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PurposeData 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.Design/methodology/approachDEA 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.FindingsThe 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.Originality/valueThe use of machine learning applications introduces advanced strategies and important factors influencing agricultural production, which may help future research in farms' performance.

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.

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This paper studies the correlation between electricity consumption and consumer income and effectiveness of alternate energy sources in the domestic sector. India is a developing country and the demand for the energy is increasing regularly. Fossil fuels are the main sources of energy in this country. As fossils fuels become more costly and harder to find with several other disadvantages, it is required a shift from fossil fuels to non conventional energy. Solar energy is available in plenty and by using it electricity can be generated. This paper also attempts to use renewable energy at the domestic sector to aid the regular power supply.