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| 2. |
- Djurfeldt, Göran, et al.
(författare)
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African farm dynamics and the sub-continental food crisis – the case of maize
- 2008
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Ingår i: Acta Agriculturae Scandinavica. Section C. Food Economics. - : Informa UK Limited. - 1650-7541 .- 1651-288X. ; 5:2, s. 75-91
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Tidskriftsartikel (refereegranskat)abstract
- This is a longitudinal study of smallholder farm dynamics in eight African countries, drawing on a sample of more than 3000 farm households. It deals with influences on smallholder maize production at three different levels: micro, meso and macro. Although the study points to inadequately exploited production potentials and a production system based primarily on self-provisioning the drivers behind dynamism in this context are clear. Dynamic production patterns are closely associated with smallholder access to modern crop technologies in combination with commercial incentives as expressed both at the meso and macro levels.
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| 3. |
- Djurfeldt, Göran, et al.
(författare)
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Afrint database
- 2011
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Annan publikation (övrigt vetenskapligt/konstnärligt)
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| 4. |
- Nasrin, Sultana, et al.
(författare)
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Drivers of rice production: evidence from five Sub-Saharan African countries
- 2015
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Ingår i: Agriculture & Food Security. - : Springer Science and Business Media LLC. - 2048-7010. ; 4:12, s. 1-19
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Tidskriftsartikel (refereegranskat)abstract
- Background: In spite of considerable rice production gains over the past 50 years, Sub-Saharan Africa is becoming increasingly dependent on rice imports as demand is outpacing domestic supply. The serious economic and social strains caused by this have urged national leaders to address production deficits. The aim of this article is to analyse and discuss the drivers behind recent changes in rice production in Africa South of the Sahara, focusing on Ghana, Malawi, Nigeria, Tanzania and Mozambique. Considering the period 2002–2008, we model production performance and changes in production amongst 317 rice-growing households using multilevel and longitudinal data. We evaluate and discuss the role of three key processes: the role of commercial drivers, farm technology and macro-level conditions. Results: We show that until 2002, production was driven by a combination of the three key processes considered, while during the period 2002–2008, production increases were primarily associated with area expansion and commercial drivers. This suggests that production lately has been more driven by processes of extensification than intensification. We also note that in none of the periods considered, the share of the state budget allocated to agriculture had a significant effect on production and that recent developments do not give any obvious support for an Asianstyle state-driven Green Revolution in rice in Sub-Saharan Africa. Conclusions: The role of commercialization in explaining changes in production suggests that policies strengthening food staple markets in the sub-continent hold great potential for driving rice production in the near future. Due to the scarcity of available land, the possibilities of further growth in the rice sector are limited without an intensification of production. Hence, farmers also need to access new farm technology, and positive development of rice production would in turn contribute to an improvement of food security.
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| 5. |
- Djurfeldt, Göran, et al.
(författare)
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Using panel survey and remote sensing data to explain yield gaps for maize in sub-Saharan Africa
- 2018
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Ingår i: Journal of Land Use Science. - 1747-423X. ; 13:3, s. 344-357
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Tidskriftsartikel (refereegranskat)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.
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