Field research stations in Senegal play a crucial role in monitoring environmental changes and managing associated risks. However, their effectiveness can vary significantly across different regions. Multilevel regression analysis was employed to assess the impact of various factors on risk reduction at both local (field level) and regional levels. The model included fixed effects for station location, variable coefficients for different environmental indicators, and random intercepts for variability between stations. The multilevel regression analysis revealed that certain environmental conditions had a significant positive effect on reducing risks by approximately 15% in specific regions, indicating the effectiveness of tailored interventions. This study highlights the importance of localized monitoring and adaptive management strategies for effective risk reduction in Senegalese field research stations. Field researchers should prioritise regular calibration and updating of their systems to align with changing environmental conditions. Policy makers can use these findings to support evidence-based decision-making. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.