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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
2022-04-25 17:16:35
@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}},
  keywords     = {{smallholders; sub-Saharan Africa; yield gaps; panel data; transdisciplinary explanation}},
  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}},
}