Browsing by Author "Mohammad Aslam Ansari"

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Determinants of farm household resilience and its impact on climate-smart agriculture performance: Insights from coastal and non-coastal ecosystems in Odisha, India
(Elsevier Ltd, 2025) Usha Das; Mohammad Aslam Ansari; Souvik Ghosh; Neela Madhav Patnaik; Saikat Maji
Context: Climate change presents severe challenges to agricultural production systems, particularly for smallholder farmers in developing nations like India. Strengthening the resilience of farm households is crucial for sustaining agricultural productivity in the face of climatic uncertainties. To enhance the effectiveness and upscaling of Climate Smart Agriculture (CSA) interventions, agricultural systems must be restructured and reformed considering resilience of farm households. Understanding the influencing factors of farm household resilience as well as effect of resilience pillars in improving the CSA performance is essential. Objective: Present study aims to identify the key determinants of farm household resilience across coastal and non-coastal ecosystems in Odisha, India, a highly climate-vulnerable state. It seeks to analyse the interrelationships between resilience determinants and explore the link between farm household resilience and CSA performance in terms of effectiveness and implementation feasibility as perceived by the farmers. Methods: The study investigates the resilience of farm households across coastal and non-coastal ecosystems, focusing on three dominant livelihood groups: crop farming, livestock farming, and combined crop-livestock farming. It employs the Resilience Index Measurement and Analysis (RIMA) framework to assess resilience through four key pillars: Access to basic services (ABS), Assets (AST), Social safety nets (SSN), and Adaptive capacity (AC). A Multiple Indicators Multiple Causes (MIMIC) model is used to examine resilience determinants, while Structural Equation Modeling (SEM) is applied to assess the relationship between household resilience and CSA performance. Additionally, multiple regression and path analysis are conducted to identify resilience drivers in terms of livelihood indicators across coastal and non-coastal ecosystems. Results and conclusions: Findings indicate that crop-livestock farming households exhibit the highest resilience in both coastal and non-coastal regions, while crop farmers demonstrate higher resilience than livestock farmers. The study uncovers distinct resilience drivers between coastal and non-coastal areas. SEM analysis highlights a differential relationship between resilience and CSA performance, revealing how resilience influences CSA outcomes. It suggests a moderately fit model highlighting AC and SSN pillars contributing to resilience. Multiple regression and path analysis have revealed key livelihood indicators (such as infrastructure, connectivity, community network, landholding, irrigation access, and income) determining the resilience of the farmers. These insights contribute to a deeper understanding of micro-level resilience among farm households and its relationship with CSA performance. Significance: By identifying key resilience determinants beyond traditional adaptive capacity, this study provides critical policy inputs for fostering climate-resilient agri-food systems. The findings emphasize the need for targeted interventions to strengthen farm household resilience and promote sustainable, climate-smart agricultural practices in vulnerable regions. © 2025