Mapping expected returns to fertilizer investments across Sub-Saharan Africa
Low and uneven fertilizer use in Sub-Saharan Africa (SSA) persists despite extensive agronomic evidence of yield responsiveness. A key constraint is strong spatial heterogeneity in biophysical conditions, prices, and market access, which limits the relevance of average fertilizer recommendations for...
| Autores principales: | , |
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| Formato: | Informe técnico |
| Lenguaje: | Inglés |
| Publicado: |
CIMMYT
2025
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/179703 |
| _version_ | 1855525649502961664 |
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| author | Gebrekidan, Bisrat Gebrekidan Chamberlin, Jordan |
| author_browse | Chamberlin, Jordan Gebrekidan, Bisrat Gebrekidan |
| author_facet | Gebrekidan, Bisrat Gebrekidan Chamberlin, Jordan |
| author_sort | Gebrekidan, Bisrat Gebrekidan |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Low and uneven fertilizer use in Sub-Saharan Africa (SSA) persists despite extensive agronomic evidence of yield responsiveness. A key constraint is strong spatial heterogeneity in biophysical conditions, prices, and market access, which limits the relevance of average fertilizer recommendations for farm-level decision-making. Existing decision-support tools rarely provide ex ante guidance on where fertilizer investments are likely to be economically viable. We develop a spatially explicit ex ante framework to evaluate fertilizer profitability in maize systems across SSA. The framework integrates large-scale yield response trial data, gridded soil and climate information, and spatially explicit output and input price surfaces to predict pixel-level yield gains, revenues, and net returns under alternative fertilizer application rates. Machine-learning models are used to estimate yield and price response surfaces, which are combined with spatially varying costs to generate high-resolution profitability maps. Results show substantial spatial variation in fertilizer profitability, with many areas indicating low or negative expected returns at commonly recommended rates, alongside regions with consistently high economic potential. Comparisons across recommended and profit-maximizing nutrient rates show systematic divergence between agronomic and economic optima. By providing scalable, data-driven estimates of fertilizer profitability ex ante, the framework offers a practical basis for improving targeting of fertilizer recommendations, extension services, and public investment strategies in SSA. |
| format | Informe técnico |
| id | CGSpace179703 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | CIMMYT |
| publisherStr | CIMMYT |
| record_format | dspace |
| spelling | CGSpace1797032026-01-13T02:07:32Z Mapping expected returns to fertilizer investments across Sub-Saharan Africa Gebrekidan, Bisrat Gebrekidan Chamberlin, Jordan fertilizers economic viability public investment machine learning Low and uneven fertilizer use in Sub-Saharan Africa (SSA) persists despite extensive agronomic evidence of yield responsiveness. A key constraint is strong spatial heterogeneity in biophysical conditions, prices, and market access, which limits the relevance of average fertilizer recommendations for farm-level decision-making. Existing decision-support tools rarely provide ex ante guidance on where fertilizer investments are likely to be economically viable. We develop a spatially explicit ex ante framework to evaluate fertilizer profitability in maize systems across SSA. The framework integrates large-scale yield response trial data, gridded soil and climate information, and spatially explicit output and input price surfaces to predict pixel-level yield gains, revenues, and net returns under alternative fertilizer application rates. Machine-learning models are used to estimate yield and price response surfaces, which are combined with spatially varying costs to generate high-resolution profitability maps. Results show substantial spatial variation in fertilizer profitability, with many areas indicating low or negative expected returns at commonly recommended rates, alongside regions with consistently high economic potential. Comparisons across recommended and profit-maximizing nutrient rates show systematic divergence between agronomic and economic optima. By providing scalable, data-driven estimates of fertilizer profitability ex ante, the framework offers a practical basis for improving targeting of fertilizer recommendations, extension services, and public investment strategies in SSA. 2025-12-25 2026-01-12T15:59:45Z 2026-01-12T15:59:45Z Report https://hdl.handle.net/10568/179703 en Open Access application/pdf CIMMYT CGIAR Gebrekidan, B., & Chamberlin, J. (2025). Mapping expected returns to fertilizer investments across Sub-Saharan Africa. CIMMYT & CGIAR. https://hdl.handle.net/10883/36653 |
| spellingShingle | fertilizers economic viability public investment machine learning Gebrekidan, Bisrat Gebrekidan Chamberlin, Jordan Mapping expected returns to fertilizer investments across Sub-Saharan Africa |
| title | Mapping expected returns to fertilizer investments across Sub-Saharan Africa |
| title_full | Mapping expected returns to fertilizer investments across Sub-Saharan Africa |
| title_fullStr | Mapping expected returns to fertilizer investments across Sub-Saharan Africa |
| title_full_unstemmed | Mapping expected returns to fertilizer investments across Sub-Saharan Africa |
| title_short | Mapping expected returns to fertilizer investments across Sub-Saharan Africa |
| title_sort | mapping expected returns to fertilizer investments across sub saharan africa |
| topic | fertilizers economic viability public investment machine learning |
| url | https://hdl.handle.net/10568/179703 |
| work_keys_str_mv | AT gebrekidanbisratgebrekidan mappingexpectedreturnstofertilizerinvestmentsacrosssubsaharanafrica AT chamberlinjordan mappingexpectedreturnstofertilizerinvestmentsacrosssubsaharanafrica |