Bulk Density of Soils in Relation to Soil Physical and Chemical Properties

Abstract

Soil scientists and engineers commonly require bulk density (Db) data as parameter input of simulation models predicting a wide range of soil processes. Field methods exist to measure Db, but are labor intensive and time consuming. Much effort has been recently devoted to evaluate alternative procedures to predict Db from soil physical and chemical data. Using data of about 12 000 soil pedons from the continental USA, Hawaii, Puerto Rico, and some foreign countries, multiple regression relationships were developed to predict Db at −33 kPa moisture content. The data base was partitioned in small homogeneous data sets according to their Soil Taxonomy classification. Regression relationships between Db and soil properties indicated that Db can be accurately predicted (R2 > 0.60) from organic-C content for Inceptisols and Spodosols. Regression relationships with organic C and clay contents, water content at −1500 kPa, and the −1500 kPa water to clay ratio gave R2 values from 0.53 to 0.74 for Ultisols, Alfisols, Vertisols, Oxisols, and Inceptisols. The predictive capability of the regression models was greatly improved when the data were partitioned by suborders for all but Aridisols, Entisols, and Mollisols. The results of this study show that, for soils grouped according to their taxonomic classification, multiple-regression models based on soil properties can provide a relatively accurate alternative for predicting Db.