Mapping livestock movements in Sahelian Africa.

In the dominant livestock systems of Sahelian countries herds have to move across territories. Their mobility is often a source of conflict with farmers in the areas crossed, and helps spread diseases such as Rift Valley Fever. Knowledge of the routes followed by herds is therefore core to guiding the implementation of preventive and control measures for transboundary animal diseases, land use planning and conflict management. However, the lack of quantitative data on livestock movements, together with the high temporal and spatial variability of herd movements, has so far hampered the production of fine resolution maps of animal movements. This paper proposes a general framework for mapping potential paths for livestock movements and identifying areas of high animal passage potential for those movements. The method consists in combining the information contained in livestock mobility networks with landscape connectivity, based on different mobility conductance layers. We illustrate our approach with a livestock mobility network in Senegal and Mauritania in the 2014 dry and wet seasons.

Remote sensing and geographic information systems to predict the density of ruminants, hosts of Rift Valley fever virus in the Sahel.

Rift Valley fever (RVF) is an acute arboviral disease of domestic ungulates and humans in Africa and the Middle East. Since the first epidemic in 1987, Senegal has been confronted with recurrent episodes of the disease. This study aimed to model spatial distribution of ruminants in the agropastoral area of Barkedji (Senegal) where the disease is enzootic. In this Sahelian ecosystem, livestock distribution mainly depends on the availability of resources. Accordingly, remote sensing and geographic information systems (GIS) were used to seek environmental indicators of livestock density. A high-resolution Landsat image was associated with landscape field data to describe the land-cover. A series of normalized difference vegetation index values gave an estimation of the phytomass. In addition the compounds of herders in the study zone were located and sampled. Three surveys were conducted during the rainy season to record the number of herds in each compound of the sample. All these data were overlaid in the GIS. A discriminant analysis was performed to associate the observed herd density with environmental data and to develop a predictive model for the entire study zone. The final result was a 1-km resolution raster map of herd density during a normal rainy season.