Bayesian Hierarchical Model for Risk Reduction in Senegal's Regional Monitoring Networks: A Methodological Evaluation

This study evaluates the effectiveness of regional monitoring networks in Senegal's Agricultural sector by applying a Bayesian hierarchical model to measure risk reduction. A Bayesian hierarchical model was developed to analyse data from multiple monitoring networks across different regions in Senegal. This approach allows for the incorporation of spatial and temporal dependencies, providing a comprehensive assessment of agricultural risks at various scales. The analysis revealed significant regional disparities in risk levels, with certain areas showing up to 40% higher vulnerability compared to others, indicating the need for targeted interventions. Bayesian hierarchical models effectively capture spatial and temporal heterogeneities in agricultural risk across Senegal's regions, offering a valuable tool for policy-makers and practitioners. Based on findings, recommendations include prioritising high-risk areas with tailored intervention programmes and enhancing network connectivity to improve data sharing and analysis efficiency. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.