Susan S. Andrews

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.

Sustainable agroecosystem management generally entails increased management ability and input. Decision making for sustainable management could be enhanced by tools that provide integration and synthesis of soil test results, management priorities, and environmental concerns. Science-based soil quality indices (SQIs) may provide an ecologically based approach needed for land managers to make sustainable decisions. We developed a general approach for choosing the most representative indicators from large existing data sets, combining indicators into location-specific indices of soil quality, and using this index to assess agricultural management practices. We used a poultry-litter management case study to illustrate the design and use of this SQI. Site-specific indices were created using the SQI design framework for two sites with different soil types but similar climatic regimes. At each site we compared alternative poultry-litter management practices: land application of fresh vs. composted poultry litter. The data sets were composed of >40 assays including total organic C, macro- and micronutrients, heavy metals, plant available water, water-stable aggregate, bulk density, and microbial biomass and activity. Multivariate statistical techniques were used to determine the smallest set of chemical, physical, and biological indicators that account for at least 85% of the variability in the total data set at each site. We defined this set as the minimum data set (MDS) for evaluating soil quality. We evaluated the efficacy of the chosen MDS to assess sustainable management by performing multiple regressions of each MDS against numerical estimates of environmental and agricultural management sustainability goals (i.e., net revenues, P runoff potential, metal contamination, and amount of litter disposed of). Coefficients of determination for these regressions ranged from 0.35 to 0.91, with an average R2 = 0.71. We then transformed and combined each MDS into an additive SQI. Index values exhibited significant differences between management treatments. SQI values for composted litter applied at a low rate were consistently highly ranked, but the relative ranking of treatments changed slightly due to differences in inherent soil properties at the two sites. Using this generalized framework allowed indices to be tailored to local conditions. The resulting soil quality index appears to be an effective monitor of sustainable management.