Mining the gaps:using machine learning to map a million data points from agricultural research from the global south
We’re entering a new era in agriculture, one that moves beyond a purely production-oriented vision and recognizes its role in contributing to a food system that prioritizes people’s livelihoods and nutrition, as well as environmental and climate outcomes. This shift in thinking will require major...
| Autores principales: | , , , , , |
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| Formato: | Informe técnico |
| Lenguaje: | Inglés |
| Publicado: |
CGIAR Research Program on Water, Land and Ecosystems
2021
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/119437 |
| _version_ | 1855513304249663488 |
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| author | Porciello, J. Lipper, L. Bourne, T. Ivanina, M. Lin, S. Langleben, S. |
| author_browse | Bourne, T. Ivanina, M. Langleben, S. Lin, S. Lipper, L. Porciello, J. |
| author_facet | Porciello, J. Lipper, L. Bourne, T. Ivanina, M. Lin, S. Langleben, S. |
| author_sort | Porciello, J. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | We’re entering a new era in agriculture, one that moves beyond a purely production-oriented vision and recognizes its role in contributing to a food system that prioritizes people’s livelihoods and nutrition, as well as environmental and climate outcomes.
This shift in thinking will require major shifts in policy, research, and investment. But where should these investments go?
What foundations should be strengthened? Which gaps need filling? What’s working? What’s not?
In order to answer these questions in an informed way, we need to examine the evidence that exists and identify areas where more research is needed. |
| format | Informe técnico |
| id | CGSpace119437 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | CGIAR Research Program on Water, Land and Ecosystems |
| publisherStr | CGIAR Research Program on Water, Land and Ecosystems |
| record_format | dspace |
| spelling | CGSpace1194372024-01-08T18:54:14Z Mining the gaps:using machine learning to map a million data points from agricultural research from the global south Porciello, J. Lipper, L. Bourne, T. Ivanina, M. Lin, S. Langleben, S. agricultural research nutrition artificial intelligence machine learning We’re entering a new era in agriculture, one that moves beyond a purely production-oriented vision and recognizes its role in contributing to a food system that prioritizes people’s livelihoods and nutrition, as well as environmental and climate outcomes. This shift in thinking will require major shifts in policy, research, and investment. But where should these investments go? What foundations should be strengthened? Which gaps need filling? What’s working? What’s not? In order to answer these questions in an informed way, we need to examine the evidence that exists and identify areas where more research is needed. 2021-12-01 2022-05-02T08:26:19Z 2022-05-02T08:26:19Z Report https://hdl.handle.net/10568/119437 en Open Access application/pdf CGIAR Research Program on Water, Land and Ecosystems Porciello, J.; Lipper, L.; Bourne, T.; Ivanina, M.; Lin, S.; Langleben, S. 2021. Mining the gaps:using machine learning to map a million data points from agricultural research from the global south. Colombo, Sri Lanka: CGIAR Research Program on Water, Land and Ecosystems (WLE). 22p. |
| spellingShingle | agricultural research nutrition artificial intelligence machine learning Porciello, J. Lipper, L. Bourne, T. Ivanina, M. Lin, S. Langleben, S. Mining the gaps:using machine learning to map a million data points from agricultural research from the global south |
| title | Mining the gaps:using machine learning to map a million data points from agricultural research from the global south |
| title_full | Mining the gaps:using machine learning to map a million data points from agricultural research from the global south |
| title_fullStr | Mining the gaps:using machine learning to map a million data points from agricultural research from the global south |
| title_full_unstemmed | Mining the gaps:using machine learning to map a million data points from agricultural research from the global south |
| title_short | Mining the gaps:using machine learning to map a million data points from agricultural research from the global south |
| title_sort | mining the gaps using machine learning to map a million data points from agricultural research from the global south |
| topic | agricultural research nutrition artificial intelligence machine learning |
| url | https://hdl.handle.net/10568/119437 |
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