Utilizing Knowledge- Based Inference Mapping to Digitally Map the Soils of the Uasin Gishu Plateau in Western Kenya.

Various Digital Soil Mapping (DSM) approaches work to enhance soil mapping, particularly in areas where current soil maps are only very general or nonexistent. In this study, we utilized knowledge-based inference mapping to develop a first generation digital soil map for the Uasin Gishu Plateau in western Kenya. Knowledge-based inference mapping utilizes terrain attributes calculated from a Digital Elevation Model (DEM) to quantitatively describe how water is redistributed across the landscape to bring about different soil patterns. These terrain attributes are then combined with field-based knowledge of soil properties and distributions to create a digital soil map. This approach works efficiently with limited data, which is often the case in developing countries. In this project, the Shuttle Radar Topographic Mission (SRTM) 30m DEM was used to generate terrain attributes, which were then used along with existing soil information and expert knowledge from soil scientists familiar with the area, to produce new raster based soil classes. The results of this study include a unique prediction of the soil classes to be used for soil, crop and land use management for each 30m square pixel. Five major soil classes or mapping units were found to occur in this area. These were representative of five soil orders: Gleysols, Luvisols, Acrisols, Ferralsols and Nitisols, according to the WRB soil classification system.