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...
| Main Authors: | , , |
|---|---|
| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Springer
2019
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/145592 |
Similar Items: 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
- Estimation of soybean grain yield from multispectral high-resolution UAV data with machine learning models in west Africa
- Deep learning methods improve genomic prediction of wheat breeding
- Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands