Faculty Publications
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Item Combining agriculture, social and climate indicators to classify vulnerable regions in the Indian semi-arid region(IWA Publishing, 2022) Kalli, R.; Jena, P.R.Climate change vulnerability is highly counter-productive for agriculture among the arid and semi-arid regions. The study constructs the agriculture vulnerability index for Karnataka, a south Indian state. The state has faced frequent climate-related shocks in the last decade. The district-wise vulnerability index is estimated using longitudinal data considering exposure, sensitivity and adaptive capacity as sub-indices. The results show that the districts in the north interior region of Karnataka are highly vulnerable to the climate change followed by the districts in the south interior and coastal regions. There is an urgent need to prioritize the most vulnerable districts while formulating the development policies to minimize the risk of climate change on agriculture. Specific technical knowledge and support need to be made available to the farmers for informative climate resilience action. © 2022 The Authors.Item Perception on climate change, access to extension service and energy sources determining adoption of climate-smart practices: A multivariate approach(Academic Press, 2023) Tanti, P.C.; Jena, P.R.Climate change has an adverse impact on rural livelihoods by increasing vulnerability and reducing crop yields. Climate-smart agricultural (CSA) practices have been advanced as a possible solution to adopt and mitigate climate change issues. Administering a structured questionnaire survey among the 494 rural farming households of an eastern Indian state, namely Odisha, the study explores the key determinants of CSA adoption. Three districts, one from the state's coastal and two from the inland regions, are chosen for the study. The majority of the respondents (85%) perceive an increase in temperature and (76%) perceive a decrease in rainfall due to climate change in the region. The respondents have adopted a range of CSA practices such as rescheduling planting (74.5%), crop rotation (59.3%), crop diversification (31.2%), soil conservation (62.1%), drought-resistant seeds (36%) and agroforestry (10.3%) to adapt to these weather anomalies. The current paper employs a multivariate probit model in which the findings of econometric modelling have been triangulated to explore the key determinants of the adoption of CSA practices. The result shows that the key determinants are – perception of climate change, agricultural extension services, and access to energy for irrigation. © 2023 Elsevier LtdItem 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 AuthorsItem Enhancing crop yields and farm income through climate-smart agricultural practices in Eastern India(Springer Science and Business Media B.V., 2024) Tanti, P.C.; Jena, P.R.; Timilsina, R.R.; Rahut, D.B.Climate-induced increase in temperature and rainfall variability severely threaten the agricultural sector and food security in the Indian state of Odisha. Climate-smart agricultural (CSA) practices, such as crop rotation and integrated soil management, help farmers adapt to climate risk and contribute to a reduction in greenhouse gas (GHG) emissions. Therefore, this paper examines the impact of CSA practices on yield and income in vulnerable semi-arid districts of Odisha—Balangir, Kendrapara, and Mayurbhanj. We use primary survey data from 494 households collected in 2019–2020, using a multi-stage stratified sampling approach and structured questionnaire. Propensity score matching (PSM) and the two-stage least square method (2SLS) have been used to analyze the impact of CSA on income and productivity. Two instrument variables, namely distance to the extension office and percentage of adopters in a village, are used to control self-selection bias and endogeneity in our model. Both models show a positive and significant impact of the adoption of CSA on farmers’ productivity and income. The study sheds light on the significant contribution of CSA practices in fostering sustainable income growth amid environmental challenges. Overall, our results suggest that small and marginal farmers of Eastern India, a highly environmentally vulnerable area, can significantly improve their income and productivity by adopting CSA technology. Hence, policymakers should scale the adoption of CSA technology through effective extension programs. © The Author(s) 2024.