Spatially targeting conservation and farm mechanization in Southern Africa: Insights from multicriteria analysis
The uptake of conservation agriculture and farm mechanization in Southern Africa has been slow and low. As a result, most smallholder farmers continue to grow crops under degraded soils using conventional tools and human powered farm operations. This leads to low productivity. Therefore, spatially v...
| Autores principales: | , , , |
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| Formato: | Brochure |
| Lenguaje: | Inglés |
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International Maize and Wheat Improvement Center
2023
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| Acceso en línea: | https://hdl.handle.net/10568/138150 |
| _version_ | 1855542289998282752 |
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| author | Membele, Garikai Martin Ngoma, Hambulo Thierfelder, Christian Marenya, Paswel P. |
| author_browse | Marenya, Paswel P. Membele, Garikai Martin Ngoma, Hambulo Thierfelder, Christian |
| author_facet | Membele, Garikai Martin Ngoma, Hambulo Thierfelder, Christian Marenya, Paswel P. |
| author_sort | Membele, Garikai Martin |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The uptake of conservation agriculture and farm mechanization in Southern Africa has been slow and low. As a result, most smallholder farmers continue to grow crops under degraded soils using conventional tools and human powered farm operations. This leads to low productivity. Therefore, spatially visualizing areas where conservation agriculture and farm mechanization can be targeted can be crucial to guide targeting and scaling. In this study, a geographical information systems-based multicriteria analysis using the analytical hierarchical process was used to map the suitability of conservation agriculture and farm mechanization in Malawi, Zambia, and Zimbabwe. This included biophysical (soil, rainfall, temperature, slope, elevation, land use and biomass) and socioeconomic (population density, farming system and livestock ownership) as recommended domains. The Super Decision software 3.2.0 was used to generate the final weights through pairwise comparison. ArcGIS Pro 2.6 through fuzzy functions was used to standardise and generate the maps. The results show that over 95% of the land area in Zimbabwe, 73% in Zambia, and 67% in Malawi are suitable for conservation agriculture and farm mechanization. Malawi, however, has a bigger proportion of land (1%) among the three countries with low suitability. There are regional differences with Lilongwe and Balaka in Malawi; Southern, Central, Eastern and Western provinces in Zambia; and Matebeleland South and North, Mashonaland West, Midlands and Masvingo provinces in Zimbabwe being the most suitable for conservation agriculture and farm mechanization. After validation, the suitability map based on varying weights showed higher levels of reliability, resulting in context-specific suitability maps and that the mapping process was robust. Thus, the suitability maps generated from this study can be used for targeting conservation agriculture and farm mechanization by stakeholders and decisionmakers in the three countries. |
| format | Brochure |
| id | CGSpace138150 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | International Maize and Wheat Improvement Center |
| publisherStr | International Maize and Wheat Improvement Center |
| record_format | dspace |
| spelling | CGSpace1381502025-05-04T09:22:16Z Spatially targeting conservation and farm mechanization in Southern Africa: Insights from multicriteria analysis Membele, Garikai Martin Ngoma, Hambulo Thierfelder, Christian Marenya, Paswel P. conservation agriculture mechanization The uptake of conservation agriculture and farm mechanization in Southern Africa has been slow and low. As a result, most smallholder farmers continue to grow crops under degraded soils using conventional tools and human powered farm operations. This leads to low productivity. Therefore, spatially visualizing areas where conservation agriculture and farm mechanization can be targeted can be crucial to guide targeting and scaling. In this study, a geographical information systems-based multicriteria analysis using the analytical hierarchical process was used to map the suitability of conservation agriculture and farm mechanization in Malawi, Zambia, and Zimbabwe. This included biophysical (soil, rainfall, temperature, slope, elevation, land use and biomass) and socioeconomic (population density, farming system and livestock ownership) as recommended domains. The Super Decision software 3.2.0 was used to generate the final weights through pairwise comparison. ArcGIS Pro 2.6 through fuzzy functions was used to standardise and generate the maps. The results show that over 95% of the land area in Zimbabwe, 73% in Zambia, and 67% in Malawi are suitable for conservation agriculture and farm mechanization. Malawi, however, has a bigger proportion of land (1%) among the three countries with low suitability. There are regional differences with Lilongwe and Balaka in Malawi; Southern, Central, Eastern and Western provinces in Zambia; and Matebeleland South and North, Mashonaland West, Midlands and Masvingo provinces in Zimbabwe being the most suitable for conservation agriculture and farm mechanization. After validation, the suitability map based on varying weights showed higher levels of reliability, resulting in context-specific suitability maps and that the mapping process was robust. Thus, the suitability maps generated from this study can be used for targeting conservation agriculture and farm mechanization by stakeholders and decisionmakers in the three countries. 2023 2024-01-19T15:42:21Z 2024-01-19T15:42:21Z Brochure https://hdl.handle.net/10568/138150 en Open Access application/pdf International Maize and Wheat Improvement Center Membele, G. M., Ngoma, H., Thierfelder, C., & Marenya, P. P. (2023). Spatially targeting conservation and farm mechanization in Southern Africa: Insights from multicriteria analysis. CIMMYT. https://hdl.handle.net/10883/22934 |
| spellingShingle | conservation agriculture mechanization Membele, Garikai Martin Ngoma, Hambulo Thierfelder, Christian Marenya, Paswel P. Spatially targeting conservation and farm mechanization in Southern Africa: Insights from multicriteria analysis |
| title | Spatially targeting conservation and farm mechanization in Southern Africa: Insights from multicriteria analysis |
| title_full | Spatially targeting conservation and farm mechanization in Southern Africa: Insights from multicriteria analysis |
| title_fullStr | Spatially targeting conservation and farm mechanization in Southern Africa: Insights from multicriteria analysis |
| title_full_unstemmed | Spatially targeting conservation and farm mechanization in Southern Africa: Insights from multicriteria analysis |
| title_short | Spatially targeting conservation and farm mechanization in Southern Africa: Insights from multicriteria analysis |
| title_sort | spatially targeting conservation and farm mechanization in southern africa insights from multicriteria analysis |
| topic | conservation agriculture mechanization |
| url | https://hdl.handle.net/10568/138150 |
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