Integrating crop models and machine learning for projecting climate change impacts on crops in data-limited environments
Context Accurately projecting crop yields under climate change is essential for understanding potential impacts and planning of agricultural adaptation in sub-Saharan Africa (SSA). Crop growth models and machine learning (ML) are often used, but their effectiveness is limited by data availability,...
| Autores principales: | , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2025
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/178106 |
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