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

Abstract

Environmental risk assessment (ERA) in agricultural ecosystems is vital for ensuring sustainable food production, ecosystem resilience, and climate adaptation. However, conventional assessment approaches often struggle to integrate the dynamic interactions between biophysical, climatic, and anthropogenic factors. This review proposes a comprehensive conceptual framework for developing predictive models that enhance the accuracy, interpretability, and decision-support capacity of environmental risk analyses in agriculture. The framework integrates data-driven modeling, machine learning algorithms, and environmental process-based simulations to quantify risks associated with soil degradation, nutrient leaching, water contamination, and biodiversity loss. It emphasizes multi-scale data fusion from remote sensing, IoT-based field sensors, and geospatial databases to enable spatiotemporal forecasting and scenario evaluation. Furthermore, the framework underscores the importance of uncertainty quantification, explainable AI techniques, and stakeholder-inclusive validation processes for model transparency and policy relevance. By linking predictive analytics with environmental monitoring systems, the study advances a holistic approach for proactive risk mitigation, sustainable land management, and evidence-based agricultural policy formulation. This conceptual model provides a foundation for interdisciplinary research and real-time environmental intelligence in precision agriculture.