Faculty Publications
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Item Prediction of crop yield using climate variables in the south-western province of India: a functional artificial neural network modeling (FLANN) approach(Springer Science and Business Media B.V., 2023) Jena, P.R.; Majhi, B.; Kalli, R.; Majhi, R.To meet the demand of the growing population, there exists pressure on food production. In this context, appropriate prediction of crop yield helps in agricultural production planning. Given the inability of the traditional linear models to provide satisfactory prediction performance, there is a need to develop a crop yield prediction model that is simple in complexity, accurate in prediction, and less time-consuming during training and validation phases. Keeping these objectives in view, the present paper focuses on building an adaptive, low complexity, and accurate nonlinear model for the prediction of crop yield. A time series dataset for the period 1991–2012 of Karnataka, a southwestern state of India, is used for yield prediction. An empirical nonlinear relation between crop yield and the four independent attributes has been obtained from the proposed ANN model. The independent attributes employed are total rainfall, the cumulative distribution of temperature, the proportion of irrigated land, and the average amount of fertilizer used. It is demonstrated that the developed model exhibits better prediction accuracy, less root mean square error in the range of 0.07–0.14, less mean square error in the range of 0.01–0.04, and mean absolute error in the range of 0.07–0.15 compared to its corresponding linear regression model. It is recommended that the proposed ANN model can also be applied to predict other agricultural products of the same or other geographical regions of the globe. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.Item Effect of irrigation on farm efficiency in tribal villages of Eastern India(Elsevier B.V., 2024) Kalli, R.; Jena, P.R.; Timilsina, R.R.; Rahut, D.B.; Sonobe, T.Irrigation is an important adaptation strategy to cope with climate change which reduces vulnerability to water stress and improves crop productivity to feed millions. There is evidence of crop yield stagnation in many developing countries, and irrigation efficiency is claimed to increase crop productivity. Therefore, this paper uses data envelopment analysis to evaluate the farmer's productivity through technical efficiency (TE), i.e., the relationship between resource inputs and outputs of 513 paddy farmers in Eastern India. The results show that the farms are, on average operating at 14% TE, leaving a considerable scope to improve up to 86% to reach the optimal level. A significant difference is observed between irrigated and rain-fed paddy farmers, such that10% of the irrigated farms achieved efficiency scores over 40% and only 2% of rain-fed farms achieved the same. The tobit and beta fit regression models are estimated to find out the factors that influence the TE. Both surface water and groundwater sources of irrigation are used as predictors, along with other socio-demographic factors. Access to surface water irrigation is identified to be a significant determinant of farm efficiency, however, surface water irrigation, such as canal irrigation, is accessible only to farmers living on plain land. Farmers living on highlands need to explore other sources of irrigation practices, such as drip and sprinkler, that can increase TE and farm productivity. Therefore, this paper calls for government intervention to provide extensive training and facilities for these micro-irrigation practices. © 2023 The Authors