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This article in SSSAJ

  1. Vol. 73 No. 5, p. 1638-1645
     
    Received: Sept 19, 2008
    Published: Sept, 2009


    * Corresponding author(s): attila.nemes@ars.usda.gov
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doi:10.2136/sssaj2008.0298

Evaluation of the Rawls et al. (1982) Pedotransfer Functions for their Applicability at the U.S. National Scale

  1. A. Nemes *a,
  2. D. J. Timlinb,
  3. Ya. A. Pachepskyc and
  4. W. J. Rawlsd
  1. a Dep. of Plant Sci. and Landscape Architecture, University of Maryland, 2102 Plant Science Building, College Park, MD 20742
    b USDA-ARS, Crop Systems and Global Change Laboratory, 10300 Baltimore Avenue, Building 001, BARC-West, Beltsville, MD 20705
    c USDA-ARS, Environmental Microbial Safety Laboratory, Powder Mill Road, Building 173, BARC-East, Beltsville, MD 20705
    d USDA-ARS, Hydrology and Remote Sensing Laboratory, 10300 Baltimore Avenue, Building 007, BARC-West, Beltsville, MD 20705

Abstract

Large scale environmental impact studies typically involve the use of simulation models and require a variety of inputs, some of which may need to be estimated when adequate measured data are absent. As an example, soil water retention needs to be estimated for a large number of soils that are to be used in the context of the U.S. national scale Conservation Effects Assessment Project (CEAP). Use of a set of well known linear regression based pedotransfer functions (PTFs) developed in 1982 was proposed to address such data need. Examination of the underlying data as well as comparative estimations to an independent US-wide data set revealed that the proposed equations were most likely meant to use organic carbon (OC) data in place of the reported organic matter (OM) data. Other discrepancies—possibly due to misreporting—were also found in a large portion of the OM data. These PTFs were also developed from data originating from only 18 U.S. states—and 48% of them dominated by 3 U.S. states—while major cropland states/regions were barely or not represented at all. Resulting estimations showed non-random distribution of estimation residuals (i.e., bias) that could however be corrected with data transformations and by using a k-Nearest Neighbor algorithm as an alternative PTF technique. We recommend that the PTF equations proposed in 1982 not be used in the context of the U.S. national scale CEAP project. Alternative solutions should ensure the proper representation of U.S. soils and their properties.

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