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

Summary, in English

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.

• Author: Göran Djurfeldt; Ola Hall; Magnus Jirström; Maria Archila; Björn Holmquist; Sultana Nasrin
• Department/s: Department of Human Geography; Sociology; Department of Statistics
• Publishing year: 2018
• Language: English
• Pages: 344-357
• Publication/Series: Journal of Land Use Science
• Volume: 13
• Issue: 3
• Links: http://dx.doi.org/10.1080/1747423X.2018.1511763
• Document type: Journal article
• Publisher: Taylor & Francis
• Topic: Human Geography
• Keywords: smallholders; sub-Saharan Africa; yield gaps; panel data; transdisciplinary explanation
• Status: Published
• ISBN/ISSN/Other: ISSN: 1747-423X