A spatial framework for guiding mechanization investments and evaluating mechanization theory in Africa: Evidence from Zimbabwe

CONTEXT: Farm mechanization has re-emerged as a development priority across sub-Saharan Africa, yet mechanization investments remain constrained by limited resources and uncertainty about where they are most likely to succeed. Existing approaches also rely on assumptions regarding the structural drivers of mechanization that are seldom evaluated empirically.OBJECTIVE: The objective of this study was to develop a spatial framework to guide mechanization investments by integrating an ex-ante, hypothesis-based approach with an ex-post, data-driven approach, and to evaluate the framework in Zimbabwe.METHODS: Principal component analysis and hierarchical clustering were used to identify mechanization recommendation domains, from five structural drivers commonly identified in mechanization theory: farming system intensity, land:labour ratio, early growing season rainfall variability, value of crop production, and market access. Using data from a national survey of 6,936 farming households, random forest models predicted the occurrence of nine mechanization outcomes from the same drivers as predictors, and quantified their relative importance.RESULTS AND CONCLUSIONS: Three mechanization recommendation domains were identified from the five hypothesized structural drivers and were strongly supported by observed mechanization ownership. Across the nine mechanization outcomes, value of crop production and early growing season rainfall variability consistently emerged as the dominant drivers of mechanization ownership at local level, whereas land:labour ratio contributed comparatively little. Contrary to prevailing mechanization theory, one recommendation domain exhibited consistently low mechanization ownership despite comparatively favourable farming system intensity, value of crop production, market access and land:labour ratio, indicating that current theory cannot fully explain mechanization patterns in Zimbabwe.SIGNIFICANCE: By integrating ex-ante and ex-post approaches, the proposed framework provides a practical basis for guiding mechanization investments and offers a means to empirically evaluate and refine mechanization theory across African farming systems.