Soil Survey: Pedotransfer Function of Linear Extensibility Percent for Soils of the United States. Issue 1 (January 2017)

• Record Type: Journal Article
• Authors: Seybold, Cathy A.; Libohova, Zamir
• Is Part Of: Soil science. Volume 182:Issue 1(2017)
• Publication Date: 2017-01
• Subjects: COLE -- linear extensibility percent -- prediction -- shrink-swell -- soil survey; Soil science -- Periodicals; 631.405
• DOI: 10.1097/SS.0000000000000191
• Languages: English
• ISSNs: 0038-075X

Abstract

Abstract : ABSTRACT: Soil survey is an ongoing process from initial soil mapping through soil survey updates. A national model of linear extensibility percent (LEP) is needed to improve this process. The objective of this work was to develop and validate models for estimating LEP using general linear models and readily available soil survey properties. Measured data from the Kellogg National Soil Survey Laboratory database (Lincoln, Nebraska) were used to develop the prediction models. Twenty LEP prediction equations were developed based on pH and major mineralogy classes. Noncarbonate clay and, depending on the soil pH, either cation-exchange capacity or effective cation-exchange capacity explained between 42% and 86% of the total variation in LEP. Model equations using cation-exchange capacity as a predictive variable collectively produced a prediction root mean square error (RMSEP ) of 1.44% and mean error (ME) of −0.16%. For low pH soils, the model equations using effective cation-exchange capacity as a predictive variable collectively produced an RMSEP of 1.29% and ME of −0.034%. The small negative MEs indicate an overall underestimation of LEP. Breaking down the validation results further among the different mineralogy groups produced a range of RMSEp from 0.42% to 1.80%. The smectitic group had the largest and the siliceous group had the lowest RMSEp . The prediction accuracy is considered adequate for soil survey purposes, and it is expected that LEP estimates will ultimately enhance soil survey interpretations. The models will be added to the soil survey database for soil scientists to use when measured data are not available.