Predicting Cation Exchange Capacity for Soil Survey Using Linear Models

sons, clay and organic matter differences between soils should beincorporated into any kind ofpredictivemodel. 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 (Drakeand Motto,1982),for sandysoilsin Florida(Yuan C content, soil pH, taxonomic family mineralogy class and CEC- et al., 1967), for some Philippine soils (Sahrawat, 1983), activity class, and taxonomic order. Models were developed for each andfor foursoils inMexico(Bell andvan 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