| 1. |
- 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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| 2. |
- Hall, Ola, et al.
(författare)
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Classification of Maize in Complex Smallholder Farming Systems Using UAV Imagery
- 2018
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Ingår i: Drones. - : MDPI AG. - 2504-446X. ; 2:3, s. 1-8
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Tidskriftsartikel (refereegranskat)abstract
- Yield estimates and yield gap analysis are important for identifying poor agricultural productivity. Remote sensing holds great promise for measuring yield and thus determining yield gaps. Farming systems in sub-Saharan Africa (SSA) are commonly characterized by small field size, intercropping, different crop species with similar phenologies, and sometimes high cloud frequency during the growing season, all of which pose real challenges to remote sensing. Here, an unmanned aerial vehicle (UAV) system based on a quadcopter equipped with two consumer-grade cameras was used for the delineation and classification of maize plants on smallholder farms in Ghana. Object-oriented image classification methods were applied to the imagery, combined with measures of image texture and intensity, hue, and saturation (IHS), in order to achieve delineation. It was found that the inclusion of a near-infrared (NIR) channel and red–green–blue (RGB) spectra, in combination with texture or IHS, increased the classification accuracy for both single and mosaic images to above 94%. Thus, the system proved suitable for delineating and classifying maize using RGB and NIR imagery and calculating the vegetation fraction, an important parameter in producing yield estimates for heterogeneous smallholder farming systems.
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| 3. |
- Jirström, Magnus, et al.
(författare)
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African Smallholder Farmers on the Move: Farm and Non-Farm Trends for Six Sub-Saharan African Countries, 2002–15
- 2018
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Ingår i: Agriculture, Diversification, and Gender in Rural Africa. - : Oxford University Press. - 9780198799283 ; , s. 17-53
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Bokkapitel (refereegranskat)abstract
- This chapter provides a broad descriptive background of central aspects of smallholder agriculture in six countries in sub-Saharan Africa (SSA). It offers an up-to-date picture of the current trends of crop production, area productivity, levels of commercialization, and sources of cash incomes among 2,500 farming households. Structured around smallholder production, commercialization, and diversification in the period 2002–15, the chapter points on the one hand at persistent challenges such as low crop yields, low levels of output per farm, and a high degree of subsistence farming, and on the other hand at positive change over time in terms of growth in crop production and increasing levels of commercialization. It points at large variations not only between countries and time periods but also at the village levels, where gaps in crop productivity between farms remain large. Implicitly it points at the potential yet to be exploited in the SSA smallholder sector.
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