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

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Abstract

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.

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Nyckelord

Originalspråkengelska
Sidor (från-till)344-357
TidskriftJournal of Land Use Science
Volym13
Utgåva nummer3
StatusPublished - 2018
PublikationskategoriForskning
Peer review utfördJa