Assessing spatial suitability for agricultural mechanization in Ethiopia using expert-based and data-driven approaches
Despite its well documented role in improving agricultural productivity, agricultural mechanization adoption remains very low in Ethiopia. Within the context of agricultural mechanization, tractors represent the dominant source of mechanical farm power used to perform farm operations. This study dev...
| Autores principales: | , , |
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| Formato: | Informe técnico |
| Lenguaje: | Inglés |
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CIMMYT
2025
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/180244 |
| _version_ | 1855527696587554816 |
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| author | Gebrekidan, Bisrat Gebrekidan Tadesse, Ephrem Sida, Tesfaye Shiferaw |
| author_browse | Gebrekidan, Bisrat Gebrekidan Sida, Tesfaye Shiferaw Tadesse, Ephrem |
| author_facet | Gebrekidan, Bisrat Gebrekidan Tadesse, Ephrem Sida, Tesfaye Shiferaw |
| author_sort | Gebrekidan, Bisrat Gebrekidan |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Despite its well documented role in improving agricultural productivity, agricultural mechanization adoption remains very low in Ethiopia. Within the context of agricultural mechanization, tractors represent the dominant source of mechanical farm power used to perform farm operations. This study develops a national-scale spatial assessment of tractor suitability by integrating expert-based multi-criteria analysis, Analytic Hierarchy Process (AHP) weighting, and machine-learning models trained on observed tractor use from LSMS survey data. Using harmonized 1 km resolution datasets on soils, topography, accessibility, rainfall, and farm size, we map both technical suitability and predicted adoption intensity. Results show strong spatial heterogeneity, with slope, farm size, and accessibility emerging as dominant constraints. The framework highlights where mechanization investments are most viable and where complementary policy interventions might be required. |
| format | Informe técnico |
| id | CGSpace180244 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | CIMMYT |
| publisherStr | CIMMYT |
| record_format | dspace |
| spelling | CGSpace1802442026-01-25T02:08:17Z Assessing spatial suitability for agricultural mechanization in Ethiopia using expert-based and data-driven approaches Gebrekidan, Bisrat Gebrekidan Tadesse, Ephrem Sida, Tesfaye Shiferaw agricultural mechanization tractors spatial analysis machine learning farm size Despite its well documented role in improving agricultural productivity, agricultural mechanization adoption remains very low in Ethiopia. Within the context of agricultural mechanization, tractors represent the dominant source of mechanical farm power used to perform farm operations. This study develops a national-scale spatial assessment of tractor suitability by integrating expert-based multi-criteria analysis, Analytic Hierarchy Process (AHP) weighting, and machine-learning models trained on observed tractor use from LSMS survey data. Using harmonized 1 km resolution datasets on soils, topography, accessibility, rainfall, and farm size, we map both technical suitability and predicted adoption intensity. Results show strong spatial heterogeneity, with slope, farm size, and accessibility emerging as dominant constraints. The framework highlights where mechanization investments are most viable and where complementary policy interventions might be required. 2025-12-25 2026-01-20T20:15:08Z 2026-01-20T20:15:08Z Report https://hdl.handle.net/10568/180244 en Open Access application/pdf CIMMYT Gebrekidan, B., Tadesse, E., & Sida, T. S. 2025. Assessing spatial suitability for agricultural mechanization in Ethiopia using expert-based and data-driven approaches. CIMMYT. https://hdl.handle.net/10883/36775 |
| spellingShingle | agricultural mechanization tractors spatial analysis machine learning farm size Gebrekidan, Bisrat Gebrekidan Tadesse, Ephrem Sida, Tesfaye Shiferaw Assessing spatial suitability for agricultural mechanization in Ethiopia using expert-based and data-driven approaches |
| title | Assessing spatial suitability for agricultural mechanization in Ethiopia using expert-based and data-driven approaches |
| title_full | Assessing spatial suitability for agricultural mechanization in Ethiopia using expert-based and data-driven approaches |
| title_fullStr | Assessing spatial suitability for agricultural mechanization in Ethiopia using expert-based and data-driven approaches |
| title_full_unstemmed | Assessing spatial suitability for agricultural mechanization in Ethiopia using expert-based and data-driven approaches |
| title_short | Assessing spatial suitability for agricultural mechanization in Ethiopia using expert-based and data-driven approaches |
| title_sort | assessing spatial suitability for agricultural mechanization in ethiopia using expert based and data driven approaches |
| topic | agricultural mechanization tractors spatial analysis machine learning farm size |
| url | https://hdl.handle.net/10568/180244 |
| work_keys_str_mv | AT gebrekidanbisratgebrekidan assessingspatialsuitabilityforagriculturalmechanizationinethiopiausingexpertbasedanddatadrivenapproaches AT tadesseephrem assessingspatialsuitabilityforagriculturalmechanizationinethiopiausingexpertbasedanddatadrivenapproaches AT sidatesfayeshiferaw assessingspatialsuitabilityforagriculturalmechanizationinethiopiausingexpertbasedanddatadrivenapproaches |