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: | , , |
|---|---|
| Formato: | Artículo preliminar |
| Lenguaje: | Inglés |
| Publicado: |
International Food Policy Research Institute
2018
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/145591 |
Ejemplares similares: Predicting high-magnitude, low-frequency crop losses using machine learning: An application to cereal crops in Ethiopia
- Predicting high-magnitude, low-frequency crop losses using machine learning: An application to cereal crops in Ethiopia
- Predicting High-Magnitiude, Low-Frequency Crop Losses Using Machine Learning: An Application to Cereal Crops in Ethiopia
- Machine Learning Approach for Prediction of Area Under Cultivation and Production for Vegetatively Propagated Crops
- A critical synthesis of remote sensing and machine learning approaches for climate hazard impact on crop yield
- Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands
- Drought vulnerability of central Sahel agrosystems: a modelling-approach based on magnitudes of changes and machine learning techniques