A CLIMATE-RESPONSIVE FRAMEWORK FOR CROP INSURANCE: YIELD FORECASTING AND RISK-BASED PREMIUM OPTIMIZATION

The Indian agriculture sector is highly exposed to climatic risks, creating financial uncertainty for farmers and other stakeholders. While crop insurance programs can mitigate these impacts, their effectiveness is often undermined by inaccurate pricing, delayed claim settlement caused by inefficient loss assessment processes. This study develops a data-driven yield forecasting and premium rate estimation model that combines meteorological and remote sensing variables with district-fixed effects to account for spatial heterogeneity. The model demonstrates a strong predictive performance in both in-sample and out-of-sample tests, effectively capturing region and season-specific weather impacts on yields. By leveraging tech-enabled weather data, the framework can enhance the accuracy of risk-sensitive premium pricing, which enhances risk pooling efficiency by insurers. The proposed climate-responsive framework has the potential to mitigate imperfect market conditions and promote sustainable and inclusive agricultural insurance schemes.