Climate Resilient Agriculture Vulnerability Mapping of Indian Districts – Directions for Future Policy Planning

Swati Hans , Anandajit Goswami, Tanu Dua, Indu Kashyap 

Climate change poses significant risks to agriculture, especially in agro-dependent, climate-vulnerable regions and states of India. This study applies a machine learning-based Long Short-Term Memory (LSTM) model to assess agricultural risks, climate vulnerability in various Indian states with diverse climatic variables to address India's 2070 net-zero goal. It addresses the existing research gaps between the predictive analytical models for climate vulnerability mapping and their application for policy implementation in India.
Our predictive modelling analysis based on the AI ML applications presents a district-wise climate vulnerability mapping across India's four major climatic zones. Based on district-specific vulnerability mapping across the four zones of India, this paper proposes a comprehensive framework for policy implementation and an action plan to address climate-induced agricultural vulnerability in the country. The model leverages climate variables such as temperature and rainfall, along with agronomic factors, to forecast systemic and non-systemic risks across states. Through our LSTM Model, the effect of climate factors has been analyzed in various districts of India for the Kharif and Rabi seasons. Our LSTM Model assists in finding the key districts requiring immediate attention in terms of policy execution and implementation at a sub-national level to address the district-specific climate and agricultural vulnerability.
Key findings indicate substantial variability in risk profiles of the chosen districts of India, underscoring the need for tailored policies to enhance crop resilience and mitigate future climate-led agricultural vulnerabilities. By integrating predictive analytics, the research provides actionable insights for policymakers to design adaptive measures, ensuring sustainable agricultural practices, improved farmer incomes, and food security. As an outcome, this novel approach bridges the gap between predictive modelling and policy applications for mitigating future agricultural and climate vulnerability of chosen Indian states and districts, paving the way for climate-resilient agricultural systems driven by subnational, decentralized, climate-resilience-based governance systems for the future.