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. 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.

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.