Using Soil Physical and Chemical Properties to Estimate Bulk Density

Abstract

A stepwise multiple regression procedure was developed to predict oven‐dried bulk density from soil properties using the 1997 USDA‐NRCS National Soil Survey Characterization Data. The database includes both subsoil and topsoil samples. An overall regression equation for predicting oven‐dried bulk density from soil properties ( R 2 = 0.45, P < 0.001) was developed using almost 47000 soil samples. Partitioning the database by soil suborders improved regression relationships ( R 2 = 0.62, P < 0.001). Of the soil properties considered, the stepwise multiple regression analysis indicated that organic C content was the strongest contributor to bulk density prediction. Other significant variables included clay content, water content and to a lesser extent, silt content and depth. In general, the accuracy of regression equations was better for suborders containing more organic C (most Inceptisols, Spodosols, Ultisols, and Mollisols). Bulk density was poorly predicted for suborders of the Aridisol and Vertisol orders which contain little or no organic C. Although organic C was an important variable in the suborder analysis, water content explained most (>30%) of the variation in bulk density for Udox, Xererts, Ustands, Aquands, and Saprists. Relationships between bulk density with soil volume measured on oven‐dried natural clods and bulk density with soil volume measured at field‐moisture content and one‐third bar were also determined ( R 2 = 0.70 and 0.69, respectively; P < 0.001). Utilizing the regression equations developed in this study, oven‐dried bulk density predictions were obtained for 71% of the 85608 samples in the database without bulk density measurements. While improving on methods of previous analyses, this study illustrates that regression equations are a feasible alternative for bulk‐density estimation.