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

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T1 - Using panel survey and remote sensing data to explain yield gaps for maize in sub-Saharan Africa

AU - Djurfeldt, Göran

AU - Hall, Ola

AU - Jirström, Magnus

AU - Archila, Maria

AU - Holmquist, Björn

AU - Nasrin, Sultana

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

KW - smallholders

KW - sub-Saharan Africa

KW - yield gaps

KW - panel data

KW - transdisciplinary explanation

U2 - 10.1080/1747423X.2018.1511763

DO - 10.1080/1747423X.2018.1511763

M3 - Article

VL - 13

SP - 344

EP - 357

JO - Journal of Land Use Science

JF - Journal of Land Use Science

SN - 1747-423X

IS - 3

ER -