Agri-environmental Indicators Research Articles

Evaluation and selection of aromatic plants based on agri-environmental indicators

Evaluation and selection of aromatic plants based on agri-environmental indicators

How sustainable are agricultural practices in European Union countries? A spherical fuzzy logic based evaluation

ABSTRACT Climate change and population growth have heightened the need for a comprehensive framework to analyse the environmental impacts of agricultural practices. While traditional agricultural activities have detrimental environmental effects such as soil degradation, water pollution, and greenhouse gas emissions, sustainable agriculture is an innovative approach to minimizing humanity’s significant problems, such as depletion of natural resources, climate change, and biodiversity loss. In this study, a model is developed to evaluate the agri-environmental performance of countries. This model evaluates and ranks countries using the spherical fuzzy analytical hierarchy process and grey relational analysis methods within the European Union’s agri-environmental indicators framework. This study is among the pioneering studies to utilise the spherical fuzzy logic approach for determining the relative importance of agri-environmental performance criteria. The findings reveal that crucial factors such as the irrigable utilised agricultural area, the sales of pesticides per utilised agricultural area, and the share of main livestock types in total livestock units are critical to agri-environmental performance. In addition, in the implementation phase of the study, the performances of the European Union member countries are evaluated using the proposed model. This constitutes another contribution of the research. The results of this study indicate that Finland, Lithuania, and Sweden are the frontrunners in agri-environmental performance, while Italy, the Netherlands, and Malta rank lower. The model developed in this study has far-reaching implications for developing targeted environmental protection policies and advancing sustainable agricultural practices on a global scale.

Unveiling the Epoch: Exploring Pesticide Utilization and Trade Trends Globally and Regionally from 1990 to 2020

Over the past three decades, a rapid increase in pesticide usage has played a central role in transforming the landscape of modern agriculture. The objective of this research is to conduct a comprehensive analysis of changes in pesticide usage from 1990 to 2020, emphasizing the complexity of global trends, environmental impacts, and pesticide trade dynamics. This study utilizes agri-environmental indicators and relies on the latest statistical data from FAOSTAT in 2022. The applied research method is a quantitative approach based on secondary data.The research findings indicate that global pesticide usage reached 2.7 million tons in 2020, with an average of 1.8 kg/ha. Although reaching a stable level, pesticide trade experienced a significant increase, particularly in the disinfectant category. Over the past three decades, there has been an approximately 50 percent increase in pesticide usage, especially in herbicides. Per capita analysis shows a relatively stable consumption rate, although there is an increase in pesticide usage per unit of agricultural land. Regional levels reveal significant disparities, with Asia as the largest contributor, Europe implementing strict policies, the Americas having the highest usage rates, Oceania experiencing low trade, and Africa applying low usage rates. Some countries, such as Saint Lucia, exhibit high pesticide application rates per unit of agricultural land, and this variation requires in-depth analysis to understand influencing factors. To mitigate negative impacts, international cooperation, the development of sustainable technologies, and a profound understanding of pesticide applications are necessary. Therefore, more effective and sustainable agricultural policies need to be designed to protect the environment and human health.

Changes in agricultural nitrogen (N) balance of OECD countries and its causes and impacts

The Organization for Economic Co-operation and Development (OECD) developed soil surface nutrient balance and made it mandatory for member countries to report annual nutrient budgets since 1990. This study aimed to evaluate the status of nitrogen (N) management in member countries and to figure out why N surplus levels differ across countries and how they relate to other agri-environmental indicators, by analyzing the N budgets from 35 OECD countries over the last 30 years. Of the three factors determining N balance (agricultural land area, N input, and N output), agricultural land area decreased in most OECD countries, negatively affecting N balance reduction. However, OECD's average N balance highly decreased from 91 to 46 kg ha−1 over the last 30 years due to the decrease in N input through inorganic fertilizers and manure, especially in EU countries with high N input levels, while N output did not meaningfully change. In comparison, in Japan and Korea, the N balance slightly increased and they became the highest N balance country recently. A higher N balance led to lower N use efficiency and higher ammonia (NH3) and nitrous oxide (N2O) emission intensities. More densely populated countries with smaller agricultural land per capita (ranging from 0.03 to 0.47 ha capita−1) showed a higher N balance (228–80 kg ha−1), presumably due to higher N input for more agricultural production on limited land. The most densely populated countries among OECD members (Belgium, the Netherlands, Korea, and Japan) had similar N input levels. However, two EU countries had much higher N output than two Asian countries due to higher pasture production, which led to a lower N balance and higher N use efficiency. Therefore, highly populated countries with small arable land areas per capita might need multilateral efforts to alleviate agricultural N balance.

Nitrogen inputs by irrigation is a missing link in the agricultural nitrogen cycle and related policies in Europe

Irrigation, one of the 28 agri-environmental indicators defined in the European Common Agricultural Policy, is often neglected in agricultural nitrogen (N) budgets, while it can be a considerable source of N in irrigated agriculture. The annual N input from irrigation water sources (NIrrig) to cropping systems was quantified for Europe for 2000–2010 at a resolution of 10 × 10 km, accounting for crop-specific gross irrigation requirements (GIR) and surface- and groundwater nitrate concentration. GIR were computed for 20 crops, while spatially explicit nitrate concentration in groundwater was derived using a random forest model. We show that although GIR were relatively stable (46–60 km3 yr-1), the Nirrig in Europe increased over the 10-year period (184 to 259 Gg N yr-1), approximately 68 % of which occurred in the Mediterranean region. The main hotspots appeared in areas with both high irrigation requirements and high groundwater nitrate concentration, reaching up to averaged values of 150 kg N ha-1 yr1. These were mainly located in Mediterranean Europe (Greece, Portugal and Spain) and to a lesser extent in Northern Europe (The Netherlands, Sweden and Germany). By not including NIrrig, environmental and agricultural policies are underestimating the real extent of N pollution hotspots in European irrigated systems.

Model Application for Estimation of Agri-Environmental Indicators of Kiwi Production: A Case Study in Northern Greece

Due to the sensitivity of kiwifruit to soil water and nutrient availability, kiwi production is often associated with over-watering and over-fertilization, especially with nitrogen (N), resulting in increased environmental risks. Crop models are powerful tools for simulating crop production and environmental impact of given management practices. In this study, the CropSyst model was applied to estimate soil N budget and environmental effects of kiwi production, with particular regard to N losses, in two grower-managed kiwi orchards in northern Greece, involving two seasons and different management practices. Management options included N fertilization and irrigation. Model estimates were compared with yield and soil mineral N content (0–90 cm depths) measured three times within the growing season. Agri-environmental indicators were calculated based on the N budget simulation results to assess the environmental consequences (focusing on N losses and water use efficiency) of the different management practices in kiwi production. According to model simulation results, kiwifruit yield and N uptake were similar in both orchards. N losses to the environment, however, were estimated on average to be 10.3% higher in the orchard with the higher inputs of irrigation water and N fertilizer. The orchard with the lower inputs showed better water and N use efficiency. N leaching losses were estimated to be higher than 70% of total available soil N in both study sites, indicating potential impact on groundwater quality. These findings demonstrate the necessity for improved irrigation and N fertilization management in kiwi production in the area.

The Risk of Agricultural Land Abandonment as a Socioeconomic Challenge for the Development of Agriculture in the European Union

In this article, the risk of agricultural farmland abandonment was assessed with the use of a synthetic measure of socioeconomic problems as challenges to the quality of life in rural areas in the European Union. To determine the direction and dynamics of changes in farmland abandonment in the EU countries, variables based on EUROSTAT and FADN data were analyzed using Hellwig’s method, and data for 2010, 2013, 2016, and 2019 were compared. The EUROSTAT methodology for the agri-environmental indicator “risk of land abandonment” was adapted for the needs of this study. Agricultural land is abandoned for many reasons, including conversion to other uses, but also abandonment of farming. The results of the analysis indicate that the risk of farmland abandonment was highest in countries with difficult farming conditions, such as Greece, Spain, Portugal, Romania, and Finland. In turn, Belgium, Denmark, Germany, and the Netherlands, i.e., the most economically developed countries, were most resilient to this risk. An analysis of the factors that contribute to farmland abandonment demonstrated that the likelihood of this risk decreases with a rise in agricultural investments, farm income, population density, prices of agricultural land, road quality, and density. A high proportion of ageing farm owners was the only factor that increased the risk of agricultural land abandonment.

Land Productivity and Agri-Environmental Indicators: A Case Study of Western Balkans

Due to the environmental radicalization of European politics, which is reflected in the European Green Deal, Farm to Fork strategy, and new CAP 2023–2027, this paper aims to determine the impact of agri-environmental indicators on soil productivity based on the land productivity function model. The paper focuses on the Western Balkans countries, which are in the process of European integration and which, in the coming period, need to harmonize their agricultural policy with the CAP. First, the aggregate Cobb–Douglas production function has been used to create a land productivity function. Then, the sources of land productivity growth have been calculated, which can be particularly interesting in the context of agri-environmental indicators, such as fertilizer use and livestock density. The research results showed that land productivity is the most elastic concerning changes in the number of livestock units per hectare. Consequently, reducing livestock units had a markedly negative effect on productivity. In addition, the research results showed that using mineral fertilizers is a crucial source of growth in land productivity in these countries. These results imply that the creators of the agricultural policy must carefully assess the pace at which they will harmonize ecological and economic goals, especially if they take into account the current Ukraine crisis that can disrupt the food market.

Soil indicators for comparing medium-term organic and conventional agricultural systems

Evaluation of the sustainability and environmental footprint of different agricultural production systems, such as organic and conventional systems, is an open scientific debate. Presently, several agri-environmental indicators have been proposed; however, their reliability and interpretation are still under discussion. The investigation of biological, chemical, and physical soil health variables has been advocated for understanding the sustainability of agricultural systems, even though they are not univocal indicators. In the present study, both biological (abundances of ammonia-oxidizing archaea, AOA; ammonia-oxidizing bacteria, AOB; and nosZ) and physicochemical (pH; soil organic carbon, SOC; total Kjeldahl nitrogen, TKN; available phosphorus, P; and texture) soil variables were investigated to compare two soils under organic (OS) and conventional (CS) management after 15 years. Among the physicochemical variables analyzed, TKN content was higher whereas P content and pH values were lower in the OS than in the CS. The SOC content was not affected by agricultural management. Biological indicators showed significant differences in the soil nitrogen cycle. After 15 years, the OS showed a higher potential for N2O emission reduction (higher nosZ) and NO3- losses in the environment (higher AOA) than the CS. Therefore, we conclude that biological (AOA and nosZ) and chemical (P, TKN, and pH) variables can be regarded as soil health indicators for comparing different agricultural management systems.

Financing the Agri-Environmental Policy: Consequences on the Economic Growth and Environmental Quality in Romania.

The aim of this research is to point out the impact that the application of the agri-environmental policy has on the economic growth and on the quality of the environment, these being the main aspects targeted by the practice of a sustainable agriculture. The research is conducted based on the agri-environment indicators for Romania for the period of time between 1997 and 2019. In order to answer the objectives of this whole research, we performed stationarity tests, a cointegration test and used the Fully Modified Least Squares (FMOLS) method to estimate the relationships between the variables included in the three proposed models. The obtained results highlighted the positive influence exerted by the area that was arranged for irrigation and the agricultural area that was arranged with drainage works on the GDP, but also the negative influence of the amount of natural fertilizers used in agriculture. The use of chemical fertilizers and pesticides generates an increase in environmental degradation, meaning CO2 emissions, while an increase in the agricultural area arranged with erosion control and land improvement works, leads to reducing environmental degradations. The limitations of this research lie in the fact that the agri-environmental indicators are specific to each country in the European Union and, therefore, it is difficult to make comparisons with other member states or to apply the measures recommended for Romania to other states with similar agricultural and economic systems.

Environmental Impact Assessment of Maize Production in Northern India

Agricultural production has been recognized as a one of the major contributors to environmental degradation and a source of several forms of pollution that have far-reaching consequences for ecosystems, water supplies, soil quality, air quality, and human health. Maize cultivation has also placed a major environmental cost, particularly when considering resource utilization and the environmental impacts of fertilizer, insecticides, and electricity production. With the goal of measuring the impacts and identifying the major environment hotspot, a life cycle assessment was performed using the cradle-to-farm-gate approach to evaluate the environmental effects of maize cultivation. The environmental impact of the inputs and outputs studied by life cycle assessment was estimated using the Sima Pro version 9.0 and the CML 2 baseline 2000 technique. According to the findings, the fertilization phase had the most detrimental influence on the ecosystem, followed by the harvesting period. The current research makes a number of recommendations for agricultural practices to be used in conjunction with maize production, based on agri-environmental indicator scores and the correlations between them for eco-friendly maize production.

A geospatial framework for the assessment and monitoring of environmental impacts of agriculture

The agriculture sector plays a critical role in global food security and economy, but it is also among the greatest contributors to environmental degradation and global warming through practices such as clearing of forests and wetlands, water management, and use of fertilizers and pesticides. This study proposes a geospatial framework for the systematic assessment and monitoring of environmental impacts of agriculture practices using agri-environmental impact indicators and the environmental impact assessment (EIA) method. Geospatial approaches are identified and synthesized for four key phases of the EIA method: (1) screening; (2) scoping; (3) impact prediction & assessment; and (4) impact management, monitoring & follow up. The study shows the potential of remote sensing and geospatial methods such as mapping, geostatistical interpolation, spectral indices, image classification, multi-criteria decision analysis, and GIS watershed analysis for the different EIA phases. The proposed framework can assess impacts at flexible spatial (within-farm up to the national level) and temporal scales (daily to annual). The framework was exemplified for Canada, where its use could integrate existing programs from Agriculture and Agri-Food Canada, such as environmental farm plans and agri-environmental indicators. The framework can be used by farmers, farmer organizations, environmental agencies, and consultancies, as well as provincial, territorial and federal agencies.

Integrating agri-environmental indicators, ecosystem services assessment, life cycle assessment and yield gap analysis to assess the environmental sustainability of agriculture

Agriculture’s primary function is the production of food, feed, fibre and fuel for the fast-growing world population. However, it also affects human health and ecosystem integrity. Policymakers make policies in order to avoid harmful impacts. How to assess such policies is a challenge. In this paper, we propose a conceptual framework to help evaluate the impacts of agricultural policies on the environment. Our framework represents the global system as four subsystems and their interactions. These four components are the cells of a 2 by 2 matrix [Agriculture, Rest of the word]; [Socio-eco system, Ecological system]. We then developed a set of indicators for environmental issues and positioned these issues in the framework. To assess these issues, we used four well-known existing approaches: Life Cycle Assessment, Ecosystem Services Analysis, Yield Gap Analysis and Agro-Environmental Indicators. Using these four approaches together provided a more holistic view of the impacts of a given policy on the system. We then applied our framework on existing cover crop policies using an extensive literature survey and analysing the different environmental issues mobilised by the four assessment approaches. This demonstration case shows that our framework may be of help for a full systemic assessment. Despite their differences (aims, scales, standardization, data requirements, etc.), it is possible and profitable to use the four approaches together. This is a significant step forward, though more work is needed to produce a genuinely operational tool.

A Comparative View of Agri-Environmental Indicators and Stakeholders’ Assessment of Their Quality

The degree to which economic goals have been prioritized over environmental and social objectives has caused dissatisfaction with conventional agricultural practices and stimulated the adoption of sustainable farming methods. One way to consider the multidimensionality of sustainable agriculture is to refer to indicators, more precisely, to agri-environmental indicators (AEIs). This study provides a comparative overview of the 28 AEIs of the European Union (EU) and those of the OECD and FAO, additionally revealing how these 28 indicators are reflected in the literature regarding agri-environmental indicators. Furthermore, since much of human behavior is influenced by perceptions, it was critical to reveal the stakeholders’ assessment of the 28 AEIs based on four criteria (“Availability”, “Relevance”, “Target-oriented”, and “Operational simplicity”). The stakeholders’ opinions of the 28 AEIs were assessed using the evaluation matrix. The highest overall evaluation considering the four criteria was received by “Irrigation” and “Soil quality indicators”. The study concludes that tripartite cooperation between stakeholders—farmers, agri-environmental researchers, and policymakers—is needed to successfully implement the AEIs of the EU.

Agri-Environmental Indicators: A Selected Review to Support Impact Assessment of New EU Green Deal Policies

Every intervention of planning, implementation, and monitoring of agricultural and agri-environmental policies requires assessment tools that should have the characteristics of relevance, completeness, interpretability, data quality, efficiency, and overlapping. Despite the extensive selection of bibliographies and numerous projects designed to develop agri-environmental indicators necessary for assessing the sustainability of new policies, it is difficult to have an integrated and updated set of indicators available, which can be an effective and practical application tool to assists policymakers, researchers, and actors in policy design, monitoring and impact assessment. Particularly, such a need is pressing to face the new environmental challenges imposed by the upcoming European Union Green Deal on the Common Agricultural Policy (CAP) post 2023. This study, therefore, aims to fill this gap by proposing a selection methodology and different pools of agri-environmental indicators differentiated based on a scale approach (crop-farm-district-region). Furthermore, we have attempted to apply our approach by quantifying selected indicators for a specific evaluation necessity, represented in this case by an assessment of the environmental impact of land use change induced by CAP greening requirements in the Northern Italy context. Results of this validation show original crops’ impacts comparison, but also highlight great knowledge gaps in the available literature.

Selection and application of agri-environmental indicators to assess potential technologies for nutrient recovery in agriculture

The adverse effects of agriculture and livestock production on the environment are well-known and require mitigation in order to achieve sustainability in the food production chain. This study focused on adverse effects related to biogeochemical flows of phosphorus and nitrogen cycles which natural balances have been greatly disturbed by current practices. To assess the potential benefits and detrimental effects of proposed mitigation measures, adequate impact indicators are required. The challenge lies in identifying and providing indicators that cover the important aspects of environmental sustainability and allow a direct comparison of policy alternatives. A review of potential indicators that are also consistent with those used to indicate the performance of agricultural and general sustainability (i.e. the European Green Deal) led to the selection of fifteen agri-environmental indicators covering the main environmental issues in agriculture. The indicators identified offered an effective representation of environmental behaviour and would be useful in communicating a comprehensive ‘dashboard’ for professional end users of solutions to nutrient recovery and nutrient efficiency improvement in arable and livestock systems. The selected dashboard indicators (DBI) covered the dimensions of ‘use of primary resources’, ‘emissions to the environment’ and ‘resilience to climate change’. Five case studies were investigated to test the DBI using an Excel questionnaire applying the qualitative approach of the Delphi method together with expert knowledge. As expected, the results indicated that there were potential benefits of the technologies in terms of improved ‘nutrient recovery’ and decreased ‘nitrate leaching’. Potential disadvantages included increased electricity and oil consumption and greater ammonia volatilisation due to the increased use of organic fertilisers. The indicator ‘water’ received more neutral responses; thus, the specific technology was not expected to consistently affect the indicator. In relation to ‘particulate matter’, the results were indicated to be ‘unknown’ for some solutions due to the difficulty of predicting this indicator. Furthermore, methodologies for estimating quantitative values for the dashboard indicators were proposed, and a quantitative assessment was performed for the solution ‘catch crops to recover nutrients’, confirming the responses in the qualitative assessment. The dashboard indicators selected covered the main aspects of the solutions, identified in more comprehensive studies of environmental impacts, as being suitable for the rapid assessment of technologies for nutrient recovery in agriculture. As such, they can be used as a pre-screening method for technologies designed to improve the environmental sustainability of arable and livestock systems.

Deep Q-Learning and Preference Based Multi-Agent System for Sustainable Agricultural Market.

Yearly population growth will lead to a significant increase in agricultural production in the coming years. Twenty-first century agricultural producers will be facing the challenge of achieving food security and efficiency. This must be achieved while ensuring sustainable agricultural systems and overcoming the problems posed by climate change, depletion of water resources, and the potential for increased erosion and loss of productivity due to extreme weather conditions. Those environmental consequences will directly affect the price setting process. In view of the price oscillations and the lack of transparent information for buyers, a multi-agent system (MAS) is presented in this article. It supports the making of decisions in the purchase of sustainable agricultural products. The proposed MAS consists of a system that supports decision-making when choosing a supplier on the basis of certain preference-based parameters aimed at measuring the sustainability of a supplier and a deep Q-learning agent for agricultural future market price forecast. Therefore, different agri-environmental indicators (AEIs) have been considered, as well as the use of edge computing technologies to reduce costs of data transfer to the cloud. The presented MAS combines price setting optimizations and user preferences in regards to accessing, filtering, and integrating information. The agents filter and fuse information relevant to a user according to supplier attributes and a dynamic environment. The results presented in this paper allow a user to choose the supplier that best suits their preferences as well as to gain insight on agricultural future markets price oscillations through a deep Q-learning agent.

Can Long-Term Experiments Predict Real Field N and P Balance and System Sustainability? Results from Maize, Winter Wheat, and Soybean Trials Using Mineral and Organic Fertilisers

Agri-environmental indicators such as nutrient balance may play a key role in soil and water quality monitoring, although short-term experiments might be unable to capture the sustainability of cropping systems. Therefore, the objectives of this study are: (i) to evaluate the reliability of long-term experimental N and P balance estimates to predict real field (RF) (i.e., short-term transitory) conditions; and (ii) to compare the sustainability of short- and long-term experiments. The LTE-based predictions showed that crops are generally over-fertilised in RF conditions, particularly maize. Nutrient balance predictions based on the LTE data tended to be more optimistic than those observed under RF conditions, which are often characterised by lower outputs; in particular, 13, 44, and 47% lower yields were observed for winter wheat, maize, and soybean, respectively, under organic management. The graphical evaluation of N and P use efficiency demonstrated the benefit of adopting crop rotation practices and the risk of nutrient loss when liquid organic fertiliser was applied on a long-term basis. In conclusion, LTE predictions may depend upon specific RF conditions, representing potential N and P use efficiencies that, in RF, may be reduced by crop yield-limiting factors and the specific implemented crop sequence.

Simulating alfalfa and pasture yields at regional and national scales in Canada from 1981 to 2019

CONTEXTThere has been a great deal of uncertainty in the reported alfalfa and pasture yields in the agricultural database as these commodities are typically used internally on farms and are not sold in the same way as other crops such as wheat, canola or corn. However, yields from alfalfa and pasture crops are important inputs (biological fixation) and outputs (feed) that can impact the estimates of agri-environmental indicators as the land area in Canada under these crops is very large. One of the indicators which uses this data is the residual soil nitrogen (RSN) which is the amount of inorganic N remaining in soils after harvest. High RSN levels can lead to impaired water quality (e.g., eutrophication) and global warming (N2O). Therefore, it is critical to obtain accurate alfalfa and pasture yield estimates. In previous versions of the Canadian Agricultural Nitrogen Budget Model (CANB), only provincial average yield values were used for alfalfa or pasture yields across all of the soils and landscapes. Soils, climates, and crop yields do vary within provinces, and a more accurate estimate of these yields over time and space is required. OBJECTIVEThe objective of this study was to collect recent published yield data and use the Environmental Policy Integrated Climate (EPIC) to estimate yields for the 2780 soil landscapes of Canada (SLC) polygons. METHODSThe EPIC model was used to calibrate and evaluate yields at field scale based on a total of 109, 59 and 47 treatments collected for alfalfa, improved and unimproved pasture yields, respectively, from Canadian publications. Due to satisfactory evaluation, we extended simulation to all SLC polygons based on SLC v3.2 soil and weather databases, representative crops as proxies for planting and harvesting date, and historical rates of fertilization. RESULTS AND CONCLUSIONSThe average simulated alfalfa, improved and unimproved pasture yields were 5.00, 3.85 and 1.22 Mg ha−1, respectively over the period from 1981 to 2019. Generally, the EPIC model showed reliable predictions for alfalfa and pasture yields under variable weather, soil, and management practices. SIGNIFICANCETherefore, it enabled us to provide improved estimates across soil landscapes polygons in Canada and thereby improve our agri-environmental indicator models.

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

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.