Measuring the agricultural sustainability of India: An application of Pressure-State-Response (PSR) model

Surendra Singh JATAV , Kalu NAIK

a Babasaheb Bhimrao Ambedkar University, Lucknow, 226025, India

b India Council of Agricultural Research (ICAR)-National Institute of Agricultural Economics and Policy Research, New Delhi, 110012, India

Keywords:Indicator approach Agro-climatic region Sustainable Development Goals(SDGs)Pressure-State-Response (PSR) model Environmental sustainability index Economic security index Social security index Agricultural sustainability index

A B S T R A C T

1.Introduction

Food security and environmental protection are two key areas where sustainable agriculture may contribute to advance global sustainable development (Singh, 2020a).Sustaining and improving both economic capability and life quality are central to the Sustainable Development Goals (SDGs), which aim to meet people’s needs over long term without causing irreversible harm to environment and renewable resources, while also reducing the use of nonrenewable resources (DeClerck et al., 2016).The SDG-2 (zero hunger), SDG-12 (responsible consumption and production), SDG-13 (climate action), and SDG-15 (life on land) all regard agriculture as a crucial component of sustainable development by the year 2030.Natural resources should be utilized more efficiently and waste should be cut down, if we want to increase overall output and productivity per unit of land.In addition, any harmful outcomes such as pollution, must be reduced or entirely eliminated from the system.

Changes in farm size (Jatav, 2021), altered land use patterns (Jatav et al., 2022), unsustainable agricultural policies(Concepción et al., 2008), natural disasters (Singh and Singh, 2019), environmental pollution, and climate change are some of economic, social, and environmental issues currently affecting agricultural systems (Ali and Erenstein, 2017).Nearly 11.00% of the world’s population (7.42 billion people) live in severe poverty, most of them reside in rural areas of Southern Asia and Sub-Saharan Africa (World Bank, 2018).Since 78.00% of poor people rely on agriculture and related businesses for income, agricultural development has the potential to greatly alleviate rural poverty.The World Bank (2007) found that Gross Domestic Product (GDP) growth in agricultural sector is at least twice that in other sectors in terms of poverty reduction, i.e., a 1.00% rise in GDP in any non-farm sector leads to a 1.00% reduction in poverty, whereas a 1.00% increase in agricultural sector reduces poverty by 2.00%.Hence, agricultural growth should be at the center of anti-poverty strategies in India (Mottaleb, 2018).

In terms of its contribution to India’s economic growth, agriculture is responsible for around 42.00% of the country’s total employment and 16.00% of GDP, and agriculture land accounts for 60.00% of the total land area(World Bank, 2016).Despite being a significant source of employment, the share of agriculture in GDP has been on decline over the last 60 years, going from around 54.00% in 1960-1961 to 16.00% in 2020-2021 (GoI, 2021).The major reasons for the decline include climate change, poor infrastructure, poor management of agricultural markets,farmers’ poor socio-economic conditions, inefficient state agricultural policy execution, and farmers’ lack of understanding about agricultural technology (Swami et al., 2018; Jatav, 2022; Jatav et al., 2022).

Now, India is facing a four-dimensional agricultural crisis of declining farm income, rising farm expenditure,depletion of natural resources, and impaired ago-biodiversity (GoI, 2020; Singh, 2020a).Additionally, there is a positive correlation between average farm revenue and farm size (Chand et al., 2011).However, the percentage of big farms fell from 6.00% to 3.00%, while the percentage of small farms rose from 59.00% to 67.00% in India from 1991 to 2011 (GoI, 2021).According to the report of Food and Agriculture Organization (FAO, 2017), due to rapid urbanization and increased family wealth, the Indian diet is gradually diversifying.The percentage of cereal to overall food spending decreased from 52.00% in 1972 to 29.00% in 2006.The dynamics of food market system and food processing system is impacted by dietary change towards non-staple food industry in India.The consumption of fertilizer is 165.00 kg/hm2in India, higher than the global average of 138.00 kg/hm2, indicating that fertilizer is being used excessively and inefficiently in India, which leads to insect infestation, soil contamination, and crop nutrition issues (World Bank, 2016; FAO, 2019).Multiple food insecurity is a serious issue, India has a 14.50% prevalence of undernourishment and a 37.90% prevalence of stunting among children under 5 years old, despite concerted efforts to improve nutritional security, which have significantly reduced the population suffering from food insecurity (FAO,2019).

Researchers in the field of sustainable agriculture are attempting to figure out how to rise crop yields while minimizing negative impacts on the natural environment via the use of green technologies (Campbell and Garmestani,2012; Jatav et al., 2022; Singh, 2020a).Environmentalists, ecologists, industrialists, scientists, government officials,and economists are all paying close attention to the term “sustainability”, as it applies to agriculture right now(Campbell and Garmestani, 2012).Sustainability in agricultural ecosystem is crucial for meeting the global need for food, protecting environment, bolstering economy, and fostering positive social change.

The Organisation of Economic Cooperation and Development (OECD) introduced the Pressure-State-Response(PSR) model for addressing the problem of systematic identification of indicator, and it is the most widely accepted model for measuring sustainability (Woodhouse et al., 2000; Suresh et al., 2022).The concept of PSR model relies on the principal of causality, i.e., human activities put pressure on environment and change its state, and eventually evoke human response.Accordingly, PSR model has three important dimensions: pressure, state, and response.The pressure in PSR model refers to the effect of human activities on environment such as changes in environment quality or state.Other models like Driver-Pressure-State-Response (DPSR) framework and Driver-Pressure-State-Impact-Response (DPSIR) framework, can be considered as variants of PSR model.Figure 1 shows the schematic representation of DPSIR framework.

With a growing sustainability knowledge, sustainability indicators are commonly employed in sustainable agriculture models (Yigitcanlar et al., 2015) and can be quantifiable and measurable for a system related to sustainability (Pannell and Schilizzi, 1999).However, the indicator approach has several shortcomings for estimating sustainability (Saisana and Tarantola, 2002; Gómez-Limón and Sanchez-Fernandez, 2010).In this study, we identified agricultural sustainability indicators following DPSIR framework, taking account of environmental, economic, and social dimensions.

Knowledge and evaluation of agro-climatic regions are crucial for agricultural sustainability management and agricultural resilience; but there is a dearth of sources that used to evaluate agricultural output in most common agroclimatic regions of India.Several agricultural development initiatives throughout India have failed or underperformed,due to a lack of attention to regional issues of sustainability (Singh and Nayak, 2020).In the late 1990s, the Planning Commission of India conducted a prominent regional agricultural output assessment, in which the Indian subcontinent was divided into 15 mainstream agro-climatic regions based on its physical characteristics, topography, soil,geographical formation, rainfall pattern, cropping system, irrigation development, and mineral resources.By integrating agro-climatic areas, plans, and policies with state and national plans, agro-climatic zoning aims at maximizing the synergy effect between technology-led development and resource use efficiency (Singh et al., 2021).

Due to a lack of information, the island zone was not included in the estimation, hence, this study covers only 14 mainstream agro-climatic regions of India, involving 33 states and 674 districts.The 14 mainstream agro-climatic regions are Western Himalayan Region (WHR), Eastern Himalayan Region (EHR), Central Plateau and Hills Region(CPHR), Eastern Plateau and Hills Region (EPHR), Upper Gangetic Plains Region (UGPR), Western Dry Region(WDR), Middle Gangetic Plains Region (MGPR), Gujarat Plains and Hills Region (GPHR), East Coast Plains and Hills Region (ECPHR), Trans-Gangetic Plain Region (TGPR), Lower Gangetic Plain Region (LGPR), West Coast Plains and Ghats Region (WCPGR), Southern Plateau and Hills Region (SPHR), and Western Plateau and Hills Region (WPHR).This study aims to examine the agricultural sustainability of the 14 mainstream agro-climatic regions of India, covering three dimensions: environment, society, and economy.Hence, robust and reproducible agricultural sustainability index is calculated at regional and district levels.

Fig.1.Driver-Pressure-State-Impact-Response (DPSIR) framework of sustainability.

2.Methods and materials

Composite indices have been extensively used in studying sustainable agriculture development since they are valuable tools for policy-making (Jatav et al., 2022).In this study, we developed a new composite index to measure agricultural sustainability, i.e., agricultural sustainability index.

2.1.Selection of indicators

The first stage in creating the composite agricultural sustainability index is selecting specific sustainability indicators.A total of 29 agro-ecological indicators were used to assess the agricultural sustainability status of different mainstream agro-climatic regions of India using district-level secondary data.The availability of comparable secondary data is an important consideration when selecting indicators.

There are three stages involved in selecting the individual indicators of this study.First, we compiled a set of indicators from studies on agricultural sustainability measurement.Second, we used consolidation method to merge and integrate terminology.Third, we chose the 29 indicators shown in Table 1 based on four criteria: simplicity,importance to sustainability, measurability, and availability of data.The selected 29 indicators of agricultural sustainability include 10 indicators in environmental dimension, 11 indicators in economic dimension, and 8 indicators in social dimension (Table 1).

Table 1Components and indicators of agricultural sustainability.

2.1.1.Indicators of environmental sustainability

The 10 indicators of agricultural sustainability in environmental dimension, which constitute the environmental sustainability index of this study, are as follows.

(1) Area under forest.Forest provides several useful ecosystem services for agriculture like regulating climate and water cycle, regulating carbon dioxide and oxygen content, and contributing to carbon sequestration (Singh and Nayak, 2020).Forests provide services such as supply of products (both timber and non-timber), food and livelihood sources for people, especially for marginalized section of society and tribal communities, thereby minimizing pressure on cultivated land.Besides, the modern crop breeding programme is founded on the genetic diversity of forest, which is the source of genetic materials.The share of area under forest to total geographical area was only 17.22% in India in 2019.

(2) Agricultural land use intensity.The pressure of food production triggers land conversion, which is mostly from ecologically sensitive lands to cultivation land.Land conversion from natural ecosystems has destructive impacts on ecosystem services, as in the case of land conversion from tropical forest and temperate grassland to agriculture land(Foley et al., 2005; Rodrigues et al., 2013).The increased monoculture and mono-cropping result in the loss of productivity.Under this background, land intensification, which is measured as a percentage of net sown area to total geographical area, is assumed to have a negative influence on agricultural stability.At the national level, the agricultural land use intensity was about 51.37% in 2019.

(3) Agricultural chemical use intensity.The increased cereal production in the world over the past 50 years has resulted from an increased application of agricultural inputs like water, pesticides, and nutrients (Matson et al., 1997).The increased production by intensified agriculture is also accompanied by the degradation of land and water and non-point source of pollution, constraining the growth of agricultural production and agricultural stability (Pingali,2012; Sun et al., 2012).The major negative impacts of chemical utilization on environment include the eutrophication of surface water (particularly freshwater streams and coastal seas), loss of biodiversity, degradation of water quality,depletion of ozone layer, and acidification of soil (MacDonald et al., 2011).In addition, toxicity to non-target organisms and humans is also a major risk of elevated pesticide use (Henry et al., 2012).In this study, we used the application amount of chemical fertilizer like nitrogen (N), phosphorous (P), and potassium (K) per hectare to depict agricultural chemical use intensity, which was 133.44 kg/hm2in India in 2016 and assumed to have a negative effect on agricultural sustainability.

(4) Groundwater depletion.From an environmental sustainability perspective, the expansion of irrigated area,particularly groundwater-based irrigated farmland has led to the degradation of water resources and soil deterioration(Shah, 2008).Therefore, we hypothesized that groundwater depletion has a negative effect on agricultural sustainability in this study.Groundwater has dropped to alarming levels in many parts of India.According to the MoAFW (2019), the groundwater-based irrigated area accounted for 54.32% of the total irrigated area in India in 2019.

(5) Livestock ownership.The livestock industry is an important part of the global food system, contributing to poverty alleviation, food security, and the growth of agricultural production.In 2019, livestock industry generated 40.00% of worldwide agricultural outputs and supported the livelihood and food and nutrition security of about 1.30 billion people (FAO, 2019).Further, livestock industry plays an important role in sustainable food systems.For example, manure is an important source of natural fertilizer, and livestock can be utilized as draught animal in areas where automation is limited.For vulnerable communities, livestock is valuable resource.The recent agriculture situation assessment data collected by National Sample Survey Organization (NSSO) show that 75.58% of farmers owned livestock to diversify their income sources in India in 2019.

(6) Rainfall variability.We used the coefficient of variation of rainfall during the preceding decades (1991-2020)to estimate rainfall variability, which is assumed to have a negative effect on agricultural sustainability.Climate change, manifesting as short-term variability of weather variables, exacerbates the threat of food insecurity among many farming communities (Singh, 2020a).The negative consequences of rainfall variability on agriculture include reduced productivity, increased incidence of crop disease, and drastic reduction in soil fertility (Gadgil and Gadgil,2006).According to Kavi Kumar and Viswanathan (2015), the monsoon variation, particularly those resulting in severe drought, caused approximately 2.00% to 5.00% reduction of Indian GDP.

(7) Minimum temperature variability and maximum temperature variability.Anthropogenic induced climate change has been realized all over the world and has resulted in the increase of global surface temperature by 0.85°C over the past 100 years.According to the report of Intergovernmental Panel on Climate Change (IPCC, 2014), the global surface temperature is predicted to increase further by at least 1.50°C by the end of the 21stcentury.Poverty and disadvantage have increased with recent warming and are expected to increase as average global temperature increase from 1.00°C to 1.50°C and higher (IPCC, 2018).In this study, we estimated the variability of minimum and maximum temperatures by the coefficient of temperature variation during 1991-2020, and hypothesized that both of the indicators have negative effects on agricultural sustainability.

(8) Cropping intensity.High cropping intensity promotes integrated farming and better resource usage and imparts resilience.It also contributes to nutrient recycling and climate regulation, and is considered to have a positive effect on environment (Cassman, 1999).Further, it avoids the necessity for clearing the forest lands.Cropping intensity increases consistently at the national level, and it was 139.35% in 2019.

(9) Farmers’ perception on natural calamities.Analysis of farmer’s perception on climate change is a prerequisite for assessing adaptation and achieving agricultural sustainability.The literature on climate change perception has clearly identified the important of timing and types of climate change instances that farmers usually observe and utilize to frame their perceptions (Singh, 2020a, c).Weather extremes are uncertain, so farmers need to act instantaneously to avoid losses.Decision making by farmers under such circumstances is quite difficult (Jatav and Singh, 2023), as the time lag between gathering and processing information and decision-making is quite short (Jatav,2022).The results show that about 64.63% of Indian farmers perceived that natural calamities destroy crops in 2019.

2.1.2.Indicators of economic security

The 11 indicators of agricultural sustainability in economic dimension, which make up the economic security index of this study, are as follows.

(1) Human-land ratio.The carrying capacity of agricultural system determined by the rural population on per unit net cropped area, is a pressure variable.Population pressure triggers agricultural intensification (Boserup, 2005).A high human-land ratio coupled with low productivity leads to disguised unemployment.At the national level of India,the human-land ratio reached 21 persons/hm2of net sown area in 2019.

(2) Per capita income.Per capita income is an indicator of living standard and has a significant influence on access to inputs and resources (Singh and Nayak, 2020).It also influences access to health care and educational facilities(Nayak and Jatav, 2023).Per capita income consists of income from agriculture, industry, and service sectors.We hypothesized that per capita income has positive association with agricultural sustainability in this study.At the national level of India, the per capita income is 689.63 USD in 2013.

(3) Irrigation intensity.Irrigation helps to augment productivity and farm income with a reduction in risk (Singh and Nayak, 2020).Irrigation development is limited by water availability, capital constraint, and technological feasibility nowadays, therefore, high irrigation efficiency is desired.At the national level of India, irrigation intensity was 119.13% in 2019.

(4) Road transport.The main role of transport in agriculture is to deliver agricultural products from farms to markets or cities worldwide.Proper logistics is the key to managing the assets or goods from the place of origin to consumers.In many parts of the world, farmers and producers live far away from the places of distributing agricultural products,which means that many of the supplies have to be transported to the locations of collection to be stored or simply sold(Singh and Nayak, 2020).At the national level of India, 59.20% of villages had access to road in 2011.

(5) Institutional credit.Credit is one of the critical inputs for agricultural development (Jatav and Nayak, 2022),providing funds to farmers to undertake new investments or adopt new technologies.The importance of agricultural credit is further reinforced by the unique role of Indian agriculture in the macroeconomic framework along with its significant role in poverty alleviation.Realizing the importance of agricultural credit in fostering agricultural growth and development, the institutional framework for agricultural credit is emphasized since the beginning of planned development era of India.Farmers depend on a multitude of sources of credit to meet short-term crop loans or longterm farm investment loans.Despite several attempts by the government to channelize institutional credit for agricultural sector (Kumar et al., 2015), many farmers still continue to avail credit from non-institutional sources often at a usurious rate of interest such as money lenders.At the national level of India, 51.41% of farmers took credit from institutional sources in 2019.

(6) Food grain productivity.Maintaining the growth of food grain production above the population growth is important to meet the increased food demand.A negative trend in food grain productivity leads to inadequate domestic production, high food-price inflation, and negative impacts on food and nutritional security.The growth of food grain production higher than the population growth is essential to maintain food availability, which is one of the components of food security (Jatav, 2022).At the national level of India, the value of food grain productivity was 2.49 t/hm2in 2019.

(7) Crop diversification.The term “crop diversification” is used to describe the practice of increasing a farm’s agricultural outputs by adding additional and non-traditional crops or cropping systems (Jatav, 2022).The aim of crop diversification is to increase crop portfolio to avoid farmers relying on a single crop to generate income.Crop diversification can be one of the important technologies in increasing the farmers’ income to a certain extent, and also an important stress-relieving option for economic growth of farming community.In states such as Punjab, Haryana and Western Uttar Pradesh, where the first Green Revolution took place, agricultural diversification seems to be a priority.At the national level of India, we observed that only 13.55% of farmers planted more than one crop in 2016.

(8) Aware of minimum support price.In absence of procurement, a farmer can refuse to settle price below the minimum support price, if he knows the minimum support price (Chand, 2003).If he does not know the minimum support price of crops, traders and middlemen will turn to be exploitative and offer price less than the minimum support price (Chand, 2008).At the national level of India, only 19.59% of farmers were aware of the minimum support price of crops in 2019.

(9) Working in the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA).The goal of MGNREGA is to provide a strong safety-net for vulnerable groups by providing a back-up employment chance when alternative employment chances are scare or inadequate.MGNREGA is not only an act against unemployment, but also an act to guarantee the sustainable development in all fields, including sustainability of economy, agriculture,forest, health care, and many more ideals and principles of sustainable development (Singh, 2013).At the national level of India, 45.69% of farmers worked in MGREGA in 2019.

(10) Agriculture training.Agriculture training centers such as Kisan Vikas Kendra (KVK) play a key role in disseminating agricultural knowledge, technologies, and information, as well as in linking farmers with other stakeholders in India.KVK, which frequently offers in-service training to public sector employees, training to farmers,and short courses on demand to people in public or private sectors in India, is a critical change agent in transforming traditional subsistence farming to modern commercial agriculture to improve household food security, rise income,and reduce poverty (Jatav, 2022).At the national level of India, the statistical results of agriculture training are not promising, only 1.20% of farmers took agricultural training from KVKs, agricultural university, or India Council of Agricultural Research (ICAR)-institutes in 2019.

(11) Tractor ownership.Modern farming developed through technological development such as tillage and harvesting machines, controlled irrigation, fertilizers, pesticides, crop breeding, genetics research, and biotechnological tools for trait improvement (Singh and Nayak, 2020).These innovation technologies help farmers dramatically increase yields of high-quality crops.At the national level of India, only 1.79% of farmers owned tractors in 2019, indicating that the development of modern farming still has a long way to go for India.

2.1.3.Indicators of social security

The eight indicators of agricultural sustainability in social dimension, which form the social security index of this study, are as follows.

(1) Literacy rate.Literacy enhances agricultural production by reducing information asymmetry, facilitating the flow of social capital, and improving technical efficiency (Singh, 2020a).In the transformation of a rural economy to a non-farm oriented economy, investment in education would have high return, as the educated person stands to gain more (Jatav, 2022).In this context, we hypothesized a positive effect of literacy rate on agricultural sustainability.At the national level of India, 74.04% of Indians were literate in 2011.

(2) Infant mortality rate (IMR).According to Ruttan (1992), health is a cause and effect of agricultural development.Infant mortality rate, which is a comprehensive variable that reflects the health of both child and mother, is a social outcome of the complex interaction of agricultural growth, food and nutritional security, availability of safe drinking water, and health care facilities.The IMR of children under 5 years old has shown a significant reduction from 80 children per 1000 births in 1991 to 54 children per 1000 births in 2019.

(3) Sex ratio.There is evidence that the traditional agriculture tends to prefer men over women, due to the physical power required for agricultural operation (Singh, 2020c).The sex ratio is influenced by several social and economic factors such as migration and labor participation in agriculture (Jatav and Singh, 2020).Gender empowerment is a main factor of agricultural growth.Sex ratio, expressed as number of females per 1000 males, can serve as an indicator of gender justice and social sustainability.As the census conducted in 2011, sex ratio of India was 945 women over 1000 men in 2011 (OGRI, 2011).

(4) Knowledge sharing.Knowledge sharing is an important practice to help farmers and other stakeholders in gaining more production.The results of agricultural situation assessment survey conducted by National Sample Survey Organization (NSSO) show that 21.14% of farmers in 2019 have taken advice from the progressive farmers on cropping pattern, modern technology, etc.

(5) Joint family.The cohesion of a society is bolstered by the flow of mutually beneficial collective activities that are generated by high levels of social capital.Norms, beliefs, and attitudes make individuals more likely to work together; trust, mutually beneficial relationships, and mutually agreed-upon and enforced regulations and consequences are all examples that social assets make up social capital.Farmers living in a joint family system are more capable to deal with any distresses and positively contribute to agriculture sustainability (Jatav, 2022).At all India level, 28.43% of farmers lived in joint family system in 2019.

(6) Working population.Knowledge about modern techniques provides an advantage to deal with the distresses faced by Indian farmers such as variability in rainfall and temperature and fluctuation in market prices (Jatav, 2022).Further, it is expected that an educated farmer is more aware of agriculture system and would be more capable to deal with any constraints.Likewise, young farmers with compact modern technology are likely able to utilize agriculture resources more efficiently and sustainably.At all Indian level, 56.52% of rural population aged 15-45 years old belonged to working population in 2019.

(7) Receiving remittance.A significant part of the debate about the impacts of remittance on sustainable development focuses on specific development indicators that measure poverty and inequality.Remittances tend to reduce poverty, albeit modestly, but the impact on inequality is uneven and sometimes negative.In some cases,increased investment in human capital associated with the receipt of remittance improves health and education outcomes.Financial inclusion has also recently been highlighted as a key tool for maximizing the impact of remittance on development.Overall, the ability of remittance to advance sustainable development is necessarily linked to the policy and investment environment (Singh and Nayak, 2020; Jatav and Nayak, 2022).At all India level, 68.47% of farmers in 2019 received remittance from family members working in non-farm sectors.

(8) Membership of agricultural credit society.The primary aim of agricultural credit society is to promote saving habits among its members.Besides, the agricultural credit society arranges the supply of agricultural inputs such as seeds, fertilizers, insecticides, etc., and helps its members by providing marketing facilities that could enhance the sale of agricultural products at proper prices in the market (Jatav, 2022).At all India level, 12.69% of farmers had membership of agricultural credit society in 2019.

2.2.Standardization of the value of indicator

We used indicator approach to standardize the data, as data are different and it is prerequisite to standardize the data before calculating agricultural sustainability index.The advantage of indicator approach is that researchers can create agricultural sustainability index at any level like person, home, village, district, state, or nation, even if they don’t have precise data.In addition, the indicator approach has a number of features that allow it to be widely used in the planning process and policy communication over the years.These features include the ability to consolidate a large volume of information into a manageable format, which makes it easy to comprehend and evaluate the current state of performance in complex and elusive fields that cannot be directly measured (Singh and Singh, 2019), to identify, prioritize, and rank the most important factors, and to communicate policy objectives and progress in a clear and concise manner (Singh and Nayak, 2020; Jatav et al., 2022).

Before calculating agricultural sustainability index, the property of data and the research objectives of the composite indicator should be taken into consideration in the standardize process.To standardize indicators into a common range (0, 1) based on their functional link with agricultural sustainability, this study utilized the min-max approach (Iyengar and Sudarshan, 1982).Equations 1 and 2 were adopted for larger-better-type and smaller-worsetype indicators, respectively.

whereZijis the standardized value ofithindicator in thejthagro-climatic region;Xijis the actual value ofithindicator in thejthagro-climatic region; and max(Xij) and min(Xij) are the maximum and minimum values ofithindicator in thejthagro-climatic region, respectively.

2.3.Assigning weight

Given the assignment of appropriate weight for different components is an important issue in the construction of an index, we adopted the statistical weight method (Iyengar and Sudarshan, 1982).

whereWiis the weight ofithindicator; and var(Zij) is variance of standardized value ofithindicator in thejthagroclimatic region.The calculated weights were used to construct the component indexPjfor thejthagro-climatic region using Equation 4.

Finally, the agriculture sustainability index for each agro-climatic region is calculated as an average of three components, i.e., environmental sustainability index, economic security index, and social security index.Based on the index scores, we ranked the agro-climatic regions in descending order.Agro-climatic region with higher index score indicates that it has greater agriculture sustainability.Further, we categorized the homogenous districts under each component into three groups: low (0.0%-33.0%), medium (34.0%-66.0%), and high (67.0%-100.0%) based on the quartile estimation by QGIS mapping software (Open Source Geospatial Foundation, Oregon, the United States)to capture the heterogeneity of agriculture sustainability status at district level.

3.Results

Due to its multifaceted nature, measuring agricultural sustainability is difficult.Given data constraints, this study provides a new suite of quantitative indicators for assessing agricultural sustainability at regional and district levels.

3.1.Environmental sustainability index

Among the mainstream 14 agro-climatic regions of India, WHR is more ecological sustainable, whereas SPHR is significantly less environmental sustainability (Table 2).According to the cross-indicator analysis, the main influencing factors for lower environmental sustainability in SPHR compared to WHR are less forest coverage, less livestock, and less cropping intensity.In WHR, the forest area accounts for 24.13% of total geographical area, whereas it accounts for just 16.60% in SPHR.Further, about 86.63% of farmers own livestock in WHR, while corresponding statistics for SPHR is only 70.00%.Irrigation intensity is 125.25% in WHR, while it is only 116.73% in SPHR.Similarly, farmers in SPHR utilize more chemical fertilizers than farmers in WHR.

Table 2Rank of the 14 agro-climatic regions in environmental dimension of agricultural sustainability.

3.2.Economic security index

The most crucial component of agricultural sustainability is the economic security index.Among the 14 mainstream agro-climatic regions of India, the results show that the farmers in TGPR have considerably better economic security,while those in EHR have substantially lower economic security (Table 3).The results of cross-indicator analysis reveal that, with exception of working in the MGNREGA, all other indicators in economic dimension of agricultural sustainability have better performance in TGPR than EHR.According to the data, the TGPR has a human-land ratio of 34 persons/hm2, whereas the ratio is just 12 persons/hm2in EHR.Moreover, the per capita income in TGPR was 839.16 USD, but it was just 222.85 USD in EHR in 2013.Irrigation intensity is also greater in TGPR 170.16%) than in EHR (64.18%).In 2011, about 90.00% of farmers have access to road in TGPR, but only 36.02% of farmers have access to road in EHR.Additionally, in TGPR, more than 55.00% of farmers obtain finance from institutional sources,while the proportion only reaches 35.25% in EHR.The food grain productivity of TGPR is also twice more than (4.13 t/hm2) that of EHR (1.68 t/hm2).Crop diversification is more than 10.00% in TGPR but just 3.31% in EHR.In TGPR,more than 38.20% of farmers are aware of the minimum support price, but in EHR, the percentage is just 4.75%.Farmers in TGPR are more economic secure than those in EHR.

Table 3Rank of the 14 agro-climatic regions in economic security dimension of agricultural sustainability.

3.3.Social security index

In India, social security is the most crucial pillar for agricultural sustainability and livelihood security.The results suggest that social security is comparatively high in SPHR, while the worst social security is in EHR (Table 4).

Table 4Rank of the 14 agro-climatic regions in social security dimension of agricultural sustainability.

According to the cross-indicator analysis, the main influencing indicators responsible for higher social security in SPHR compared to EHR include higher knowledge sharing among farmers, younger and larger working population,higher remittance from relatives, and relatively higher membership in agricultural credit societies.In SPHR, over 37.00% of farmers communicate with other farmers regarding farm management, while just approximately 9.00% of farmers consult with others in EHR.Also, it was discovered that more than 80.00% of people are between the age of 15 and 45 in SPHR, while the statistics result is just 36.00% in EHR.Approximately 20.00% of farmers are members of agricultural credit societies in SPHR, but just about 3.00% of farmers are in EHR.Farmers enjoy greater social security in SPHR than farmers in EHR and other agro-climatic regions.

3.4.Agricultural sustainability index

Among the 14 mainstream agro-climatic regions of India, TGPR gets the highest score of agricultural sustainability,while EHR gets the lowest (Table 5).While environmental sustainability index of EHR is rather high, social and economic security indices are quite poor, placing the region at the bottom of agricultural sustainability ranking.This shows how important it is to involve social and economic contexts in discussing agricultural sustainability.Given the complexity and diversity of Indian society, it is social structure that ultimately defines the country’s economic and ecological well-being.

Table 5Rank of the 14 agro-climatic regions of India in agricultural sustainability.

Higher livestock ownership, cropping intensity, per capita income, irrigation intensity, access of transportation facilities, share of institutional credit, food grain productivity, crop diversification, awareness of minimum support price, knowledge sharing with fellow farmers, young and working population, and remittance from relatives or friends, and better membership of agricultural credit societies are influencing indicators responsible for higher agricultural sustainability in TGPR compare with EHR.Except the disparity between TGPR and EHR in economic security dimension, the results of cross-indicator analysis show that more than 90.00% of farmers in TGPR own livestock, while only 57.19% of farmers in EHR own livestock; the cropping intensity in TGPR is 180.42%, while it is only 140.27% in EHR; the irrigation intensity is 170.16% in TGPR, while it is only 64.18% in EHR; moreover,33.92% of farmers in TGPR share their knowledge with fellow farmers, while corresponding figure is only 10.00%for EHR; lastly, more than 95.00% of farmers in TGPR receive remittance from their relatives working in urban areas,while the corresponding figure for EHR is only 3.00%.

According to our findings, all the indicators of agricultural sustainability vary widely at district level.Based on the score of agricultural sustainability index, we divided all the 674 districts into three categories: low (0.00%-33.00%),medium (34.00%-66.00%), and high (67.00%-100.00%).

About 21.07% of districts fall under high category of environmental sustainability, while only 13.20% fall under low category, and rest (65.73%) fall under medium category (Table 6).The majority of low environmental sustainability districts locate in the Andhra Pradesh (8 districts), Bihar (2 districts), Chhattisgarh (7 districts), Goa (1 district), Haryana (1 district), Kerala (1 district), and Uttar Pradesh (1 district), Gujarat (7 districts), Jharkhand (2 districts), Karnataka (12 districts), Maharashtra (24 districts), Punjab (5 districts), Tamil Nadu (6 districts), Telangana(8 districts), and West Bengal (4 districts) states, while the majority of high environmental sustainability districts belong to the Himalayan regions.

Table 6Category analysis of agricultural sustainability status of India at district level.

The results of economic security index show that economic security exist relatively higher heterogeneity across districts, states, and agro-climatic regions of India (Table 6).The calculated district-level scores of economic security index show that about 32.20% of districts fall under low economic security category, while only 8.16% of districts fall under high category.The majority of low economic security districts mainly locate in the Arunachal Pradesh (15 districts), Assam (27 districts), Bihar (15 districts), Chhattisgarh (12 districts), Daman and Diu (1 district), Gujarat (1 district), Jammu and Kashmir (19 districts), Jharkhand (16 districts), Karnataka (3 districts), Manipur (6 districts),Madhya Pradesh (12 districts), Maharashtra (10 districts), Meghalaya (7 districts), Mizoram (8 districts), Nagaland(11 districts), Orissa (12 districts), Rajasthan (4 districts), Sikkim (4 districts), Tripura (2 districts), Uttar Pradesh (9 districts), Uttarakhand (9 districts), West Bengal (9 districts) states, while the majority of high economic security districts belong to Bihar (2 districts), Himachal Pradesh (2 districts), Uttar Pradesh (2 districts), Chandigarh (1 district), Chhattisgarh (1 district), Delhi (1 district), Dadra and Nagar Haveli (1 district), Kerala (1 district), Gujarat(4 districts), Karnataka (4 districts), Haryana (6 districts), Punjab (14 districts), Rajasthan (3 districts), Telangana (3 districts), and Tamil Nadu (9 districts) states.

The calculated district-level scores of social security index show that about 25.67% of districts fall under low social security category, while only 18.25% of districts fall under high social security category.The majority of low social security districts belong to the states like Arunachal Pradesh (15 districts), Assam (22 districts), Goa (1 district),Kerala (1 district), Telangana (1 district), Gujarat (2 districts), Haryana (2 districts), Tripura (2 districts), Sikkim (4 districts), Tamil Nadu (4 districts), Karnataka (5 districts), Rajasthan (5 districts), Himachal Pradesh (6 districts),Jammu and Kashmir (6 districts), Madhya Pradesh (6 districts), Punjab (6 districts), Manipur (8 districts), Mizoram(8 districts), Nagaland (9 districts), Orissa (9 districts), Uttarakhand (9 districts), Meghalaya (11 districts), and Uttar Pradesh (24 districts), while the majority of high social security districts locate in the Chandigarh (1 district), Delhi(1 district), Dadra (1 district), Nagar Haveli (1 district), Goa (1 district), Uttar Pradesh (1 district), Jammu and Kashmir(1 district), Daman and Diu (2 districts), Chhattisgarh (3 districts), Puducherry (3 districts), Punjab (4 districts),Telangana (4 districts), Andhra Pradesh (5 districts), Gujarat (6 districts), Kerala (6 districts), Rajasthan (6 districts),Madhya Pradesh (6 districts), Orissa (7 districts), Bihar (8 districts), Karnataka (8 districts), Haryana (9 districts),Tamil Nadu (9 districts), Maharashtra (13 districts), and Jharkhand (18 districts) states.

Lastly, the calculated district-level scores of agricultural sustainability index show that about 27.89% of districts fall under low agricultural sustainability category, while only 5.49% of districts fall under high agricultural sustainability category, and the rest of districts (66.62%) fall under medium agricultural sustainability category.The majority of low agricultural sustainability districts belong to the Goa (1 district), Kerala (2 districts), Punjab (2 districts), Tripura (2 districts), Uttarakhand (2 districts), Rajasthan (3 districts), Sikkim (3 districts), Jharkhand (4 districts), Nagaland (4 districts), Madhya Pradesh (5 districts), Telangana (5 districts), Andhra Pradesh (5 districts),Tamil Nadu (6 districts), Manipur (7 districts), Gujarat (8 districts), Bihar (9 districts), Chhattisgarh (7 districts),Orissa (10 districts), Arunachal Pradesh (13 districts), Karnataka (13 districts), West Bengal (15 districts), Uttar Pradesh (19 districts), and Assam (21 districts) states, while the majority of high agricultural sustainability districts locate in the Bihar (1 district), Chandigarh (1 district), Dadra and Nagar Haveli (1 district), Daman and Diu (1 district),Gujarat (1 district), Puducherry (1 district), Punjab (1 district), Jharkhand (2 districts), Kerala (2 districts), Madhya Pradesh (2 districts), Himachal Pradesh (4 districts), Rajasthan (6 districts), Haryana (7 districts), Jammu and Kashmir(3 districts), and Tamil Nadu (3 districts) states.

4.Discussion

Using the PSR model, this study provides a set of guidelines for the long-term viability of agriculture in the mainstream 14 agro-climatic regions of India.To aid in the long-term prosperity of Indian agriculture, this model helps policymakers pinpoint pressing sustainability issues and provides a wide range of policy recommendations.Comparing with other research, our study emphasizes the following.First, the PSR model allows for both quantitative and qualitative answers to be included in sustainability evaluation (Thomson et al., 2008; Qi et al., 2020).Second, the PSR model is helpful in achieving SDGs.Pressure, state, and response assessment indicators are developed using the SDGs as a reference.SDGs can be more effectively monitored and improved with the use of such an indicator system,which allows policymakers to keep a close eye on the sustainability performance of agricultural sector.The adaptable design of PSR-based framework allows users simply modify its parts to meet their own purposes of sustainability assessment.Changing the framework’s goals, indicators, and assessment models to fit various agricultural sustainability initiatives is straight forward.Suresh et al.(2022) employed the PSR model to examine the relative contribution of environment, social, and economic indicators in agricultural sustainability in India and found that in developing countries, farm households’ decisions on livelihood transition are influenced by micro-level household and community assets.

Analyzing agricultural sustainability is essential for designing and assessing rural development initiatives.However,accurately measuring agricultural sustainability is complicated since it involves so many different factors, such as social, economic, and environmental factors.The results of present study, similar with Singh et al.(2021), show that in different agro-climatic regions, social and economic assets perform well, while due to rapid decline in natural resources such as forest and groundwater, agriculture is unsustainable for rural population.Mishra and Sahu (2014), Narayanan and Sahu (2016), Jatav and Sanatan (2022), and Jatav (2022) reported the importance of financial and natural assets,for example, access to credit sources, access to irrigation facilities, and area of land holding play decisive roles in agricultural sustainability.Likewise, the results of this study show that the farmers living in SPHR hold a lower level of natural assets, which should be given proper attention to reduce their vulnerability and improve adaptation measures to bring resilience in their farming and thereby in their livelihoods as well (Jatav and Singh, 2023).

The benchmark method of normalization has an advantage over other methods, as it helps in computing an index that can be compared across the agro-climatic regions and time periods (Chand et al., 2015; Singh, 2020a).The unitary normalization method provides only the relative status of sustainability in different regions, which may alter with the change in the value of reference unit used for normalization.The government may be interested to see how the sustainability improves over time and how it compares with any other regions in the country or outside.The results of present study are most suitable for agriculture development policy, as it covers mainstream agro-climatic regions of India, which have diverse agro-ecological settings, including desert, semi-arid, wet, rainfed, and flood prone.

This study contributes to the literature on agricultural sustainability in three different ways.First, it provides a methodological model, which integrates environmental, economic, and social dimensions by DPSIR framework.Second, it utilizes indicators that relate to policies, including national agriculture, forest, water, land use, livestock,and fertilizer pricing policies, and policies with respect to marketing, credit, and the incentives for crop diversification.The indicators included in this study are based on secondary data that have been considered in policy deliberations,which are easily available at national and sub-national levels and therefore, can be replicated for similar locations.Lastly, the previous attempts to assess the sustainability of agriculture in India consider a limited set of indicators,while this study includes diverse and robust indicators covering main three dimensions of sustainability (Saleth, 1993;Kareemulla et al., 2017).

Though agro-climatic regions are homogeneous, agriculture is a subject matter of state.Agriculture policies are in majority governed by states and vary from state to state based on different situations of each state.For instance, in the high farm productivity states like Punjab, Haryana, and Madhya Pradesh, state government provides power subsidies and free water from canal irrigation and purchases more farm products at remunerative prices from farmers compared with states like Bihar, Chhattisgarh, and Jharkhand (Gulati and Narayanan, 2003; Finance Commission of India, 2019).Further, organic farming is also a major component of agricultural sustainability, but it is only limited to a few northeastern states like Meghalaya, Mizoram, Tripura, and Assam (Singh et al., 2021).Furthermore, soil quality, the availability of water resources, topography, socio-economic characteristics of farmers, as well as availability and accessibility to extension services are also major determining factors for agricultural sustainability(Singh and Nayak, 2020).We developed agricultural sustainability index for different agro-climatic regions to capture grass-root level heterogeneity.

5.Conclusions, policy implications, and limitations

5.1.Conclusions and implications

Agricultural sustainability is a concept, which is difficult to define and measure.The term of “sustainability”reflects our understanding of systems that in continual transition, so sustainability should be approached as a concept to strive for, like social wellbeing, rather than an objective that can be measured with common analytical techniques.In this sense, it is more useful to be able to track the performance of core indicators towards sustainability than set specific targets to be achieved, although setting targets is often useful to identify levels of satisfaction.It is no different with tracking the performance of natural economy, in most cases, it is simple to know how to do and what direction does the economy trend to go.Although progress has been made in developing approaches for monitoring agricultural sustainability, considerable additional work remains to be done to better identify sustainable strategic, use core indicators, and integrate recommended procedures.In this direction, the present study provides a new suite of quantitative indicators to assess agricultural sustainability at regional and district levels, taking into account the limitations in available data.In this study, we discovered that the diversity of people’s means of subsistence fluctuate greatly across space and time.The results show that TGPR ranks first in agricultural sustainability among the 14 mainstream agro-climatic regions in India, while EHR ranks lowest.Although the environment sustainability of EHR is quite good, its scores on social and economic security indices are fairly low, putting it at the bottom of the rank of agricultural sustainability index among the 14 mainstream agro-climatic regions in India.

As this study shows, achieving agricultural sustainable development requires planners and decision-makers to have access to reliable information about agriculture and sustainability.To boost agricultural revenue, authorities should devise novel non-farm job opportunities.Integrating environmental and economic interests in a comprehensive manner is necessary to achieve the objectives of sustainable land and crop management.This requires that environmental concerns be given equal importance to economic performance in evaluating the impacts of technological innovations and that the reliable indicators of environmental performance and land quality be developed.The results of PSR model demonstrate the interconnections of spatial change among sustainability indicators, reveal prevalent trade-offs between economic, social, and environmental performance in agricultural production, and thus facilitate potential collaboration and coordination among policymakers who influence a wide range of topics, including food and sustainable agricultural policies, rural development, and environmental policies.

5.2.Limitations

Although this study makes an effort to assess agricultural performance in the context of SDGs by using robust and rational data of different agro-climatic regions of India and reasonable methodology (i.e., PSR model), it also offers some caveats that may help guide future research.First, although we adopted the statistical techniques to validate indicators in this work, other scientific approaches such as expert review, are equally legitimate.Second, indicators were normalized using the min-max approach, and weights were assigned using statistical weight method in this study, while the agricultural sustainability index may be normalized and weighted in a variety of ways, which can be investigated by future researchers.Third, sensitivity analysis, which may be done in many ways, including using econometric and regression strategies, is not undertaken in this study.Fourth, different multi-criteria decision analysis approaches should be used to compare the findings obtained from basic sum-weighted procedures.Fifth, the selected indicators are extracted using literature review in this study.We tried to use the most commonly and robust indicators;however, it should be noted that sustainability indicators are very dynamic like the concept of sustainability.Thus future researchers, depending on the scope of their research, can use other indicators to design and validate agricultural sustainability index.Lastly, this study does not capture the farm-level impacts, does not take into account more nuanced physical, chemical, and biological characteristics of agricultural systems, and is not able to use a continuous set of data to understand the time-series nature of advances in agricultural sustainability.

Authorship contribution statement

Surendra Singh JATAV: data curation, formal analysis, methodology, writing-original draft; Kalu NAIK:conceptualization, methodology, writing-review & editing.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The authors are thankful to the Dr.Naveen Prakash SINGH (Official Member of the Commission for Agricultral Costs and Prices, Ministry of Agriculture and Farmers Welfare, Government of India) and Prof.Sanatan NAYAK(Department of Economics, Babasaheb Bhimrao Ambedkar University) for their valuable suggestions and improvements on the previous draft of this article.