Satellite monitoring uncovers progress but large disparities in doubling crop yields
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866909378882306048 |
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| author | Fankhauser, Katie Thomas, Evan Mehrabi, Zia |
| author_facet | Fankhauser, Katie Thomas, Evan Mehrabi, Zia |
| contents | High-resolution satellite-based crop yield mapping offers enormous promise for monitoring progress towards the SDGs. Across 15,000 villages in Rwanda we uncover areas that are on and off track to double productivity by 2030. This machine learning enabled analysis is used to design spatially explicit productivity targets that, if met, would simultaneously ensure national goals without leaving anyone behind. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_03322 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Satellite monitoring uncovers progress but large disparities in doubling crop yields Fankhauser, Katie Thomas, Evan Mehrabi, Zia Computers and Society Machine Learning High-resolution satellite-based crop yield mapping offers enormous promise for monitoring progress towards the SDGs. Across 15,000 villages in Rwanda we uncover areas that are on and off track to double productivity by 2030. This machine learning enabled analysis is used to design spatially explicit productivity targets that, if met, would simultaneously ensure national goals without leaving anyone behind. |
| title | Satellite monitoring uncovers progress but large disparities in doubling crop yields |
| topic | Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2411.03322 |