RSS recently published two collaborative studies in the agricultural sector, focusing on the identification of food (in)security and crop diseases. The studies are located in Burkina Faso and Kenya.
In the first publication, RSS developed an approach for modeling yields at a very small scale (household level) together with partners from the Heidelberg Institute of Global Health and governmental and research institutions in Burkina Faso. The model is based on time-series of vegetation indices and harvest measurements of smallholder fields that were collected during an extensive field campaign in Burkina Faso in 2018. This new method supports studies on food security and child undernutrition at household level.
The upper figure displays the graphical abstract of the yield model. The yield was estimated for the main food crops. Each of these fields is associated with a household, which permits a quantitative yield estimate and consequently enables a novel link between remotely sensed data, food security, and health-related issues. This link and model output is especially valuable for rural subsistence farming systems, where harvest deficits translate to household food insecurity and potentially child undernutrition. For future studies, the potential of the approach is of central importance. The project’s publication, therefore, discusses the risks and potentials in detail and underlines how the study can be utilized for variable political and scientific sectors in the future. The full publication titled “Estimating Yields of Household Fields in Rural Subsistence Farming Systems to Study Food Security in Burkina Faso” in the open-access journal “Remote Sensing” can be found here.
The second study, titled “Investigation of Maize Lethal Necrosis (MLN) severity and cropping systems mapping in agro-ecological maize systems in Bomet, Kenya utilizing RapidEye and Landsat-8 Imagery”, discusses the potential of Earth Observation to prepare for food security restraints in Africa. The study was conducted with the International Centre of Insect Physiology and Ecology (icipe) in Kenya and presents an innovative method to identify and map the severity of the MLN disease on subsistence farms. The resulting map (bottom figure) identified maize fields and those affected by MLN.
Moreover, various cropping systems variables, such as inter- and mono-cropping, as well as precipitation amounts, were included in the modelling of the disease severity. On the basis of this additional data, factors contributing to the spread of the disease can be understood and identified. For example, mono-cropped fields in areas with high precipitation levels were found to be less severely affected by the disease. The MLN severity mapping method can, therefore, potentially be used to guide the allocation of resources to priority areas in order to manage and contain the disease more sustainably. The publication can be downloaded here