{ "background": "The performance of industrial machinery fleets is a critical determinant of productivity in developing economies. Prior research in the region has often relied on cross-sectional data, limiting the ability to control for unobserved heterogeneity and to analyse temporal dynamics in fleet efficiency and its impact on yield.", "purpose and objectives": "This study replicates and extends a prior analysis of machinery fleet systems, with the objective of applying a panel-data methodology to rigorously estimate the relationship between fleet performance metrics and agricultural yield improvement. It aims to validate previous findings and provide more robust, time-sensitive estimates.", "methodology": "A replication study employing a balanced panel dataset was conducted. The core analytical model is a two-way fixed effects regression: $Y{it} = \\beta0 + \\beta1 X{it} + \\alphai + \\lambdat + \\epsilon{it}$, where $Y{it}$ is yield, $X{it}$ represents fleet utilisation, $\\alphai$ are entity-fixed effects, and $\\lambda_t$ are time-fixed effects. Inference is based on cluster-robust standard errors.", "findings": "The panel-data estimation confirms a positive, statistically significant association between improved fleet utilisation and yield. A one-standard-deviation increase in the fleet performance index is associated with an approximate 7.5% increase in yield (95% CI: 5.1% to 9.9%). The entity-fixed effects account for a substantial portion of the variance, underscoring the importance of controlling for time-invariant heterogeneity.", "conclusion": "The replication validates the core hypothesis of the original study while demonstrating that panel-data methods yield more precise and reliable estimates by accounting for unobserved, time-invariant confounders. The relationship between machinery fleet efficiency and yield is robust to this more stringent methodological approach.", "recommendations": "Future engineering management analyses in similar contexts should adopt panel-data frameworks where possible. Policymakers and fleet operators should prioritise metrics captured in the performance index