|Halvorson, Jonathan - BATTELLE LABORATORIES|
Submitted to: Biology and Fertility of Soils
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: December 15, 1994
Publication Date: N/A
Interpretive Summary: Soil quality is of utmost importance to every person on earth. The soil provides a medium for plant growth, a sink for atmospheric pollutants and a filter for lithosphere pollutants. The soil is so 'rich' that it provides food for the world on only 10% of the land surface of the earth. Thus soil quality must be maintained in order to maintain the food supply for today and for the future. However, soil quality is determined by numerous indicators or parameters. We have developed methodology to integrate any number of parameters into an index of soil quality or a function thereof. This methodology requires some decisions as to threshold levels of acceptable soil quality. These ideas will carry us into the future of quantifying the attributes of soil quality and in management decisions of controlling soil quality for the benefit of mankind.
Technical Abstract: Development of a method to assess and monitor soil quality is critical to soil resource management and policy formation. To be useful, a method for assessing soil quality must be able to integrate many different kinds of data, allow evaluation of soil quality based on alternative uses or definitions and estimate soil quality for unsampled locations. We demonstrate one such method, based on nonparametric geostatistics, to evaluate the soil quality. We evaluated soil quality from the integration of six soil variables measured at 220 locations in an agricultural field in southeastern Washington State. We converted the continuous data values for each soil variable at each location to a binary variable indicator transform (VIT) based on thresholds. We then combined indicator transformed data for individual soil variables into a single integrative indicator of soil quality termed a multiple indicator transform (MVIT). We observed that soil chemical variables, pools of soil resources, populations of microorganisms, and soil enzymes covaried spatially across the landscape. These ensembles of soil variables were not randomly distributed, but rather were systematically patterned. Soil quality maps calculated by kriging showed the joint probabilities of meeting specific MVIT selection were influenced by the critical threshold values used to transform each individual soil quality variable and the MVIT selection criteria. If MVIT criteria adequately reflect soil quality then the kriging can produce maps of the probability of a soil being of good or poor quality.