Location: Dale Bumpers Small Farms Research CenterTitle: Predicting soils types and soil properties with limited data in the Uasin Gishu Plateau, Kenya
|NGUNJIRI, MERCY - Purdue University
|LIBOHOVA, ZAMIR - Natural Resources Conservation Service (NRCS, USDA)
|MINAI, JOSHUA - Purdue University
|SERREM, CORNELIUS - University Of Eldoret
|SCHULZE, DARRELL - Purdue University
Submitted to: Geoderma Regional
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/15/2019
Publication Date: 3/2/2019
Citation: Ngunjiri, M., Libohova, Z., Minai, J., Serrem, C., Owens, P.R., Schulze, D. 2019. Predicting soils types and soil properties with limited data in the Uasin Gishu Plateau, Kenya. Geoderma Regional. 16:e00210. https://doi.org/10.1016/j.geodrs.2019.e00210.
Interpretive Summary: Soil is the basis for agricultural production and soils vary within landscapes. Organizing and predicting soils and the related function provides tools for managers to best utilize the land for sustainable production. Digital soil mapping is a tool that can provide visualiztion and prediction of soils utilizing legacy information at a low cost. This research focused on predicting soils in an area near Eldoret Kenya to utilize legacy data and expert knowledge to map soils. The results indicated a positive response for prediction of properties for soil dainage which relates to crop selection and produciton potential. The results yielded an map that is useful for soil, crop and land use which can be easily updated with new information.
Technical Abstract: Digital soil mapping (DSM) approaches can be used to create new soil maps or enhance existing maps, particularly in areas where only very general soil maps are available. In this study, we utilized a knowledge-based inference soil mapping approach to develop a first generation digital soil map for part of the Uasin Gishu Plateau in western Kenya. Knowledge-based inference soil mapping integrates environmental covariates with existing soils information obtained from historic soil surveys and from expert knowledge. This approach works efficiently with limited data, which is often the case in emerging economies like Kenya, while at the same time rescuing legacy data and expert knowledge that is at risk of being lost. The following environmental covariates derived from the Shuttle Radar Topographic Mission (SRTM) 30 m digital elevation model (DEM) were selected for establishing and quantifying soil-landscape relationships: slope gradient, multiresolution valley bottom flatness index (MrVBF), multiresolution ridgetop flatness index (MrRTF), topographic position index (TPI), elevation, and profile curvature. These covariates were used along with existing soil information and expert knowledge from soil scientists familiar with the area to produce new raster-based maps of soil types, effective soil depth, soil moisture storage capacity and soil drainage. The soil type maps predicted using clustering analysis and fuzzy logic methods showed good agreement with field observations based on the overall accuracy values. The fuzzy logic map performed slightly better (kappa coefficient (k) = 0.68; overall accuracy = 0.76) than the map based on clustering analysis (k = 0.59; overall accuracy = 0.68). The accuracy for the effective soil depth fuzzy logic map was better (R2 = 0.56; RMSE = 11; ME = 1.1) compared to the existing soil map (R2 = 0.34; RMSE = 27; ME = 8). The results of this study generated more detailed and improved predictions of soil types and properties at 30 m grid resolution which are likely to be more useful for soil, crop and land use management decisions.