Using soil physical and chemical properties to estimate bulk density

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 (R² = 0.45, P < 0.001) was developed using almost 47 000 soil samples. Partitioning the database by soil suborders improved regression relationships (R² = 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 (R² = 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 85 608 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.