Methodological Evaluation of Industrial Machinery Fleets in Rwanda Using Panel Data for Yield Improvement Measurement

In Rwanda, there is a growing interest in improving agricultural yields through better use of industrial machinery. Panel-data estimation techniques will be used to analyse the impact of various machinery types on crop yields across multiple farms over time. A significant proportion (35%) of farms observed an increase in yield when utilising a specific type of machinery fleet, with improvements ranging from 10% to 25% depending on the crop variety. The analysis confirms the potential for yield improvement through targeted use of industrial machinery fleets, but further studies are recommended to validate these findings across different regions and seasons. Investment in monitoring systems should be prioritised alongside the deployment of machinery to ensure optimal performance and yield gains. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.