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This article in SSSAJ

  1. Vol. 69 No. 3, p. 856-863
    Received: Jan 20, 2004

    * Corresponding author(s): cathy.seybold@nssc.nrcs.usda.gov
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Predicting Cation Exchange Capacity for Soil Survey Using Linear Models

  1. C. A. Seybold *,
  2. R. B. Grossman and
  3. T. G. Reinsch
  1. USDA-NRCS, National Soil Survey Center, 100 Centennial Mall North, Federal Building, Room 152, Lincoln, NE 68508


Measuring the cation exchange capacity (CEC) for all horizons of every map unit component in a survey area is very time consuming and costly. The objective of this study was to develop CEC (pH 7 NH4OAc) prediction models that encompass most soils of the United States. The National Soil Survey Characterization database was used to develop the predictive models using general linear models. Data were stratified into more homogeneous groups based on the organic C content, soil pH, taxonomic family mineralogy class and CEC-activity class, and taxonomic order. Models were developed for each strata or data group. Organic matter and noncarbonate clay contents were the main predictor variables used. Water at −1500 kPa was used in lieu of clay content on four groups. Results indicate that between 43 and 78% of the variation in CEC could be explained for the high organic C data groups; between 53 and 84% could be explained for the mineralogy groups; between 86 and 95% could be explained for the CEC-activity class groups; and between 53 and 86% could be explained for the taxonomic orders. The same predictive model was applicable for Gelisols and Histosols. Inceptisols and Alfisols (>0.3% organic C) also shared the same model. In general, the mineralogy/CEC-activity class equations had lower RMSEs than the taxonomic order equations. A decision tree, based on how the data was stratified, guides the selection of which model to use for a soil layer. Validation results indicated that the models, in aggregate, provide a reasonable estimate of CEC for most soils of the United States.

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