Data Science for Food Security
Data Science for Food Security
Using agricultural and economic characteristics in African nations as test cases, new research by David Lobell and Marshall Burke demonstrates the use of satellite data to address the long-standing problem of accurate data collection in developing countries. An often cited challenge in achieving development goals aimed at poverty and hunger reduction is the lack of reliable on-the-ground data. Limited or insuffiient data makes it difficult to establish baseline conditions and to assess effectiveness of various aid programs. In the past, researchers and policymakers had to rely on ground surveys, which are expensive, time-consuming, and rarely conducted. This has led to large data gaps in mapping sustainable development goal progress, such as in agricultural and poverty statistics.
This brief is based on findings from the papers “Satellite-based assessment of yield variation and its determinants in smallholder African systems,” published in Proceedings of the National Academy of Sciences in 2017 and “Combining satellite imagery and machine learning to predict poverty,” published in Science in 2016.