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This article in SSSAJ

  1. Vol. 69 No. 1, p. 51-56
     
    Received: Feb 9, 2004
    Published: Jan, 2005


    * Corresponding author(s): brandtcc@ornl.gov
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doi:10.2136/sssaj2005.0051

Using Soil Physical and Chemical Properties to Estimate Bulk Density

  1. Sonja A. Heuscher,
  2. Craig C. Brandt * and
  3. Philip M. Jardine
  1. Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831-6038

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.

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