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A framework for soil management assessment: Four case studies.
Andrews, Susan*,1, Karlen, Douglas2, 1 USDA-NRCS Soil Quality Institute, Ames, IA, 500112 USDA-ARS National Soil Tilth Lab, Ames, IA, 50011
ABSTRACT- Current soil testing, usually of chemical fertility interpreted on the basis of crop nutrient requirements, provides an imcomplete picture of soil function. Meaningful information for agroecosystem management could be gained from a tool that suggests chemical, biological and physical indicators, offers site-specific interpretations of those indicators, and provides an overall assessment of those interpretations in relation to soil function. Designing this assessment tool as a framework allows researchers to continually update and refine the interpretations for many soils, climates, and land use practices. We designed and assessed a framework consisting of three steps: indicator selection, indicator interpretation, and integration into an index. We tested transferability of the framework using data from four case studies in GA, IA, CA, and the Pacific Northwest (NRI) that differed in climate, management, spatial extent, and soil type. Applying decision rules in the selection step successfully identified indicators that were present in the existing data sets. The interpretation step resulted in site-specific and scientifically defensible differences in indicator scores. We found four main patterns of indicator results, including significantly different scores when no difference was detected in the measured data (before scoring). As a check of the efficacy of this framework approach, we performed stepwise regressions using the scored (and observed) indicators as independent variables and endpoint data as iterative dependent variables for each case study. The scored indicators usually had coefficients of determination (R2s) that were similar or greater than those of the observed indicator values. The R2s between indicators and endpoints were higher when examined for one treatment at a time rather than the entire data set. For instance, when using all NRI data, there was no relationship between indicators and nematode diversity but when only the data for no-tilled Xerolls cropped to continuous small grains were examined alone, the R2 was 0.92. The results of this study show significant progress toward development of an assessment framework for adaptive management that is transferable to a variety of climates, soil types, land uses and management systems
Key words: decision tools, site-specific indicator assessment, soil quality, agroecosystem management