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,...
| Main Authors: | , , , , , |
|---|---|
| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Elsevier
2025
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/178106 |
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