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.
Minimizing wind erosion on agricultural fields is of great interest to farmers. There is a general understanding that vegetation can greatly minimize the wind erosion taking place. However, after harvest, a low vegetation cover can be inevitable, whereby the amount of stubble that remains on a field is dependent on the crop type and land management. This study aims at quantifying the vulnerability to wind erosion of different crops, and the possibility to predict the vulnerability based on high precision aerial images. The study area was the semi-arid Free State, which holds large intensive agriculture on sandy soils. These croplands have been identified as the largest emitter of dust in South Africa. The main crop in the region is maize, but also sunflower, peanut and fallow fields are common land-use types. On these fields, the horizontal sediment flux, the saltation threshold, and aerodynamic roughness length were measured, and the soil cover was assessed using Unmanned Aerial Vehicle (UAV) imagery. The results showed a strong relationship between the soil cover and the sediment flux, whereby fallow and groundnut fields have the highest wind erosion risk. These results emphasize the great importance of soil cover management to prevent wind erosion.
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