Regional monitoring networks have been established in Kenya to track adoption rates of agricultural innovations. A longitudinal study using the difference-in-differences (DiD) model was conducted to analyse regional monitoring network data. The DiD approach accounts for potential confounding factors by comparing changes within regions before and after innovation introduction versus those in control regions without the intervention. The analyses revealed that the DiD model significantly improved the estimation of adoption rates, with a confidence interval around the adjusted coefficient indicating an estimated increase of 15% in adoption following the network's implementation. The difference-in-differences methodology provided robust estimates for regional monitoring networks' impact on agricultural innovation adoption, offering a reliable tool for future studies and policy development. Future research should consider expanding the DiD model to include more detailed control groups or incorporate additional variables to enhance model accuracy. Agricultural innovations, Monitoring networks, Adoption rates, Difference-in-differences (DiD), Regional monitoring systems The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.