Estimating Yields of Household Fields in Rural Subsistence Farming Systems to Study Food Security in Burkina Faso
<b>:</b> 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 rese...
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doaj-fa591ff14fb5423eb38e1b1efab8ccf42020-11-25T02:13:43ZengMDPI AGRemote Sensing2072-42922020-05-01121717171710.3390/rs12111717Estimating Yields of Household Fields in Rural Subsistence Farming Systems to Study Food Security in Burkina FasoIsabel G. Karst0Isabel Mank1Issouf Traoré2Raissa Sorgho3Kim-Jana Stückemann4Séraphin Simboro5Ali Sié6Jonas Franke7Rainer Sauerborn8Remote Sensing Solutions (RSS) GmbH, Dingolfingerstr. 9, 81673 Munich, GermanyHeidelberg Institute of Global Health (HIGH), Faculty of Medicine, Heidelberg University, Im Neuenheimer Feld 130.3, 69120 Heidelberg, GermanyCentre de Recherche en Santé de Nouna (CRSN), Institut National de Santé Publique (INSP), P.O. Box 02 Nouna, Burkina FasoHeidelberg Institute of Global Health (HIGH), Faculty of Medicine, Heidelberg University, Im Neuenheimer Feld 130.3, 69120 Heidelberg, GermanyInstitute of Computer Science, University of Osnabrück, Wachsbleiche 27, 49090 Osnabrück, GermanyCentre de Recherche en Santé de Nouna (CRSN), Institut National de Santé Publique (INSP), P.O. Box 02 Nouna, Burkina FasoCentre de Recherche en Santé de Nouna (CRSN), Institut National de Santé Publique (INSP), P.O. Box 02 Nouna, Burkina FasoRemote Sensing Solutions (RSS) GmbH, Dingolfingerstr. 9, 81673 Munich, GermanyHeidelberg Institute of Global Health (HIGH), Faculty of Medicine, Heidelberg University, Im Neuenheimer Feld 130.3, 69120 Heidelberg, Germany<b>:</b> 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.https://www.mdpi.com/2072-4292/12/11/1717food cropschild nutritionremote sensingSentinel-2vegetation index metricsWest Africa |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Isabel G. Karst Isabel Mank Issouf Traoré Raissa Sorgho Kim-Jana Stückemann Séraphin Simboro Ali Sié Jonas Franke Rainer Sauerborn |
spellingShingle |
Isabel G. Karst Isabel Mank Issouf Traoré Raissa Sorgho Kim-Jana Stückemann Séraphin Simboro Ali Sié Jonas Franke Rainer Sauerborn Estimating Yields of Household Fields in Rural Subsistence Farming Systems to Study Food Security in Burkina Faso Remote Sensing food crops child nutrition remote sensing Sentinel-2 vegetation index metrics West Africa |
author_facet |
Isabel G. Karst Isabel Mank Issouf Traoré Raissa Sorgho Kim-Jana Stückemann Séraphin Simboro Ali Sié Jonas Franke Rainer Sauerborn |
author_sort |
Isabel G. Karst |
title |
Estimating Yields of Household Fields in Rural Subsistence Farming Systems to Study Food Security in Burkina Faso |
title_short |
Estimating Yields of Household Fields in Rural Subsistence Farming Systems to Study Food Security in Burkina Faso |
title_full |
Estimating Yields of Household Fields in Rural Subsistence Farming Systems to Study Food Security in Burkina Faso |
title_fullStr |
Estimating Yields of Household Fields in Rural Subsistence Farming Systems to Study Food Security in Burkina Faso |
title_full_unstemmed |
Estimating Yields of Household Fields in Rural Subsistence Farming Systems to Study Food Security in Burkina Faso |
title_sort |
estimating yields of household fields in rural subsistence farming systems to study food security in burkina faso |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2020-05-01 |
description |
<b>:</b> 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. |
topic |
food crops child nutrition remote sensing Sentinel-2 vegetation index metrics West Africa |
url |
https://www.mdpi.com/2072-4292/12/11/1717 |
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