefore irrigation at right time with adequate quantity is critical in the Indian context (Yadav, 2006).

Pollution—industrial, domestic or agricultural—effectively limits the availability of freshwater. This is particularly true of thirdworld countries, where surface water and ground water resources have been thoroughly contaminated by domestic, agriculture and industrial effluents and by solid wastes leaches. Pollution restricts the water availability and results in shortages.

The Annual precipitation including snowfall is the main source of water in India and is estimated to be of the order of 4000 cu km. The total water resource potential of the country, which occurs as natural run off in the rivers is estimated at 1869 cu km considering both surface and groundwater into account. Due to various constraints of topography, uneven distribution of resource over space and time, it has been estimated that only about 1123 cu km be put to beneficial use – out of which only 690 cu km is surface water. However, 370 cu km of estimated utilizable surface water comes from the nonclassified river basins. As per the distribution of water resources potential in the country, the national per capita annual availability of water is 1731 cu m (estimated as on 1st March 2004). The average availability in Brahmaputra and Barak basin is as high as 14057 cu m while it is as low as 308 cu m in Sabarmati basin in 2000. Brahmaputra and Barak basin with 7.6 % of geographical area and 5.2 % of population of all the basins in the country has 31 % of the annual water resources. Per capita annual availability for rest of the country excluding Brahmaputra and Barak basin works out to about 1345 cu m. An availability of less than 1000 cu m per capita is considered by international agencies as scarcity condition. Cauvery, Sabarmati, East flowing rivers and west flowing rivers are some of the basins which fall into this category (CWC, 2009).

Agriculture is a vital for the Indian economy, as India has traditionally been an agrarian economy by virtue of not only employing 70% of the workforce but also contributing to a great part of the national income as agricultural production. 1. India undergoes census every 10 years and last time it took place was in the year 2001 2. Source: Directorate of Economics & Statistics, Ministry of Agriculture

(1) Relates to agriculture year : July to June. (2) Excludes data in respect of Jammu and Kashmir under unlawful occupation

of China & Pakistan.

India possesses 16% of the world’s population but just 4% of its water resources (Planning Commission, 2001a). India’s finite and fragile water resources are stressed and



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depleting, while sectoral demands for water (including drinking water, industry, agriculture, and others) are growing rapidly. At Independence in 1947, India’s population was less than 400 million, with per capita water availability being over 5,000 cubic meters per year. With the population having grown to over a billion now, the per capita water availability has fallen to barely 2,000 cubic meters per year and the actual per capita usable quantity is around 1,122 cubic meters per year (Planning Commissions 2002b). The total water utilization for all uses 12a in 1990 was about 518 billion cu. meters (BCM) and is estimated to increase to 1,422 BCM by year 2050 (Planning Commission, 2002b), which will be in excess of the total utilizable average water resources. Increasing population has so far, and will continue to lead to stress on availability of water. Significant environmental problems have also emerged due to economic development fuelled by burgeoning agriculture and industrial production and has resulted in declining per capita water availability due to deteriorating raw water qualities. Efficient usage of water across sectors has thus emerged as a major issue in the modern times (Planning Commission, 2002b; National Water Policy, 2002 2 ). Since irrigation augments agricultural production, irrigation and wealth have been closely linked. Hence, agricultural areas continue to expand and limited water resources become a key factor of socioeconomic development. Due to inadequate water conveyance systems which lead to high water losses, nonadapted type of crops, lack of maintenance of irrigation systems, and farmer’s lack of knowledge of appropriate irrigation practices, it is certain that the current use of water for irrigation purposes is inefficient. Therefore, the modernization of irrigation techniques should be encouraged and more productive use of water should be a fundamental objective not only for the agriculture but also for all water using sectors.

Data envelopment analysis (DEA) is a multifactor productivity analysis model for measuring the relative efficiencies of a homogenous set of decision making units (DMUs). Inputoriented DEA models strive to maximise the proportional decrease in input variables while remaining within the envelopment space (production possibility set). The inputoriented model is suited to the measurement of cost saving efficiency while an outputoriented model is better for revenue efficiency evaluation, thus an inputoriented DEA model is chosen to service the purpose of this study because of its emphasis on cost savings. In terms of the assumption of returns to the scale, DEA models are distinguished as constant returns to scale models (CRS) or variable returns to scale models (VRS). Although from the economic viewpoint and from the public interest, cost efficiency measurement should always be taken under constant returns to scale in a pricing regulation context thereby encouraging water providers to operate at the most economic scale, a variable return to scale (VRS) model may be adopted to measure cost efficiency because operating scale is often uncontrollable for DMUs in the short and medium term. The difference in operating scale between the irrigation schemes included in this study is remarkably large. So, potential cost savings will be measured according to the nature of the returns which characterise efficient operation and this may not be the most efficient scale. DEA, the technique which measures internal managerial efficiency, identifies

1a This includes water from any source, and is inclusive of raw, reused, and recycles waters.



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better practices and sets plausible targets, thereby providing operating cost savings which can be factored into the setting of reasonable pricing limits by water regulators (Gang et al, 2002).

The objective of this study is to determine the irrigation inefficiencies so that these can be addressed timely by using best practices and implementing other modernisation plan. Vidisha is a historical place. Samrat Ashok Sagar Project (also known as Halali Project) is a major irrigation cum flood protection project constructed across Halali river which is a tributary of Betwa River. The dam site of the project is 40kms away from Bhopal and 16 kms from Salamatpur railway station, which is on Bhopal Delhi main line. It is also connected by road from Vidisha and Raisen.

The administrative approval of the project was accorded in the year 1963 for Rs. 404.27 Lakhs vide Government of Madhya Pradesh, Irrigation department letter no. 966/1100/XIX/W3 dated 27th April 1963. The latest revised cost as reported by the project authorities is Rs.2471.10 lakhs.

The total gross command area of project is 37419 ha out of which culturable command area is 27924 ha. Net area served is 25091 ha against the annual irrigation of 37636 ha. The intensity of irrigation is thus 135%. The annual irrigation in Vidisha and Raisen district will be 31536 ha and 6070 ha respectively.

In the year 189697 and 18991900, two famines attacked central India which resulted in death of animal as well as human population. In the year 1905, excessive cold had greatly damaged the spring crop (poppy and gram). During the year 1965 there were abnormal floods in Betwa river damaging properties worth lakhs of rupees and loss of several human lives and live stock in low lying areas. All these calamities and deficiencies prompted the authorities to take up this project for providing irrigation facilities and flood control to the area. In view of this the project was started in the year 1973 and dam stream closure (central portion of river stream) was completed in 1976. The irrigation from the project was started in the year 1978. Huge investment is done for creating water resources potential in India. However, due to large gap between the potential created and potential utilized in most of the irrigation projects, the benefits of created infrastructure could not be achieve. Similar to other irrigation project, Samrat Ashok Sager Irrigation Project (Halali Project) is also showing same deficiency. Huge investment is done for creating water resources potential in India. However, due to large gap between the potential created and potential utilized in most of the irrigation projects, the benefits of created infrastructure could not be achieve. Similar to other irrigation project, Samrat Ashok Sager Irrigation Project (Halali Project) is also showing same deficiency. The large gap between potential created and utilized as area proposed for kharif irrigation is reported nil as against 12545 ha (WALMI, 2006). Apart from this, the area proposed in Rabi by flow irrigation is not fully developed but it is compensated due to higher commands of adjoining canals area which are doing irrigation by pumps and therefore it is appearing that full potential is achieved in Rabi. However entire water planned for Kharif is utilized in Rabi crop season itself.



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The overall performance may be satisfactory for the project, but in the project regions wide disparities exist due to inadequate carrying capacity of discharge in entire distribution system, and same is reflected in terms of performances of the entire Water Users Associations, therefore these are captured by assessing the relative performances of these WUAs. Looking to said reasons, it is mandatory for irrigation projects to study of project inefficiencies at micro level i.e. Water Users Associations level. In present study, data envelopment analysis is used in evaluation of inefficiencies.

Development of Farmers Organization i.e. Water Users Associations in Madhya Pradesh Water Resources Department has been done in following manner:

• In the year 198485, Irrigation Panchayats were constituted under M.P.Irrigation Act,1931 but could not deliver the goods since the functions, duties, powers of Panchayat were not well defined therefore these Panchayats were defunct.

• In the year 199495, 65 Farmers Management Committees were formed on pilot basis under Cooperative Society Act but these could not extend their whole hearted interest in Irrigation Management and therefore resulted in defunct.

• In the year 1997 98, it was decided to create a public support at all levels regarding transfer of power to manage the state Irrigation System to their real beneficiaries i.e. farmers.

• In the year 199899, Merits of PIM and success stories of Andhra Pradesh PIM Model and achievements of Maharashtra and Gujarat Irrigation Societies were publicized for awareness amongst farmers and politicians.

• In the year 19992000, “Madhya Pradesh Sinchai Prabandhan Me Krishko ki Bhagidari Adhinium 1999” was enforced by Government of Madhya Pradesh in September 1999. Participatory Irrigation Management Programme was launched in whole state.

• In year 2003, Samrat Ashok Sagar Project was considered under Indo Canada Environmental Facility programme to enhance the capacity building of farmer’s organisation with the external support of Non Governmental Organisation (NGOs). Under this programme rehabilitation of existing canal system with active participation of farmers which includes 30% financial contribution in physical improvement was proposed (ICEF, 2003).

• In year 200405, Madhya Pradesh Water Sector Restructuring Project was started for modernization and rehabilitation of deteriorated schemes funded by the World Bank for Rs 1919 crores which includes special programme for Capacity Building of Water Users Associations (ESA, 2004).



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• MPFarmer Participation in Irrigation Management (MPFPIM) Act came into force since 1999 and under this act the structure of participatory model is as below:

• The WUAs are constituted over a population of 1001000 water users. WUA has a general body including farmer and wives of male farmers who are members of the general body of the WUAs.

• It is important to distinguish between the member of the WUA general body including in particular women members and a member of the Management Committee (MC) of the WUA.

• The demographic area covered by a WUA will be a ‘hydrological boundary’ ranging from 100 to 2000 Ha.

• The number and the boundary of a WUA are notified by the District Collector in accordance with the President and Territorial Constituency members (ranging from 4 to 10) depending on the WUA.

• The medium irrigation schemes have a two tier system in which WUA are involved with Project Committees.

• In the major irrigation schemes, WUAs are involved with a three tier committee consisting of Distibutory Committees and Project Committees (PC).

• A State level Apex Committee headed by the Minister of WRD consists of representatives of Project Committees across the State.

• All these committees and WUA Management Committees work in partnership and share different responsibilities.

• The WUAs are expected to work in close partnership with other stakeholders like WRD, Agriculture and other relevant line departments with Panchayati Raj Institutions (PRIs), for financial and other help.

• WUA are involved in identifying and pin pointing the problems and deficiencies in physical system of canal in joint walkthrough process. Their suggestions are taken for understanding background of the problems and its remedies.

2. Data Envelopment Analysis

DEA is a nonparametric frontier method for the study of production functions. The use of DEA analysis based on inputs and outputs of an irrigation system enables us to determine the relative efficiency of an organization or a productive function within an organization and to determine its position in relation to the optimal situation by providing a numerical



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quantification of the direction in which the organization must direct its efforts in the future (RodríguezDíaz et al., 2004). The Constant Return to Scale (CRS) assumption is appropriate when all firms are operating at an optimal scale. However, imperfect competition, government regulations, constrains on finance, etc. may cause a firm to be not operating at optimal scale. Various authors, such as Fare, Grosskopf and Logan (1983) and Banker, Charnes and Cooper (1984) suggested adjusting the CRS DEA model to account for variable returns to scale (VRS) situations. The use of the CRS specification when not all firms are operating at the optimal scale, results in measures of TE that are confounded by scale efficiencies (SE). The use of the VRS specification permits the calculation of TE devoid of these SE effects.

Scale efficiency measures can be obtained for each firm by conducting both a CRS and a VRS DEA, and then decomposing the TE scores obtained from the CRS DEA into two components, one due to scale inefficiency and one due to “pure” technical inefficiency (i.e. VRS TE). If there is a difference in the CRS and VRS TE scores for a particular firm, then this indicates that the firm has scale inefficiency. This is computationally intensive when the number of DMUs is large (Raju and Kumar, 2006). In addition, incident factors, variation of organization structure, climate, geographical location, soil type, economic conditions and measurement errors are not considered in the DEA method. Therefore, confident data of homogenous DMUs should be employed in the analysis, and the results should be investigated in depth through field studies.

Although the DEA approach has been widely and successfully used in different areas, its application to water resources management problems, particularly to irrigation water management alternatives, is surprisingly rare. However, specific examples include the efficiency studies of the water companies in UK (Thanassoulis, 2000), the irrigation districts in Andalusia (Spain) (RodríguezDíaz et. al., 2004), and the reservoir system in the Paraguacu river basin in Brazil (Srdjevic et.al., 2005). In agriculture, when the water volume applied to crops is increased, we do not necessarily obtain a linearly proportional increase in agricultural production. In order to account for this effect, the DEA model for variable returnstoscale (BCC) was developed (Banker et.al., 1984). Efficiency measures can be beneficially employed by a range of stakeholders to identify areas where there is scope for enhanced performance. Some of the areas that benefit from efficiency analysis are delineated below:

• A typical efficiency and productivity analysis will help water managers and administrators to identify inefficient units. Also the water utility managers can use efficiency measures to identify gaps between actual and existing best practices. These measures can thus form a useful part of utility benchmarking initiatives. Such an analysis can also help identify performance targets based on which incentive schemes may be devised for managers to induce productivity.

• Governments can use efficiency measures to implement incentive based regulation i.e., to identify and reward those service providers who are meeting their objectives in the most cost effective manner.



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• Policymakers can assess the impact of reforms, such as output–based funding or contractingout of service provisions.

• Community and business users can utilize the public information on performance of different service providers to keep governments accountable and force the decisionmaking authorities to make favorable decisions with regard to their preferred service providers.

• Regulators can use these measures to assess the scope of furthering efficiency improvements and to determine if the water enterprises are exploiting monopoly power. Regulators can also employ these measures to set tariffs.

In the recent times, DEA is receiving increasing importance as a tool for evaluating and improving the performance of manufacturing and service operations. DEA is a multi factor productivity analysis for measuring the relative efficiencies of a homogenous set of decisionmaking units (DMUs) that perform similar tasks by consuming multiple inputs to produce multiple outputs. It can be applied to analyze multiple outputs and multiple inputs without preassigned weights and without imposing any functional form on the relationships between variables. DEA was suggested by Charnes, Cooper and Rhodes (1978) (hence the CCR model), and built on the idea of Farrell (1957) which is concerned with the estimation of technical efficiency and efficient frontiers.

The main characteristics of DEA are: • It can be applied to analyze multiple outputs and multiple inputs without pre

assigned weights, • It can be used for measuring relative efficiency based on the observed data DEA

model without imposing any functional form on the relationships between inputs and outputs.

• Decision makers’ preferences can be incorporated in DEA models • It can be used as a resource allocation tool

There are a number of models and modifications that exist. For water utilities, input quantities act as decision variables that need minimization as the output is often fixed. Hence, the basic DEA model discussed below has an input orientation. DEA input oriented approach was also chosen since the objective of the analysis was to suggest benchmarks for input reductions. The following sections describe the formulations employed in the current study for analysis.

2.1 The CCR Formulation

This model was suggested by Charnes, Cooper and Rhodes (1978), and hence is named CCR formulation. This formulation assumes a constant return to scale hypothesis. The efficiency score in the presence of multiple input and output factors is defined as:



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Efficiency = weighted sum of outputs/weighted sum of inputs (1)

Assuming that the chosen sample has z utilities (called Decision Making Units (DMUs) in the popular DEA terminology), each with m inputs and n outputs, the relative efficiency score of a test DMU p is obtained by solving the model proposed by Charnes et al. (1978):

where i = 1 to z, j = 1 to m, k = 1 to n, yki = amount of output k produced by DMU i, xji = amount of input j utilized by DMU i, vk = weight given to output k, uj = weight given to input j. The fractional program in (2) is subsequently converted to a linear programming format and a mathematical dual is employed as shown in (3), to solve the linear problem. The dual is required as it reduces the number of constraints from z+m+n+1 in the primal to m+n in the dual; thereby, rendering the linear problem easier to solve. Charnes et al. (1978) spell this model development and can be referred for greater details.

where, θ = efficiency score, and λi = dual variables (weights in the dual model for the inputs and outputs of the z DMUs).

The above problem is run z times in identifying the relative efficiency scores of all the DMUs, and values of θ (efficiency score), and λi (weights in the dual model for the inputs and outputs) are computed. The weights obtained show the target utility in the

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most favorable mode. The linear program is to be solved for each individual DMU in the sample. The method creates a frontier using information on the assumed most efficient utilities and measures the efficiency relative to the rest of the utilities. DEA attempts to approximate the efficient frontier by a ‘‘piecewise’’ linear approximation based on the sample. Efficiency scores are constructed by measuring how far a utility is from the frontier. A test DMU is considered inefficient if a composite DMU (defined as linear combination of units in the set) can be identified which utilizes less input than the test DMU while maintaining the same or greater output levels. In general, a DMU is efficient if it obtains a score of 1; while a score of less than 1 indicates that it is inefficient. Koopmans (1951) had provided a more comprehensive definition of efficiency: A DMU is efficient if it operates on the frontier and also has zero associated slacks, a description now widely accepted (Banker et al, 2004). The units involved in the construction of the composite DMU can then be utilized as benchmarks for the inefficient test DMU. The technique also computes the input and output refinements that would turn an inefficient unit into an efficient one.

2.2 The BCC Formulation

When the utilities do not perform at optimal scales, this formulation can be modified to account for variable return to scale conditions as shown by Banker, Charnes and Cooper (1984) (hence the BCC formulation), by adding a convexity constraint. This formulation employs the same equation as employed in the CCR model, with the modification that a convexity constraint is now added to equation (3) as shown in equation (4).

3. Literature Review: Previous Efficiency evaluation studies in various sectors

DEA is receiving increasing importance as a tool for evaluating and improving the performance of manufacturing and service operations. It has been applied in diverse sectors and industries for efficiency evaluation (Table 1).

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Table1: Some DEA applications to infrastructure in the past decade

Sector References Airports Gillen and Lall (1997), Parker (1999), Vasigh and Hamzaee

(2000), Sarkis (2000), Pels et al. (2001), Martin and Roman (2001), Chin and Siong (2001) , Abbott and Wu (2002), Pacheco and Fernandes (2003)

Banking Sherman and Ladino (1995), Miller and Noulas (1996), Schaffnit et al. (1997), Berger and Humphrey (1997), Kantor and Maital (1999), Golany and Storbeck (1999), Zenios et al,(1999),Athanassopoulos and Giokas (2000), Cook et al. (2000), Xueming Luo (2003), Mercan et al (2003)

Coal sector Russell et al(1995),Kulshreshtha and Parikh (2002) Educational Schools, Institutes and Universities

Johnes and Johnes (1995), Post and Spronk (1999), Korhonen et al (2001), Avkiran(2001), Rajiv et al (2004)

Gas Industry Hawdon (2003) , David (2003)

Hospitals Jaume PuigJunoy (2000),Ouellette and Vierstraete (2004) , Chen et al (2005)

Ports Tongzon (2001)

Power sector Goto and Tsutsui (1998), Kumbhakar (1998), Chitkara (1999), Martý´n (2000), Olatubi (2000), Jamasb and Pollitt (2001), Domah 2002, Pacudan and Guzman (2002), Pahwa et al (2002), Korhonen and Luptacik (2004), Edvardsen and Førsund (2003), Oliveira and Tolmasquim (2004)

Railways Tim Coelli and Sergio Perelman (1999)

Road Transportation Viton (1998), Karlaftis (2004)

Telecommunication Sueyoshi (1998), Zhu (2003)

Irrigation and Drainage Sector

Huynh Viet Kha, Mitsuyasu Yabe, Hiroshi Yokogawa and Goshi Sato (2008), Rodriguez Diaz, E. Camacho Poyato and Lopez Luque (2004), Hector Malano, Martin Burton and Ian Makin (2004), Malana Naeem M., Hector M. Malana (2006), Lieu Gang and Bruce Felingham (2002), Chieko Umetsu, Sevgi Donama, Takanori Nagano and Ziya Coskun (2004), Baris Yilmaz, Nilgun B. Harmancioglu ( 2008), Eliane Goncalves Gomes, 2009,

4. Study Area

The Madhya Pradesh state is a central part of India and has created 23 lakhs hectare irrigation potential, but performance in potential utilisation is as low as 40%. Madhya Pradesh has been divided in 11 agroclimatic zones and 5 crop zones. The command of the Samrat Ashok Sagar Project comes under Vindhyan plateau climatic zone and wheat zone. It is need of the hour to minimise gap between these two parameters, and ensure optimal irrigation water by using best practices to use of available water resources. There is a growing concern and realization among the prime stakeholder i.e. the farmers and the Government that old organization setup on the pattern of early British India is no longer



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appropriate and a paradigm shift in irrigation management is needed. The wide gap between actual and desirable performance threatens the sustainability of irrigated agriculture. Production of food grain, adequate power supply and safe drinking water for growing population will be a great challenge for India and worldwide both in near future. Therefore it is the need of the hour to develop a strategy for equitable and optimal utilization of Canal Irrigation Water for better productivity through Community Participation. Now, it has been widely accepted that promoting community participation through Water Users Association can be the best strategy for long term sustainability of Irrigated agriculture (Palanisami, 2006).

Vidisha is a historical place. Samrat Ashok Sagar Project (also known as Halali Project) is a major irrigation cum flood protection project constructed across Halali River which is a tributary of Betwa River. The dam site of the project is 40kms away from Bhopal and 16 kms from Salamatpur railway station, which is on Bhopal Delhi main line. It is also connected by road from Vidisha and Raisen.

Figure 1: Drainage and Canal Map showing Water Userswise Area (Source: Secondary Data from Project Office)

Project covers 2 revenue districts in command namely Vidisha and Raisen. This is a Major Multipurpose Irrigation Project. It caters demand of Drinking Water supply to



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Vidisha Township, Irrigation to part area of Vidisha and Raisen District. In addition to this fisheries and Tourism is also generates huge revenue. Basically this project was cleared under flood protection scheme.

5. Work Methodology

Data Envelopment Analysis has been selected to know the inefficiencies at WUAs level for selected input and output in present study. The inefficiencies based on benchmarking at project level may not give realistic solution to resolve the conflicts and issues at individual WUA level within a major irrigation project where more than one WUA exists. Therefore, it is necessary to start benchmarking process at WUA level. DEA models are distinguished as constant returns to scale models (CRS) or variable returns to scale models (VRS) for Year 200304 and Year 200708. Efficiency concepts are used to describe the performance of production units. There are two reasons to measure the efficiency of natural resources using DEA. First of all, efficiency estimates are success indicators – performance measures whereby production units are evaluated. Secondly, it is only by measuring efficiency and separating their effects on production that hypotheses concerning the sources of efficiency or productivity differentials can be explored. In evaluation process, Output are used as operation and maintenance cost; and labour/ staff deployed for services; similarly input are Beneficiary Members of WUAs, No. of watering, Irrigation potential utilised (ha), Agriculture Production (Rs.) etc.

Problem Definition and structuring

Select the Input and Output variables

Construct the DEA Model

Efficiency analysis by CCR and BCC Models

Evaluation of Potential Targets/Savings

Select the participating utilities

Establishment of benchmarks / best practices

Sensitivity Analysis on selected utilities



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Figure 1: Research methodology In order to achieve such an assessment, this study proposes the use of data envelopment analysis (DEA), a methodology for the study of data envelopment using nonparametric frontiers. With a series of inputs and outputs for each Water Users Associations, DEA permits one to assess the relative efficiency of a given WUA and to obtain the optimal configuration by numerically assigning to each WUA for its objective and by using the guidelines to follow [RodríguezDíaz et. al., 2004]. The main advantages of DEA are: (1) multiple inputs and outputs can be used effectively, while ascertaining efficiency and a specific production function is not required; (2) the decision maker doesn’t need prior information about weights of inputs and outputs; (3) for each decision making unit (DMU), efficiency is compared to that of an ideal operating unit, rather than to the average performance. On the other hand, the main limitation in the method is that the standard formulation of DEA creates a separate linear program for each DMU.

6. Results and Discussion

Water User Associations level Input Oriented Model where input was Operating Cost and Total Staff deployed including labour deployed in numbers and outputs were Beneficiary members of WUAs Irrigation Potential utilised in ha, No. of Watering and Agriculture Production in Rupees. The efficiency scale for both 200304 and 200708 is given in table 2 & figure 2. The average operating cost saving for VRS is Rs. 210363 (Table 3 & figure 4) and average staff saving 2104 (Table 3 & figure 3) for year 200304 similarly the average operating cost saving for VRS is Rs. 191463 (Table 3 & figure 4) and average staff saving 1167 for year 200708 (Table 3 & figure3) .

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Scale Effic

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Scale efficiency Year 2003‐04 Scale efficiency Year 2007‐08

Figure 2: Scale Efficiency for Year 200304 and 200708 at WUA level



185

0 2000 4000 6000 8000

10000 Kh

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Total Staff Sav

ings

VRS Savings of Total staff deployed Year 2003‐04

VRS Savings of Total staff deployed Year 2007‐08

CRS Savings of Total staff deployed Year 2003‐04

CRS Savings of Total staff deployed Year 2007‐08

Figure 3: Savings of Staff Deployed for Year 200304 and 200708 at WUA level

0 100000 200000 300000 400000 500000 600000 700000 800000

Khamkheda

Laskarpur

Jewajeepur

Bamoria

Duparia

Sayar

Sunpura

Andiakhurd

Uchher

Dhaniakhedi

Name of WUA

Saving

in Rs.

VRS Savings of OPEX (Rs) Year 2003‐04 VRS Savings of OPEX (Rs) Year 2007‐08

CRS Savings of OPEX (Rs) Year 2003‐04 CRS Savings of OPEX (Rs) Year 2007‐08

Figure 4: Savings in Operating Cost for Year 200304 and 200708 at WUA level Table 2: Efficiency scores : WUA InputOriented Agriculture Production Model

Inputs Total staff deployed, OPEX (Rs) Outputs Beneficiary Members of WUAs, No. of watering, Irrigation potential utilised (ha), Agriculture Production (Rs.)



186

InputOriented 200304

Scale efficiency 200304

InputOriented 2007 08

Scale efficiency 200704

DMU Name

CRS Efficien

cy

VRS Efficien

cy

CRS Efficiency

VRS Efficien

cy

Ratio of CRS to VRS

Mean 0.74 0.81 0.92 0.76 0.83 0.91

SD 0.18 0.17 0.14 0.17 0.16 0.12

Min 0.39 0.51 0.54 0.38 0.52 0.74

Max 1.00 1.00 1.00 1.00 1.00 1.00

Table 3: Potential savings for WUAs InputOriented Agriculture Production Model

Inputs Total staff deployed, OPEX (Rs) Outputs Beneficiary Members of WUAs, No. of watering, Irrigation potential utilised (ha), Agriculture Production (Rs.)

CRS Savings 2003 04

VRS Savings 200304

CRS Savings 200708

VRS Savings 2007 08

DMU Name

Total staff

deploye d

OPEX (Rs)

Total staff

deploye d

OPEX (Rs)

Total staff

deploye d

OPEX (Rs)

Total staff

deploye d

OPEX (Rs)

Mean 2646 270579 2104 210363 1500 257404 1167 191463

SD 2136 217119 2004 200368 1231 207918 1146 187985

Min 0 0 0 0 0 0 0 0

Max 8282 844051 6805 680567 4986 849192 4062 666289

Out of 19 DMUs 13 are efficient on CRS and VRS model with constant return to scale and total 6 DMUs show increasing return to scale. This implies that decreasing the input level in the case of the inefficient 6 DMUs will result in proportionate increase in output for year 200304 (Table 4) and 12 DMUs out of 19 total DMUs are efficient on CRS and VRS model with constant return to scale and total 7 DMUs show increasing return to scale for year 200708. This implies that decreasing the input level in the case of the inefficient 7 DMUs will result proportionate increase in output for year 200708 (Table 5). Table 4: Efficiency scores and potential savings for WUAs InputOriented Agriculture

Production Model ( 200304) Inputs Total staff deployed, OPEX (Rs) Outputs Beneficiary Members of WUAs, No. of watering, Irrigation potential utilised (ha), Agriculture Production

(Rs.)



187

Scale efficie ncy

CRS Efficient Input Target

CRS Savings VRS Efficient Input Target

VRS Savings

DMU Name

Ratio of CRS to

VRS

RTS Benchmar ks VRS

Total staff deplo yed

OPEX (Rs)


OPEX (Rs)


OPEX(Rs )


OPEX (Rs)


OPEX(Rs )

Khamkhe da

1.00 Consta nt

7413 80479 2

7413 80479 2

0 0 7413 80479 2

0 0

Kararia 1.00 Consta nt

7037 76723 2

7037 76723 2

0 0 7037 76723 2

0 0

Laskarpur 1.00 Consta nt

9992 10627 28

6663 72973 7

3329 3329 91

6663 72973 7

3329 3329 91

Cheerkhe da

1.00 Consta nt

10099 10733 68

6815 74501 5

3284 3283 53

6815 74501 5

3284 3283 53

Jewajeep ur

1.00 Consta nt

Duparia (0.354)

10349 10983 68

6961 75960 9

3388 3387 59

6961 75960 9

3388 3387 59

Billore 1.00 Consta nt

7175 78095 2

7175 78095 2

0 0 7175 78095 2

0 0

Bamoria 1.00 Consta nt

Duparia (0.575)

10261 10895 68

6893 75284 0

3368 3367 28

6893 75284 0

3368 3367 28

Sankalkh eda

1.00 Consta nt

Chitoria (0.140)

10228 10863 28

6831 74662 1

3397 3397 07

6831 74662 1

3397 3397 07

Duparia 1.00 Consta nt

6780 74151 2

6780 74151 2

0 0 6780 74151 2

0 0

Gadla 0.76 Increas ing

6517 71523 2

4956 54154 6

1561 1736 86

6517 71523 2

0 0

Sayar 1.00 Consta nt

Chitoria (0.842)

9979 10614 08

6695 73296 2

3284 3284 46

6695 73296 2

3284 3284 46

Neem Khera

0.77 Increas ing

Sunpura (0.441)

9713 10348 48

5050 55259 9

4663 4822 49

6533 71679 2

3180 3180 56

Sunpura 0.54 Increas ing

6255 68895 2

3377 36944 9

2878 3195 03

6255 68895 2

0 0

Chitoria 1.00 Consta nt

6648 72827 2

6648 72827 2

0 0 6648 72827 2

0 0

Andiakhu rd

0.78 Increas ing

6646 72807 2

5203 56796 5

1443 1601 07

6646 72807 2

0 0

Sarchamp a

1.00 Consta nt

9942 10576 88

6669 73034 0

3273 3273 48

6669 73034 0

3273 3273 48

Uchher 1.00 Consta nt

9933 10568 08

6675 73102 4

3258 3257 84

6675 73102 4

3258 3257 84

Mendki 0.77 Increas ing

Chitoria (0.409)

13269 13903 84

4987 54633 3

8282 8440 51

6464 70981 7

6805 6805 67

Dhaniakh edi

0.77 Increas ing

Chitoria (0.446)

9860 10494 88

4986 54620 4

4874 5032 84

6459 70932 2

3401 3401 66

Table: 5 Efficiency scores and potential savings for WUAs InputOriented Agriculture Production Model (Year 200708)

Inputs Total Staff deployed, OPEXOutputs Beneficiary Members of WUAs, No. of watering, Irrigation potential utilised, Agriculture Production (Rs.)



188

Scale effici ency

Input CRS Efficient Input Target

CRS Saving VRS Efficient Input Target

VRS Saving

D M UNo .

DMU Name

Ratio of CRS to

VRS

RTS Benchma rks VRS

Total Staff

deploye d

OPEX (Rs)

Total Staff deploy ed

OPE X (Rs)

Total Staff

deploye d

OPEX (Rs)

Total Staff deploy ed

OPEX (Rs)

Total Staff deplo yed

OPEX (Rs)

1 Khamkh eda

1 Consta nt

4988 91250 4

4988 9125 04

0 0 4988 912504 0 0

2 Kararia 1 Consta nt

4491 83106 4

4491 8310 64

0 0 4491 831064 0 0

3 Laskarpu r

1 Consta nt

5980 10752 36

4224 7871 44

1756 288092 4224 787144 1756 288092

4 Cheerkh eda

1 Consta nt

6135 11006 76

4362 8098 22

1773 290854 4362 809822 1773 290854

5 Jewajeep ur

1 Consta nt

6285 11252 36

4373 8116 18

1912 313618 4373 811618 1912 313618

6 Billore 1 Consta nt

4692 86402 4

4692 8640 24

0 0 4692 864024 0 0

7 Bamoria 1 Consta nt

Duparia (0.558)

6369 11389 96

4352 8081 81

2017 330815 4352 808181 2017 330815

8 Sankalkh eda

1 Consta nt

6183 11085 16

4302 7999 71

1881 308545 4302 799971 1881 308545

9 Duparia 1 Consta nt

4224 78714 4

4224 7871 44

0 0 4224 787144 0 0

10 Gadla 0.750 Increas ing

3838 72386 4

2880 5353 49

958 188515 3838 723864 0 0

11 Sayar 1 Consta nt

5937 10681 96

4285 7972 00

1652 270996 4285 797200 1652 270996

12 Neem Khera

0.742 Increas ing

5627 10172 36

2816 5247 63

2811 492473 3732 706399 1895 310837

13 Sunpura 0.788 Increas ing

3582 68186 4

2821 5256 52

761 156212 3582 681864 0 0

14 Chitoria 0.752 Increas ing

Andiakhu rd

(0.430)

4080 76354 4

3067 5687 74

1013 194770 4077 763059 3 485

15 Andiakh urd

0.787 Increas ing

3822 72130 4

3009 5567 55

813 164549 3822 721304 0 0

16 Sarcham pa

1 Consta nt

5830 10505 16

4224 7871 44

1606 263372 4224 787144 1606 263372

17 Uchher 1 Consta nt

5828 10502 76

4224 7871 44

1604 263132 4224 787144 1604 263132

18 Mendki 0.743 Increas ing

Andiakhu rd

(0.280)

7827 13781 28

2841 5289 36

4986 849192 3765 711839 4062 666289

19 Dhaniak hedi

0.743 Increas ing

Andiakhu rd

(0.548)

5818 10486 76

2867 5331 43

2951 515533 3802 717915 2016 330761

The VRS analysis for year 200304 shows that only 13 out of 19 DMUs are efficient. These efficient DMUs can be selected by inefficient DMUs as best practice DMU, making them as a benchmark. For example, in the case of DMU 19, the DMU that represents the best practice or reference benchmark DMU is formed by the DMU 14(0.446). This means DMU 19 is close to the efficient frontier segment formed by these



189

efficient DMUs represented in the DMU. The selection of these efficient DMUs is made on the basis of their comparable level of inputs and outputs to DMU 14. The benchmark DMU for DMU 19 is expressed as DMU 14, where 4 is the DMU number while the values between brackets are the intensity vector l for the respective DMUs. On the other hand, the DMU 14 appears 4 times in the reference set of inefficient DMUs. This places DMU 14 closest to the input and output levels of most of the inefficient DMUs but uses fewer inputs. The VRS analysis for year 200708 shows that only 12 out of 19 DMUs are efficient. These efficient DMUs can be selected by inefficient DMUs as best practice DMUs, making them a as a benchmark. For example, in the case of DMU 19, the DMU that represents the best practice or reference benchmark DMU is formed by the combination of DMU 15(0.548). This means DMU 19 is close to the efficient frontier segment formed by these efficient DMUs represented in the DMU 15. The selection of these efficient DMUs is made on the basis of their comparable level of inputs and outputs to DMU 15. The benchmark DMU for DMU 19 is expressed as DMU 15 where 15 is the DMU number while the values between brackets are the intensity vector l for the respective DMUs. The higher value of the intensity vector l for DMU 15 indicates that its level of inputs and outputs is closer to DMU 19. On the other hand, the DMU 15 appears 3 times in the reference set of inefficient DMUs. This places DMU 15 closest to the input and output levels of most of the inefficient DMUs but uses fewer inputs.

7. Conclusions

In this study, the system is transferred to Water Users Associations without improving environmentally degraded system. Results recommend that it needs urgent attention to modernise and restructure the system to desired level so that productivity targets can be achieved. The analysis shows that more input is used by many areas where proportionate productivity can be increased by reducing inputs. While the DEA results highlight the lower productivity of inefficient DMUs, a more detailed analysis of the physical environments, climatic conditions and agricultural practices will be required to investigate the causes of inefficiency. Furthermore, these DMUs are not perfectly competitive and therefore cannot be treated on equal grounds. However, by identifying those DMUs with lower productivity, this analysis provides a quantification of the productivity in these DMUs in relation to those performing at the frontier of high productivity, thus enabling planners and scientists to focus their attention on those DMUs with lower performance to determine the actual underlying causes of that underperformance (Malana 2006). For every inefficient DMU, DEA has identified a set of corresponding efficient DMUs that can be utilized as benchmarks (reference DMUs) for improvement, and also allows computing the projected values of inputs and outputs to make them efficient. It should be noted that DEA is primarily a diagnostic tool and does not prescribe any reengineering strategies to make inefficient units efficient. Such improvement strategies need further studied for implementation by understanding the operations of the efficient units. Therefore, the result needs to be evaluated in the light of field conditions and prevailing natural conditions like climate rainfall and soil quality etc. Since the principal aim is to use water efficiently, irrigation methods and irrigation



190

systems are extremely important. Due to inadequate water conveyance systems which lead to high water losses, nonadapted type of crops, lack of maintenance of irrigation systems, and farmer’s lack of knowledge of appropriate irrigation practices, it is certain that the current use of water for irrigation purposes is inefficient. Therefore, the modernization of irrigation techniques should be implemented for more productive use of water not only for the agriculture but also for all water using sectors. It is concluded that DEA is a highly useful tool for detecting local inefficiencies and determining possible improvements for irrigation districts that offer the greatest potential for growth (Yilmaz at el 2008).

8. References

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2. Charnes A, Cooper WW and Rhodes E. 1978. Measuring the Efficiency of Decision Making Units, European Journal of Operational Research 2: pp 429–444

3. Chieko Umetsu, Sevgi Donma, Takanori Nagano and Ziya Coşkun 2006. The Efficient Management of Water User Associations: A Case Of Lower Seyhan Irrigation Project In Turkey, Turkey Journal Of Agriculture And Forestry, pp 21 29

4. Fare R., S. Grosskopf, M. Norris and Z. Zhang 1994. “Productivity Growth, Technical Progress and Efficiency Changes in Industrialized Countries”, American Economic Review, 84, pp 66 83

5. Farrell, M.J. 1957. “The Measurement of Productive Efficiency”, Journal of the Royal Statistical Society, 120, pp 253290.

6. Frija Aymen , Stijn Speelman , Ali Chebil , Jeroen Buysse , Guido Van Huylenbroeck ,2008, Assessing the efficiency of irrigation water users' associations and its determinants: evidence from Tunisia, Journal Irrigation and Drainage, Volume 58 Issue 5, pp 538 550

7. Gang Liu et al 2002. The Technical Efficiency Of Australian Irrigation Schemes, pp 12

8. Malana Naeem M. and Hector M. Malano 2006. Benchmarking Productive Efficiency of Selected Wheat Areas in Pakistan and India Using Data Envelopment Analysis, Irrigation and Drainage Journal, 53, pp 383–394

9. National Water Policy Of Government Of India (GoI), 1987

10. Palanisami K. 2006. Sustainable Management Of Tank Irrigation Systems In India, Journal Of Developments In Sustainable Agriculture, 1: pp 34, 38

11. Raju, K. S and Kumar, D. N., 2006, “Ranking irrigation planning alternatives using data envelopment analysis”, Water Resources Management 20, pp 553566.

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12. RodríguezDíaz J.A J. A., E. Camacho Poyato1 and R. Lo´Pez Luque 2004. Applying Benchmarking and Data Envelopment Analysis (DEA) Techniques to Irrigation Districts in Spain, Irrigation and Drainage Journal, 53, pp 135–143

13. Srdjevic, B., Medeiros, Y.D.P., Porto, R.L.L., 2005, “Data envelopment analysis of reservoir system performance”, Computer & Operations Research 32, pp 3209 3226.

14. Thanassoulis E. 2000. The Use of Data Envelopment Analysis in the Regulation of UK Water Utilities: Water Distribution. European Journal of Operational Research 126, pp 436–453

15. Thanassoulis E. 2000a. “DEA and its Use in the Regulation of Water Companies”, European Journal of Operational Research, 127, pp 1 13

16. Yadav Krishna 2006. Application of Benchmarking in irrigation Industry, Workshop on Benchmarking of Irrigation Projects, MPWALMI, Bhopal

17. Yilmaz Barışa e al 2008, The Use Of Data Envelopment Analysis In Assessment Of Irrigation Efficiency, International Congress On River Basin Management, pp 346357

evaluation of irrigation inefficiencies in samrat ashok … is a historical place. samrat ash ok...

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