Predicting high-magnitude, low-frequency crop losses using machine learning: An application to cereal crops in Ethiopia
Timely and accurate agricultural impact assessments for droughts are critical for designing appropriate interventions and policy. These assessments are often ad hoc, late, or spatially imprecise, with reporting at the zonal or regional level. This is problematic as we find substantial variability in...
| Autores principales: | , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Springer
2019
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/145592 |
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