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Open AccessArticle

Estimating Yields of Household Fields in Rural Subsistence Farming Systems to Study Food Security in Burkina Faso

1
Remote Sensing Solutions (RSS) GmbH, Dingolfingerstr. 9, 81673 Munich, Germany
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Heidelberg Institute of Global Health (HIGH), Faculty of Medicine, Heidelberg University, Im Neuenheimer Feld 130.3, 69120 Heidelberg, Germany
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Centre de Recherche en Santé de Nouna (CRSN), Institut National de Santé Publique (INSP), Rue Namory Keita, PO Box 02, Nouna, Boucle du Mouhoun, Burkina Faso
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Institut Universitaire de Formations Initiale et Continue (IUFIC), Université Ouaga II, 12 BP 417 Ouagadougou 12, Burkina Faso
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Institute of Computer Science, University of Osnabrück, Wachsbleiche 27, 49090 Osnabrück, Germany
*
Author to whom correspondence should be addressed.
Shared first authorship.
Shared last authorship.
Remote Sens. 2020, 12(11), 1717; https://doi.org/10.3390/rs12111717
Received: 6 April 2020 / Revised: 10 May 2020 / Accepted: 22 May 2020 / Published: 27 May 2020
Climate change has an increasing impact on food security and child nutrition, particularly among rural smallholder farmers in sub-Saharan Africa. Their limited resources and rainfall dependent farming practices make them sensitive to climate change-related effects. Data and research linking yield, human health, and nutrition are scarce but can provide a basis for adaptation and risk management strategies. In support of studies on child undernutrition in Burkina Faso, this study analyzed the potential of remote sensing-based yield estimates at household level. Multi-temporal Sentinel-2 data from the growing season 2018 were used to model yield of household fields (median 1.4 hectares (ha), min 0.01 ha, max 12.6 ha) for the five most prominent crops in the Nouna Health and Demographic Surveillance (HDSS) area in Burkina Faso. Based on monthly metrics of vegetation indices (VIs) and in-situ harvest measurements from an extensive field survey, yield prediction models for different crops of high dietary importance (millet, sorghum, maize, and beans) were successfully generated producing R² between 0.4 and 0.54 (adj. R² between 0.32 and 0.5). The models were spatially applied and resulted in a yield estimation map at household level, enabling predictions of up to 2 months prior to harvest. The map links yield on a 10-m spatial resolution to households and consequently can display potential food insecurity. The results highlight the potential for satellite imagery to provide yield predictions of smallholder fields and are discussed in the context of health-related studies such as child undernutrition and food security in rural Africa under climate change. View Full-Text
Keywords: food crops; child nutrition; remote sensing; Sentinel-2; vegetation index metrics; West Africa food crops; child nutrition; remote sensing; Sentinel-2; vegetation index metrics; West Africa
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MDPI and ACS Style

Karst, I.G.; Mank, I.; Traoré, I.; Sorgho, R.; Stückemann, K.-J.; Simboro, S.; Sié, A.; Franke, J.; Sauerborn, R. Estimating Yields of Household Fields in Rural Subsistence Farming Systems to Study Food Security in Burkina Faso. Remote Sens. 2020, 12, 1717.

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