Predicting Cation Exchange Capacity for Soil Survey Using Linear Models

sons, clay and organic matter differences between soils should be incorporated into any kind of predictive model. Measuring the cation exchange capacity (CEC) for all horizons of Several researchers have attempted to predict CEC every map unit component in a survey area is very time consuming from clay and organic C contents alone, using multiple and costly. The objective of this study was to develop CEC (pH 7 regression. Results show that greater than 50% of the NH4OAc) prediction models that encompass most soils of the United States. The National Soil Survey Characterization database was used variation in CEC could be explained by the variation in to develop the predictive models using general linear models. Data clay and organic C content for several New Jersey soils were stratified into more homogeneous groups based on the organic (Drake and Motto, 1982), for sandy soils in Florida (Yuan C content, soil pH, taxonomic family mineralogy class and CECet al., 1967), for some Philippine soils (Sahrawat, 1983), activity class, and taxonomic order. Models were developed for each and for four soils in Mexico (Bell and van Keulen, 1995). strata or data group. Organic matter and noncarbonate clay contents Only a small improvement was obtained by adding pH were the main predictor variables used. Water at 1500 kPa was used to the model for four Mexican soils (Bell and van Keuin lieu of clay content on four groups. Results indicate that between len, 1995). In B horizons of a toposequence, the amount 43 and 78% of the variation in CEC could be explained for the high of fine clay ( 0.2 m) was shown to explain a larger organic C data groups; between 53 and 84% could be explained for percent of the variation in CEC than the total clay the mineralogy groups; between 86 and 95% could be explained for the CEC-activity class groups; and between 53 and 86% could be content (Wilding and Rutledge, 1966). In gleyed subsoil explained for the taxonomic orders. The same predictive model was horizons of lowland soils in Quebec, surface area (of the applicable for Gelisols and Histosols. Inceptisols and Alfisols ( 0.3% soil) gave a better prediction of CEC than did total clay organic C) also shared the same model. In general, the mineralogy/ (Martel et al., 1978). Martel et al. (1978) also showed that CEC-activity class equations had lower RMSEs than the taxonomic the variations in mineralogical composition, although order equations. A decision tree, based on how the data was stratified, small, were sufficient to explain nearly 50% of the variguides the selection of which model to use for a soil layer. Validation ation in CEC. Similarly, Miller (1970) found that the results indicated that the models, in aggregate, provide a reasonable type of clay alone could explain up to 50% of the variaestimate of CEC for most soils of the United States. tion in CEC. Many of the above predictive models are specific to a region or area and confined to only a few soil types. Our approach is to develop predictive models C exchange capacity is the total of the exthat provide a comprehensive coverage of soils of the changeable cations that a soil can hold at a speciUnited States. fied pH. Soil components known to contribute to CEC When using least squares estimates in CEC models, are clay and organic matter, and to a lesser extent, silt the assumption is made that the compositions of the (Martel et al., 1978; Manrique et al., 1991). The exchange clay and organic matter are identical from one sample sites can be either permanent or pH-dependent. Mineral to another and that the soils vary only in the amounts soils have an exchange capacity that is a combination of the components present (Stevenson, 1994). For this of permanent and pH-dependent charge sites, while that reason, regression equations tend to be accurate only of organic soils is predominantly pH-dependent. In any within a limited geographic and climatic zone, where given soil, the number of exchange sites is dependent on the composition of the clay and organic fractions are the soil pH; type, size, and amount of clay; and amount, reasonably homogenous (Helling et al., 1964). When soils decomposition state, and source of the organic material of diverse genesis are included in the analyses and little (Kamprath and Welch, 1962; Parfitt et al., 1995; Syers or no attempt is made to control for variables such as et al., 1970; Miller, 1970). The relationship between clay mineralogical composition, soil properties become less content (% by weight) and CEC can be highly variable predictive (Syers et al., 1970). When soils are grouped because different clay minerals have very different CECs, by similarities in origin or properties, accuracy of preand the relative proportion of pH-dependant and perdictive models (in general) has been shown to improve manent CEC varies among clay minerals (Miller, 1970). (Pachepsky and Rawls, 1999). Drake and Motto (1982) Cation exchange capacity of organic soils increases markgrouped soils by taxonomic order or province, which edly with increases in pH, and increases with greater proved superior in defining groups for predicting CEC. degrees of humification (Stevenson, 1994). For these reaSimilarly, Asadu and Akamigbo (1990) predicted CEC from organic matter and clay content grouped by taxonomic order (Inceptisols, Alfisols, Ultisols, and Oxisols). USDA-NRCS, National Soil Survey Center, 100 Centennial Mall North, Federal Building, Room 152, Lincoln, NE 68508. Received They indicated that partitioning the data by taxonomic 20 Jan. 2004. Pedology. *Corresponding author (cathy.seybold@nssc. order resulted in regression equations that were signifinrcs.usda.gov). cantly distinct from each other, as the groupings tended to reduce the variability in soil properties. The U.S. Soil Published in Soil Sci. Soc. Am. J. 69:856–863 (2005). doi:10.2136/sssaj2004.0026 © Soil Science Society of America Abbreviations: CEC, cation exchange capacity; NASIS, National Soil Information System; OC, organic carbon. 677 S. Segoe Rd., Madison, WI 53711 USA 856 Published online May 6, 2005