Using panel survey and remote sensing data to explain yield gaps for maize in sub-Saharan Africa
(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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/9676e985-82e2-4aac-af6d-b4091b3d7e11
- author
- Djurfeldt, Göran LU ; Hall, Ola LU ; Jirström, Magnus LU ; Archila, Maria LU ; Holmquist, Björn LU and Nasrin, Sultana LU
- organization
- publishing date
- 2018
- 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}}, }