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

Djurfeldt, Göran LU orcid ; Hall, Ola LU ; Jirström, Magnus LU ; Archila, Maria LU ; Holmquist, Björn LU orcid and Nasrin, Sultana LU (2018) In Journal of Land Use Science 13(3). p.344-357
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... (More)
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. (Less)
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author
; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
smallholders, sub-Saharan Africa, yield gaps, panel data, transdisciplinary explanation
in
Journal of Land Use Science
volume
13
issue
3
pages
344 - 357
publisher
Taylor & Francis
external identifiers
  • scopus:85055635837
ISSN
1747-423X
DOI
10.1080/1747423X.2018.1511763
language
English
LU publication?
yes
id
9676e985-82e2-4aac-af6d-b4091b3d7e11
date added to LUP
2018-09-17 11:16:37
date last changed
2021-09-22 04:50:33
@article{9676e985-82e2-4aac-af6d-b4091b3d7e11,
  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.},
  author       = {Djurfeldt, Göran and Hall, Ola and Jirström, Magnus and Archila, Maria and Holmquist, Björn and Nasrin, Sultana},
  issn         = {1747-423X},
  language     = {eng},
  number       = {3},
  pages        = {344--357},
  publisher    = {Taylor & Francis},
  series       = {Journal of Land Use Science},
  title        = {Using panel survey and remote sensing data to explain yield gaps for maize in sub-Saharan Africa},
  url          = {http://dx.doi.org/10.1080/1747423X.2018.1511763},
  doi          = {10.1080/1747423X.2018.1511763},
  volume       = {13},
  year         = {2018},
}