Conceptual Framework for Developing Predictive Models for Environmental Risk Assessment in Agricultural Ecosystems

Environmental risk assessment in agricultural ecosystems is essential for safeguarding sustainable food production, ecosystem health, and climate resilience. The paper notes that conventional ERA methods struggle to capture the complex and dynamic interactions among biophysical, climatic, and human factors. It proposes a comprehensive conceptual framework for predictive modeling that integrates machine learning, data-driven analytics, and environmental process-based simulations to quantify risks such as soil degradation, nutrient leaching, water contamination, and biodiversity loss. The framework emphasizes multi-scale data fusion from remote sensing, IoT-based field sensors, and geospatial databases to enable spatiotemporal forecasting and scenario analysis. It also highlights the need for uncertainty quantification, explainable AI, and stakeholder-inclusive validation to improve model transparency and policy relevance. By linking predictive analytics with environmental monitoring systems, the study promotes proactive risk mitigation, sustainable land management, and evidence-based agricultural policy.