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Verfasst von:Karst, Isabel G. [VerfasserIn]   i
 Mank, Isabel [VerfasserIn]   i
 Traoré, Issouf [VerfasserIn]   i
 Sorgho, Raissa [VerfasserIn]   i
 Stückemann, Kim-Jana [VerfasserIn]   i
 Simboro, Séraphin [VerfasserIn]   i
 Sié, Ali [VerfasserIn]   i
 Franke, Jonas [VerfasserIn]   i
 Sauerborn, Rainer [VerfasserIn]   i
Titel:Estimating yields of household fields in rural subsistence farming systems to study food security in Burkina Faso
Verf.angabe:Isabel G. Karst, Isabel Mank, Issouf Traoré, Raissa Sorgho, Kim-Jana Stückemann, Séraphin Simboro, Ali Sié, Jonas Franke and Rainer Sauerborn
E-Jahr:2020
Jahr:27 May 2020
Umfang:20 S.
Teil:volume:12
 year:2020
 number:11
 extent:20
Fussnoten:Gesehen am 06.08.2020
Titel Quelle:Enthalten in: Remote sensing
Ort Quelle:Basel : MDPI, 2009
Jahr Quelle:2020
Band/Heft Quelle:12(2020,11) Artikel-Nummer 1717, 20 Seiten
ISSN Quelle:2072-4292
Abstract: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.
DOI:doi:10.3390/rs12111717
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext ; Verlag: https://doi.org/10.3390/rs12111717
 Volltext: https://www.mdpi.com/2072-4292/12/11/1717
 DOI: https://doi.org/10.3390/rs12111717
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:child nutrition
 food crops
 remote sensing
 Sentinel-2
 vegetation index metrics
 West Africa
K10plus-PPN:1726558142
Verknüpfungen:→ Zeitschrift

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