Faculty Publications
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Item Can minimum tillage enhance productivity? Evidence from smallholder farmers in Kenya(Elsevier Ltd, 2019) Jena, P.R.Conservation agriculture has been touted as a sustainable and productivity enhancing agricultural practice and increasingly being promoted in the developing countries. Previous research shows that this practice has increased agricultural productivity in the developed countries. This paper revolves around the central question – whether minimum tillage practice, which has succeeded in the developed countries under large scale farming, could bring out similar impacts for smallholder farmers in the developing countries. To examine this, plot level survey data are collected from a randomly selected sample households from the maize-dominant farming system of Kenya. Quasi experimental impact evaluation methods like endogenous switching regression has been applied to elucidate the impact of adoption of minimum tillage. Results show that adoption of minimum tillage has saved on labour by reducing the average total and female labour use in maize production thereby creating scope for undertaking other income generating activities. However, maize productivity is not found to have increased as an effect of minimum tillage adoption. Findings show that a major reason for such absence of yield impact is due to the fact that farmers adopting minimum tillage are often not practicing it together with other components of conservation agriculture. More importantly there is also serious irregularities in which other required supporting inputs, namely fertilizer and irrigation and agricultural management practices are used. © 2019 Elsevier LtdItem An application of artificial neural network classifier to analyze the behavioral traits of smallholder farmers in Kenya(Springer Science and Business Media Deutschland GmbH, 2021) Jena, P.R.; Majhi, R.This paper develops and employs a novel artificial neural network (ANN) model to study farmers’ behavior towards decision making on maize production in Kenya. The paper has compared the accuracy level of ANN based models and the statistical model. The results show that the ANN models has achieved higher accuracy and efficiency. The findings from the study reveal that the farmers are mostly influenced by their demographic characteristics and food security conditions in their decision making. Further to examine the relative importance of different demographic and food security characteristics, an ANOVA test is undertaken. The results found that education and food security indices are instrumental in influencing farmers’ decision making. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.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 Regenerative agriculture practices and multidimensional poverty in eastern rural India(Nature Research, 2025) Khosla, S.; Timilsina, R.R.; Jena, P.R.; Rahut, D.B.Rural households in developing countries face multidimensional poverty (MDP), i.e., deprivations beyond income, including health, education, empowerment, and living standards, while they are also being highly vulnerable to climatic risks. Regenerative agriculture (RA), a set of practices aimed at restoring soil health, enhancing biodiversity, and improving long-term farm resilience, has emerged as a promising strategy to boost productivity, diversify incomes, and promote sustainability. However, little is known about the impact of RA practices on MDP, and empirical evidence linking the two remains scarce. To this end, the present study examines the relationship between RA practices, such as crop rotation, agroforestry and crop diversification, and MDP reduction in eastern rural India. We administer household survey data from 917 households to construct an MDP index based on Alkire and Foster’s counting method and estimate the impact of RA adoption through Propensity Score Matching (PSM). The results show that RA practices significantly reduce MDP by improving access to education, healthcare, and living standards. These findings underscore the potential of RA as a pathway for sustainable rural development and call for targeted policy interventions to support its broader adoption. © The Author(s) 2025.