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This article in SSSAJ

  1. Vol. 68 No. 6, p. 1945-1962
     
    Received: Nov 20, 2003
    Published: Nov, 2004


    * Corresponding author(s): andrews@nstl.gov
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doi:10.2136/sssaj2004.1945

The Soil Management Assessment Framework

  1. Susan S. Andrews *a,
  2. Douglas L. Karlenb and
  3. Cynthia A. Cambardellab
  1. a USDA-NRCS, Soil Quality Institute, 2150 Pammel Dr., Ames, IA 50011-4420
    b USDA-ARS, National Soil Tilth Lab., 2150 Pammel Dr., Ames, IA 50011-4420

Abstract

Erosion rates and annual soil loss tolerance (T) values in evaluations of soil management practices have served as focal points for soil quality (SQ) research and assessment programs for decades. Our objective is to enhance and extend current soil assessment efforts by presenting a framework for assessing the impact of soil management practices on soil function. The tool consists of three steps: indicator selection, indicator interpretation, and integration into an index. The tool's framework design allows researchers to continually update and refine the interpretations for many soils, climates, and land use practices. The tool was demonstrated using data from case studies in Georgia, Iowa, California, and the Pacific Northwest (WA, ID, OR). Using an expert system of decision rules as an indicator selection step successfully identified indicators for the minimum data set (MDS) in the case study data sets. In the indicator interpretation step, observed indicator data were transformed into unitless scores based on site-specific algorithmic relationships to soil function. The scored data resulted in scientifically defensible and statistically different treatment means in the four case studies. The efficacy of the indicator interpretation step was evaluated with stepwise regressions using scored and observed indicators as independent variables and endpoint data as iterative dependent variables. Scored indicators usually had coefficients of determination (R 2) that were similar or greater than those of the observed indicator values. In some cases, the R 2 values for indicators and endpoint regressions were higher when examined for individual treatments rather than the entire data set. This study demonstrates significant progress toward development of a SQ assessment framework for adaptive soil resource management or monitoring that is transferable to a variety of climates, soil types, and soil management systems.

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