december 2005 - cbddecember 2005 estimating the value of agricultural cropland and pastureland in...

December 2005

Estimating the value of agricultural cropland andpastureland in India

Monograph 2GAISP (Green Accounting for Indian States Project)

Haripriya Gundimeda <[email protected]>Associate Professor, Madras School of Economics

Sanjeev Sanyal <[email protected]>Director, GAISP

Rajiv Sinha <[email protected]>Professor, Arizona State University, USA, and Director, GAISP

Pavan Sukhdev <[email protected]>Director, GAISP

Project Sponsors

Green Indian States Trust Deutsche Bank India Foundation

© Green Indian States Trust, 2005

AcknowledgementsThe directors of GAISP wish to thank Deutsche Bank India Foundation for theirsponsorship to create a ‘Deutsche Bank India Foundation Research Assistantship’for the duration of this project. We also thank Centurion Bank of Punjab for their kinddonation towards work in 2005/06. We would also like to thank Mr P Yesuthasen (Trustee,GIST), Ms Suma Sunny (Research Associate), and TERI Press for their support. In addition,we would like to thank Dr Giles Atkinson and Mr Bhaskar Baruah for their valuable inputs.

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Contents

List of acronyms......................................................................... v

Background .............................................................................. vii

Introduction ................................................................................. 1

Objectives..................................................................................... 4

Literature review ........................................................................... 4

Profile of agricultural land and pastureland in thestudy area ..................................................................................... 6

Framework for accounting............................................................. 9

Operationalizing the framework for physical andmonetary accounts ........................................................................ 15

Discussion and conclusions ........................................................... 35

Annexure I: Standard definitions of various categories ofland use adopted in the land utilization statistics ............................. 37

Annexure II: Commodities included in various groups .................. 38

Annexure III: Summary of working assumptions .......................... 39

References .................................................................................... 40

List of acronyms

CSO Central Statistical Organization

EPIC Erosion productivity index calculator

ESI Environmental sustainability index

GAISP Green Accounting for Indian States and UnionTerritories Project

GDP Gross domestic product

GHG Greenhouse gas

GSDP Gross state domestic product

GCF Gross capital formation

HDI Human development index

NAEB National Afforestation and Eco-development Board

NBSSLUP National Bureau of Soil Survey and Land Use Planning

NRSA National Remote Sensing Agency

NSDP Net state domestic product

SEEA System of Integrated Environmental and EconomicAccounting

SNA System of National Accounts

SPWD Society for Promotion of Wastelands Development

USLE Universal soil loss equation

Like most developing nations, India faces many trade-offs in its attemptsto reduce poverty and improve the living standards of its people. There isa need for an empirical basis on which to base policy decisions on thetrade-offs between the many competing priorities of a developing nation,including intergenerational claims — trade-offs between the needs ofpresent and future generations. Available mechanisms and measures ofdevelopment, including the current SNA (system of national accounts)with its primary focus on GDP (gross domestic product) growth rates,do not capture many vital aspects of national wealth such as changes inthe quality of health, changes in the extent of education, and changes inthe quality and extent of India’s environmental resources. All theseaspects have a significant impact on the well-being of India’s citizensgenerally, and most of them are critical to poverty alleviation in providingincome opportunities and livelihood security to the poor. GDP accountsand their state-level equivalents GSDP (gross state domestic product)accounts are, therefore, inadequate for properly evaluating the trade-offsencountered by India’s policy-makers.

The GAISP (Green Accounting for Indian States and Union TerritoriesProject) was launched in 2004 largely in recognition of the reality thatthe available yardsticks (in particular, ‘GDP growth percentages’) aresubstantially misleading as measures of either growth or development,wealth or well-being, and nevertheless, they continue to be used exten-sively and often exclusively by planners, policy-makers, businesses, andthe media. GAISP proposes to build a framework of adjusted nationalaccounts that represents genuine net additions to national wealth. Theseare sometimes referred to in literature as ‘Green Accounts’. Such asystem of environmentally adjusted national income accounts will notonly reflect, in economic terms, the depletion of natural resources andthe health costs of pollution, but will also reward additions to the stock ofhuman capital through education. Thus, we hope that ‘Green Accounts’will provide a more holistic measure of development, welfare, and wealththan GDP (national income) and GSDP (state income) growth percent-ages. They will thus enable and encourage the emergence of sustainabledevelopment as a focus of economic policy at the operative state level.Other indicators and indexes such as HDI (human development index)and ESI (environmental sustainability index) provide qualitative mea-sures of sustainability; however, only Green Accounting can quantita-tively answer the question: ‘is this economy sustainable or not?’

GAISP aims to set up ‘top-down’ economic models for state-wise annualestimates of adjusted GSDP for all major Indian state and union terri-tory economies. A top-down or macroeconomic approach is adapted tomodel adjustments to GDP/GSDP accounts, for two reasons. Firstly, it

Background

Estimating the value of agriculturalcropland and pastureland in India

Estimating the value of agricultural cropland and pastureland in Indiaviii

has the advantage of providing a consistent and impartial nationalframework to value hitherto unaccounted aspects of national and statewealth and production. Secondly, it optimizes existing research, which isalready extensive, albeit not yet tied together in a manner that makes ituseful for policy analysis. The publication of the results and methodologyof GAISP will provide a much improved toolkit for India’s policy-makersto evaluate in economic terms the trade-offs faced by the nation. Theywill also enable policy-makers and the public to engage in a debate onthe sustainability of economic growth, both at the national level as well asthrough interstate comparisons.

The first phase of GAISP consists of eight monographs, each of whichwill evaluate a particular area or related set of areas of adjustments toGSDP accounts.

These eight monographs are as follows.

1 The value of timber, carbon, fuelwood, and non-timber forest pro-duce in India’s forests (published in February 2005)

2 Estimating the value of agricultural cropland and pastureland in India(the current paper)

3 The value of India’s sub-soil assets

4 Eco-tourism and biodiversity values in India

5 Estimating the value of educational capital formation in India

6 Investments in health and pollution control and their value to India

7 Estimating the ecological values of India’s forests: water augmenta-tion, soil conservation, and flood prevention

8 Estimating the value of freshwater resources in India

All adjustments calculated in the above eight GAISP monographs applyto the same set of GSDP accounts (viz., for year ended March 2003) andthey are all additive. The website of GAISP (http\\:www.gistindia.org)will carry a running record of cumulative state-wise adjustments to theseGSDP accounts. To a first-order approximation, these adjustments maybe added/subtracted, as indicated, to GSDP growth percentages for theyear 2002/03.

The final report of GAISP will summarize and consolidate the workdone on these eight monographs and will include ‘adjusted GSDP’measures for the states and significant union territories in India, as wellas a commentary on the policy implications of our results.

Estimating the value of agriculturalcropland and pastureland in India

Agricultural land, constituting 57% of the geographical area of India,contributed around 20% to the total GDP in India for the year 2002/03.In SNA, agricultural land is treated as a non-produced economic asset,which provides economic benefits to its users. This is one of the severaluses to which land is put, and forms a significant part of the total wealthof the nation. The economic activities considered under ‘agriculture’ inSNA include (1) growing of field crops, fruits, nuts, seeds, andvegetables, (2) management of tea, coffee, and rubber plantations,(3) agricultural and horticultural services on a fee or contract basis, suchas harvesting, bailing and threshing, preparation of tobacco and otheragro products for marketing, pest control, spraying, pruning, picking,and packing, and (4) ancillary activities of cultivators such as making gur(raw cane sugar), transportation of produce to primary markets, andactivities yielding rental income from farm buildings and farm machin-ery and interest on agricultural loans.

The contribution of the agricultural sector1 to India’s GDP has de-creased from 50% in 1951 to 20% in 2003, which largely reflects India’smodernization and urbanization over this half century. During thisperiod, the contribution of manufacturing and utilities grew from 15%to 24%, and the contribution of services (the so-called ‘tertiary sector’)grew from 29% to 51%. The agricultural sector has, however, seenconsiderable transformation during this period such as an increase in theuse of fertilizers, pesticides, irrigation, better-quality seeds, etc. FromFigures 1a and 1b, we can see that the quantity of pesticides and fertiliz-ers consumed per tonne of food grain produced has increased manifold,whereas the gross area sown has increased at a very slow rate. However,the benefits of inputs such as fertilizers, pesticides, and irrigation oftenhave more damaging off-farm effects (on the quality of downstreamwater, soil quality, groundwater potability, human health, andbiodiversity) in the long run than the short-run on-farm benefits.

Fertilizers supplement the soil with nutrients but do not supply organicmatter, thereby lowering its water-retention capacity. This leads to soilcompaction, increased run-off, and loss of soil. Another major problemof fertilizer use is leakage/run-off into surface water and groundwater,which has serious environmental consequences such as eutrophication(nitrogen and phosphorus over-concentration due to fertilizer run-off /leaching). Eutrophication leads to explosive growth of algae, oxygendepletion, death of fish, and loss of biodiversity. Furthermore, the con-tamination of groundwater has serious health impacts because the

Introduction

1 Including the allied activities such as livestock and irrigation.

Estimating the value of agricultural cropland and pastureland in India2

majority of India’s population uses groundwater as a source of drinkingwater.

The most detrimental impact of pesticide use is on human health andecology. Human beings indirectly consume pesticides either through theconsumption of fish/shellfish/agricultural products that are contami-nated with pesticides or through direct consumption of pesticide-con-taminated water, which poses serious health hazards. The ecologicaleffects of pesticides (and other organic contaminants) are varied and areoften interrelated. Many of these effects are chronic and often go unno-ticed, yet they can affect the entire food chain. Some of these effectsrange from the death of the organism to the thinning of eggshells and lossof biodiversity. In addition to the effects of fertilizers and pesticides,irrigation of agricultural lands also causes environmental problems.

Figure 1a

Yield, fertilizer use, andpesticide use over the last

50 years

Figure 1b

Cropping Intensity, shareof irrigated area in grossarea sown, and share of

gross sown area togeographical area

3Estimating the value of agricultural cropland and pastureland in India

Irrigation results in waterlogging, desertification, salinization, erosion,etc. and may also cause downstream degradation of water quality bysalts, agrochemicals, and toxic leachates (Figure 2).

In addition to the impact of pesticides, fertilizers, and irrigation on theenvironment, agriculture can also have positive externalities (Figure 2).These generally include recreational and biodiversity benefits, althoughthese may be questionable in the Indian context. In developed econo-mies with temperate climates, the attraction of ‘farm and field’ living iswidespread, both as a lifestyle choice and as a tourist destination. Thesame cannot be said for India, where lack of attendant amenities andinfrastructure prevents agricultural locales from attracting either tourismor residential interest. Furthermore, biodiversity benefits of agricultureare offset by losses in forest biodiversity due to farming and grazingpressures. On the other hand, agricultural biomass and soil act as a sinkfor carbon dioxide, via carbon fixing in the biomass, and this type ofcarbon storage is a positive externality.

Figure 2

Agricultural impact onenvironment

Source Authors’ compilation


The main aim of this study is to develop an accounting framework thatreflects the real contribution of agricultural land and pastureland tosociety. The more specific objectives are to

1 estimate the value of the stocks and flows of agricultural land andpastureland;

2 incorporate the loss in value caused due to depletion of agriculturaland pastoral resources into the national accounts; and

3 estimate the impact of the sector on the degradation of the environ-ment and thereby estimate the sector’s real contribution to theeconomy.

While estimating the degradation of the environment, we mainlyconsidered the impact of agricultural production on land degradation,soil erosion, and sedimentation of waterways. Although we recognizedthe fact that agricultural activity has several other externalities such aschemical pollution and falling water tables, these are outside the scope ofthis monograph and will be dealt with in other monographs. Further-more, we did not address the negative impact of the production of inputslike fertilizers/pesticides. Our study looks at various states in India andmainly relies on secondary data.

Thus, it is clear that economic use of land is often connected with short-or long-term processes of deterioration (or improvement). In order to geta better understanding of the relationship between economic activitiesand the environment, we need to consider both the use of land by differ-ent economic activities and the potential of land from an ecological pointof view. This helps in assessing the sector’s net contribution to theeconomy, analysing whether the sector is growing sustainably and identi-fying the corrective measures to be taken if growth is unsustainable.However, the aggregate statistics do not show any alarming signal be-cause most of the problems relating to agriculture are micro-level andcannot be reflected at the macro level. Hence, there is a need for revisingmacro level estimates in order to have an improved understanding of theeconomic implications of the sector’s impact on environment.

Objectives

Several studies exist in other countries on agricultural accounting. Adgerand Whitby (1993) adjusted the national accounts of the UK (UnitedKingdom) for the ‘land-use sector’ as a whole. The study showed that theadjusted NDP (net domestic product) was larger than the conventionalNDP due to net appreciation of natural capital outweighing degradation.Adger and Whitby (1996) estimated the value of asset changes in the UKagricultural sector in order to discuss the contribution of agriculture tonet wealth creation. Another study by Pretty, Brett, Gee, et al. (2000)estimated the externalities without incorporating them into a set ofrevised accounts. Their study estimated the damage to natural capital

Literaturereview


such as water, air, soil, biodiversity, and landscape, and to human healthdue to pesticides, nitrates, microorganisms, and other disease agents. Thetotal damage due to agriculture was estimated to be in the range of1149–3907 million pounds. Hartridge and Pearce (2001) adjusted thenational accounts in the UK for the year 1998 (after adjusting for pre-vailing subsidies) for the negative and positive externalities of agriculturein the UK. They found that the negative externalities amounted to atleast 1072 million pounds, while the positive externalities (due to theamenity value of agricultural countryside, excluding non-use values)offset approximately one-half of these negative effects. The study arguedthat if the subsidies were removed, the sector would contract and theconfiguration of externalities would differ. A study by Atkinson et al.(Eftec 2004) estimated the economic value of the positive environmentalservices provided by agriculture as well as the negative service flowsresulting from depreciation of natural assets for the UK. The studysystematically considered environmental services and sink functionsimpacted upon by agriculture according to key impact headings such aswater, air, soil, landscape, habitats and species, waste, nuisance, resourceuse, etc. The study used the DPSIR (driving forces, pressures, state of theenvironment, impact on final end points, and policy responses) frame-work. The economic value of an environmental impact is estimated usingdata on household preferences for that impact. Preferences are measuredby people’s willingness to pay for environmental goods and services.

While all these studies were done for the UK, some studies exist for othercountries as well. Some studies like Tiezzi (1999) for Italy; Bonnieux,Rainelli, and Vermersch (1998) and Le Goffe (2000) for France; andHrubovcak, LeBlanc, and Eaking (2000), Smith (1992), and SteinerMcLaughlin, Faeth et al. (1995) for the USA attempted to address thisissue. However, not all the studies aimed at adjusting the nationalaccounts.

Hrubovcak, LeBlanc, and Eakin (2000) developed a theoretically consis-tent framework to incorporate the environmental effects of agriculturalproduction and the depletion of natural capital caused by agriculturalproduction into the existing national accounts for the USA. Usingtheoretical framework, the study estimated the adjustments to theincome attributed to agriculture to be 3.9 billion dollars in 1982, 4.2billion dollars in 1987, and 4.4 billion dollars in 1992. These adjust-ments to net income attributed to agriculture range from six per cent toeight per cent of the total net income, and the relative share of adjust-ments to the net income attributed to agriculture decreased during1987–92. Smith (1992) in his work on environmental costing consideredthe off-site effects of soil erosion, wetland erosion, and groundwatercontamination caused due to agricultural activities in each of the 10crop-producing regions in USA and estimated the environmental costsrelative to the value of crops produced in 1984. His estimates rangedfrom 0.08% to 40% of the value of crops per acre on land deemed


responsible for these impacts. Tiezzi (1999) estimated the aggregatevalue of external effects from agriculture for three provinces in Italy forthe period 1961–91. The negative external effects considered includedemissions of chemical and organic fertilizers into the environment,determination of air pollution, ground and surface-water pollution, andoil contamination. The analysis was done for three regions in Italy. It wasfound that the value of external effects from the use of chemical andorganic fertilizers amounted to around 0.4% of the total Italian valueadded nationally. Different studies followed different frameworks andthere is no standardized framework. The United Nations SEEA (Systemof Integrated Environmental and Economic Accounting) has a suggestedframework for land use accounting, of which agriculture is one. How-ever, several issues were left unaddressed by SEEA like accounting forthe damages caused to other sectors due to agriculture. In SEEA, fivemajor types of land resources are distinguished: (1) land underlyingbuildings and structures, (2) agricultural land and associated surfacewater, (3) wooded land and associated surface water, (4) major waterbodies, and (5) other land. However, we based our analysis on a nine-fold classification of land drawn from a standard system used by theDepartment of Agriculture, Government of India.

A study by Brekke, Iversen, and Aune (1999) estimated Tanzania’s soilwealth, focusing extensively on soil mining. The estimates suggested thatthe value of the eroded soil amounted to 12%–17% of the value of theHicksian income, and the savings required for maintaining consumptionamounted to 13%–29% of the contribution to the GDP. The studyindicates that the potential gains from change in agricultural manage-ment are considerable. A study by Francisco and de Los Angeles (1998)presented the results of the damage function for soil loss using a 20-yeardata projected through the application of EPIC (erosion productivityindex calculator) in one soil conservation project site in the Philippines.A study by Reddy (2003) for India measured the extent of damage due toland degradation of various types and their expected trends. It alsoexplored the linkages between degradation and policy and the institu-tional environment in the context of the agro-climatic regional planning.

Agriculture in India is the means of livelihood for almost two-thirds ofthe work force in the country and it has always been India’s most impor-tant economic sector. Based on the nine-fold classification of land use, ofIndia’s reported geographical area of 306.2 million ha (hectare) for landutilization statistics, about 22.6% of the land is forested, 46% is culti-vated, 7.7% is put to non-agricultural uses, 6.2% falls under barren anduncultivable land, 3.6% is under permanent pastures and grazing lands,1.2% is under miscellaneous tree crops and groves, 4.5% is cultivablewaste, 3.3% is fallow land other than current fallow, and 4.8% land isunder current fallow (Table 1). The nine-fold classification is primarilybased on whether a particular area is cultivated, grazed, or forested and isbased on actual use and not on how a particular piece of land can bepotentially utilized (Annexure I).

Profile ofagricultural landand pasturelandin the study area


The agricultural land resources in India suffer from indiscriminateconversions. As forestlands give way to agriculture, so do farm lands toindustrial and urban expansion. These conversions are largely offshootsof the industrialization effort in India. With commercial and industrialdevelopment offering immediate and more profitable returns, convertingagricultural lands to industrial uses has escalated. In terms of per capitaavailability, the availability of land has declined from 0.89 ha in 1951 to0.3 ha in 2001; the per capita availability of agricultural land has de-clined from 0.48 ha in 1951 to 0.14 ha in 2001. Besides the pressure ofhuman population, there are about 500 million cattle and other livestockliving off the biomass from the land.

Table 1Land-use classification in different states (2000/01) (’000 hectares)

Perma- Landnent under Fallow

Reporting Area Barren pastures misc, lands,area put to and and tree Culti- other

Geogra- for land Land non-agri- unculti- other crops vable thanphical utilization under cultural vable grazing and waste current Current Net area

State area statistics forests uses land land groves land fallow fallow sown

Andhra Pradesh 27507 27440 6199 2624 2100 675 269 728 1417 2312 11115Arunachal Pradesh 8374 5498 5154 5 21 4 36 37 47 30 164Assam 7844 7850 1932 1070 1461 163 234 80 65 110 2734Bihar 17388 17330 2949 2430 1010 105 344 321 922 1811 7437Goa 370 361 125 37 — 1 1 55 141Gujarat 19602 18812 1865 1141 2604 849 4 1982 13 911 9443Haryana 4421 4402 115 368 102 34 7 19 232 3526Himachal Pradesh 5567 4547 1094 314 807 1529 57 124 13 54 555Jammu and Kashmir 22224 4505 2747 291 291 126 72 140 8 81 748Karnataka 19179 19050 3068 1312 794 959 303 427 409 1367 10410Kerala 3886 3885 1082 382 29 15 59 34 78 2206Madhya Pradesh 44345 30755 8655 1889 1349 1585 20 1201 575 818 14664Maharashtra 30771 30758 5296 1301 1696 1341 226 903 1171 1189 17636Manipur 2233 2211 602 26 1419 24 — — 140Meghalaya 2243 2227 951 87 136 — 155 441 162 65 230Mizoram 2108 2109 1626 — 16 23 31 127 156 36 94Nagaland 1658 1589 863 66 — — 125 65 79 91 300Orissa 15571 15571 5813 999 843 443 482 392 430 340 5829Punjab 5036 5033 305 327 55 7 10 25 4 38 4264Rajasthan 34224 34265 2606 1740 2566 1707 14 4908 2444 2415 15865Sikkim 710 710 257 97 173 69 5 1 9 4 95Tamil Nadu 13006 12991 2134 1986 476 123 255 352 1228 1134 5303Tripura 1049 1049 606 131 3 27 1 1 1 280Uttar Pradesh 29441 29767 5155 2579 920 292 572 872 730 1034 17612West Bengal 8875 8688 1190 1567 27 4 57 37 29 358 5417Andaman and

Nicobar Islands 825 793 695 22 2 4 16 12 3 1 38Chandigarh 11 7 4 1 — 1 — 2Daman and Diu 11 10 — 1 2 1 2 4Dadra and

Nagar Haveli 49 49 20 4 1 — 1 1 23Delhi 148 147 1 77 13 1 10 7 4 34Lakshadweep 3 3 — — — — — — — — 3Pondicherry 48 49 — 15 0 0 1 4 3 1 24India 328726 306249 69407 23569 19259 10897 3366 13660 10191 14799 141101

Note For standard definitions of various categories of land use adopted in the land utilization statistics, refer to Annexure I.Source www.indiastat.com


Till the 1970s, agricultural productivity was very low. However, after that1970s, there was a huge increase in India’s agricultural production due tothe Green Revolution. An increase in agricultural production has beenbrought about not merely by bringing additional area under cultivation,but by the extension of irrigation facilities, use of better seeds and betteragricultural techniques, water management, and plant protection. Thegeneral perception is that the key causes for the success of the GreenRevolution were better genetic material with higher production potentialand better ability for nutrient uptake, higher demands on irrigation(which were met), improved cultivation practices, and, of course, higherprofitability which led to area expansion. The percentage of land irri-gated increased from 17% of the gross cropped area in the 1950s to 41%in 2000 (Figure 1b). Along with the area under irrigation, the composi-tion of irrigation has changed over time, especially in the recent years.The proportion of area under canal and tank irrigation has declinedwhile that under well irrigation has gone up substantially. The increase inthe area under well irrigation coupled with the decline in tank irrigationinvariably results in overexploitation of groundwater, resulting in envi-ronmental problems such as waterlogging and increased soil salinity onthe one hand and desertification on the other hand. The foodgrainproduction in the country increased from 50 MT (million tonnes) in the1950s to 209 MT in 2001. Along with this, India’s fertilizer consumptionincreased from 0.3 million tonnes in the 1960s to 16 million tonnes in2001 and the pesticide consumption increased from 2353 tonnes in the1960s to 48 350 tonnes in 2001.

This increase in the use of fertilizers, pesticides, and irrigation has alsocaused serious environmental degradation. It is estimated that about 174million ha of land (around 53%) suffers from different types and varyingdegrees of degradation (Table 5). Around 800 ha of arable land is lostannually due to the ingress of ravines. An estimate by NBSSLUP(National Bureau of Soil Survey and Land Use Planning) (1990) indi-cated that the average loss of topsoil due to erosion is 19.6 tonnes/ha. Allthis has a direct bearing on the food production and the livelihood of thepeople. This is because unlike other countries, which have considerablescope for bringing new area under cultivation, India has very limitedscope for the extension of cultivation to new lands. Already, about 49.7%of the total reported area is cultivated, although cultivable land, which isnot cultivated at present (cultivable wasteland and other fallow lands,permanent pastures and grazing lands, and miscellaneous tree crops), isestimated at about 42 million ha (around 13.6% of the total). However,most of this area comprises marginal and sub-marginal lands and theextension of agriculture into this area will be costly, as it requires exten-sive work for soil and water conservation, irrigation, and reclamation.


First, we suggest a consistent framework for physical and monetaryaccounts (land cover, land use, and production) for different states andunion territories in India. We assess the period 1992/93 to 2000/01 to seehow land use and land cover changed over the 10-year period. A 10-yeartime frame is considered because land degradation occurs slowly, andsuch a time period can sufficiently capture these changes. Finally, weannualize our results to the annual loss due to degradation by applying a‘straight line’ method. By accounting for land resources, we also accountfor the soil resources on land because the value of land depends onwhether or not the soil is fertile. It is not possible to separate landaccounts from soil accounts. Hence, we consider both land as well as soilin our paper.

Framework foraccounting

Our framework is very similar to the SEEA framework. The physicalaccounts for agricultural and pastoral lands under the SEEA frameworkinclude items such as opening and closing stocks, other accumulation,and other volume changes (Table 2). Opening and closing stocks refer tothe quantity of land (area in hectares) at the beginning and end of theaccounting period. The land area under agricultural and pastoral landscan be increased (artificially) for economic reasons by means of landreclamation (from the sea or river beds). Increase or decrease in thequantity of land comes under the other accumulation category, whichsimply pertains to the changes in the quantity of land (additions orreductions in areas devoted to specific use) caused by economic deci-sions. Included in this category are changes in land use and/or transfer ofnon-economic land from the environment to the economy for produc-tion purposes and vice versa. Lands subjected to shifting cultivationinvolve areas that are opened up for agriculture from forestry and thus,represent additions to the inventory.2 On the other hand, conversionsfrom agricultural to non-agricultural uses would decrease agriculturalareas and increase other types of land. Quantitative losses of land due toeconomic uses can be caused due to the partition of states or the transferof districts to some states or, in some cases, due to natural disasters (forexample river/sea coastal erosion—in the case of river erosion, in statessuch as Assam, the area lost is not inconsiderable; or, for example, theDecember 2004 tsunami that submerged large portions of arable land).

Physical accounts

2 It is argued that including shif ting cultivation in inventories made sense in the olden days when thecycle used to be of 15–20 years as the land area brought under cultivation in a specific year mighthave been cultivated again only after 14–19 years. However, in recent years, the shif ting cultivationcycle has come down to three to four years, in which case it may nearly be valid to include suchareas as net accrual. Such lands, in this monograph, if cultivated for one year and not cultivated forthree or four years in succession are treated as culturable wastelands, which have the potential tobe converted into agriculture (and hence fall under the other accumulation category).


Table 2A framework for accounting for agricultural land and pastureland

Activity Description

Opening stock Land under cultivation and grazing

Changes in quantity Asset increase due to land reclamation/improvement

Other accumulation Changes in land useTransfer of land from the environment to economic use

Other volume changes Changes in land use and land area due to natural, political, orother non-economic causesTransfer of land from economic use to environment

Closing stock Land under cultivation or grazing

Changes in quality of land* Soil erosion or nutrient loss (tonnes)Land/soil contamination including salinization and other changesin soil quality

Impact on other sectors of Extent of sedimentation in waterwaysthe economy** Amount of greenhouse gases released to the atmosphere

Extent of contamination of waterways by pesticides and fertilizers

Note * Quality measures are not part of the asset accounts, but are used in assessing the cost ofproductivity losses** These are not given in the SEEA (System of Integrated Environmental and Economic Accounting)framework. However, in order to mention the sector’s impact on other sectors, we are incorporatingthem here itself.

As such, these changes are entered in the category other volume changes.An adjustment was included in the physical accounts to balance theresulting closing stock of the previous year to the opening stock of thefollowing year.

Changes in land and soil quality affect land productivity and economicvalue, the most notable of which is topsoil erosion measured in tonnes ofsoil lost, which affects the productivity of agricultural land. The physicalextent of land for general and specific uses was accounted for in thesupplementary accounts and expressed in hectares. Specifically, the landuse account represents the physical area by specific type of land use andland utilization.

The next step was to develop the monetary accounts using physicalaccounting framework. In order to monetize the physical accounts,valuation is essential. At first sight, valuation of land seems straight-forward; in practice, a number of complications arise. The first problemis that although there is a market for land, relatively little land changeshands in any year and so a comprehensive set of prices to cover all land

Valuation of thestock of assets


types in all locations is seldom available. Even when prices are recorded,they may be subject to many distortions. Further, some land will neverbe exchanged on the market but will change hands as it is passed on fromone generation to the next. This also includes some of the land for whichno market transactions can take place (for example, wastelands). Salesinvolving agricultural land may also cover aspects other than the initialpurpose of the land. For instance, agricultural land with fertile soil andplenty of ground water will fetch a higher price compared to equivalentland without these. Moreover, land sale data would include sales involv-ing conversion of agricultural land to non-agricultural use. Transactionsof this nature are likely to be plentiful, and they change the essential basisof the transaction, making it inappropriate for computing cropland salevalues.

In such cases, where market prices cannot be used, SEEA suggests usingthe net present value of future benefits accruing from holding or usingthe asset as a proxy for market prices. The asset would not be a cost-effective purchase if the value of future benefits did not at least equal themarket price. Thus, the net present value should be compatible with themarket prices. If there are no market prices and it is not possible tocalculate the net present value of an asset, then the cost of producing itmay be used as a lower bound on its value. Again, the argument runs thatit would not be worthwhile to construct the asset unless the benefits areexpected to be at least as great as the costs.

In this paper we used the net present value approach to estimate thevalue of the asset and the changes in assets. The conceptual frameworkbehind the net present value approach is as follows. A piece of agricul-tural land is characterized by several attributes: soil quality, soil texture,soil fertility measured in terms of nutrients, associated water resources,etc. With the help of these natural factors and other inputs such as seeds,rainfall, fertilizers, etc. some output is produced which can be marketedat some market value. When the value of man-made inputs are deductedfrom the output, we get the economic rent or land rent, which is consid-ered as payment for the use of natural resources. The variations in theseeconomic rents or land rents are due to differences in the quality of landand the inputs mentioned earlier. The economic rent is expected tochange every year with changes in the levels of outputs/input use, theirprices, and the discount rate. Since the resource unit is expected tocontribute to the production of one or more resource commodities over aperiod of time, the asset value for any one land use, say agriculture, willbe equal to the present value of the stream of land rent over the economiclife of the resource over the relevant planning period. Similarly, the valueof the pastureland will be equal to the present value of the stream of landrent over the economic life of the resource (Francisco and de Los Angeles1998). The land rent is obtained by estimating the annual net returns


from the use of the resource over time, less a reasonable allowance3 forprofit, which can be represented by

nn

T

n iLRNPV)1(1 +

=�=

where ‘NPV’ is the net present value of the asset in year ‘n’; ‘T’ is thelength of the planning horizon/or economic life of the resource; ‘i’ is thediscount rate; and ‘LRn’ is the land rent in year ‘n’.

Five sets of assumptions are critical in this estimation procedure.

1 The first assumption relates to prices, costs, and yield data as a functionof time. If one assumes constant prices, then the observed changes inthe value of the land rent can only be attributed to changes in the yieldor productivity of the resources and in the time-value of money.

2 The second assumption relates to the expected value of the resourceat the end of the planning horizon, that is, for how much can the landbe sold for non-agricultural purposes. This is difficult to estimate andcannot be equated to zero. Some value needs to be assumed.

3 The third assumption refers to the choice of the discount rate to beused in weighting the present consumption value versus that of futureconsumption. In general, a lower discount rate favours future con-sumption, while a high discount rate gives more preference to presentconsumption.

4 The cost of production is assumed to include the value of all materialand labour inputs along with normal return on capital (that is, profit).

5 Another important assumption to be made is regarding the variable‘n’, the economic life of the resource. Basically, this variable measuresthe number of years the land can remain productive or can be used forproduction purposes. Once the asset reaches the end of its lifetime, itis not possible to produce anything on it, but such land can be usedfor some other purposes (say, construction purposes). If no suchoption exists, then the asset value at the end of the period may indeedbe zero (in some cases this can be a temporary phenomenon as thefertility of the land can be improved by leaving it fallow for someperiod).

The change in the value of assets (depreciation or appreciation) is esti-mated as the change in asset value during the accounting period. On ayear-to-year basis, land depreciation is simply measured as the differencebetween the asset value at the beginning of the year and at the end of theyear.

3 In this monograph, we took the net value added from the data published by the Central StatisticalOrganization. They usually allow a margin of 10% as return on capital.


If used sustainably, land has an infinite life. No adjustment for degrada-tion is required and the whole resource rent can be considered asincome. However, as discussed earlier, the use of land for agricultureusing unsustainable practices would mean degradation of the land due tosoil erosion in the form of the loss of nutrients from the topsoil, move-ment of soil (changes in soil depth), salinization due to improper irriga-tion practices, deposition of chemical fertilizers on land, etc. In suchcases, adjustment to income is necessary. Several techniques can be usedto estimate the extent of degradation caused which are discussed asfollows. In this study we considered only the costs of degradation due tosoil erosion and sedimentation and did not estimate the damage causedto the soil and water from eutrophication.

Cost of soil erosionSoil erosion is a natural process and only when it erodes beyond thetolerable rate, does it have an impact. Under natural conditions, the soillost is largely replenished. However, when the natural rate of replenish-ment is exceeded by erosion, a physical depreciation of soil resourcestakes place. In the absence of other forces at play, any loss of soil erosionbeyond a tolerable level can be considered as human induced. In thisstudy, we were interested in human-induced soil erosion. We considerthat soil erosion impacts the economy in two ways: (1) erosion of topsoiland (2) sedimentation of waterways. Mainly two approaches have beenused in the literature to value the on-site effects of erosion. One approachmeasures the impact on the soil as a resource and the second approach isbased on the effects of erosion on agricultural production. The effects oferosion on soil properties can be examined from the perspective ofcertain indicators of soil characteristics such as soil nutrient content, soilmoisture capacity, etc. The effects of erosion on agricultural productioncan be valued in terms of the reductions in crop yields, which can bedirectly captured through the loss in market value. The most commonapproach for valuing the loss of soil and soil nutrients is the replacementcost method. This is based on the cost of replacing soil nutrients withartificial fertilizers or the cost of physically returning eroded sediment tothe land (the labour costs or the cost of buying fertilizers).

We preferred to use the replacement cost approach over the loss inproductivity approach, though under optimal conditions, both shouldgive the same value. This is because crop yields are not dependent on soilproductivity alone but are also determined by a number of factors suchas rainfall, fertilizer application rates, climate, pests, and irrigationpractices. Moreover, the impact of erosion on crop production is compli-cated by the dynamic nature of agriculture. Though the soil hasdegraded, farmers can respond by adjusting their level of inputs byadopting the cropping patterns, that are less sensitive to erosion. If weassume everything else to be same, then erosion can have an impact ondeclining crop yields.

Estimating the valueof land degradation


Cost of sedimentationSiltation or sedimentation in a reservoir is a very serious problem, for itconsiderably reduces its life. The life of a reservoir depends on the rate ofsilt inflow and its dead storage capacity. It has been estimated that manyof the reservoirs in India are losing capacity at a rate of 1% – 2% everyyear (SOER 2001). In order to estimate the cost of sedimentation, twoapproaches can be used. The first approach is to estimate the value of loststorage capacity and the second approach is to use the maintenance costmethod, that is, how much would it cost to remove sediment from water.In this study, we used the cost of removing sediments from reservoirs asan indicative value of the costs imposed by off-site effects of erosion. Asrivers have a different hydrology compared to reservoirs, it may bepossible that the cost of treating sediments may vary considerably be-tween reservoirs and rivers. Accordingly, the estimates can be higher orlower than the estimates presented in this paper. We were conscious ofthe fact that sedimentation and soil erosion may have causes both naturalas well as human induced, and that their effects stretch beyond theagricultural value. However, as their effects are a reduction in nationalasset values and the proximate asset class is agricultural land, theseeffects were modelled and accounted for accordingly.

Cost of degraded landsDue to unsustainable practices, some of the lands become degraded andare categorized as wastelands. These lands comprise salt-affected land,land subjected to chemical deposition, land subjected to shifting cultiva-tion, gullied and ravined land, waterlogged land, etc., which can have aneffect on productivity. For example, salinity directly affects the produc-tivity of the soil by rendering it unfit for healthy crop growth. Indirectly,it lowers productivity through adverse effects on the availability ofnutrients and on the beneficial activities of soil micro flora. Apart fromsalinity, the deposition of heavy metals or industrial effluents and indis-criminate use of agro-chemicals such as fertilizers and pesticides are alsoresponsible for land degradation. The value of such changes are onlypartly reflected in the current value added in agriculture and does notgive a complete picture of the extent of degradation. Though the soildegrades, farmers can respond by adjusting their level of inputs byadopting the cropping patterns that are less sensitive to erosion. More-over, such lands, if left untreated, cause more damage to the environmentthan the estimates given by a loss in the economic productivity approach.Hence, from time to time, the government incurs some expenditure intreating these lands. We used these expenditures as a proxy to estimatethe value of degradation.


Before getting the data in the desired accounting framework, we wouldlike to comment on the basic distinction between land cover and landuse. Land cover reflects the (bio) physical dimension of the earth’ssurface and corresponds, in some sense, to the notion of ecosystems. Forexample, agricultural land and pastureland come under the category ofland cover. Land use, on the other hand, is based on the functionaldimension of land for different purposes or economic activities. Forexample, if we treat agricultural land as land cover, then in any particularyear it can be used to grow various kinds of main crops or commercialcrops, which can vary across different years, and this is referred to as landuse (Table 3). From the land-cover-change matrix, it can be seen that thearea put to non-agricultural uses, barren and uncultivable land, landunder forests, fallow land other than current fallow, and current fallowhas gone up in the last 10 years, while the area under permanent pasturesand other grazing lands, land under miscellaneous crops and groves,cultivable waste land, and the net area sown has come down (Table 4). Inreality, though, the area under forests is shown as ‘increased’, in manyplaces the actual tree cover area has gone down. These statistics are basedon the reported land utilization statistics.

Operationalizingthe framework

for physical andmonetaryaccounts

In the next step, we tried to bring the information into the accountingframework mentioned in Table 2. The opening stock of agricultural landand pastureland was taken as the opening area (net area sown) in theyear 1992. The closing stock was the stock of agricultural land present atthe end of 2001 (net area sown). The reason why we chose 1992 as theopening year was that degradation does not take place suddenly butoccurs over a period of time and we believed that a 10-year period couldsufficiently capture the land degradation. At the end all the estimateswere annualized to obtain estimates on a year-to-year basis. The areaunder agricultural land and pastureland was taken from the AgriculturalStatistics published by the Ministry of Agriculture. The land-use-changematrix was obtained from the land use classification in different years aspublished in the Statistical Abstract of India.

As seen in Table 2, the stock can change due to several reasons. It canchange either due to economic reasons or due to changes in the quantityof land under particular land use or due to transfer from the environ-ment to economic uses. Such detailed information is not available fromthe published data. Only land-use change data is available from Table 4.For example, in the state of Andhra Pradesh, the area put to agriculturaluse decreased by 82 000 ha. This decrease could be as a result of increasein the area put to non-agricultural uses (Annexure I) or due to improve-ment of land which was earlier unfit for cultivation. All the changes inland-use classification basically imply other accumulations. Reliable dataon shifting cultivation (other volume changes) was not available for thetwo time periods. Similar was the case with the changes in quantity dueto economic activity. Hence, we did not dichotomize the cause of the

Physical accounts


Table 3Area under different categories of crops in India for 2000/01 (’000 hectare)

Condi-

Drugs ments Fruits

and and and Other Fodder Pasture

State Cereals Pulses Oilseeds Sugar Fibres narcotics spices vegetables crops crops land

Andhra Pradesh 5489.3 1797.1 2675.6 217.4 1128.0 218.0 432.8 697.9 0.102 130.0 675.000Arunachal Pradesh 177.4 6.2 24.8 0 0 0 0 72.1 0.280 0 4.000

Assam 2776.0 111.5 310.4 27.0 100.0 235.0 73.5 345.3 12.117 8.0 163.000Bihar 6472.5 780.0 171.0 93.5 182.0 18.0 0 976.2 0 11.0 105.333Chhattisgarh 4005.6 563.4 275.5 0 0 0 0 96.0 0 0 852.000

Goa 57.9 10.8 1.8 0 0 0 1.6 18.1 0.872 0 1.000Gujarat 2513.2 634.7 2695.0 177.7 1545.0 165.0 154.0 376.5 275.200 1318.0 849.000Haryana 4179.0 111.9 453.5 143.0 546.0 4.0 0 172.4 147.400 510.0 34.000

Himachal Pradesh 801.4 35.1 18.9 2.8 0 3.0 0 172.4 0 10.0 1529.000Jammu and Kashmir 876.6 31.2 70.4 0.1 0 0 0 186.6 0 46.0 126.000Jharkhand 1719.7 116.3 50.1 0 0 0 0 170.7 0 0 0

Karnataka 5729.0 2061.0 2197.8 417.1 541.0 268.0 341.0 670.6 19.735 55.0 959.000Kerala 353.7 22.1 8.8 3.4 5.0 130.0 329.0 349.3 474.350 3.0 0.500Madhya Pradesh 6521.8 3324.0 5612.5 74.9 498.0 21.0 322.1 301.7 0.189 700.0 2524.000

Maharashtra 9766.9 3552.8 2523.3 595.0 3282.0 6.0 124.0 938.3 1.588 1290.0 1341.000Manipur 164.2 0 2.3 0.7 0 0 0 34.4 4.029 0 0Meghalaya 126.6 4.6 8.7 0.1 16.0 1.0 10.0 61.8 0.585 0 0

Mizoram 58.5 2.4 7.2 1.0 0 0 1.0 25.9 1.791 0 23.000Nagaland 193.1 17.4 36.3 0.8 1.0 0 0 51.6 0.493 0 0Orissa 4640.3 604.3 277.3 16.8 94.0 7.0 157.3 917.9 0 0 443.000

Punjab 6221.0 59.0 87.4 121.0 726.0 3.0 0 165.2 15.800 653.0 7.000Rajasthan 8984.1 2374.8 2645.7 13.5 594.0 85.0 458.8 115.1 3056.200 3491.0 1707.000Sikkim 70.0 6.1 10.0 0 0 0 0 22.9 18.710 0 69.000

Tamil Nadu 3190.0 745.9 1056.0 315.3 182.0 115.0 125.0 443.0 26.495 179.0 123.000Tripura 243.8 10.1 6.3 1.0 3.0 29.0 2.3 60.7 0 0 0Uttar Pradesh 17422.2 2679.3 1395.4 1938.4 16.0 176.0 0 955.9 2.900 969.0 293.333

Uttaranchal 979.8 28.5 22.0 1938.4 0 0 0 296.6 0.388 0 0West Bengal 5918.3 322.3 599.6 21.6 625.0 163.0 7.7 1208.7 0.931 2.0 4.000Andaman and

Nicobar Islands 10.9 1.3 0 0.2 0 0 4.0 6.8 0 0 4.000Chandigarh 0 0 0 0 0 0 0 0.2 0 2.0 0Daman and Diu 2.0 1.3 0 0 0 0 0 0.5 0 1.0 0.500

Dadra andNagar Haveli 16.3 4.3 0.1 0 0 0 0 2.2 0 0 1.000

Delhi 49.5 0.6 4.8 0 0 0 3.0 0 1.0 0.500

Lakshadweep 0 0 0 0 0 0 0 0.5 0 0 0Pondicherry 26.4 5.8 1.3 2.5 0 0 0 6.0 0 0 0India 99757.0 20026.1 23249.8 6123.2 10086.0 1648.0 2544.1 9923.0 4060.170 9379.0 11838.000

Source Author’s compilation based on the data taken from www.indiastat.com

changes between the opening and closing stocks. This did not affect ourestimates because any change in agricultural land or pastureland isreflected in the total production and hence the value. If the agriculturalland increases due to whatsoever reason, it is reflected in increasedproduction and vice versa. If this increase in agricultural area camebecause of land improvements, the investments on this land improve-ment would have already been recorded by CSO (Central StatisticalOrganization) in GCF (gross capital formation). If the agricultural landis converted to non-agricultural use, it indicates decrease in the capital inthe agricultural sector but increase in value in the other sector whichshould be accounted for there. From Table 4, it can be seen that in thestates of Gujarat, Karnataka, Madhya Pradesh, Maharashtra, Orissa,


Table 4Land-use-change matrix for the period 1992/93–2000/01 (’000 hectare)

Fallow

Reporting Area put Barren Permanent Land under lands,

area for land Land to non- and pastures miscellaneous other than

utilization under agricultural uncultivable and other tree crops Cultivable current Current Net area

State statistics forests uses land grazing land and groves wasteland fallows fallow sown

Andhra Pradesh 0 −82 198 42 −145 11 −47 −57 −570 649

Arunachal Pradesh −1 0 −24 −27 4 −8 37 −2 5 14Assam −2 −52 156 −80 −21 −13 −24 −19 22 28Bihar and Jharkhand 0 0 273 −4 −21 48 −43 −83 −446 275

Goa 0 0 15 −15 0 0 −4 0 0 3Gujarat −10 −21 18 −3 1 0 0 −20 155 −140Haryana 26 −56 49 16 3 3 −14 0 −26 52

Himachal Pradesh 1152 56 113 661 326 14 4 −10 6 −18Jammu and Kashmir 0 0 0 −2 1 0 −1 1 −18 18Karnataka 0 −7 108 −7 38 −14 −16 −7 282 −378

Kerala −1 0 79 −26 −2 −19 −32 7 36 −44Madhya Pradesh and

Chhattisgarh 200 553 144 −299 −273 −52 8 −47 282 −115

Maharashtra 0 151 114 105 161 −61 −45 77 −117 −384Manipur 0 0 0 0 0 0 0 0 0 0Meghalaya −12 15 3 −6 0 −1 −52 −6 6 29

Mizoram 7 323 −10 −185 19 28 53 −103 −147 29Nagaland 49 0 37 0 0 −4 −26 −27 −27 96Orissa 31 335 218 311 −220 −375 −146 246 137 −475

Punjab 0 18 −85 −21 3 6 −1 −8 −35 125Rajasthan 13 212 92 −162 −64 −4 −443 579 876 −1073Sikkim 0 0 0 0 0 0 0 0 0 0

Tamil Nadu −21 −17 117 −34 2 24 48 177 172 −510Tripura 0 0 −2 3 0 0 0 0 −12 12Uttar Pradesh and

Uttaranchal −30 1 89 −95 −8 15 −142 −169 −75 353West Bengal 2 −4 82 −32 −3 −4 −52 −9 99 −77Union territories 0 2 16 −2 −1 −10 10 −2 3 −15

India 1404 1426 1798 137 −199 −415 −929 519 611 −1544

Source Author’s compilation based on the data taken from www.indiastat.com

Rajasthan, and Tamil Nadu, there has been decline in agricultural landmostly due to conversion to fallow land. In the states of Andhra Pradesh,Bihar, Punjab, and Uttar Pradesh the agricultural land has increased dueto decrease in current fallow, and other fallow land. Where the land isconverted from other uses into agriculture, it accounts for GCF in theagricultural sector.

Changes in quality of landAs mentioned earlier, intensive agriculture and wrong managementpractices can lead to degradation of land mainly caused by soil erosion,and salt and chemical deposition on the land, and also have a negativeimpact on other assets such as surface water, ground water, and health.Here, we would like to clarify the terms ‘wasteland’ and ‘degraded land’.The terms wasteland and degraded lands are in essence, synonymous.Wasteland is defined as degraded land which can be brought undervegetative cover with reasonable efforts. These lands are currentlyunderutilized, and are deteriorating for lack of appropriate water and soilmanagement or on account of natural causes. Wastelands can also result


from inherent/imposed disabilities such as by location, environment,chemical, and physical properties of the soils or other financial or man-agement constraints (Reddy 2003). As per NRSA (National RemoteSensing Agency), wasteland/degraded land can be classified into 13categories: (1) sandy, (2) hilly, (3) rocky, (4) snow-covered regions,(5) mining and industrial wastelands (6) forest-based degradation,(7) degraded land under plantation crops (8) gullied/ravined land,(9) upland with or without scrub, (10) waterlogged and marshy land;(11) land affected by salinity and alkalinity, (12) shifting cultivation area,and (13) degraded pasture and grazing lands. Of all the categories ofwastelands, only the gullied/ravenous land, upland with or withoutscrubs, waterlogged and marshy land, land affected by salinity andalkalinity, shifting cultivation area, and degraded pasture and grazinglands are considered in this paper for further analysis.4

Estimates of land degradation were available from a number of sources—Ministry of Agriculture, UNEP (United Nations EnvironmentProgramme), NRSA, and various individual studies (Table 5). FromTable 5, it can be seen that the extent of degradation is about 14.6% ofthe geographical area in 1988/89 (NRSA 1989). Interestingly, between1984 (SPWD 1984) and 1988/89 (NRSA 1989), the extent of degrada-tion went up from 12.958 million ha to 44.39 million ha. The compositeestimate for 1986–99 (NRSA 1999) is 63.18 million ha. However, theestimates from NRSA and SPWD (Society for Promotion of WastelandsDevelopment) cannot be compared due to the differences in the assess-ment methodology and the scale.

We felt that estimates by NRSA were more accurate as they were gener-ated using remote sensing techniques covering the entire country. How-ever, we needed at least two time points for comparison. NRSA pub-lished some estimates for the year 1988/89 using a 1:250 000 scalecovering 442 districts under different agro-climatic zones. Another set ofestimates was available for the year 1999 using a 1:50 000 scale covering584 districts (the survey was done in different years from 1986) (NRSA2000). These two data sets were not strictly comparable because of thescale differences and the differences in coverage. However, as the NRSAdata was more accurate, we used the estimates published by NRSA(2000) for analysis. The NRSA estimates had to be adjusted for the landalready degraded. The estimate by SPWD (1984) indicated that about12.95 million ha, i.e. 20% of the land was already in a degraded stateduring the early 1980s, while the NRSA (2000) estimates indicated thatabout 63.81 million ha was degraded.5 This meant 20% of this land wasin degraded state by 1982. We assumed that the remaining 80% of the

4 Upland, with or without scrub, is also considered because this category is either barren due tounsustainable grazing and cultivation or is occasionally cultivated.

5 We do recognize the differences in scale and approach between these two sources. As we felt NRSA(2000) data is more authentic and cannot be compared with any other source, we adjusted thelatest estimates by a proportion to take into account the already degraded land.


Table 5Area under various categories of wastelands as given by different sources

NRSA NRSA NAEB SPWD Department of1988/89 1986–99 (1993) (1984) Agriculture(square (square (square (million (million

Category of wasteland kilometre) kilometre) kilometre) hectare) hectare) (1985)

Water erosion — — — 7.36 107.12Wind erosion — — — 1.292 17.79Salt-affected area 19883.8 20477.38 — — —

12427.96 0.716 7.61 — —Waterlogged area 12196.7 16568.45 4266.17 — 8.52Marshy/swampy land 8238.8 — — — —Gullied/ravined area 20203.3 20553.35 19433.64 — 39.45Land with or without scrubs 265145.6 fallows 194014.29 124397.7 9.369 —Sandy area 55720.9 50021.65 15241.64 — 1.46Steep sloping area 62514.1 7656.29 4121.07 — —Barren rocky area — 64584.77 25722.6 — —Snow/glacial area — 55788.49 2973.16 — —Degraded forest 162742.7 140652.31 96099.04 3.5889 19.49Forest blanks area 18138.5 — — — —Degraded land under plantation crops — 5828.09 4931.65 — —Degraded pasture/grazing land — 25978.91 13148.69 — —Shifting cultivation area — 35142.20 18542.76 — 4.91Special problem area — — — — 2.73Total wasteland 624784.4 638518.31 341580.8 12.958 173.64Total geographical area — 3166414.00 1888061.59 — —

Note The estimates published by different sources may not be comparable due to the differences in scale and assessment methods.NRSA – National Remote Sensing Agency; NAEB – National Afforestation and Eco-development Board; SPWD – Society for Promotion ofWastelands DevelopmentSource Author’s compilation from various sources.

land was degraded over 20 years. We multiplied the amount of landdegraded as per NRSA (2000) with 0.80 and then divided it by 20 toreflect the average annual land degradation.

As our aim was to develop accounts for different states, we needed datafor different states. For 2000, for the first time, the NWDB (NationalWasteland Development Board) provided data at the state level, whichrevealed substantial variations (Table 6). From Table 6, it can be seenthat the extent of wasteland ranges from 3.7% in Kerala to 64% inJammu and Kashmir. Among all the states, Jammu and Kashmir has thehighest extent of wasteland followed by Manipur at 58%, HimachalPradesh at 56%, and both Nagaland and Sikkim at 50% as these stateshave very high proportion of natural wastelands (barren rocky, steepsloping area, land under snow cover, sandy desert or coastal region).However, such natural wastelands are not treated as degraded lands inthis paper. If we consider only the land degraded as a result of humanactivities like agriculture, the states of Manipur, Mizoram, and Nagalandtop the list with more than 15% of the geographical area placed underthe degraded lands category, while the states of Jammu and Kashmir andHimachal Pradesh have wastelands of less than five per cent of theirgeographical area.


Tabl

e 6

Stat

e-w

ise

was

tela

nds

of In

dia

in th

e ye

ar 2

000

(squ

are

kilo

met

re)

% T

otal

Tota

lge

ogra

phic

al

Stat

eG

l and

RU

SW

L an

d M

LS/

ALSH

/CUU

/DF

DPG

DPC

S an

d D

LM

and

IWL

BR/S

ASS

ASC

/GA

was

tela

nds

area

Andh

ra P

rade

sh69

2.70

2025

7.00

1035

.02

603.

2613

.80

2223

7.80

709.

2952

.91

464.

7098

.88

5196

.27

388.

960

5175

0.20

18.8

1

Arun

acha

l Pra

desh

033

26.8

041

.47

030

88.1

014

16.6

721

34.9

96.

0730

9.43

0.30

1262

.36

7.93

6732

.218

326.

3021

.88

Assa

m0

843.

7216

33.5

60

8391

.50

3112

.71

2217

.85

037

64.5

00.

4354

.88

00

2001

9.20

25.5

2Bi

har

559.

2046

89.9

011

98.8

70.

5145

.45

1306

6.50

164.

9779

.80

222.

0818

4.23

688.

9197

.10

020

997.

6012

.08

Goa

029

2.83

41.0

20

071

.99

2.47

32.1

90

110.

7358

.55

3.49

061

3.27

16.5

7G

ujar

at10

13.0

021

787.

0026

56.2

676

37.3

00

5443

.02

387.

4578

.32

188.

4249

.66

3293

.39

487.

310

4302

1.30

21.9

5H

arya

na49

.50

988.

4223

8.30

285.

630

732.

5272

1.65

134.

1246

5.01

13.7

210

5.12

00

3733

.98

8.45

Him

acha

l Pra

desh

121.

9020

56.5

015

.69

1.36

045

89.9

842

78.1

724

57.5

910

5.04

85.6

638

58.0

415

29.6

712

559.

031

659.

0056

.87

Jam

mu

and

Kash

mir

21.2

544

95.3

024

6.50

00

2491

.66

267.

5164

0.56

869.

260.

3132

821.

5016

85.4

221

905.

065

444.

2064

.55

Karn

atak

a30

1.50

9087

.70

32.7

612

5.11

082

99.4

197

.46

104.

7443

.96

77.7

826

27.8

940

.97

020

839.

3010

.87

Kera

la0

357.

9313

6.00

00

609.

303.

9925

.65

27.8

70.

4914

6.46

140.

490

1448

.18

3.73

Mad

hya

Prad

esh

7569

.00

3697

8.00

51.7

216

2.81

020

437.

8030

2.44

910.

4024

.57

141.

4429

50.9

718

4.65

069

713.

8015

.72

Mah

aras

htra

1700

.00

3138

7.00

527.

5725

1.66

013

430.

7013

49.4

068

7.43

77.6

310

0.45

2587

.42

1389

.57

053

489.

1017

.38

Man

ipur

01.

3232

4.60

012

014.

0060

8.64

00

00

00

012

948.

6058

.00

Meg

hala

ya0

4190

.60

14.8

70

2086

.80

3612

.11

00

00

00

099

04.3

844

.16

Miz

oram

00

00

3761

.20

310.

450

00

00

00

4071

.68

19.3

1N

agal

and

015

96.5

00

052

24.7

015

82.9

90

00

00

00

8404

.10

50.6

9

Oris

sa18

5.80

8358

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379.

1051

.49

115.

2810

014.

1013

.43

193.

9321

2.49

35.4

515

74.0

920

7.88

021

341.

7013

.71

Punj

ab16

8.50

339.

4435

2.01

173.

290

353.

2911

3.71

81.5

861

9.67

26.8

90

00

2228

.40

4.42

Raja

stha

n49

53.0

027

153.

0028

9.66

2723

.00

012

541.

9012

208.

4021

.14

4064

0.00

128.

6547

99.0

218

2.28

010

5639

.00

30.8

7

Sikk

im0

1073

.10

00

010

60.5

70

00

010

.34

014

25.6

3569

.58

50.3

0Tr

ipur

a0

286.

870.

110

400.

8858

8.18

00

00

00

012

76.0

312

.17

Tam

il N

adu

226.

1076

97.9

041

5.80

2479

.70

0.53

9634

.25

168.

9422

1.96

590.

8012

0.46

1155

.92

301.

500

2301

3.90

17.7

0

Utt

ar P

rade

sh28

07.0

054

99.0

049

81.4

358

11.9

00

3338

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446.

3650

.44

470.

2129

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1180

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992.

8313

166.

038

772.

8013

.17

Wes

t Ben

gal

171.

9012

45.2

019

31.5

413

1.25

077

7.58

384.

972.

9387

9.13

47.3

413

0.46

16.2

40

5718

.48

6.44

Uni

on Te

rrito

ries

12.8

325

.74

24.6

039

.01

028

9.97

5.43

46.3

447

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083

.05

00

574.

305.

23

Indi

a20

553.

0019

4014

.00

1656

8.50

2047

7.00

3514

2.00

1406

52.0

025

978.

9058

28.0

950

022.

0012

52.1

064

584.

8076

56.2

955

788.

063

8518

.00

20.1

7

BR/S

A –

Bar

ren

rock

y/st

ony

was

te/s

heet

rock

y Ar

ea; D

PG –

deg

rade

d pa

stur

es/g

razi

ng la

nd; D

PC –

deg

rade

d la

nd u

nder

pla

ntat

ion

Crop

s; G

L an

d R

– gu

illie

d an

d or

ravi

nous

land

; M a

nd IW

L –

min

ing

indu

stria

l was

tela

nds;

S/A

– la

nd a

ffec

ted

by s

alin

ity/a

lkal

inity

-coa

stal

/inl

and;

SC

– sn

ow c

over

ed a

nd o

r gla

cial

are

a; S

and

DL

– sa

nds-

dese

rtic

coa

stal

; SH

/C –

shi

ftin

g cu

ltiva

tion

area

; SSA

– s

teep

slo

ppin

g ar

ea; U

U/D

F –

unde

r-ut

ilize

d de

grad

ed n

otifi

ed fo

rest

land

; WL

and

ML

– w

ater

logg

ed a

nd m

arsh

y la

nd; U

S –

upla

nd w

ith o

r with

out s

crub

Sour

ceM

inis

try

of R

ural

Dev

elop

men

t (20

00)


As land degradation takes place largely in the form of soil erosion, whichis a serious threat to our future well being, we also included soil resourcesin the accounts for agriculture. The two main agents of soil erosion arewind and water. Water-related erosion takes place directly through floodsand surface run off and indirectly through excess or inappropriate use ofwater resulting in salinity and alkalinity. Using water contaminated withindustrial pollutants can result in severe damage to croplands, withsignificant decline in the yield rates. Erosion occurs in agricultural lands,construction sites, roadways, disturbed lands, surfaces mines, and inareas where natural or geological disturbances take place. Erosion canbroadly be classified into five types: (1) sheet erosion (removal of a thin,relatively uniform layer of soil particles), (2) rill erosion (erosion innumerous small channels that are small enough to be obliterated bynormal tillage), (3) gully erosion (larger upland channels), and(4) stream channel (erosion caused by stream flow), and (5) masserosion (enmass movement of soil). Although soil erosion is causedmainly by natural factors (climate and hydrology), soil topography, soilsurface conditions and their interactions, the management and use ofland play a major role in aggravating the situation.

Soil erosion estimates are usually calculated through USLE (universalsoil loss equation), where the soil loss in tonnes per hectare is regressedagainst variables such as the erosive capacity of rainfall, erosion of soil,length of slope, steepness of slope; land cover and management, andsupport practices (contouring, strip cropping, etc.) (Table 7). Theseestimates are usually available from different studies for a specific loca-tion but are usually generalized at the aggregate level. However, the datais not available for all the climatic zones. We used a study by Singh, Babu,Narain, et al (1992), which published the soil erosion rates for differentregions of India. Based on the iso-erosion map generated by them, theydivided the entire country into five soil erosion zones ranging from anarea of no erosion to slight, medium, severe, and very severe erosionareas. We found this data to be useful in estimating the soil erosion ratesfor the entire country (Table 8). The erosion rate contributed by eachstate is then computed using the share of the agricultural area in eachstate to the total in India.

Soil erosion not only has on-site but also off-site impact by way of sedi-mentation of the waterways. The Central Water Commission has pub-lished some data on the sediment load of some of the major reservoirs.However, as the data is for reservoirs, it is difficult to get state-wisedisaggregated estimates. A study by Sharma (2002) gives the sedimentload per square kilometre per year for all major rivers and their tributar-ies in India and Nepal at various monitoring points. The study is basedupon all published sources of information and also on controlled labora-tory experiments (Table 9). However, as a particular river originates inone state and often flows in more than two to three states, we had tomake some assumptions to estimate the sediment load in different rivers.


Table 7Annual soil loss estimates in different regions of India as per the universal soil lossequation

Areacovered

Soil loss (’000(tonnes per squaresquare kilo-

Land resource region kilometre) Major land use metre)

North Himalayan forest region 287 Forest 131.70Punjab–Haryana alluvial plains 330 Agriculture 101.25Upper Gangetic alluvial plains 1440–3320 Agriculture and wasteland 200.00Lower Gangetic alluvial plains 287–940 Agriculture 145.50North-eastern forest region 2780–4095 Agriculture/shif ting cultivation 161.00Gujarat alluvial plain region 240–3320 Agriculture 62.75Red soil region 240–360 Agriculture 68.80Black soil region 2370–11250 Agriculture 67.34Lateritic soils 3930 Agriculture 61.00

Source Narayana and Babu (1983)

Table 8Estimates of soil erosion in India

Soil erosionrate range Soil erosion Area(Mg/ha/yr) class (km2) Low estimate Medium estimate High estimate

0–5 Slight 801 350 0 200 337 500 400 675 0005–10 Moderate 1 405 640 702 820 000 1 054 230 000 1 405 640 00010–20 High 805 030 805 030 000 1 207 545 000 1 610 060 00020–40 Very high 160 050 320 100 000 480 150 000 640 200 00040–80 Severe 83 300 333 200 000 499 800 000 666 400 000>80 Very severe 31 895 255 160 000 318 950 000 382 740 000Total 3 287 265 2 416 310 000 3 761 012 500 5 106 000 000

Mg/ha/yr =t/ha/yr; km2 – square kilometreSource First three columns are based on Singh, Babu, Narain, et al. 1992, and the next threecolumns are authors’ computations.

Though we tried to estimate the state-wise sediment load using thelength of the rivers in major states, published data existed only for somemajor rivers and not for the smaller rivers. Hence, first we estimated thesediment loads in various major rivers (all the tributaries of a river areadded up as they ultimately flow into the main river). A paper byAmarsinghe (2004) published the percentage of the geographical areacovered by major rivers in all states in India. Using this data, we firstfound the sediment load of the different rivers in different states. Assediments are contributed by various natural factors and also other land-use changes and not necessarily by agriculture alone, we first identifiedthe proportion of land degraded as a result of agricultural activities in thetotal geographical area of each states. Using these values as weights, we


Tabl

e 9

Sedi

men

t loa

d in

Indi

an ri

vers Se

dim

ent

Sedi

men

tSe

dim

ent

Sedi

men

tRi

ver

Loca

tion

(ton

nes)

Rive

rLo

catio

n(t

onne

s)Ri

ver

Loca

tion

(ton

nes)

Rive

rLo

catio

n(t

onne

s)

Sutle

jBh

akra

4512

8160

God

avar

iN

ande

d25

0000

00Ba

gmat

iCh

ovar

7600

00Si

naW

adak

bal

6480

000

Sutle

jG

obin

dsag

ar44

0610

00G

odav

ari

Basa

r16

0000

00Be

twa

Mat

atila

1740

4800

Tung

aSh

imog

a37

0000

Bana

sD

anto

wad

a30

0910

7G

odav

ari

Srira

msa

gar

7927

2864

Betw

aSa

hijn

a10

5373

40Tu

ngab

hadr

aTu

ngab

hadr

a14

5000

0Bh

adar

Bhad

ar39

5687

5G

odav

ari

Man

cher

ial

1330

0000

0Be

twa

Gan

dhis

agar

2940

0000

Tung

abha

dra

Tung

abha

dra

2555

9260

Mah

iKa

dana

1629

1968

God

avar

iPe

rur

1330

0000

0Ch

amba

lG

andh

isag

ar11

0000

00Va

roda

Mar

ol66

1635

Mah

iM

outh

2210

0000

God

avar

iRa

jham

undr

y17

0000

000

Cham

bal

Udi

1803

2265

Kris

hna

Vija

yaw

ada

4110

000

Taw

aTa

wa

6800

000

God

avar

iM

outh

1700

0000

0G

anda

kCo

nflu

ence

2400

0000

God

avar

iRa

mte

k28

6412

Nar

mad

aG

arud

eshw

ar69

7000

00In

drav

ati

Now

rang

pur

3000

000

Gha

gra

Conf

luen

ce12

5000

000

God

avar

iD

hulle

1000

0000

Nar

mad

aM

outh

1250

0000

0In

drav

ati

Jada

lpur

4000

000

Gom

tiCo

nflu

ence

6000

000

God

avar

iG

agra

1200

0000

Nar

mad

aM

outh

6140

0000

Indr

avat

iP.

Gud

em21

0000

00Ka

ligan

daki

Setib

eni

3200

0000

Jhel

umD

omai

l14

6390

00G

irna

and

Panz

aG

irna

4955

992

Kadd

amKa

ddam

3404

992

Kank

aiM

aina

chul

i58

0000

0Jh

elum

Man

gla

6359

5515

Tapi

Savk

hoea

2470

0000

Lakh

amva

raLa

kham

vara

m39

9588

Karn

ali

Asar

agha

t17

0000

00M

and

Kuru

bhat

a25

4000

4Ta

pti

Uka

i70

9365

00M

anai

rRa

map

pa L

.66

608

Karn

ali

Chis

apan

i10

5000

000

Ong

Sale

bhat

a18

8999

3Ta

pti

Mou

th10

2000

000

Man

jeer

aSa

igao

n30

0000

0Ke

nCh

ila63

1210

56Pa

iriBa

rond

a30

2998

4Ku

ttiy

adi

Peru

vann

am’h

i28

0784

Man

jeer

aRa

ipal

li60

0000

0Ko

siCh

atar

a13

3000

000

Seon

ath

Sim

ga18

5427

8M

alam

puzh

aM

alam

puzh

a52

6288

Man

jira

Man

jira

2381

340

Bhilg

anga

Tehr

i13

9629

49Se

onat

hJo

ndhr

a43

2994

9M

anal

iPe

echi

1433

8Ki

nner

sani

Kinn

ersa

ni11

3730

Kosi

Balta

ra80

0000

00Te

lKa

ntam

al67

9002

8Bh

arat

hpuz

hM

anga

lam

7585

2Ka

nigi

riKa

nigi

ri88

998

Kule

khan

iKu

lekh

ani

2000

Brah

man

iSa

mal

2040

0000

Aliy

arAl

iyar

2314

65M

usi

Mus

i48

3395

Loth

arkh

ola

Loth

ar14

0000

0M

ayur

aksh

iM

ayur

aksh

i44

6400

0Ka

ilman

i’har

Man

imut

haru

2757

24Ta

ndav

aTa

ndav

a36

6212

Nar

ayan

iN

aray

angh

at17

6000

000

Dam

odar

Panc

het H

ill89

1996

0Va

igai

Vaig

ai12

5492

1H

indr

iG

ajul

adin

ne16

9951

6Ph

ewa

Phew

a76

9912

Dam

odar

Mou

th28

0000

00Ku

ndah

Uppe

rbha

wan

i25

84Ta

mm

ileru

Tam

mile

ru68

8597

Gan

dak

19

5974

400

Bara

kae

Mai

thon

9000

420

Bhaw

ani

Low

erbh

awan

i17

6400

0Ar

ania

rAr

ania

r58

0160

Ram

gang

aRa

mga

nga

1006

0140

Gan

ga/B

rahm

aput

ra

1100

8640

00Ca

uver

yM

ettu

r14

8966

00Ka

dam

Swar

na25

1720

Ram

gang

aCo

nflu

ence

1000

0000

Iraw

adi

Del

a25

9780

000

Cauv

ery

Mus

iri15

0000

0M

ehad

rigad

Meh

adrig

adda

7744

00Ra

pti

Baga

soti

1700

0000

Burh

i Gan

’kRo

sera

2800

0000

Cauv

ery

Mou

th32

3100

00Ko

nam

Kona

m26

7957

Seti

Phoo

lbar

i33

0000

0Sh

akka

rG

adar

war

a17

8195

0Pa

nnal

arui

Kris

hnag

iri13

0863

0Pa

mpa

Pam

pa22

0100

Seti

Bang

a19

0000

00In

dus

Kach

ural

8700

0000

Ponn

iar

Sath

nur

1537

292

Mun

yeru

Low

er S

aile

ru60

243

Son

Conf

luen

ce50

0000

00In

dus

Prat

ab B

1600

0000

0G

undu

kam

mCu

mbu

mta

k13

7530

5M

anjir

aN

izam

saga

r14

8603

90Su

nkos

iKa

mpu

ghat

7590

0000

Indu

sD

arba

nd14

7511

133

Pala

rPa

lar

3374

0Pe

ngan

gaP.

G.B

ridge

1400

0000

Tam

orM

ulgh

at41

4000

00In

dus

Tarb

ela

1880

0000

0Pe

nnar

Som

asila

6900

000

Pran

ahita

Tekr

a65

0000

00To

nsIc

hari

9138

18In

dus

Kala

bagh

7503

0000

0Al

iaru

Poch

aram

9422

0Pu

rna

Purn

a10

0000

00To

nsIc

hari

7924

000

Indu

sKo

tri

4809

1600

0Bh

ima

Gho

d22

8600

0Sa

bari

Kont

a60

0000

0Tr

ishu

liBe

traw

ati

3400

000

Arun

Trib

eni

4844

0000

Bhim

aTa

kali

1407

5104

Sidh

apan

aM

agle

aon

2000

000

Yam

una

Taje

wal

a18

0815

08W

yra

and

Pang

iW

yra

2854

20Bh

ima

Yedg

ir46

0397

17Si

leru

Mac

hkud

6000

00Ya

mun

aD

elhi

2608

9200

Nira

Sara

ti10

2960

0D

indi

Din

di94

080

Wai

ngan

gaPa

uni

2100

0000

Yam

una

Okh

la28

7524

54M

ahan

adi

Tika

rapa

ra30

7000

00Is

saH

imay

atsa

gar

8175

00W

ardh

aBa

min

i51

0000

00Ya

mun

aEt

awah

8445

691

Mah

anad

iN

araj

6786

5476

Koyn

aSh

ivaj

isag

ar96

1389

Wai

ngan

gaAs

thi

2200

0000

Yam

una

Alla

haba

d64

3277

28G

anga

Brah

ma

Conc

e52

0000

000

Kris

hna

Kara

d16

9000

0H

amp

A’Ko

re49

7515

Yam

una

Conf

luen

ce12

5000

000

Brah

map

utra

Gan

ga C

onflu

ence

5400

0000

0Kr

ishn

aBa

galk

ot28

1000

0H

asde

oBa

mni

dih

4850

016

Gan

gaH

arid

war

1400

0000

Mun

eru

Shan

igra

m L

5103

9Kr

ishn

aH

uvan

pur

9170

000

IbSu

ndar

garh

2760

015

Gan

gaKa

nnau

j15

0000

00M

ahan

adi

Hira

kud

7730

7165

Kris

hna

Baw

apur

am63

8000

0Jo

nkRa

mpu

r68

0010

Gan

gaAl

laha

bad

2280

0000

0G

anga

Del

ta, B

. D14

5000

0000

Kris

hna

Sris

aila

m11

6825

247

Mah

anad

iRa

jim34

9962

0G

anga

Gha

zipu

r34

0000

000

Ken

Chill

a63

1210

56Kr

ishn

aM

orva

kond

a67

7200

00M

ahan

adi

Tika

rapa

ra29

8898

20G

anga

Fara

kka

7290

0000

0Ba

raka

eM

aith

on90

0042

0Kr

ishn

aM

outh

6400

0000

Mah

anad

iBa

sant

pur

1879

9879

Gan

gaCa

lcut

ta32

8000

000

IbSu

ndar

garh

2760

015

Sour

ceSh

arm

a (2

002)


To construct the monetary accounts, we used various approaches asmentioned earlier. For estimating the value of change in asset accounts,we used the net present value, method. To compute the net present valuewe had to estimate the present value of the future net returns from theland, which depend on the cropping patterns, quality of soil, rainfall, etc.(Figure 3 shows how the cropping patterns have changed over thedecade. Annexure II gives the details of various commodities consideredunder various commodity groups.) We felt that this could be captured bytaking the time-series data on the value of output in agriculture. Hence,to estimate the present value of future net returns from agriculture, weused the data on the value of output in agriculture from 1950/51 to2000/01. We fitted a linear regression model using time as an indepen-dent variable and the value of output as the dependent variable. Usingthis trend variable (time), we predicted the average future net returns.

found the contribution of agricultural practices to the sediment load ofeach state6 (Table 10).

Construction ofmonetary accounts

Figure 3

Change in croppingpattern in 1992 and 2001

6 As river sediment load is due to many reasons, by using the land degraded as a result of agriculturalpractices as weight , we attribute only a small portion of the sediment load to agricultural practices,netting out other effects.

25Estimating the value of agricultural cropland and pastureland in IndiaTabl

e 10

Phys

ical

acc

ount

s fo

r agr

icul

tura

l lan

d an

d pa

stur

elan

d

Agric

ultu

ral l

and

(’000

hec

tare

)Pa

stur

elan

d (’0

00 h

ecta

re)

Land

deg

rada

tion

(’000

hec

tare

)So

il er

osio

n (m

illio

n to

nnes

per

yea

r)

(Cha

nge

in 1

0 ye

ar p

erio

d)(A

s pe

r the

late

st e

stim

ate)

On-

site

impa

ctO

ff-si

te im

pact

s

Wat

er

Ope

ning

Chan

ges

Clos

ing

Ope

ning

Chan

ges

Clos

ing

Gl

Soil

eros

ion

sedi

men

tatio

n

stoc

ksin

qua

ntity

stoc

kst

ocks

in q

uant

ityst

ock

and

RU

SW

L/M

LS/

AD

P an

d D

GSC

Tota

l(M

T/ye

ar)

N L

oss

P Lo

ssK

Loss

(MT/

year

)

Stat

e(2

)(3

)(4

)(5

)(6

)(7

)(8

)(9

)(1

0)(1

1)(1

2)(1

3)(1

4)(1

5)(1

6)(1

7)(1

8)(1

9)

Andh

ra P

rade

sh10

466

649

1111

510

78−1

3494

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.27

2025

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103.

5060

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5.9

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239

3.68

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989

Arun

acha

l Pra

desh

150

1416

444

−440

033

2.68

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3.50

308.

8185

9.14

4.0

0.00

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003

0.05

313

.068

Assa

m27

0628

2734

431

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397

084

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360

221.

7983

9.15

1308

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0.02

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062

0.95

312

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r71

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02

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33.6

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001

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9

Guj

arat

9583

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9443

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1.30

2178

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6376

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38.7

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3348

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097

0.21

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imac

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rade

sh57

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512

4634

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7.36

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0.00

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445

Jam

mu

and

Kash

mir

730

1874

819

71

198

2.13

449.

5324

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atak

a10

788

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3887

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15.6

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rala

2250

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hya

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aras

htra

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0031

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ipur

140

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024

024

00.

1332

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alan

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sa63

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njab

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Utt

ar P

rade

sh17

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77

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7054

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498.

1458

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1954

.57

455.

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175

0.39

56.

075

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951

Wes

t Ben

gal

5494

−77

5417

68−7

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338

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038

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056

0.12

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934

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nion

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itorie

s14

3−1

512

835

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282.

572.

463.

900.

050

10.2

73.

80.

001

0.00

30.

050

0

Indi

a14

2645

−154

414

1101

1487

7−6

1414

263

2055

.30

1940

1.40

1656

.85

2047

.70

2597

.89

2570

3.84

2775

9.14

3761

.01.

448

3.26

150

.213

741.

713

Not

es

Gl a

nd R

– g

ullie

d an

d ra

vine

d la

nd; U

S –

upla

nd w

ith o

r with

out s

crub

; WL

and

ML

– w

ater

logg

ed a

nd m

arsh

y la

nd; S

/A –

land

affe

cted

by

salin

ity/a

lkal

inity

– c

oast

al/i

nlan

d; D

P an

d D

G –

deg

rade

d pa

stur

es a

nd d

egra

ded

graz

ing;

SC

– sn

ow c

over

ed, N

– n

itrog

en;

P –

phos

phor

us; K

– p

otas

sium

; MT

– m

etric

tonn

es


The net present value of future net returns was obtained using twodifferent discount rates of 4% and 10%.7

Table 11 gives the values of output and input for the year 2001/02. As theagricultural sector has subsidies going into it, in reality we should adjustthe values for the subsidies as well (Table 12). As we could not get infor-mation on state-wise subsidies, we assumed the percentage of agricul-tural subsidies in the GDP and distributed this subsidy in proportion tothe value added by the agricultural sector. This would not be far off themark, as it is likely that the bulk of the subsidies are provided by centralfunding, the major state-specific subsidy being free or near-free irrigationor electricity for irrigation, and in a minor way for other agriculturaloperations. We deducted these subsidies from the respective GSDPs, andthese adjustments are given in Table 13.

To find the net present value we made some assumptions about thediscount rate, life of the agricultural plot, and value at the end of thelifetime etc. We assumed the lifespan to be 30 years and the discount rateto be five per cent. However, we explored the sensitivity of the estimatesfor different time frame (50 years) and discount rate (10%) as well (Table14). All the entries in columns 2–7 in Table 10 were multiplied with thenet present value of land to obtain the monetary estimates given in Table15 (columns 2–7). For the purpose of estimating the value of depletion,we used a lower bound of 30 years. The estimates would have been quitedifferent if a different life span was assumed. Moreover, we did not makeany assumption about the value of agricultural land at the end of thelifespan of 30 years: it might remain in agriculture or may be convertedto other uses, which should be included as well. The opening stocks weremultiplied with the net present value of agricultural land from 1992 till2030. The values of closing stocks were multiplied with the net presentvalue of agricultural land from 2001 till 2030. As the difference in valuescould be because of the change in prices used, we introduced the revalua-tion term, which took into account the difference in values between theopening and closing stocks.

Similarly, the opening stock of pasture and grazing lands was multipliedwith its net present value. Unfortunately, we do not have good publisheddata on the price of fodder in different states. Along with agriculturaloutput, we get by-products like straws and stalks and this value is re-corded by CSO, so we used this figure as a proxy. We assumed that theseby-products came from cereals, pulses and millets, oil seeds, sugar caneand fibres (as assumed by various demand projections on availability ofthe fodder). We extrapolated the value of these by-products per hectarefrom 1950/51 to 2001/02 into the future. Using the mean contribution ofdifferent states in the value of by-products, we estimated the net presentvalue in different states. However, as these prices reflected the price of

7 We wanted to analyse the impact of using different discount rates.


Tabl

e 11

Valu

e of

inpu

ts a

nd o

utpu

t fro

m a

gric

ultu

re in

200

2/03

(rup

ees

lakh

)

Repa

irO

rgan

icCh

emic

alm

aint

e-Fe

ed o

fIr

rigat

ion

Tota

l val

ueTo

tal v

alue

Stat

eSe

edm

anur

efe

rtili

zers

nanc

eliv

esto

ckch

arge

sAg

ricu

lture

Live

stoc

kEl

ectr

icity

Pest

icid

esD

iese

l oil

of in

put

of o

utpu

t

Andh

ra P

rade

sh65

967

2600

714

8503

1699

624

0810

2585

3653

543

1579

211

273

1918

658

3697

2832

180

Arun

acha

l Pra

desh

1900

152

1822

711

7232

651

110

2429

4216

5045

2As

sam

2973

552

4475

1830

8130

883

4615

526

1698

436

1659

9424

212

0358

3Bi

har

5570

732

916

6434

884

7019

2687

1811

2798

319

518

3017

7544

240

4319

6221

6923

3G

oa13

8617

362

343

1990

665

62

210

7849

7850

850

Guj

arat

5756

281

9192

886

1565

625

3838

5720

2103

27

1913

671

7454

428

5356

3016

3040

3H

arya

na30

042

2155

564

941

6428

1532

4926

1920

099

323

472

7795

3102

636

1229

1558

075

Him

acha

l Pra

desh

1043

542

2926

9921

6927

426

2041

801

191

307

256

5191

332

4063

Jam

mu

and

Kash

mir

6237

3064

754

8251

641

833

148

5482

1499

412

184

292

316

4249

36Ka

rnat

aka

5367

819

667

9842

427

117

1643

6126

0031

160

3816

001

4576

1751

743

5139

2415

514

Kera

la67

6567

749

2015

018

468

4854

370

015

485

5518

6192

825

0818

3212

1200

354

Mad

hya

Prad

esh

1309

9133

816

1138

1230

740

3011

2935

9127

613

1626

492

2534

3744

870

8182

2140

561

Mah

aras

htra

7642

031

772

1422

3244

208

4120

5357

9854

498

7633

011

5641

4234

684

8055

4224

688

Man

ipur

1512

418

931

5545

4446

707

315

3221

384

7654

774

Meg

hala

ya38

3670

711

131

846

204

1061

77

1611

110

798

8224

9M

izor

am13

9545

3030

1747

452

51

024

938

1040

706

Nag

alan

d28

7439

852

178

4595

315

183

016

1196

4811

7711

Oris

sa34

388

4297

2621

310

713

1425

5583

916

193

2618

9414

2824

5124

0997

1255

268

Punj

ab38

578

1429

910

5210

1835

815

0736

2143

2945

53

011

039

5143

242

1253

2283

305

Raja

stha

n46

473

1018

1371

263

1779

429

6413

4999

1745

821

1784

649

6347

728

6267

7113

5333

2Si

kkim

1629

9244

1013

271

599

00

240

3726

4646

3Ta

mil

Nad

u31

419

6087

279

449

1124

311

1094

474

1962

221

669

2792

1304

333

0698

1521

059

Trip

ura

3010

699

1031

8656

142

1950

223

332

7612

735

1511

26U

ttar

Pra

desh

2205

2218

214

2565

9737

918

6022

6420

471

8062

212

148

372

1149

915

2248

1448

848

6249

755

Wes

t Ben

gal

5054

923

952

9553

211

680

2421

2361

149

944

242

3425

5996

2384

750

7901

3871

656

Anda

man

and

Nic

obar

Isla

nds

157

224

015

620

022

00

08

1712

6117

091

Dad

ra a

nd N

agar

Hav

eli

141

296

055

563

067

01

857

1188

5155

Dam

an a

nd D

iu14

129

053

108

015

03

025

374

1188

Del

hi20

616

412

3511

7013

091

6376

19

160

9720

617

162

5901

0La

ksha

dwee

p2

90

140

029

00

02

8322

12Po

ndic

herr

y25

031

016

8636

1074

1021

92

160

129

5539

3116

973

Chan

diga

rh4

4931

1224

90

150

118

037

911

35Jh

arka

nd13

889

4931

1224

90

6470

011

80

2071

950

1558

Chha

ttis

garh

2338

549

3112

249

074

720

118

031

217

5792

62U

ttar

anch

al69

3349

3112

249

061

660

118

013

459

4779

57To

tal

9639

1250

9005

1401

050

2838

4434

5335

355

346

4818

8093

821

1695

8069

554

3094

7984

812

3891

3838

Sour

ceCe

ntra

l Sta

tistic

al O

rgan

izat

ion


Table 12Extent of subsidies

Agriculture subsidies (rupees crore)

Year Fertilizer Electricity Irrigation Others Total GDP Percentage

1993/94 4562 2400 5872 1235 14 069 781 345 1.8006131994/95 5769 2338 6772 1246 16 125 838 031 1.9241531995/96 6735 1977 7931 1034 17 677 899 563 1.9650651996/97 7578 8356 9221 895 26 050 970 083 2.6853371997/98 9918 4937 10 318 983 26 156 1 016 399 2.5733991998/99 11 596 3819 11 827 1182 28 424 1 082 472 2.6258421999/2000 13 244 4276 11 487 1937 30 944 1 148 500 2.6942972000/01 13 800 6449 13 681 854 34 784 1 902 998 1.827853

Note GDP – gross domestic productSource Ministry of Agriculture (2004)

dry fodder and not green fodder, we used the assumption that 12 tonnesof green fodder is equivalent to 4 tonnes of dry fodder.8 Using thisassumption we converted the dry fodder values into green fodder values.Here, instead of multiplying the opening stocks with the net presentvalue in 1992 and the closing stocks with the net present value in 2001,we multiply the opening and closing stocks of pasture and grazing landswith the average net present value of these two years (Table 14).

As discussed earlier, changes in the quality of soil and land can be cap-tured through the tonnes of soil lost or through the lost output approach.We attempt to value both in this paper to get an idea about how theseestimates would differ. For the loss in production method, we used thenet present value of agricultural land as discussed above. In the case ofsalinity, the NBSSLUP (1990) has estimated the loss of production at25% across soil qualities and crops. However, some individual estimatesput the losses at about 50% on an average for different crops and intensi-ties of degradation (Reddy 2003). We took the former value as it gives anaggregate estimate for the whole of India. In the case of water-logging noaggregate was available, as waterlogging is mostly confined to commandareas. At the micro level, the losses due to waterlogging are estimated at40% in the case of paddy and 80% in the case of potato (Reddy 2003).Since most of the waterlogged regions dominantly grow paddy or wheat,we took the average of 40% loss in paddy production because of water-logging. However, for the purpose of this analysis, we assumed that thislost productivity is already reflected in the present output and hence notconsidered. For the rest of the degraded land categories like gullies andmarshy land, degraded pasture and uplands with or without scrubs, weassumed that entire value is lost.

8 (http: dhad.nic.in/chapter6/chap6.htm)


e 13

Adju

stm

ents

in th

e na

tiona

l acc

ount

s fo

r 200

1/02

(rup

ees

mill

ion)

L =

G/K

Dep

letio

n

A B

CD

E

FG

=C

+D

+E+

F F=

BC G

=F/

BH

I

J

= H

-IK

= F

-I

and

degr

adat

ion

Tota

lVa

lue

adde

dES

DP

afte

ras

per

cen

t

NSD

PCh

ange

sCo

stRe

plac

emen

tad

just

men

t for

by a

gric

ultu

read

just

ing

for

of E

SDP

GSD

Pcu

rren

t pric

esin

qua

ntity

of la

ndco

st o

f soi

lCo

st o

fde

plet

ion

and

Valu

e ad

ded

Agri

cultu

ral

adju

sted

agri

cultu

ral

(adj

uste

d fo

r

Stat

e20

02/0

320

02/0

3of

land

recl

amat

ion

nutr

ient

sse

dim

enta

tion

degr

adat

ion

ESD

PES

DP/

NSD

Pby

agr

icul

ture

subs

idie

sfo

r sub

sidi

essu

bsid

ies

subs

idie

s)

Andh

ra P

rade

sh16

0768

4.0

1439

754.

069

6.14

337

2.51

1767

1.75

974.

61−1

7720

.62

1422

033.

380.

988

2832

18.0

2275

3.75

2604

64.2

513

9927

9.6

1.27

Arun

acha

l Pra

desh

1945

0.5

1739

5.1

0.28

813

7.29

253.

2715

94.2

5−5

27.5

716

867.

530.

970

5045

.240

5.33

4639

.87

1646

2.2

3.20

Assa

m35

4314

.231

7208

.06.

828

209.

1245

69.0

615

20.5

3−4

980.

4831

2227

.52

0.98

412

0358

.396

69.5

911

0688

.71

3025

57.9

1.65

Biha

r89

7150

.278

7033

.025

1.67

510

6.41

1209

2.97

3770

.32

−120

54.1

277

4978

.88

0.98

526

7079

.121

457.

1524

5621

.95

7535

21.7

1.60

Goa

7771

1.0

6735

6.0

0.04

95.

3723

3.01

1.05

−243

.71

6711

2.29

0.99

650

85.0

408.

5346

76.4

766

703.

80.

37G

ujar

at13

8285

0.0

1144

047.

0−1

09.0

6853

5.03

1618

0.81

495.

86−1

7359

.93

1126

687.

070.

985

1630

40.3

1309

8.67

1499

41.6

311

1358

8.4

1.56

Har

yana

6583

72.0

5793

74.0

4.65

336

.49

5865

.82

710.

67−5

934.

1557

3439

.85

0.99

015

5807

.512

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Estimating the value of agricultural cropland and pastureland in India30 Tabl

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S –

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out s

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ted


Another way to estimate the value of degraded land is the maintenancecost approach. Given the scale of land degradation and soil erosion, fromtime to time the government incurs some expenditure in repairing andrehabilitating the degraded land (for example) various watershed devel-opment programmes). We took the expenditures incurred from theNinth Plan onwards during 1998–2002 to estimate the average costincurred to rehabilitate the lands and deducted these from the estimatesaccordingly (Table 16).

To estimate the cost of the loss of nutrients through soil erosion, we usedthe replacement cost approach. As soil erosion represents a major causeof on-site nutrient loss, the volume of soil loss can be used to estimate thenutrient loss of the study area. This will help in estimating the value ofloss in non-marketed environmental attribute (soil) occurring as a resultof farming activities (marketed good). In order to estimate the value ofloss in environmental attribute, we required data on macronutrient loss.This loss is specific to the site similar to the soil erosion data. We havevery few estimates on the extent of nutrient loss due to soil erosion. Somesite-specific studies on the extent of nutrient loss are available. A study byVerma, Bhola, Prakash, et al. (1983) for medium black soils indicatedthat on cultivated fallow land, of the 3.4 tonnes of soil loss per hectarethe nutrient losses of nitrogen, phosphorus and potassium are 10 kg/ha(kilogram per hectare), 3 kg/ha, and 0.06 kg/ha respectively. Similarly, onother kinds of crops, depending on the management practice, out of the1 tonne/ha loss of soil, the average loss in nitrogen, phosphorus and

Table 16Total investments made in treating the degraded lands under various schemes

Up to Eighth Plan Ninth Plan (1997–2001)

Area Total Area TotalYear of treated investment treated investment

Department of Agriculture and Cooperation Start of (Lakh (rupees (Lakh (rupeesScheme scheme hectare) Crore) hectare) Crore)

1 NWDPRA (National Watershed Development Project) 1990/91 42.33 967.93 21.19 792.152 RVP and FPR (River valley projects and Flood prone rivers) 1962/1981 38.89 819.95 8.17 470.143 WDPSCA (Watershed development Programme in 1974/75 0.74 93.73 1.30 63.40

shifting cultivation areas)4 Alkali soil 1985/86 4.84 62.29 1.00 13.755 EAPs (Externally aided projects) 10.00 646.00 5.00 1425.00

Department of Land Resources1 DPAP ( Drought Prone Area Programme ) 1973/74 68.60 1109.95 44.94 657.312 DDP (Desert Development Programme) 1977/78 8.48 722.79 24.77 518.673 IWDP ( Integrated Wasteland Development Programme ) 1989/90 2.84 216.16 35.65 496.32

Ministry of Environment and Forests IAEPS (Integrated Afforestation and Ecodevelopment 1989/90 2.98 203.12 1.23 141.54Project Scheme)

Source Tenth Five-year Plan report (2002–07)


potassium is 6 kg/ha, 1.31 kg/ha, and 0.54 kg/ha, respectively. Suchestimates on site-specific studies are available. However, this cannot beextrapolated for the entire country. At the aggregate level, some effortshave been made to estimate the loss of available nutrients in the topsoilof each of the 24 soil types in India, the land area under each type, andthe annual erosion rate in these soils by the Central Soil and WaterConservation Research and Training Institute. Based on this, they foundthat India lost nearly 74 MT of major nutrients annually due to erosion.However, 61% of the soil is moved and the effective loss is 39%. Of theremaining, the country loses 0.8 MT of nitrogen, 1.8 MT of phosphorus,and 26.3 million tonnes of potassium every year. According to anotherestimate by Government of India, the quantity of nutrients lost due toerosion each year ranges from 5.8 MT to 8.4 MT. As per the estimates ofNBSSLUP (1990), the average topsoil loss due to erosion is 19.6 tonnes/ha. Of this, 1.39% is actual nutrient loss in terms of nitrogen, phospho-rus, and potassium. We used the same proportion of nitrogen, phospho-rus, and potassium (as given by NBSSLUP) lost through erosion in ourstudy as well.

However, while computing the replacement cost, we did not computethe cost of the organic matter lost. This can be quite significant as can beseen by taking Punjab as an example. Due to intensive farming, the soilorganic content went down to 0.2% in 1990 from 0.5% in 1960. Loss insoil organic carbon means wasteful application of fertilizers, loss in soilbiological activity, and poor moisture retention. The soil yield lossfunction can be derived from empirical studies relating the productivitylevel of soils for a given land use/crop to the varying rates of erosion.Using this technique, the yield response functions measure the differencein yields between each soil type as compared to normal fertile soils.However, given the large number of crops that are grown in various partsof the country, it is difficult to come up with damage functions for all thecrops in different states taking into account their specific conditions (forexample, land use, crop management factor, altitude, slope, etc.).Though some studies have been done to estimate the organic carbonstock in Indian soils, the amount of organic carbon lost due to agricul-tural practices is not available for the entire country, thus limiting the useof this method. Moreover, the loss in organic matter is partly reflectedthrough reduced profits due to increasing use of fertilizers for the samelevel of output.

We estimated the on-site cost of soil erosion by analysing soil nutrientexpressed per tonne of soil basis. Due to the important role played bymacronutrients in the soil and because most data are only available forthese soil nutrients, the analysis has focused on nitrogen, phosphorus,and potassium. The values of available nitrogen, phosphorus, and potas-sium are estimated in terms of the equivalent levels of urea (46 0 0),single superphosphate or P2O5 (0 16 0), and murate of potash or K2O(0 0 60). Valuation was done using the price of fertilizer per kilogram of


nutrient published by the Fertilizer Association of India (2000). Thenutrients lost were multiplied with the price of fertilizer per kilogram ofthe nutrient to get the replacement costs.

To get the monetary estimates for the cost of sedimentation, we used themaintenance cost approach. There are mainly three methods – sedimentsluicing, flushing, and dredging – to reduce the amount of sedimentsflowing into the reservoir, thereby prolonging the life of the reservoir.Sediment sluicing is the name given to a type of reservoir operation thatpulls down the sediment level at the start of the flood season and thenallows as much sediment-heavy floodwater as possible to pass throughthe dam before it has a chance to settle. This method can drastically slowdown the rate of reservoir sedimentation but has been used successfullyonly in a few projects (Jauhari 1999). Sediment flushing is a method ofwashing out the accumulated deposits from a reservoir. Flushing out along reservoir will require several months and in general will have littleimpact on a seriously sedimented reservoir. An obvious way of restoringreservoir capacity is dredging. However, this is extremely expensive andis normally viable only for small, urban water supply reservoirs wherewater consumers can afford the cost, and landfill sites are available totake the dredged sediment. A study by Mahmood (1987) cited inBruijzneel and Bremmer (1989) cites the cost of dredging at 2–3 dollarsper cubic metre in 1987; around 20 times more than the cost of provid-ing additional storage in a new dam. Restoring the original capacity of amajor reservoir would require the removal (and transport and dumping)of billions of cubic metres of sediment. We used this value (after adjustingfor inflation) as an approximate cost incurred in removing sediments.

Table 15 gives the monetary estimates. In the monetary accounts thevalue of the change in quantity of land was observed over a 10-yearperiod. Hence, we divided this value by 10. Similarly, for the extent ofland degradation, as some land already existed in a degraded state beforethe study period, we made adjustment for this. The earliest estimateavailable was of the one by SPWD (1984) (Table 5). We could have takenthe NRSA estimate as it is more reliable, but between the NRSA (1989)and NRSA (1999) reports, there was a difference in the coverage of areaand scale as well. Hence, we decided to use the earlier estimate of SPWD(1984). We deducted the value of the land degraded from the alreadyexisting wasteland from our final estimates. Soil erosion estimates andsedimentation rates were already expressed on an annual basis, hencethey were used without further adjustment. A summary of the assump-tions we made is given in Annexure III.


Our ultimate objective was to adjust the national accounts for the degra-dation of the environment due to land use for agriculture and grazing(Table 13). The estimates in columns 6–9 of Table 13 were derived fromthe monetary asset accounts (Table 15). The total adjustments fordepletion and degradation were computed by summing up the depletionand externality costs imposed by agriculture on the environment. Thecost of externalities considered included the replacement cost of soilnutrients, cost of treatment of sediments from the waterways, and thecost of rehabilitating the degraded land. The reason we deducted the costof rehabilitating the lands was because from time to time the governmentincurs some expenditure in rehabilitating these lands, which should bededucted. Moreover, any land if left untreated causes more harm thangood to the environment. Assuming that these lands are treated in thecourse of time, the rent captured in the current year must be adjusted forthe costs the sector imposes on the environment. We computed theESDP (environment adjusted state domestic product) for all the statesafter adjusting for subsidies. From Table 13 it can be seen that in most ofthe states agriculture does impose significant external costs on theenvironment in the form of soil erosion and sedimentation of waterways.We did not consider the other impacts on human health due to thecontamination of the waterways with pesticides and fertilizers, which canbe quite significant. Despite this, the extent of impact on the environ-ment is quite high. It can be seen that the costs range from 0.3% – 4.5%of the NSDP (net state domestic product) (adjusted for subsidies) indifferent states. It is surprising that in Arunachal Pradesh a high value ofthree per cent is recorded despite the region being more forested thanagricultural. This may be because of factors like frequent flooding of theBrahmaputra followed by the nature of cropping pattern and uneventerrain. All these factors contribute to higher environmental costs else-where downstream, which should be deducted from the state product.Similarly in the states Madhya Pradesh, Orissa, and Rajasthan the gapbetween ESDP and NSDP is around three per cent to four per cent andin the other states it is about one per cent to two per cent. Our estimatesindicate that if environmental externalities are taken into account, thecontribution of agriculture to GDP is lower than what the estimatesindicate. The results also indicate the proportion of NSDP that has to beset aside to maintain the environmental capital in tact.

Our study has certain limitations. We could not get data on the extent ofcontamination of waterways by fertilizers and pesticides, which in turnlead to many health hazards. We did not consider the net emissions ofgreenhouse gases from agricultural activities. In our replacement costestimates for soil erosion, we did not address the loss of organic carbondue to agriculture and instead modelled only nitrogen, phosphorus, andpotassium replacement. Our study did not consider other aspects oferosion, such as its effects on the soil’s physical structure, moisturecapacity, organic matter content, soil fauna, and the levels of many othernutrients. Moreover, the replenishment of soil nutrients by itself is

Discussionand conclusions


insufficient to restore original soil productivity. Erosion removes nutri-ents from the soil not only in the form available to plants but also fromsoil reserves of nutrients in ‘fixed’ form that are unavailable to plants.However, artificial fertilizers supply nutrients only in an available form.The replacement of soil nutrients with fertilizers thereforeoversupplies nutrients in available form and fails to replenish soil re-serves of fixed nutrients. Furthermore, artificial fertilizers are subject tovolatilization and leaching, which makes them highly inefficient inreplacing soil nutrients. These losses should be taken into account in thecalculation of the replacement cost, though in practice they are ignored(Clark 1996). Regarding the cost of sedimentation, we used an estimateof the cost of sedimentation in reservoirs. Given that rivers have a differ-ent hydrology from reservoirs and the sediment load is different, thisestimate may be much lower than the actual. Our estimate thus repre-sents only a lower bound.

Agriculture has some positive externalities as well. In contrast to thestudies done for developed nations our study did not consider theenvironmental benefits of agriculture largely because these are notmaterial in an Indian context. Unlike Europe/the UK where the value ofa ‘farm and field’ lifestyle and agritourism is significant due to its charmvis-à-vis the urban life, in India farms and fields neither have the samefacilities nor is there similar demand for experiencing such a lifestyle.

Our results should thus be viewed with an active consciousness of thelimitations of the available data. Data required for agricultural account-ing is site-specific. It depends on the local conditions, topography, cropmanagement factors, etc. However, in line with our stated objectives, weused aggregate estimates available from various secondary sources. Site-specific estimates can be used at a more disaggregated level of accounts(such as the state and its districts) at a later stage.


Forests Forests include all lands classed as forests under any legalenactment dealing with forests or administered as forests,whether state owned or private, and whether wooded or main-tained as potential forestland. The area of crops raised in theforests and grazing lands or the area open for grazing within theforests should be included in the forest area category.

Land under non-agricultural use This category includes all lands occupied by buildings, roads, andrailways or underwater, for example, rivers and canals, and otherlands put to uses other than agriculture.

Barren and uncultivable land This includes all barren and uncultivable lands, such as moun-tains, deserts, etc. which cannot be brought under cultivation,except at a high cost, such land is classified as uncultivable,whether it is in isolated blocks or within cultivated holdings.

Permanent pastures and This category covers all grazing lands whether or not theyother grazing land are permanent pastures and meadows. Village commons and

grazing lands are included under this category.

Miscellaneous tree All cultivable land which is not included under the net areacrops and groves sown, but is put to some agricultural use is included under this

category. This means lands under Casuarina trees, thatchinggrass, bamboo bushes, and other groves for fuel, etc. which arenot included under ‘orchards’ are classified under this category.

Cultivable wasteland This category includes all lands available for cultivation, whethertaken up for cultivation or not, or taken up for cultivation once butnot cultivated during the current years and for the last five years ormore in succession. Such lands may be either fallow or coveredwith shrubs and jungles, which are not put to any use. They may beassessed or unassessed and may lie in isolated blocks or withincultivated holdings. Land once cultivated, but not cultivated forfive years in succession, shall also be included in this categoryafter the period of five years.

Current fallows This class comprises cropped areas, which are kept fallow duringthe current years only. For example, if any seedling area is notcropped again in the same year, it may be treated as currentfallow.

Other fallow land This category includes all lands, which were taken up forcultivation but are temporarily out of cultivation for a period of notless than one year and not more than five years. The reason forkeeping such lands fallow may be any one of the following.1 Poverty of the cultivators,2 Inadequate supply of water,3 Silting of canals and rivers,4 Un-remunerative nature of farming, and5 Unfavourable climate.

Net area sown This term denotes the net area sown under crops and orchards,wherein the areas sown more than once in the same year onlyonce.

Source Ministry of Rural Development (2000)

Annexure I Standard definitions of various categories of land use adopted in the landutilization statistics.


Commodities included in various groups

Cereals Paddy, wheat, jowar, bajra, barley, maize, ragi, small millets, and othercereals

Pulses Gram, arhar, urad, moong, masoor, horse gram, and other pulses

Oilseeds Linseed, sesame, groundnut, rapeseed and mustard, castor, coconut,nigerseed, safflower, sunflower, soyabean, and other oilseeds

Sugar Sugarcane and gur, other sugar, and total sugar

Fibre Kapas, jute, sunhemp, mesta, and other fibres

Drugs and narcotics Tea, coffee, tobacco, and other drugs and narcotics

Condiments and spices Cardamom, dry chillies, black pepper, dry ginger, turmeric, arecanut,garlic, coriander, and other condiments and spices

Fruits and vegetables Banana, cashewnut, potato, sweet potato, tapioca, onion, otherhor ticulture crops, floriculture

Other crops Rubber, guarseed, miscellaneous crops, and total other crops

Source CSO (2002)

Annexure II


Summary of working assumptions

We have used the following assumptions and methods in our study.

Opening stocks were multiplied with the net present value of agriculture in 1992 and closing stockswith the net present value of agriculture in 2001. The difference between opening stock and closingstock gave the loss due to changes in quantity. However, the difference in value could also be due tothe price changes between the opening and closing years. This had to be netted out.

To compute the net present value, we used the value added by agriculture, published by CentralStatistical organization.

To estimate the present value of future net returns from agriculture, we used the data on the value ofoutput in agriculture from 1950/51 to 2000/01. We fitted a linear model using time as the indepen-dent variable and the value of output as the dependent variable. Using this trend (time) variable, weextrapolated into future. We used a discount rate of 4% and 10%.

For computing productivity losses due to land degradation we used the following assumptions.

• For gullied and ravenous lands, upland with or without scrub – entire productivity loss.

• For waterlogged / marshy land – 40% loss in productivity

• For saline or alkaline lands – 25% loss in productivity

• For degraded pastures and grazing land — entire productivity loss

For estimating the value of soil erosion, we used the replacement cost method, i.e. the cost ofreplacing the nutrients in the soil by fertilizers.

• Replacement cost of nitrogen = nitrogen lost in tonnes * price per kilogram of nitrate in urea*content of nitrogen in the fertilizer (urea)

• Replacement cost of phosphorus = phosphorus lost in tonnes * price per kilogram of phosphorusin super phosphate * content of phosphorus in the fertilizer (super phosphate)

• Replacement cost of potassium = potassium lost in tonnes * price per kilogram of potassium inpotash * content of potassium in the fertilizer (potash)

To estimate the cost of removing sedimentation, we used an estimate of 3 dollars. Converting this intorupees we got the cost of sedimentation as 122 rupees per tonne of sediment.

We also estimated the value of land degradation using the maintenance cost method. For this usingthe expenditures incurred on various land development schemes, we estimated the average costs.

The accounts were adjusted as follows.

Depletion = change in value of agricultural land + change in pastureland value

Degradation (loss in productivity) = Total loss in productivity due to land degradation * adjustment forpreviously degraded land

Total adjustment = Depletion – replacement cost of soil nutrients – cost of sedimentation – cost ofrehabilitating the lands.

ESDP = adjusted SDP after agricultural subsidies +total adjustment for depletion and degradation

For agricultural subsidies, we computed the extent of subsidies going into fertilizers, pesticides,electricity, seeds, and water. The data is available only at the all India level. We estimated the extent ofsubsidies as a percentage of the GDP and distributed this among the states based on farm outputvalue. We adjusted the NSDP in different states using the estimated subsidy.

Annexure III


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Monograph 2

This monograph is part of a larger effort to build an empirically-based frame-work that would allow policy judgements regarding the accumulation anddepletion of natural and human capital. In this monograph, we have at-tempted to adjust national accounts for land degradation associated withagriculture. Specifically, we have considered the cost of soil nutrients re-placement, sedimentation of waterways and rehabilitation of degradedland. For now, we have ignored other externalities such as the environmentalcontamination caused by the use of pesticides and fertilizers. Our studysuggests that the ‘true’ economic contribution of agriculture in many statesmay be significantly smaller than generally assumed. Of course, we recog-nize that the results need to be treated with caution due to data limitations.Nonetheless, we believe that we have established a workable and consis-tent methodology as well as have identified gaps in the available data.

Green Accounting for India’s Statesand Union Territories Project

In common with most developing nations, India faces many trade-offs in its attempt toimprove the living standards of its people. The trade-of fs emerge in various arenas, andseveral mechanisms for decision-making (including political institutions) have beendeveloped to help choose between competing alternatives. Unfor tunately, most of thesedecision mechanisms do not take into account intergenerational choices, i.e. trade-offsbetween the needs of the present and the future generations. In our view, it is urgentlynecessary to develop a mechanism to do this because many of the choices we make todaycould severely affect the welfare of our children tomorrow.

Therefore, we propose to build a framework of national accounts that presents genuine netadditions to national wealth. This system of environmentally-adjusted national incomeaccounts will not only account for the depletion of natural resources and the costs ofpollution but also reward additions to the stock of human capital.

The Green Accounting for Indian States and Union Territories Project (GAISP) aims to setup economic models for preparing annual estimates of ‘genuine savings’, i.e. true ‘valueaddition’, at both state and national levels. The publication of the results will enable policy-makers and the public to engage in a debate on the sustainability of growth as well as makecross-state comparisons. It is hoped that a policy consequence of the project is gradualincreases in budgetar y allocations for improvements in education, public health, andenvironmental conservation, all of which are key elements needed to secure India’s long-term future.

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