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ARS Home » Southeast Area » Booneville, Arkansas » Dale Bumpers Small Farms Research Center » Research » Publications at this Location » Publication #362909

Research Project: Sustainable Small Farm and Organic Production Systems for Livestock and Agroforestry

Location: Dale Bumpers Small Farms Research Center

Title: Predicting soil types and soil properties with limited data in the Uasin Gishu Plateau, Kenya

Author
item NGUNJIRI, MERCY - Orise Fellow
item LIBOHOVA, ZAMIR - Natural Resources Conservation Service (NRCS, USDA)
item MINAI, JOSHUA - Purdue University
item SERREM, C - University Of Eldoret
item Owens, Phillip
item 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.W., Libohova, Z., Minai, J.O., Serrem, C., Owens, P.R., Schulze, D.G. 2019. Predicting soil 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.
DOI: https://doi.org/10.1016/j.geodrs.2019.e00210

Interpretive Summary: Soil is the foundation for agricultural production. In the United States, we have detailed soil information provided by the USDA that has been developed over the past 100 years. In other developing countries, soil information is provided on a more regional basis and provides very little specific information. This study focused on a region in Kenya to utilize new technological tools, computing power and digital soil mapping applications to develop detailed soil maps for agricultural management. Soil type, effective soil depth, soil moisture, water holding capacity and soil drainage class maps produced by this study were more spatially detailed and better captured the soil-landscape relationships when compared to existing Kenyan soil maps. The accuracy of soil type and property maps was assessed and showed good agreement with collected field data. The new improved and detailed soil type map should be useful to support land management decisions at finer scales compared to the existing soil maps. The digital soil mapping approach in this study is new because it provides a platform to organize, incorporate and rescue historic data and human knowledge. These results demonstrate the utility of combining historic soil data and expert knowledge with new technologic tools as an effective way to provide detailed soil maps for crop selection, crop management and yield expectations. Additional advantages of this type of mapping is that the process is much quicker than traditional methods and can be accomplished at a much lower cost.

Technical Abstract: Digital soil mapping (DSM) approaches can be used to create new soil maps or enhance existing maps, particularly in areas where only 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. We calculated the following environmental covariates from the Shuttle Radar Topographic Mission (SRTM) 30 m digital elevation model (DEM): slope gradient, multi-resolution valley bottom flatness index (MrVBF), multi-resolution ridgetop flatness index (MrRTF), topographic position index (TPI), elevation, and profile curvature. These covariates were then 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 class. 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 higher (R2 = 0.56; RMSE = 11; ME = 1.1) compared to the existing soil map (R2 = 0.34; RMSE = 27; ME = 8). Seven major soil types occur in the study area: Ferralsols, Nitisols, Gleysols, Luvisols, Acrisols, Cambisols and Regosols, according to the World Reference Base soil classification system. This study generated detailed and improved predictions of soil types and properties at 30 m grid resolution. These maps should be more useful for soil, crop and land use management decisions than existing maps.