Integrating AI-Driven Parametric Models For Agricultural Risk Assessment Under Data Scarcity: An Extension Of Simulation-Based Decision Support In India

Abstract

The lack of comprehensive databases on agricultural risk factors in India has impeded the adoption of advanced risk management systems commonly used in developed countries. To address this gap, this study extends the foundational economic-mathematical model originally designed for incomplete data processing in India's agricultural sector. The updated model integrates AI-driven parametric simulation techniques with a genetic algorithm framework and enhanced risk elasticity analysis. This facilitates real-time decision support for farms operating under high uncertainty, especially those cultivating cereals, legumes, and sunflowers. Tested with pseudo-random risk variable generation and expert-informed inputs, the model shows promising results in identifying significant risk contributors such as price volatility. The research advocates the formation of dynamic model libraries and adaptive decision-making frameworks to improve resilience in agricultural operations.