We now come full circle to an issue that was raised in Chapter 1: rapidly increasing human population growth providing ever-increasing pressure on a finite base of natural resources. As noted by Daily (1997), the direct substitution cost of a hydroponic plant production system for one hectare of soil is the equivalent of $850,000, and still rising. When one adds to that the cost of cleansing and recycling, this is a sizable fraction of the more than $30 trillion dollar cost of annual goods and services provided by ecosystems globally (Costanza et al., 1997).
In dealing with environmental remediation and environmental assessment in general, what is a "healthy" soil? Is there a simple one-
sentence definition of "soil quality," sensu Doran (2002)? Is there a clean soil similar to either clean air or water? The short answer is no. The longer answer is quite complex, but informative, if one takes an ecosystem-level approach (Coleman et al., 1998). A general definition of soil quality is "the capacity of a soil to function within ecosystem boundaries to sustain biological productivity, maintain environmental quality, and promote plant and animal health" (Doran and Parkin, 1994). This is a beginning, but the healthy activity of all organisms, including microorganisms, should be considered explicitly (Coleman et al., 1998). As noted in Chapter 7, the state of our knowledge of microbial diversity, indeed that of a majority of the organisms active in soil, is still at a rudimentary stage. As a heuristic concept, soil quality has been useful for both education and assessment. These education and assessment tools encourage land managers to examine biological, chemical, and physical properties and processes occurring within their soil resources and to use that information as a framework for helping to make adaptive soil management decisions (Karlen et al., 2001). Soil quality has not been embraced universally, because some soil scientists have been concerned that value-based decisions could supplant value-neutral science and thus lead to premature interpretations and assertions of soil quality before the concept has been thoroughly and analytically challenged (Sojka and Upchurch, 1999).
It is possible to examine the health of the litter-soil subsystem of terrestrial ecosystems by utilizing indicator indices. One example is the use of the ratio of microbial biomass carbon to soil organic carbon (Cmic/Corg). This index is related to soil carbon availability and the tendency for a soil to accumulate or lose organic matter. It has been used successfully in evaluating the status of restored ecosystems, for example, restored coal mine lands (Insam & Domsch, 1988).
A wide range of soil quality indices has been calculated, related to specific groups of microbes and fauna (Coleman et al., 1998). These include nitrogen mineralization, soil respiration, respiration to micro-bial biomass ratios, faunal populations, and rates of litter decomposition (Knoepp et al., 2000). Considerably less attention has been paid to ecosystem-level analyses. The following is an overview of several studies, undertaken in two agroecosystems in the Georgia Piedmont, in an aggrading forested ecosystem in western North Carolina, and in an agroecosystem in Nebraska.
In the agroecosystem study, a wide range of biological, chemical, and physical factors were measured in two field sites, in which alternative poultry-litter management practices were compared. Multivariate statistical techniques were used to determine the smallest set of chemical, physical, and biological indicators that accounted for at least 85% of the variability in the total data set at each site. This set was defined as the minimum data set (MDS) for evaluating soil quality (Andrews and Carroll, 2001). The efficacy of the chosen MDS was evaluated by performing multiple regressions of each MDS against numerical estimates of environmental and agricultural management sustainability goals (e.g., net revenues, phosphorus runoff potential, metal contamination, and amount of litter disposed of). Coefficients of determination ranged from 0.35 to 0.91, with an average R2 of 0.71. Each MDS was then transformed and combined into an additive soil quality index (SQI). SQ indexes varied between the two sites, but Andrews and Carroll (2001) noted that this "designed SQI" enabled the indices to be tailored to local conditions.
In the forest ecosystem study, a combination of chemical and biological indices was used to measure soil quality in five watersheds arranged along an elevational gradient at the Coweeta Hydrologic Laboratory, in the southern Appalachian Mountains of western North Carolina. The selected characteristics of the elevation gradient stands are presented in Table 8.3 (Knoepp et al., 2000). The sites represented a gradient in vegetation and elevation and included xeric oak-pine (OP), cove hardwood (CH), mesic mixed-oak at low and high elevations (MO-L, MO-H), and mesic northern hardwood (NH) vegetation. The sites were then ranked on a range of soil chemical characteristics, nitrogen availability, litter decomposition rates, forest floor mass, coarse woody debris standing crop, soil oribatid mite populations as numbers and total species, and Shannon-Wiener biodiversity index (Table 8.4) (Knoepp et al., 2000), and then several measures of soil carbon availability: CO2 flux, microbial carbon, qCO2 (|igCgsoil-1), and qCmic (|MgCmicgC total-1). Note that all sites had approximately equal diversity of overstory tree species, with H values ranging between 1.93 for the NH, and 2.25 for OP.
The five sites were compared for overall soil/site quality, ranked using biological and chemical or physical quality and the aboveground indices of wood production, net primary productivity, and biodiversity. Overall, soil biological quality was highest for OP and MO-L, with the highest scores in nitrogen and carbon availability and fauna population indicators. Based on soil chemical and physical properties, NH ranked highest with the greatest cation and carbon and nitrogen concentration, and lowest bulk density. In sum, the highest-quality site is dependent on the goal desired for that site. In terms of wood production, MO-H was the highest-quality site. Both mixed-oak sites had the highest productivity using the total litterfall index. If one desired to maximize biodiversity, both aboveground and in the soil, all sites ranked highly (Knoepp et al., 2000). The overall take-home message is important for land use managers and ecologists in general: the site quality really depends on the objectives of the users and the context in which the sites (in this case, sit-
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