Title: Farm efficiency estimation using a hybrid approach of machine-learning and data envelopment analysis: Evidence from rural eastern India

Year: 2020

Published: https://doi.org/10.1016/j.jclepro.2020.122106 


In agricultural sector, farm efficiency evaluation is an important means of farm management. To evaluate the farm efficiency for effective allocation of agricultural resources, data envelopment analysis (DEA) is used. Commonly, a two-stage regression analysis is used to treat the obtained efficiency values on a set of explanatory variables. However, majority of the past studies explained the variables influencing efficiency rather than efficiency prediction. This paper aims to use DEA in combination with Machine learning approach to examine and predict the impact of environmental variables on farms’ performance. Random forest (RF) algorithm has been employed, which is one of the most useful machine learning algorithms of recent times. First, DEA was used to evaluate efficiency of all the farms. Then the RF method was employed to examine the variables crucial in predicting farm performance. This multi-stage model was applied to 450 paddy producers in rural Eastern India. The results of the RF algorithm revealed that land ownership, Kisan Credit Card (KCC), and educational status were the most crucial variables which affected the performance of the paddy producers. With the identification of the major factors influencing agricultural production, new policy actions may be developed to assist the small farmers. Furthermore, this joint DEA-Machine-learning approach may help future researchers not only to investigate but also to predict the impact of the important environmental variables on farms’ performance.

[ Journal of Cleaner Production, Elsevier Publications, ABDC "A", SCI/SCOPUS ]


Title: Optimizing Energy Use Efficiency for Agricultural Sustainability

Year: 2021

Published: https://doi.org/10.1007/978-3-030-63654-8_25


Energy is a critical prerequisite of modern society. Access to adequate energy resources shapes the socio-economic status, welfare of individuals and nations alike. However, ensuring efficient utilization of energy and its continued access for everyone is one of the most pressing issues that we face today. The agricultural sector consumes a sizeable portion of energy in terms of fossil fuels, chemical fertilizers, human labour, and electricity, which are used in production. Inefficiencies in production processes lead to over-utilization of these energy resources and generate undesirable outputs, such as agricultural wastes and greenhouse gas emissions (GHGs). A detailed farm-level energy analysis could provide insights regarding how to optimize energy consumption. The primary goal of the chapter is, therefore, to elaborate the concepts energy analysis and its importance in agriculture to gauge farm efficiency and discuss possible techniques for efficient energy management at the farm-level that will ensure the sustainability of agriculture in the context of developing countries. Lastly, we look at a case of paddy farmers from India and measuring their energy efficiency through the benchmarking approach to identify the best management practices employed by proficient farmers with the help of empirical data collected through field survey. This chapter provides actionable information for lawmakers to help us devise policies that can meet the increasing energy needs of their growing economies while reducing the adverse environmental effects of energy production systems.

[ Energy and Environmental Security in Developing Countries, Springer Nature Publications, SCI/SCOPUS ]


Title: Application of fuzzy DEA and machine learning algorithms in efficiency estimation of paddy producers of rural Eastern India

Year: 2021

Published: https://doi.org/10.1108/BIJ-01-2020-0012


Data envelopment analysis (DEA) has wide applications in the agricultural sector to evaluate the efficiency with crisp input and output data. However, in agricultural production, impreciseness and uncertainty in data are common. As a result, the data obtained from farmers vary. This impreciseness in crisp data can be represented in fuzzy sets. This paper aims to employ a combination of fuzzy data envelopment analysis (FDEA) approach to yield crisp DEA efficiency values by converting the fuzzy DEA model into a linear programming problem and machine learning algorithms for better evaluation and prediction of the variables affecting the farm efficiency.

DEA applications are focused on the use of a common two-step approach to find crucial factors that affect efficiency. It is important to identify impactful variables for minimizing production adversities. In this study, first, FDEA was applied for efficiency estimation and ranking of the paddy growers. Second, the support vector machine (SVM) and random forest (RF) were used for identifying the key leading factors in efficiency prediction.

The proposed research was conducted with 450 paddy growers. In comparison to the general DEA approach, the FDEA model evaluates fuzzy DEA efficiency giving the user the flexibility to measure the performance at different possibility levels.

The use of machine learning applications introduces advanced strategies and important factors influencing agricultural production, which may help future research in farms' performance.

[ Benchmarking, Emerald Publications, ABDC "B", SCI/SCOPUS ]

GBR 2021

Title: Systematic Review and Meta-regression Analysis of Technical Efficiency of Agricultural Production Systems

Year: 2019

Published: https://doi.org/10.1177/0972150918811719


The existing state of over-utilization of input resources affected the efficient production of the agricultural output, which created a challenge for the profitability of the farming community as well as sustainability of different agricultural production systems (APSs). Hence, it is crucial to explore the important input variables, which affect farming efficiency across different APSs. In past studies, data envelopment analysis (DEA) has been used extensively to estimate the mean technical efficiency (MTE) of agricultural farms. In this study, a meta-regression analysis has been performed to examine variables that affect the MTE variation in 100 studies. The selected studies have been classified based on the study period, farm location, journals, product type, sample size and their outcomes. Results revealed that the year of study, location and sample size were not significant, whereas agricultural products such as vegetables, fruits, flowers and livestock significantly affected the performance of MTE across studies. These empirical results establish the importance of related variables in the MTE estimation of different APSs, which will lead and assist better-quality future research in the agricultural efficiency domain.

[ Global Business Review, SAGE Publications, ABDC "C", SCI/SCOPUS ]