Using panel survey and remote sensing data to explain yield gaps for maize in sub-Saharan Africa

Research output: Contribution to journalArticle


The aim of this paper is to combine remote sensing data with geo-coded household survey data in order to measure the impact of different socio-economic and biophysical factors on maize yields. We use multilevel linear regression to model village mean maize yield per year as a function of NDVI, commercialization, pluriactivity and distance to market. We draw on seven years of panel data on African smallholders, drawn from three rounds of data collection over a twelve-year period and 56 villages in six countries combined with a time-series analysis of NDVI data from the MODIS sensor. We show that, although there is much noise in yield forecasts as made with our methodology, socio-economic drivers substantially impact on yields, more, it seems, than do biophysical drivers. To reach more powerful explanations researchers need to incorporate socio-economic parameters in their models.


Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Human Geography


  • smallholders, sub-Saharan Africa, yield gaps, panel data, transdisciplinary explanation
Original languageEnglish
Pages (from-to)344-357
JournalJournal of Land Use Science
Issue number3
Publication statusPublished - 2018
Publication categoryResearch