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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| Acceso en línea: | https://hdl.handle.net/10568/119437 |
| Sumario: | 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. |
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