Agri-Environmental Sustainability of Indian Agriculture: A State Level Analysis

Abstract

Improving economic viability of Indian agriculture is contingent upon agri-environmental sustainability (AES). Objective assessment of environmental costs of agriculture is lacking in India. Unless internalise environmental impacts of agriculture will be borne by the society at large, in terms of depletion and degradation of water resources, land degradation and emissions of greenhouse gases, etc. To assess AES of Indian agriculture, the present article builds a comprehensive agri-environmental sustainability index (AESI) based on 40 agri-environmental indicators. The study captures both spatial and temporal aspects of AES by covering 17 major Indian states over 24 years (1990–1991 to 2013–2014). The estimated AESI scores are validated with outcome indicators (e.g., groundwater depletion, depletion of soil nutrients). The results show that states having higher score in Sustainable Irrigation Index are facing lower fall in groundwater level and there are negative correlations across sub-indices of AESI and macronutrient deficiencies in soil. An inverse relationship between AESI scores and agricultural intensity (as measured by average productivity of foodgrains in kilograms per hectare) is also observed. The study comes out with policy suggestions which could help to attain AES of Indian agriculture.

Keywords

Agro-ecosystem agri-environmental sustainability agri-environmental indicators sustainable agriculture environmental sustainability Indian states

Introduction

Agricultural sustainability is defined as ‘when current and future food demands can be met without unnecessarily compromising economic, ecological, and social/political needs’ (Agricultural Sustainability 2004 as cited by Królczyk and Latawiec 2015). Agricultural sustainability depends on agri-environmental sustainability (AES) (Hayati et al. 2011). In absence of ‘system of integrated environmental and economic accounting’ (SEEA), objective assessments based on agri-environmental sustainability index (AESI) could be useful in understanding the state of environmental debt of Indian agriculture.¹ Achieving environmental sustainability of Indian agriculture is crucial not only to protect livelihoods of a large section of the populace but also to eradicate poverty and malnutrition. Intensive agricultural practices followed since 1960s have resulted in depletion of soil nutrients and degradation of soil, conversion of forest land, depletion and degradation of groundwater resources, diversions of surface water and loss of biodiversity in various parts of India (Damerau et al. 2020; Gill and Nehra 2018; Shah 2012; Srivastava et al. 2016). Agriculture touches upon all spheres of environment and natural resources (e.g., land, water and air) to source inputs as well as sink of wastes. Agricultural sustainability is largely depended on sustainability of natural resources (like land and water) as well as ecosystem services. There is a two-way relationship between agriculture and environment. Agriculture and related activities impact environment and also polluted environment impacts agriculture, for example, impacts of climate change on agriculture (UNDP 2019). Deteriorating water environment, land degradation, loss of biodiversity and growing demand for land from alternative uses are the major challenges that Indian agriculture is facing today (Srivastava et al. 2016). Though agriculture is the major user of water, diversions of water for alternative uses is rising with rise in population, urbanisation, per capita income and unavailability or pollution of local sources of water. Moreover, due to scarcity of water in semi-urban and urban locations, marginal quality water is often used in agriculture, which is not only polluting groundwater resources but also posing potential public health hazards in terms of contamination of food chains by heavy metals and other emerging pollutants. Large-scale depletion and degradation of groundwater resources is observed in various parts of India (CGWB undated). Deterioration of soil fertility and rising cost of accessing desirable quality and adequate quantity of water for irrigation are the major factors influencing the rising cost of agriculture (Mukherjee 2008). Water scarcity and degradation of agricultural land are the major hurdles in alleviating poverty, especially in developing countries, as it reduces the productivity of land on which the poor depend more. Hence, environmental degradation can perpetuate poverty and inequality (Barbier and Hochard 2016).

Rising anthropogenic pressures on the earth systems in general and agri-environment (land, water, biogeochemical cycle and biodiversity) in particular may result in abrupt global environmental change (Rockström et al. 2009). Growing pressures on agri-environment due to unsustainable agricultural practices may not only diminish the country’s ability to produce sufficient food in the future (Damerau et al. 2020) but also transgress ‘planetary boundaries’ (e.g., climate change, rate of biodiversity loss, changes to the nitrogen cycle) (Rockström et al. 2009). A comprehensive assessment of AES of Indian agriculture may help to take necessary actions to reduce pressure on agri-environment.

Given the importance of AES, United Nations set the following target (Target 2.4) under the Sustainable Development Goals (SDGs):

Target 2.4: By 2030, ensure sustainable food production systems and implement resilient agricultural practices that increase productivity and production, that help maintain ecosystems, that strengthen capacity for adaptation to climate change, extreme weather, drought, flooding and other disasters and that progressively improve land and soil quality.²

To assist countries to assess the achievement in SDG Target 2.4, Food and Agriculture Organisation (FAO) has brought out a draft list of agri-environmental indicators (AEIs) for discussion (FAO 2017). However, many targets set under SDGs are affected by AES and also AES is affected by many other targets (Mukherjee 2020). It is to be acknowledged that any assessment of AES based on AEIs must be based on local conditions and availability of data on target indicators.

Given the Government of India’s initiative to lunch ‘National Mission for Sustainable Agriculture’, development of a comprehensive AESI for India could help to focus on issues related to long-run AES. Moreover, AESI could also help to assess the present state of AES across Indian states and devise state-specific policies/programmes to achieve SDG’s Target 2.4. In the next section, we review literature that develops AEIs to assess AES. In the third section, we present our methodology and data sources and in the fourth section we present our results. In the fifth section, we validate our results with various outcome indicators. We draw our conclusions in the sixth section.

Literature Review

Literature on agri-environmental index has evolved over the last two decades. The existing studies on agri-environmental index could be classified into two broad categories: (a) macro- or indicator-based assessment of environmental impacts of agriculture (e.g., Binder et al. 2010; Girardin et al. 2000; Hajkowicz et al. 2008) and (b) field level experiments (either based on simulations or model-based analysis, experiments, e.g., Bockstaller et al. 2008; Langeveld et al. 2007; van der Werf and Petit 2002). In this article, we mostly focus on studies that assess the overall status of AES based on indicators. The effectiveness of AEIs can be judged from their quantifiability, scientific soundness, reference to relevant issues and cost-effectiveness.

Sustainable agriculture is the way of managing the agricultural ecosystem in terms of maintaining biological diversity, regeneration capacity, productivity, vitality and ability to function so that it can fulfil current as well as future economic, social and ecological functions without harming the other ecosystems (Lewandowski et al. 1999). The basic attributes of sustainable agriculture include the efficient use of natural resources (land, water, energy, nutrients, etc.), productive use of human capital through reducing ecological footprints of production and consumption, and use of traditional and modern scientific knowledge to minimise emissions of greenhouse gases (GHGs), soil and water pollution (Koohafkan et al. 2012). Agricultural sustainability has different attributes, indicators and components that capture the complex interactions among economy, society and environment (Lopez-Ridaura et al. 2005; Wilson and Buller 2001). These dimensions are also interlinked to other three dimensions, viz., spatial, temporal and normative, which help to capture the dynamic aspects of AES (von Wirén-lehr 2001; Zhen and Routray 2003). Given the importance of AES, several studies capture different indicators to reflect state of AES of a specific country or region, for example, Astier et al. (2011) for Latin America, Dillon et al. (2010) for Ireland, Eilers et al. (2010) and Huffman et al. (2000) for Canada, Brower and Crabtree (1999) and Gomez-Limon and Fernandez (2010) for Spain, Kelly et al. (2018) and Wilson and Buller (2001) for European Union, Langeveld et al. (2007) for the Netherlands, Roy and Chan (2012) for Bangladesh, Sands and Podmore (2000) for Colorado, USA, Vandermeulen and Huylenbroeck (2008) for Belgium, Xavier et al. (2018) for Portugal. Based on the analysis of 84 studies conducted in the United States, Asia, Africa, New Zealand and Australia, Rasmussen et al. (2017) identified unique indicators—180 environmental indicators, 65 economic indicators and 49 cultural/social indicators—for measuring AES. The study concludes that there is no ideal set of indicators that will represent all desired perspective of AES and suggested researchers to use the existing data and indicators for better alignment of different approaches.

The OECD Compendium of Agri-Environmental Indicators (AEIs) provides a set of 18 AEIs across 34 OECD countries from 1990 to 2010 (OECD 2013). Moreover, various organisations of the United Nations Economic Commission for Europe (UNECE) are preparing AEIs for Eastern Europe, Caucasus and Central Asia. The major indicators include fertiliser consumption, pesticide consumption, irrigation, energy consumption, agriculture land use, cropping and livestock pattern, gross nitrogen balance, agriculture ammonia emission, emission of methane and nitrous oxides and other GHGs, water abstraction, soil erosion and nitrates in water.

Different aspects of AES have been studied extensively for developed countries; however, literature specific to Indian context is sparse. Earlier Mukherjee and Kathuria (2006) and Mukherjee and Chakraborty (2009) constructed Environmental Quality Index (EQI) for Indian states to assess environmental quality for two time periods 1990–1996 and 1997–2004. These studies build three sub-indices—‘depletion and degradation of water resources’, ‘nonpoint source water pollution potential’ and ‘pressure and degradation of land resources’—which have some relations with AES. However, those studies have limitations in terms of coverage of indicators and period of analysis. Kareemulla et al. (2017) construct agricultural sustainability index based on 13 indicators for 2 years, viz., 2001 and 2011 and assesse performance of 19 Indian states. The study concludes that performance of states vary over periods. However, the study includes indicators like productivity of foodgrains (kg/ha), value of agriculture output (₹/ha, crops only), per capita income (₹/head), human development index (HDI) and female work participation rate (%), which are either outcome indicators (e.g., productivity of foodgrains, value of agricultural output, depletion of groundwater) or may influence adoption of sustainable agri-environmental practices (e.g., HDI index, per capita income, female work participation rate). In another study, Veluguri et al. (2019) assesse ecological sustainability of agriculture of 29 states based on 24 indicators. The study also includes outcome indicators (e.g., groundwater development, per cent of wells classified as ‘safe’, per cent of districts with nitrate concentration above permissible limits) and indicators selected for the study are not all pertaining to a single year but drawn from different years between 2011 and 2018. The study is single point assessment of state of ecological sustainability of agriculture across Indian states. However, state of AES is dynamic and changes over time and space.

State-specific studies exploring agricultural sustainability are also sparse in India. Ghosh and Chakma (2019) assess agriculture sustainability by considering interactions among land, water and energy in agricultural production systems at Bardhhaman district in West Bengal. The results indicate that increasing use of chemical inputs and specialisation of energy and water intensive crops are reducing sustainability of agriculture. Sajjad et al. (2014) explore agriculture sustainability in Bihar. The study indicates that improper management of resources, increasing population growth, natural calamities and increasing inequality are the major hurdles in achieving agriculture sustainability. The study by Hatai and Sen (2008) assesses agriculture sustainability in Orissa by using a composite index of ecological security, social equity and economic efficiency. In another study in Bihar, Sharma and Shardendu (2011) develop agriculture sustainability index with the help of 30 indicators. The study highlights the importance of soil quality, air quality, water quality, biodiversity, ecological literacy, population, intensity of agriculture and agricultural output, which are the major indicators contributing to the sustainability of agriculture.

Existing studies on AES of Indian agriculture have three major limitations. Firstly, both state-specific and cross-state studies consider specific time point(s) or period(s) for the assessment. Therefore, the exiting studies are not adequately capturing the dynamic aspects of AES by covering large period of analysis. Secondly, some existing studies include outcome indicators in the construction AES index and this is likely to impact the results. Thirdly, limited number of indicators is considered, and therefore the results of the exiting studies may not reflect the actual state of AES of Indian agriculture. The present article attempts to overcome the limitations of the existing studies by expanding number of indicators and covering a large period (1990–1991 to 2013–2014) of analysis. In addition, the present study validates the estimated AESI scores with the outcome indicators. Therefore, the present article is a robust estimation of AES of Indian agriculture and fills the gap in the existing literature.

Methodology and Data Sources

Based on literature review and assessment of availability of time series data at state level, we have identified a list of 40 AEIs to measure AES of Indian states. We have classified these indicators into eight groups or sub-indices of AESI (Table 1). In selecting indicators, we gave emphasis on ‘causes’ of agri-environmental impacts whereas the ‘outcome’ indicators (e.g., groundwater depletion, depletion of nutrients in soils) are used to validity the estimated AESI scores. To capture temporal variations of agricultural impacts on environment, we have divided the entire period of our analysis (i.e., 1990–1991 to 2013–2014) into three sub-periods: 1990s (1990–1991 to 1999–2000), 2000s (2000–2001 to 2009–2010) and 2010s (2010–2011 to 2013–2014). While 1990s mark the early years of economic liberalisation with moderate average annual growth rate of agricultural GDP (3.2%), 2000s mark high economic growth period but lower average annual growth rate in agriculture (2.5%) and 2010s mark the lower economic growth period but higher average annual growth rate in agriculture (4.9%). To capture spatial variations, we have considered 17 major Indian states (excluding North Eastern and Hilly States and United Territories with legislature). The list of indicators and sub-indices of AESI are provided in the appendix (available online).

Table 1.

Sub-indices of Agri-environmental Sustainability Index (AESI)

Sub-index	Number of Indicators
Sustainable Land Use Index (SLUI)	9
Sustainable Cropping Index (SCI)	5
Sustainable Irrigation Index (SII)	7
Sustainable Livestock Index (SLI)	8
Sustainable Agro-chemical Index (SACI)	2
Sustainable Farm Mechanisation Index (SFMI)	3
Population Pressure on Land Index (PPLI)	2
Sustainable Forest Index (SFI)	4
Total	40

Source: The author.

Note: Online appendix of this article provides detail descriptions of each indicator and group.

Each indicator has been re-scaled using appropriate scaling indicator keeping in mind diversity (heterogeneity) among states in their geographical area, size of the economy, size of the population, etc. (see online appendix for details). Since there is no performance benchmark (either national or international) available for the indicators, we have used the best performing state for each indicator as an ideal benchmark and standardised the indicators by the methods described below. Therefore, all estimates are relative estimates with reference to the best performing state for each indicator.

In line with the United Nations’ Human Development Index (HDI) method, the indicators are transformed into their standardised form, by which the normalised value of Xij (i.e., NXij) becomes

N X_{i j} = \frac{(X_{i j} - X_{i}^{*})}{(X_{i}^{* *} - X_{i}^{*})}

(1)

N X_{i j} = \frac{(X_{i}^{* *} - X_{i})}{(X_{i}^{* *} - X_{i}^{*})}

(2)

where X_ij is the value of jth state with reference to ith indicator and $X_{i}^{*}$ and $X_{i}^{* *}$ are the minimum and maximum values for the ith indicator, respectively. For indicators having positive (favourable) impact on AES, we have used formula 1 of normalisation and for indicators having negative (detrimental) impact on AES, we have used formula 2 of normalisation (see online appendix for indicator-wise application of these two alternate formulas). After normalisation, a higher value of an indicator implies better for AES.

Now, AESIkj is score of the kth sub-index of AESI for the jth state (which constitutes of n number of indicators, n varies from 2 to 9 across sub-indices), and it is arrived at by averaging of NXij over i by using the following formula:

{AESI}_{k j} = \frac{1}{n} \sum_{i = 1}^{n} N X_{i j}

In the similar manner, AESIj, that is, the overall AESI score for the jth state, is derived by averaging the AESIkj over k by using the following formula:

{AESI}_{j} = \frac{1}{8} \sum_{k} {AESI}_{k j}

The obtained AESIs are relative measure of agri-environmental sustainability (AES) of the states where equal weightages are given to each sub-index. The states with higher score in AESI (or any sub-index of AESI) having relatively higher AES. The obtained AESIj (where j = 1 to 17) leads to the ranks of the jth state, where states having higher score in AESIj get higher rank.

For a few indicators, data is available only for different time points. However, we have taken only those indicators that have at least three observations, and one of these observations falls within the boundary of our three sub-periods (1990–2000, 2001–2010 and 2011–2014). This analysis is based on the state level secondary information available in various published government reports and databases (see online appendix for details). There may be several other sources of data on Indian agriculture, but those are either not published regularly or not available for all States.

Results

Analysis of Rankings of States in AESI During 1990s

Table 2 gives the AESI scores and ranks of 17 states across time periods. It shows that during 1990s, Madhya Pradesh (MP), Rajasthan, Odisha, Karnataka, Goa and Maharashtra are the six best performing states. On the other hand, Punjab, West Bengal (WB), Haryana, Kerala, Tamil Nadu (TN) and Uttar Pradesh (UP) are the six worst performing states. MP and Rajasthan have the highest AESI ranks, as both the states have done well in almost all sub-indices of AESI, except in Sustainable Farm Mechanisation Index (SFMI) and Sustainable Land Use Index (SLUI) by MP and Sustainable Forest Index (SFI) and Sustainable Irrigation Index (SII) by Rajasthan. Punjab and Haryana are the laggards in all sub-indices of AESI. From Table 2 it is evident that different states have different strengths and weaknesses in managing various aspects of AESI. For instance, during 1990s TN has performed well in Sustainable Land Use Index (SLUI), whereas WB has done well in SFMI. Therefore, a comprehensive assessment of AES better reflects the reality than individual indicator–based assessment.

Table 2.

State-wise Scores and Ranks in Agri-environmental Sustainability Index (AESI) and Sub-indices of AESI

State	Sustainable Land Use Index (SLUI)			Sustainable Croping Index (SCI)			Sustainable Irrigation Index (SII)			Sustainable Livestock Index (SLI)			Sustainable Agro-Chemical Index (SACI)			Sustainable Farm Mechanisation Index (SFMI)			Population Pressure on Land Index (PPLI)			Sustainable Forest Index (SFI)			Sustainable Agri-Environmental Index (SAEI)
State	1990s	2000s	2010s	1990s	2000s	2010s	1990s	2000s	2010s	1990s	2000s	2010s	1990s	2000s	2010s	1990s	2000s	2010s	1990s	2000s	2010s	1990s	2000s	2010s	1990s	2000s	2010s
Andhra Pradesh	0.72	0.68	0.55	0.44	0.47	0.33	0.64	0.59	0.56	0.66	0.59	0.40	0.21	0.44	0.20	0.75	0.58	0.56	0.79	0.80	0.84	0.40	0.54	0.57	0.58	0.59	0.50
Bihar	0.70	0.53	0.43	0.47	0.50	0.51	0.61	0.49	0.30	0.50	0.64	0.31	0.79	0.67	0.60	0.93	0.88	0.88	0.41	0.33	0.01	0.35	0.30	0.18	0.60	0.54	0.40
Chhattishgarh		0.61	0.60		0.57	0.54		0.72	0.68		0.77	0.51		0.75	0.77		0.88	0.79		0.92	0.93		0.34	0.37		0.70	0.65
Goa	0.50	0.63	0.59	0.52	0.55	0.53	0.62	0.69	0.79	0.70	0.89	0.69	0.95	1.00	0.99	0.98	0.93	0.90	0.67	0.65	0.71	0.48	0.55	0.57	0.68	0.74	0.72
Gujarat	0.63	0.54	0.49	0.35	0.29	0.48	0.63	0.50	0.48	0.79	0.82	0.55	0.60	0.60	0.67	0.68	0.51	0.65	0.85	0.83	0.85	0.39	0.23	0.31	0.61	0.54	0.56
Haryana	0.36	0.26	0.23	0.37	0.47	0.40	0.43	0.41	0.33	0.48	0.62	0.32	0.18	0.11	0.15	0.71	0.38	0.34	0.68	0.63	0.63	0.24	0.23	0.26	0.43	0.39	0.33
Jharkhand		0.80	0.70		0.56	0.66		0.68	0.73		0.69	0.45		0.91	0.74		0.97	0.92		0.69	0.69		0.36	0.40		0.71	0.66
Karnataka	0.61	0.55	0.59	0.63	0.76	0.65	0.77	0.67	0.68	0.74	0.72	0.55	0.68	0.67	0.64	0.81	0.62	0.46	0.83	0.83	0.85	0.43	0.37	0.41	0.69	0.65	0.60
Kerala	0.54	0.62	0.52	0.53	0.55	0.54	0.68	0.67	0.56	0.59	0.75	0.51	0.70	0.77	0.61	0.92	0.82	0.80	0.02	0.07	0.19	0.35	0.55	0.41	0.54	0.60	0.52
Madhya Pradesh	0.60	0.47	0.38	0.64	0.68	0.66	0.69	0.56	0.53	0.88	0.80	0.61	0.79	0.94	0.89	0.68	0.72	0.71	0.93	0.94	0.92	0.71	0.48	0.44	0.74	0.70	0.64
Maharashtra	0.64	0.53	0.48	0.49	0.56	0.50	0.75	0.68	0.65	0.75	0.80	0.59	0.77	0.75	0.53	0.60	0.65	0.54	0.80	0.79	0.80	0.47	0.34	0.37	0.66	0.64	0.56
Odhisha	0.69	0.75	0.74	0.53	0.52	0.46	0.71	0.59	0.57	0.68	0.75	0.54	0.93	0.92	0.78	1.00	0.98	0.96	0.83	0.82	0.82	0.44	0.49	0.38	0.72	0.73	0.66
Punjab	0.26	0.29	0.18	0.33	0.35	0.41	0.30	0.35	0.31	0.65	0.58	0.61	0.00	0.00	0.00	0.17	0.34	0.32	0.64	0.63	0.66	0.23	0.15	0.27	0.32	0.34	0.34
Rajasthan	0.70	0.50	0.57	0.71	0.80	0.74	0.63	0.65	0.47	0.75	0.80	0.56	0.93	0.91	0.90	0.84	0.74	0.67	1.00	0.98	0.99	0.32	0.29	0.29	0.74	0.71	0.65
Tamil Nadu	0.72	0.83	0.70	0.49	0.56	0.54	0.63	0.63	0.55	0.62	0.42	0.51	0.37	0.34	0.40	0.64	0.32	0.37	0.52	0.55	0.57	0.40	0.28	0.37	0.55	0.49	0.50
Uttar Pradesh	0.54	0.39	0.36	0.43	0.47	0.46	0.47	0.44	0.40	0.75	0.75	0.40	0.58	0.49	0.45	0.85	0.78	0.77	0.49	0.45	0.36	0.39	0.26	0.28	0.56	0.51	0.43
West Bengal	0.47	0.41	0.26	0.40	0.46	0.48	0.38	0.39	0.37	0.16	0.36	0.34	0.49	0.48	0.45	0.99	0.96	0.95	0.02	0.00	0.03	0.26	0.35	0.55	0.39	0.43	0.43
Andhra Pradesh	1	4	8	10	13	17	6	9	7	9	14	14	13	14	15	9	13	12	7	7	6	6	3	1	9	10	11
Bihar	4	11	12	9	11	9	11	13	17	13	12	17	4	10	10	4	6	5	13	15	17	10	11	17	8	11	15
Chhattishgarh		7	4		4	6		1	4		6	11		7	5		5	7		3	2		10	9		6	5
Goa	12	5	6	6	8	8	10	2	1	7	1	1	1	1	1	3	4	4	9	10	9	2	2	2	5	1	1
Gujarat	7	9	10	14	17	12	7	12	11	2	2	6	9	11	7	12	14	11	3	4	4	9	15	12	7	12	8
Haryana	14	17	16	13	14	16	13	15	15	14	13	16	14	16	16	10	15	16	8	11	12	14	16	16	13	16	17
Jharkhand		2	3		6	2		4	2		11	12		5	6		2	3		9	10		7	7		4	2
Karnataka	8	8	5	3	2	4	1	6	3	6	10	7	8	9	8	8	12	14	4	5	5	5	6	6	4	7	7
Kerala	10	6	9	4	9	5	5	5	8	12	7	9	7	6	9	5	7	6	15	16	15	11	1	5	12	9	10
Madhya Pradesh	9	13	13	2	3	3	4	11	10	1	3	3	5	2	3	11	10	9	2	2	3	1	5	4	1	5	6
Maharashtra	6	10	11	8	7	10	2	3	5	5	4	4	6	8	11	14	11	13	6	8	8	3	9	11	6	8	9
Odhisha	5	3	1	5	10	13	3	10	6	8	9	8	2	3	4	1	1	1	5	6	7	4	4	8	3	2	3
Punjab	15	16	17	15	16	15	15	17	16	10	15	2	15	17	17	15	16	17	10	12	11	15	17	15	15	17	16
Rajasthan	3	12	7	1	1	1	9	7	12	4	5	5	3	4	2	7	9	10	1	1	1	12	12	13	2	3	4
Tamil Nadu	2	1	2	7	5	7	8	8	9	11	16	10	12	15	14	13	17	15	11	13	13	7	13	10	11	14	12
Uttar Pradesh	11	15	14	11	12	14	12	14	13	3	8	13	10	12	13	6	8	8	12	14	14	8	14	14	10	13	13
West Bengal	13	14	15	12	15	11	14	16	14	15	17	15	11	13	12	2	3	2	14	17	16	13	8	3	14	15	14

Source: The author.

Note: Upper panel shows the AESI and sub-index-wise scores of states and lower panel shows the corresponding ranks.

Rankings of States in AESI During 2000s

Except Andhra Pradesh (AP), Goa, Kerala, Punjab, WB and Odisha, performances of all other states have deteriorated during 2000s, as compared to 1990s. Performance deterioration is the most prominent for Gujarat, Bihar, TN and UP. During 2000s, Goa, Odisha, Rajasthan, Jharkhand, MP and Chhattisgarh are the six best performing states. Punjab, Haryana, WB, TN, UP and Gujarat are the worst performing states during 2000s. Punjab and Haryana continue to be the laggards. However, performances of Gujarat and MP have deteriorated during 2000s. Except in Sustainable Livestock Index (SLI), performance of Gujarat has deteriorated in all other sub-indices of AESI during 2000s. For MP, considerable fall in performance observed in SLUI and SII. Performance of Goa has improved considerably during 2000s as compared to 1990s.

Rankings of States in AESI During 2010s

Except Gujarat, Punjab, TN and WB, all other states have recorded lower AESI score during 2010s as compared to 2000s. Goa, Jharkhand, Odisha, Rajasthan, Chhattisgarh and MP are the six best performing states during 2010s. Haryana, Punjab, Bihar, WB, UP and TN are the six worst performing states during the period. Performances of Bihar, AP and Kerala have deteriorated considerably during 2010s. Except in Sustainable Cropping Index (SCI) and SFMI, performance of Bihar has deteriorated during 2010s in all other sub-indices of AESI. Performance of Gujarat has improved marginally during 2010s. The analysis supports the hypothesis of temporal variations in AES. Regular assessment of environmental sustainability of Indian agriculture is important to capture the dynamic aspects of AES.

Our analysis shows that Goa, Jharkhand, Odisha and Rajasthan maintain their AES consistently during the period of our analysis. However, performances of Karnataka and MP have deteriorated over the periods. Similarly, the performance of Chhattisgarh has improved during 2010s. For middle-order states (having rankings 6 to 10), deterioration in performance in AES is observed for Bihar, Gujarat, UP during 2000s. Though Gujarat has improved the performance during 2010s, the others could not. Haryana, Punjab, TN and WB are consistently lagging behind other states in AES. Performance improvement of Kerala during 2000s and deterioration of AP during 2010s are worthy to highlight. The analysis shows that temporal and spatial variation AES across Indian states needs to be captured to make any attempt to improve performance of states. There is no distinct factor specific to location (e.g., semiarid/arid vs. alluvial) or natural resource endowments (e.g., access to water/irrigation) of a state which is determining the performance of the state. For example, when Bihar and WB are laggards, neighbouring states Jharkhand and Odisha are doing well in AES. When Rajasthan is one of the forerunners in AES, Gujarat is a middle order state. Similarly, when Karnataka and Kerala are middle order states, TN is a laggard.

Sensitivity Analysis

To understand sensitivity of the constructed AESI scores with respect to agricultural intensity, we have tested the relationship between ‘state-wise average productivity of foodgrains (in kg/ha)’—as an alternative measure of agricultural intensity—and AESI score. Figure 1 shows that there is an inverse relationship between AESI score and average productivity of foodgrains across periods of analysis. However, measuring statistical strength of the relationship requires further research. This implies that states are achveing higher productivity of foodgrains at the cost of agri-environmental sustainability. Agricultural activities are the decision of individuals and they individually may not know that their activities are detrimental for long-run sustainability of agriculture. Studies show that environmental awareness, availability of agricultural extension services and sharing agricultural information with farmers may influence individual farmer’s decision to adopt agricultural best management practices to protect agri-environment (Mukherjee 2008).

Agriculture is not just an economic activity for developing countries like India; it is source of food security and livelihoods. For populated country like India, self-sufficiency in food production is important for macroeconomic stability and overall economic development. To check whether there is any relationship between food security and AES, we have plotted AESI scores over ‘state-wise average share in India’s total foodgrains production’ in Figure 2. We found that there is an inverse relationship between the two for all the periods of our analysis. It implies that states having higher share in total foodgrains production also have lower AESI score. In other words, states that produce maximum foods are deteriorating their agri-environment. Decoupling food production from AES is a challenge not only for developing countries like India but also for developed countries (González de Molina et al. 2017; Yang et al. 2017). Therefore, better targeting of agri-environmental policies for states where maximum foodgrains are produced could help to achieve overall AES in India.

Impacts of Agriculture on Environment

The impacts of agriculture on environment and natural resources are multi-dimensional. To validate whether the constructed AESI and sub-indices reflect reality with reference to existing evidences of agri-environmental impacts, we have considered groundwater depletion and depletion of soil nutrients.

Depletion of Groundwater

Central Ground Water Board (CGWB) monitors groundwater levels across states/UTs in India through a network of monitoring wells. As on March 2017, there are 21,555 monitoring wells (15,482 dug wells and 6,073 piezometers) are located in 18 states in India (CGWB undated). Falling water level is observed for more than half of the monitoring wells located across all states (Table 3). Growing emphasis on groundwater based irrigation and providing free/subsidised electricity for agricultural uses in some states are the major causes of groundwater depletion in India.

Table 3.

Decadal Changes in Groundwater Level in Major States

Fall in Water Level	Pre-monsoon 2016 with Decadal Mean of Pre-monsoon (2006–2015)		August 2016 (Post-monsoon) with Decadal Mean of August (2006–2015)		November 2016 (Pre-monsoon) with Decadal Mean of November (2015–2016)a		January 2017 (Post-monsoon) with Decadal Mean of January (2007–2016)a
0–2 meters	6,408	(46.2)	5,159	(38.1)	6,043	(42.7)	6,397	(42.9)
2–4 meters	1,727	(12.5)	1,255	(9.3)	1,409	(10.0)	1,554	(10.4)
> 4 meters	1,025	(7.4)	787	(5.8)	908	(6.4)	1,049	(7.0)
Total	9,160	(66.1)	7,201	(53.2)	8,360	(59.1)	9,000	(60.3)
Total sample wells tested	13,864	(100)	13,539	(100)	14,152	(100)	14,923	(100)

Source: CGWB (undated).

Notes: Figures in the parenthesis show the percentage share in total sample wells tested.

^aFor states receiving retreating (northeast) monsoon, November is the pre-monsoon and January is the post-monsoon.

Depletion of groundwater is largely depends on agricultural withdrawal of water. Sustainable irrigation practices could help in sustainable management of groundwater resources. Figure 3 shows that states having higher score in Sustainable Irrigation Index (SII) are facing lower fall in groundwater level. This not only validates our methodology and selection of indicators for construction of SII but also supports the existence of a relationship between irrigation practices and groundwater management.

Depletion of Soil Nutrients

Under the National Mission for Sustainable Agriculture (NMSA), soil health management (SHM) is initiated to promote Integrated Nutrient Management (INM).³ Under this programme, soil testing is an integrated part, and in Cycle I (2015–2016 to 2016–2017) 14.97 million soil samples are tested for major Indian states (excluding North Eastern and Hilly States) till 23 January 2018. Out of these samples, 2.82 million (18.8%) samples have boron (B) deficiency, 0.63 million (4.2%) samples have copper (Cu) deficiency, 4.37 million (29.2%) samples have iron (Fe) deficiency, 1.75 million (11.7%) samples have manganese (Mn) deficiency, 3.86 million (25.8%) samples have sulphur (S) deficiency, 4.63 million (30.9%) samples have zinc (Zn) deficiency. In macronutrients, 86.6% samples have nitrogen (N) deficiency (very low and low), 21.7% samples have phosphorous (P) deficiency, 15.3% samples have potassium (K) deficiency, and 56.4% samples have organic carbon (OC) deficiency (Table 4). This shows that large-scale depletion of soil nutrients (both micro and macro) in India. Though farmers manage deficiencies in macronutrients by applying both chemical fertilisers and farmyard manure, compensating for micronutrient deficiencies is a major challenge for farmers, given the existing system of fertiliser subsidy. In the absence of sustainable soil management programme at the firm level, meeting India’s long-run agricultural productivity would be a challenge.

Table 4.

Nutrients Deficiency of Indian Soil

Macronutrient	Very Low	Low	Medium	High	Very High	Total Samples
Nitrogen (N)
Reference guideline (N kg/ha)	<140	140–280	280–560	560–700	>700
No. of samples (in lakh)	29.4	54.9	10.7	1.0	1.3	97.4
% of total samples	30.2	56.4	11.0	1.0	1.3
Phosphorous (P)
Reference guideline (P kg/ha)	<15	15–30	30–65	65–80	>80
No. of samples (in lakh)	21.3	11.2	65.0	21.6	30.4	149.5
% of total samples	14.3	7.5	43.5	14.5	20.3
Potassium (K)
Reference guideline (K kg/ha)	<120	120–180	180–300	300–360	>360
No. of samples (in lakh)	14.8	7.9	73.9	42.6	9.6	148.9
% of total samples	10.0	5.3	49.6	28.6	6.5
Organic carbon (OC)
Reference guideline (%)	<0.20	0.20–0.40	0.40–0.80	0.80–1.00	>1.00
No. of samples (in lakh)	9.6	73.8	25.8	27.3	11.5	148.0
% of total samples	6.5	49.9	17.4	18.4	7.8

Source: Data accessed from https://soilhealth.dac.gov.in/ (last accessed on 19 July 2019).

There are many aspects of AES that impact soil health. To assess the impact of sub-indices of AESI on state-wise soil nutrient deficiency, we present correlation between individual score of sub-indices and percentage of soil samples having macronutrients deficiency (very low and low) in Table 5. It shows that there are negative correlations across sub-indices and macronutrient deficiencies. This implies that states having higher scores in sub-indices of AESI have lower soil nutrient deficiencies. This finding supports that sub-indices are able to capture state of soil health across Indian states.

Table 5.

Correlation Among Sub-indices of AESI and State Level Macronutrient Deficiency for 2010s

Sub-indices of AESI	Percentage of Soil Samples Having Very Low and Low Nutrient Deficiencies
Sub-indices of AESI	Nitrogen (N)	Phosphorus (P)	Potassium (K)	Organic Carbon (OC)
Sustainable Land Use Index (SLUI)	0.07	−0.24	−0.33	−0.12
Sustainable Cropping Index (SCI)	−0.16	−0.42	−0.42	−0.13
Sustainable Irrigation Index (SII)	−0.26	0.00	−0.33	−0.40
Sustainable Livestock Index (SLI)	−0.54	0.21	0.01	−0.25
Sustainable Agro-chemical Index (SACI)	−0.13	−0.31	−0.65	−0.43
Sustainable Farm Mechanisation Index (SFMI)	−0.06	−0.26	−0.36	−0.57
Sustainable Forest Index (SFI)	−0.15	−0.17	−0.10	−0.59
Population Pressure on Land Index (PPLI)	−0.15	0.13	−0.14	0.24
Sustainable Agri-Environmental Index (SAEI)	−0.22	−0.19	−0.48	−0.37

Source: The author.

Conclusion and Policy Suggestions

The results of the present study show that different states have different strengths and weaknesses in managing various aspects of AESI. Therefore, a comprehensive assessment of AES better reflects the reality than individual indicator based assessment. The analysis shows that temporal and spatial variation AES across Indian states needs to be captured by regular assessment of environmental sustainability of Indian agriculture to make any attempt to improve performance of states. There is no distinct factor specific to location (e.g., semi-arid/arid vs. alluvial) or natural resource endowments (e.g., access to water/irrigation) of a state which is determining AES of the state.

There is an inverse relationship between AESI scores and agricultural intensity (as measured by average productivity of foodgrains). It implies that states are achieving higher productivity of foodgrains at the costs of their agri-environmental sustainability. Growing intensity agriculture to meet demands for food, fibre and fodder results in stress on environment and natural resources. We find an inverse relationship between AESI score and average share of a state in total foodgrains production. Decoupling food production from AES is a challenge not only for developing countries like India but also for developed countries. Better targeting of agri-environmental policies in states where maximum foodgrains are produced may help in achieving overall AES in India.

States having higher score in Sustainable Irrigation Index (SII) are facing lower fall in groundwater level. There are many aspects of AES that impact soil health. We find that there are negative correlations across AESI sub-indices and macronutrient deficiencies. These findings not only validate our methodology but also selection of indicators for the construction of AESI.

It would be important to adopt ‘precautionary principle’ and include agri-environmental sustainability as an objective in overall policies/programmes of the government. Integration agricultural policies with environment, water and land use policies could be the first step towards achieving sustainability in Indian agriculture. Agriculture is the predominant user of freshwater in India. Pricing of irrigation water is a contentious political issue. In the absence of proper pricing and recovery of water charges, true cost of water is not reflected and as a result water use efficiency of Indian agriculture remains low. In addition, many states provide free/subsidised electricity to farmers (e.g., Punjab, Haryana), as a result cost of abstraction of water becomes zero. High reliance on groundwater-based irrigation system and over extraction of groundwater has resulted in fall in groundwater level in many parts of India. Proper pricing of irrigation water and electricity for agricultural uses may help to reduce stress on Indian water resources.

Union government provides subsidy on fertilisers (urea, phosphorous, potash, city compost). Approximately 66% of the annual subsidy on fertiliser goes to urea (indigenous as well as imported) and the rest to other farm nutrients. Farmers do not pay actual price of fertilisers and since the fertiliser subsidy policy favours urea over other nutrients. Therefore, unbalanced and overuse of urea lead to runoff and leaching of nitrates into surface water bodies and groundwater, respectively. Application of nitrogen (N) fertilisers by foodgrain crops accounts for 69% of total consumption of N-fertiliser in India, out of which rice and wheat consume about 61% (Prasad 2011). Average nitrogen use efficiency varies from 21% to 33% whereas agronomic efficiency value is 4–17 kg grain/kg N in rice-wheat cropping system (Prasad 2011). It implies that two-thirds of applied N-fertiliser is lost in the environment. Improving fertiliser management practices—for example, crop-specific application of fertilisers after soil tests could increase fertiliser use efficiency. Recovery and recycling of farm nutrients (e.g., farm yard manures) may help to reduce dependence on chemical fertilisers.

Existing studies show that improving environmental awareness, availability of publicly funded agricultural extension services and sharing agricultural information with farmers may influence individual farmer’s decision to adopt agricultural best management practices to protect agri-environment. Linking agricultural produces procurement policy of the government with green certification of agricultural produces may encourage farmers to adopt sustainable agricultural practices.

Agriculture, being the primary activity, particularly for a large populated country like India, remains the most important part of the economy, with direct impact on people’s livelihood. Therefore, finding out environmentally sustainable solutions requires detailed understanding regarding space and people, located in major parts of the country. In this context, the state wise analysis of agri-environmental sustainability may not be the first best method to capture the environmental impacts of Indian agriculture. However, given the data constraints this analysis cannot be extended beyond state level at this time. There are several state-specific factors/policies (e.g., economic, social, institutional, political) which influence AES of states. A single framework may not necessarily capture all dynamic aspects of AES. It is expected that future research will identify state-specific factors/policies influencing performance of states in AES. In this article, for simplicity, the AESI is computed by taking equal weights of eight sub-indices, and indicators are given equal weights within each sub-index. The assessment of exact weights of components and indicators of AESI can be considered as a part of future research.

Supplemental Material

Supplemental material for this article is available online.

Supplemental Material for Agri-Environmental Sustainability of Indian Agriculture: A State Level Analysis by Sacchidananda Mukherjee, in International Journal of Rural Management

Footnotes

Acknowledgements

Research assistance provided by Trisha Chandra and Shivani Badola is gratefully acknowledged. Comments and suggestions received from Professor U. Sankar, Professor Amita Shah, Dr Kaushal Garg, Associate Editor and anonymous reviewer of the journal helped me to revise the article substantially. Usual disclaimers apply.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author received no financial support for the research, authorship and/or publication of this article.

Notes

ORCID iD

Sacchidananda Mukherjee

References

Astier

, Speelman

E. N.

, López-Ridaura

, Masera

O. R.

, and Gonzalez-Esquivel

C. E

. 2011. “Sustainability Indicators, Alternative Strategies and Trade-offs in Peasant Agroecosystems: Analysing 15 Case Studies from Latin America.” International Journal of Agricultural Sustainability 9(3): 409–22.

Barbier

E. B.

, and Hochard

J. P

. 2016. “Does Land Degradation Increase Poverty in Developing Countries?” PloS ONE 11(5): e0152973.

Binder

, Feola

, and Steinberger

. 2010. “Considering the Normative, Systemic and Procedural Dimensions in Indicator-based Sustainability Assessments in Agriculture.” Environmental Impact Assessment Review 30(2): 71–81.

Bockstaller

Christian

, Guichard

Laurence

, Makowski

David

, Aveline

Anne

, Girardin

Philippe

, and Plantureux

Sylvain

. 2008. “Agri-environmental Indicators to Assess Cropping and Farming Systems: A Review.” Agronomy for Sustainable Development 28(1): 139–49.

Brower

, Crabtree

, eds. 1999. Environmental Indicators and Agricultural Policy. Wallingford, UK: CABI Publishing.

Central Ground Water Board (CGWB). Undated. Ground Water Year Book—India 2016–17. Ministry of Water Resources, River Development and Ganga Rejuvenation, Government of India, Faridabad, Haryana.

Damerau

, Davis

K. F.

, Godde

, Herrero

, Springmann

, Bhupathiraju

S. N.

, Myers

S. S.

, and Willett

. 2020. “India Has Natural Resource Capacity to Achieve Nutrition Security, Reduce Health Risks and Improve Environmental Sustainability.” Nature Food 1: 631–9. https://doi.org/10.1038/s43016-020-00157-w.

Dillon

E. J.

, Hennessy

, and Hynes

. 2010. “Assessing the Sustainability of Irish Agriculture.” International Journal of Agricultural Sustainability 8(3): 131–47.

Eilers

, Mackay

, Graham

, and Lefebvre

. 2010. “Environmental Sustainability of Canadian Agriculture.” Agri-Environmental Indicators Report Series, Report No. 3, Agricultural and Agri-Food Canada, Ottawa, Canada.

10.

FAO. 2017. “A Literature Review on Frameworks and Methods for Measuring and Monitoring Sustainable Agriculture.” Draft Version 2, Rome: Food and Agriculture Organisation.

11.

Ghosh

, and Chakma

. 2019. “Composite Indicator of Land, Water and Energy for Measuring Agricultural Sustainability at Micro Level, Barddhaman District, West Bengal, India.” Ecological Indicators 102: 21–32.

12.

Girardin

, Bockstaller

, and van der Werf

. 2000. “Assessment of Potential Impacts of Agricultural Practices on the Environment.” Environmental Impact Assessment Review 20(2): 227–39.

13.

Gill

Singh S.

, and Singh

K. N

. 2018. “Subsidy and efficiency of groundwater use and power consumption in Haryana.” Economic and Political Weekly 53(50): 32–40.

14.

Gomez-Limon

J. A.

, and Sanchez-Fernandez

. 2010. “Empirical Evaluation of Agricultural Sustainability Using Composite Indicators.” Ecological Economics 69(5): 102–75.

15.

Molina

González de M.

, Fernández

D. S.

, Infante-Amate

, Aguilera

, Traver

J. V.

, and Guzmán

G. I

. 2017. “Decoupling Food from Land: The Evolution of Spanish Agriculture from 1960 to 2010.” Sustainability 9(12): 2348. doi:10.3390/su9122348.

16.

Hajkowicz

, Collins

, and Cattaneo

. 2008. “Review of Agri-environment Indexes and Stewardship Payments.” Environmental Management 43(2): 221–36.

17.

Hatai

L. D.

, and Sen

. 2008. “An Economic Analysis of Agricultural Sustainability in Orissa.” Agriculture Economics Research Review 21: 273–82.

18.

Hayati

Dariush

, Ranjibar

Zahra

, and Karami

Ezatollah

. 2011. “Measuring Agricultural Sustainability.” In Biodiversity, Biofuels, Agroforestry and Conservation Agriculture, edited by Eric Lichtfouse, Chapter 2, pp. 73–100. Dordrecht, The Netherlands: Springer.

19.

Huffman

, Eilers

R. G

, Padbury

, Wall

, and Macdonald

K. B

. 2000. “Canadian Agri-environmental Indicators Related to Land Quality: Integrating Census and Biophysical Data to Estimate Soil Cover, Wind Erosion and Soil Salinity.” Agriculture, Ecosystems and Environment 81(2): 113–23.

20.

Kareemulla

, Venkattakumar

, and Samuel

Manoj O

. 2017. “An Analysis on Agricultural Sustainability in India.” Current Science 112(2): 258–66.

21.

Kelly

, Latruffe

, Desjeux

, Ryan

, Uthes

, Diazabakana

, Dillon

, and Finn

. 2018. “Sustainability Indicators for Improved Assessment of the Effects of Agricultural Policy Across the EU: Is FADN the Answer?” Ecological Indicators 89: 903–11.

22.

Koohafkan

, Altieri

M. A.

, and Gimenez

E. H

. 2012. “Green Agriculture: Foundations for Biodiverse, Resilient and Productive Agricultural Systems.” International Journal of Agricultural Sustainability 10(1): 61–75.

23.

Królczyk

Jolanta B.

, and Latawiec

Agnieszka E

. 2015. “Sustainability Indicators for Agriculture in the European Union.” In Sustainability Indicators in Practice , edited by Agnieszka Latawiec and Dorice Agol, Chapter 10. Warsaw, Poland: De Gruyter Open.

24.

Langeveld

J. W. A

, Verhagen

, Neeteson

J. J.

, van Keulen

, Conjin

J. G

, Schils

R. L. M.

, and Oenema

. 2007. “Evaluating Farm Performance Using Agri-environmental Indicators: Recent Experience for Nitrogen Management in the Netherlands.” Journal of Environmental Management 82(3): 363–76.

25.

Lewandowski

, Härdtlein

, and Kaltschmitt

. 1999. “Sustainable Crop Production: Definition and Methodological Approach for Assessing and Implementing Sustainability.” Crop Science 39(1): 184–93.

26.

Lopez-Ridaura

, van Keulen

, van Ittersum

, and Leffelaar

. 2005. “Multi-scale Methodological Framework to Derive Criteria and Indicators for Sustainability Evaluation of Peasant Natural Resource Management Systems.” Environment, Development and Sustainability 7(1): 51–69.

27.

Mukherjee

. 2008. “Economics of Agricultural Nonpoint Source Water Pollution: A Case Study of Groundwater Nitrate Pollution in the Lower Bhavani River Basin, Tamil Nadu.” Unpublished PhD thesis, University of Madras ( Madras School of Economics ), Chennai, Tamil Nadu.

28.

Mukherjee

. 2020. “Agri-environmental Sustainability of Indian States during 1990–91 to 2013–14.” NIPFP Working Paper No. 290. New Delhi: National Institute of Public Finance and Policy ( NIPFP ), January 2020.

29.

Mukherjee

, and Chakraborty

. 2009. “Environment, Human Development and Economic Growth: A Contemporary Analysis of Indian States.” International Journal of Global Environmental Issues 9(1&2): 20–49.

30.

Mukherjee

, and Kathuria

. 2006. “Is Economic Growth Sustainable? Environmental Quality of Indian States after 1991.” International Journal of Sustainable Development 9(1): 38–60.

31.

OECD. 2013. “OECD Compendium of Agri-environment Indicators.” Paris, France: OECD Publishing. Available at http://dx.doi.org/10.1787/9789264181151-en ( last accessed on 19 July 2019 ).

32.

OECD. Undated. “Glossary of Statistical terms: Environmental Debt.” Available https://stats.oecd.org/glossary/detail.asp?ID=820> (last accessed on 9 June 2016 ).

33.

Prasad

. 2011. “Nitrogen and Foodgrain Production in India.” Indian Journal of Fertilisers 7(12): 66–76.

34.

Rasmussen

L. V.

, Bierbaum

, Oldekop

J. A.

, and Agrawal

. 2017. “Bridging the Practitioner-researcher Divide: Indicators to Track Environmental, Economic, and Sociocultural Sustainability of Agricultural Commodity Production.” Global Environmental Change 42: 33–46.

35.

Rockström

, Steffen

, Noone

, Persson

Å.

, Chapin

F. S.

, Lambin

, Lenton

T. M.

, Scheffer

, Folke

, Schellnhuber

, Nykvist

, De Wit

C. A.

, Hughes

, van der Leeuw

, Rodhe

, Sörlin

, Snyder

P. K.

, Costanza

, Svedin

, Falkenmark

, Karlberg

, Corell

R. W.

, Fabry

V. J.

, Hansen

, Walker

, Liverman

, Richardson

, Crutzen

, and Foley

. 2009. “Planetary Boundaries: Exploring the Safe Operating Space for Humanity.” Ecology and Society 14(2): 32.

36.

Roy

, and Chan

N. W

. 2012. “An Assessment of Agricultural Sustainability Indicators in Bangladesh: Review and Synthesis.” The Environmentalist 32(1): 99–110.

37.

Sajjad

, Nasreen

, and Ansari

S. A

. 2014. “Assessing Spatiotemporal Variation in Agricultural Sustainability Using Sustainable Livelihood Security Index: Empirical Illustration from Vaishali District of Bihar, India.” Agroecology and Sustainable Food Systems 38(1): 46–68.

38.

Sands

Gary R.

, and Podmore

Terence H

. 2000. “A Generalised Environmental Sustainability Index for Agricultural Systems.” Agriculture, Ecosystems and Environment 79(1): 29–41.

39.

Shah

Amita

. 2012. “Agriculture and Environment in India: Policy Implications in the Context of North-South Trade.” In Environmental Scenario in India : Successes and Predicaments, edited by S. Mukherjee and D. Chakraborty, Chapter 12, pp. 219–42. UK: Routledge.

40.

Sharma

, and Shardendu

. 2011. “Assessing Farm-level Agricultural Sustainability over a 60-year Period in Rural Eastern India.” The Environmentalist 31(3): 325–37.

41.

Srivastava

, Singh

, Tripathi

, and Raghubanshi

A. S

. 2016. “An Urgent Need for Sustainable Thinking in Agriculture—An Indian Scenario.” Ecological Indicators 67: 611–622.

42.

United Nations Development Programme (UNDP). 2019. “Human Development Report 2019—Beyond Income, beyond Averages, beyond Today: Inequalities in Human Development in the 21st century.” UNDP: New York.

43.

van der Werf

H. M. G.

, and Petit

. 2002. “Evaluation of the Environmental Impact of Agriculture at the Farm Level: A Comparison and Analysis of 12 Indicator-based Methods.” Agriculture, Ecosystems and Environment 93(1-3): 131–45.

44.

Vandermeulen

, and Huylenbroeck

G. V

. 2008. “Designing trans-disciplinary research to support policy formulation for sustainable agricultural development.” Ecological Economics 67(3): 352–361.

45.

Veluguri

, Ramanjaneyilu

G. V.

, and Jaacks

. 2019. “State-wise Report Cards on Ecological Sustainability of Agriculture in India.” Economic and Political Weekly 54(26-27): 19–27.

46.

von Wirén-Lehr

. 2001. “Sustainability in Agriculture: An Evaluation of Principal Goal-oriented Concepts to Close the Gap between Theory and Practice.” Agriculture, Ecosystem and Environment 84(2): 115–29.

47.

Wilson

G. A.

, and Buller

. 2001. “The Use of Socio-economic and Environmental Indicators in Assessing the Effectiveness of EU Agri-environmental Policy.” European Environment 11: 297–313.

48.

Xavier

António

, Freitas de Belém

C. M.

, Fragoso

, and Rosário do Socorro

. 2018. “A regional composite indicator for analysing agricultural sustainability in Portugal: A goal programming approach”. Ecological Indicators 89(June 2018): 84–100.

49.

Yang

, Liu

, and Zhang

. 2017. “Decoupling Agricultural Nonpoint Source Pollution from Crop Production: A Case Study of Heilongjiang Land Reclamation Area, China.” Sustainability 9(6): 1024. https://doi.org/10.3390/su9061024.

50.

Zhen

Lin

, and Routray

Jayant Kumar

. 2003. “Operational Indicators for Measuring Agricultural Sustainability in Developing Countries.” Environmental Management 32(1): 34–46.

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