A study published in September in Global Food Security, shows how machine learning of data from multiple sources can help make farming more efficient and productive even as the climate changes.

The researchers used big-data tools, based on the data farmers helped collect, and yields increased substantially.

“Today we can collect massive amounts of data, but you can’t just bulk it, process it in a machine and make a decision,” said Daniel Jimenez, the study’s lead author, data scientist at CIAT and Lead of the Community of Practice on Data-Driven Agronomy at the CGIAR Platform for Big Data in Agriculture.