Satellite monitoring uncovers progress but large disparities in doubling crop yields

TL;DR

This study uses satellite data and machine learning to monitor crop yield progress in Rwanda, identifying disparities and setting spatially explicit targets to achieve national productivity goals by 2030.

Contribution

It introduces a satellite-based, machine learning approach to assess and guide crop yield improvements at a fine spatial scale in Rwanda.

Findings

Identifies areas on and off track for doubling crop yields by 2030.

Designs spatially explicit productivity targets aligned with national SDG goals.

Highlights disparities in crop yield progress across villages.

Abstract

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.