Using panel survey and remote sensing data to explain yield gaps for maize in sub-Saharan Africa
Forskningsoutput: Tidskriftsbidrag › Artikel i vetenskaplig tidskrift
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.
|Enheter & grupper|
Ämnesklassifikation (UKÄ) – OBLIGATORISK
|Tidskrift||Journal of Land Use Science|
|Status||Published - 2018|
|Peer review utförd||Ja|