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

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Bibtex

@article{9676e98582e24aacaf6db4091b3d7e11,
title = "Using panel survey and remote sensing data to explain yield gaps for maize in sub-Saharan Africa",
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.",
keywords = "smallholders, sub-Saharan Africa, yield gaps, panel data, transdisciplinary explanation",
author = "G{\"o}ran Djurfeldt and Ola Hall and Magnus Jirstr{\"o}m and Maria Archila and Bj{\"o}rn Holmquist and Sultana Nasrin",
year = "2018",
doi = "10.1080/1747423X.2018.1511763",
language = "English",
volume = "13",
pages = "344--357",
journal = "Journal of Land Use Science",
issn = "1747-423X",
publisher = "Taylor & Francis",
number = "3",

}