Public health surveillance systems in Kenya have been established to monitor diseases and track public health trends. However, their effectiveness in yield improvement needs methodological evaluation. Panel data was collected from multiple years to analyse trends and patterns. A fixed effects model was employed to estimate the impact of the surveillance systems on yield improvement, accounting for potential confounding variables. The panel data revealed significant variation in surveillance effectiveness across different regions with a mean improvement rate of 15% over the study period, indicating room for enhancement through targeted interventions. Panel-data analysis provided insights into the efficacy and areas needing improvement within Kenya's public health surveillance systems. Enhanced training programmes should be implemented to improve data collection accuracy and regular system reviews are recommended to ensure ongoing effectiveness. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.