Stochastic Risk Assessment of Cotton Pest Outbreaks in Tropical India: Entropy, Gini Coefficients, and Machine Learning for Sustainable Agroecosystem Management

This study developed an integrated stochastic framework to forecast cotton pest outbreaks across six tropical Indian agroecosystems. Methodologically, the approach fused entropy and Gini inequality indices, Random Forest machine learning, SHAP-based feature interpretation, fuzzy logic risk assessment, and climate scenario simulations (+2 °C, +20% rainfall) to quantify outbreak variability, driver importance, and system resilience. Findings revealed extreme pest stochasticity (mean = 15.7, variance > 4200), with low entropy (0.06) and a high Gini coefficient (0.82) confirming highly concentrated spatial and temporal outbreaks. While Random Forest demonstrated limited predictive skill (RMSE = 68.9, R 2 = 0.07), SHAP analysis transparently identified evaporation, wind speed, and humidity as dominant drivers. Fuzzy logic yielded an average risk score of 1.0, reflecting frequent exceedance of biological thresholds. Scenario simulations demonstrated pronounced climate sensitivity: a +2 °C temperature increase raised mean incidence to 18.7, and +20% rainfall increased it to 18.6, resulting in a resilience index of 1.51 that indicates disproportionate vulnerability. In conclusion, combining stochastic variability metrics, explainable machine learning, and threshold-based risk modeling significantly advances tropical pest forecasting under uncertainty. Importantly, this framework contributes to sustainability by enabling climate-resilient cotton production, reducing reliance on chemical pesticides, and supporting adaptive advisory systems that strengthen long-term agroecosystem resilience. These results emphasize the critical need for adaptive, location-specific management strategies to mitigate climate-driven pest intensification and enhance resilience in cotton production systems.