"background": "Agricultural productivity in Uganda remains constrained by inefficiencies in mechanisation. Policy interventions have historically focused on subsidising machinery acquisition, with limited evaluation of the operational performance of existing machinery fleets and their direct impact on crop yield. ", "purpose and objectives": "This policy analysis evaluates the efficacy of national machinery fleet diagnostics as an engineering tool for yield improvement. It aims to quantify the relationship between fleet diagnostic metrics and agricultural output, providing an evidence base for future mechanisation policy. ", "methodology": "A multilevel regression model was employed, nesting farm-level yield data within district-level machinery fleet diagnostic assessments. The core model is specified as Y{ij = \0 + \1Xij + \ Zj + uj +, where Yij is yield for farm i in district j, Xij represents farm-level covariates, and Zj is a district-level fleet diagnostic index. Inference is based on robust standard errors clustered at the district level. ", "findings": "The analysis indicates a statistically significant positive association between higher fleet diagnostic scores and improved yield. A one-standard-deviation increase in the district-level diagnostic index was associated with an estimated 17% increase in mean yield (95% CI: 12% to 22%), controlling for farm size and seed type. ", "conclusion": "Systematic machinery fleet diagnostics are a potent, underutilised policy instrument for yield enhancement. Current policy overly emphasises procurement over operational performance optimisation. ", "recommendations": "Policy should mandate regular, standardised machinery fleet diagnostics as a condition for support. Investment should be redirected towards establishing regional diagnostic centres and training programmes for maintenance engineers. ", "key words": "mechanisation policy, multilevel modelling, agricultural engineering, fleet management, diagnostics, sub-Saharan Africa", "contribution statement": "This study provides the first quantitative evidence linking systematic machinery fleet diagnostics to agricultural yield in
Nakato Kigozi (Thu,) studied this question.