The Impact of Meteorological Factors on Crop Price Volatility in India: Case studies of Soybean and Brinjal
This study investigates the causal mechanisms through which meteorological factors drive price volatility of soybean and eggplant in India, focusing on Madhya Pradesh and Odisha. To model conditional price volatility, an Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) framework is employed; Granger causality tests—extended to capture nonlinear dependencies—are applied to identify statistically significant meteorological drivers. Furthermore, a meteorology-augmented hybrid SARIMAX-LSTM forecasting architecture is developed. The study provides the first systematic, regionally granular evidence in India demonstrating statistically significant causal effects of rainfall and temperature on both perishable and staple crop prices (p < 0.01). The proposed hybrid paradigm integrates econometric rigor with machine learning interpretability, achieving 18–23% lower Mean Absolute Error (MAE) relative to standard benchmarks. These findings deliver actionable quantitative insights for designing climate-resilient agricultural finance instruments, supporting smallholder risk management decisions, and optimizing crop rotation policies.