|Herrick, Jeffrey - Jeff|
Submitted to: Soil Science Society of America Annual Meeting
Publication Type: Abstract Only
Publication Acceptance Date: 8/30/2017
Publication Date: 10/22/2017
Citation: Salley, S.W., Herrick, J.E. 2017. Assessing quality of citizen scientists’ soil texture estimates to evaluate land potential [abstract]. Soil Science Society of America Annual Meeting. October 22-25, 2017, Tampa, Florida. pg. 48-6.
Technical Abstract: Texture influences nearly all soil processes and is often the most measured parameter in soil science. Estimating soil texture is a universal and fundamental practice applied by resource scientists to classify and understand the behavior and management of soil systems. While trained soil scientist can accurately estimate soil textural class, percent sand, and percent clay, doubt is often cast on the quality of texture estimates produced by inexperienced citizen scientists. We present analysis of citizen scientist texture-by-feel and laboratory hydrometer derived soil texture classes collected from Namibia, Africa. Participants received minimal training and were provided with the Land Potential Knowledge System (LandPKS) app LandInfo module containing soil texture-by-feel walkthrough instructions. LandPKS is a free and open source mobile and web app that facilitates the collection and recording of georeferenced data on soil properties and site characteristics. Analysis of texture accuracy was compared using confusion matrix analysis to evaluate user and classification accuracy. Classification of sandy and clayey textural classes were most accurate while loamy soil texture classes were poorly predicted. Results showed poor total classifier accuracy (38%), although classification improved significantly (59%) when sand and sandy loam categories where combined as misclassification between the sandy texture classes was generally due to soil material containing higher organic matter content. Most incorrect identifications were not considered a serious problem because estimates fell within adjacent textural classes. When combined with LandPKS geotagged smart phone location, our analysis suggests that surface and subsurface texture class estimates were sufficient to match key soil components from the FAO Harmonized World Soil Database and the ISRIC SoilGrids global databases