Geospatial Models for Predictive Agricultural Risk Assessment and Mitigation in Vulnerable Landscapes

Abstract

This study addresses complex agricultural risk assessment under simplified conditions through a multi-pronged approach. The research problem focuses on the interactions among soil moisture, vegetation cover, and land use patterns influencing agricultural risks. Using mixed methods, we research soil internal analysis, mathematical modelling, and stakeholder insights. Stratified objective sampling ensures representative data sets and various geospatial tools, including Geographic Information System (GIS) software and remote sensing platforms, are subject to data analysis. Our study reveals a positive relationship between soil moisture and vegetation cover and establishes the role of highlighting the importance of water use in agricultural resilience -Use distribution analysis reveals spatial patterns, which identify targeted strategies for risk mitigation. Soil composition data enhance our understanding of soil health, providing usable insights for sustainable agriculture. These results contribute significantly to the existing body of knowledge and emphasize the importance of understanding detailed agricultural systems under sensitive conditions. Future research should examine temporal dynamics, socioeconomic implications, and adaptive geospatial models to support decision-making. Our research provides valuable insights for practitioners, policymakers and researchers and advances the understanding of agricultural risk in dynamic contexts.