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: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Springer
2019
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/145592 |
| _version_ | 1855536710180405248 |
|---|---|
| author | Mann, Michael L. Warner, James Malik, Arun S. |
| author_browse | Malik, Arun S. Mann, Michael L. Warner, James |
| author_facet | Mann, Michael L. Warner, James Malik, Arun S. |
| author_sort | Mann, Michael L. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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 losses at the village-level, which is missing when reporting at the zonal level. In this paper, we propose a new data fusion method—combining remotely sensed data with agricultural survey data—that might address these limitations. We apply the method to Ethiopia, which is regularly hit by droughts and is a substantial recipient of ad hoc imported food aid. We then utilize remotely sensed data obtained near mid-season to predict substantial crop losses of greater than or equal to 25% due to drought at the village level for five primary cereal crops. We train machine learning models to predict the likelihood of losses and explore the most influential variables. On independent samples, the models identify substantial drought loss cases with up to 81% accuracy by mid- to late-September. We believe the proposed models could be used to help monitor and predict yields for disaster response teams and policy makers, particularly with further development of the models and integration of soon-to-be available high-resolution, remotely sensed data such as the Harmonized Landsat Sentinel (HLS) data set. |
| format | Journal Article |
| id | CGSpace145592 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | CGSpace1455922025-02-24T06:46:34Z Predicting high-magnitude, low-frequency crop losses using machine learning: An application to cereal crops in Ethiopia Mann, Michael L. Warner, James Malik, Arun S. forecasting machine learning agriculture drought crop losses cereal crops climate change 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 losses at the village-level, which is missing when reporting at the zonal level. In this paper, we propose a new data fusion method—combining remotely sensed data with agricultural survey data—that might address these limitations. We apply the method to Ethiopia, which is regularly hit by droughts and is a substantial recipient of ad hoc imported food aid. We then utilize remotely sensed data obtained near mid-season to predict substantial crop losses of greater than or equal to 25% due to drought at the village level for five primary cereal crops. We train machine learning models to predict the likelihood of losses and explore the most influential variables. On independent samples, the models identify substantial drought loss cases with up to 81% accuracy by mid- to late-September. We believe the proposed models could be used to help monitor and predict yields for disaster response teams and policy makers, particularly with further development of the models and integration of soon-to-be available high-resolution, remotely sensed data such as the Harmonized Landsat Sentinel (HLS) data set. 2019-04-25 2024-06-21T09:04:43Z 2024-06-21T09:04:43Z Journal Article https://hdl.handle.net/10568/145592 en https://doi.org/10.2499/1046080770 Open Access Springer Mann, Michael L.; Warner, James M.; and Malik, Arun S. 2019. Predicting high-magnitude, low-frequency crop losses using machine learning: An application to cereal crops in Ethiopia. Climatic Change 154(1-2): 211–227. https://doi.org/10.1007/s10584-019-02432-7 |
| spellingShingle | forecasting machine learning agriculture drought crop losses cereal crops climate change Mann, Michael L. Warner, James Malik, Arun S. Predicting high-magnitude, low-frequency crop losses using machine learning: An application to cereal crops in Ethiopia |
| title | Predicting high-magnitude, low-frequency crop losses using machine learning: An application to cereal crops in Ethiopia |
| title_full | Predicting high-magnitude, low-frequency crop losses using machine learning: An application to cereal crops in Ethiopia |
| title_fullStr | Predicting high-magnitude, low-frequency crop losses using machine learning: An application to cereal crops in Ethiopia |
| title_full_unstemmed | Predicting high-magnitude, low-frequency crop losses using machine learning: An application to cereal crops in Ethiopia |
| title_short | Predicting high-magnitude, low-frequency crop losses using machine learning: An application to cereal crops in Ethiopia |
| title_sort | predicting high magnitude low frequency crop losses using machine learning an application to cereal crops in ethiopia |
| topic | forecasting machine learning agriculture drought crop losses cereal crops climate change |
| url | https://hdl.handle.net/10568/145592 |
| work_keys_str_mv | AT mannmichaell predictinghighmagnitudelowfrequencycroplossesusingmachinelearninganapplicationtocerealcropsinethiopia AT warnerjames predictinghighmagnitudelowfrequencycroplossesusingmachinelearninganapplicationtocerealcropsinethiopia AT malikaruns predictinghighmagnitudelowfrequencycroplossesusingmachinelearninganapplicationtocerealcropsinethiopia |