designing a soil quality assessment tool for sustainable agroecosystem management

Designing a Soil Quality Assessment Tool for Sustainable Agroecosystem ManagementAuthor(s): Susan S. Andrews and C. Ronald CarrollSource: Ecological Applications, Vol. 11, No. 6 (Dec., 2001), pp. 1573-1585Published by: Ecological Society of AmericaStable URL: http://www.jstor.org/stable/3061079 .

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Ecological Applications, 11(6), 2001, pp. 1573-1585 (C) 2001 by the Ecological Society of America

DESIGNING A SOIL QUALITY ASSESSMENT TOOL FOR SUSTAINABLE AGROECOSYSTEM MANAGEMENT

SUSAN S. ANDREWSI AND C. RONALD CARROLL

Institute of Ecology, University of Georgia, Athens, Georgia 30602-2202 USA

Abstract. 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 avcrage 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.

Key words: agroecosystem management; compost; decision making tools; Festuca arundinaceae; minimum data set; poultry litter management; principal-components analysis; soil quality index; sustainable agriculture.

1NTRODUCTION

Ecologists often evaluate the ecological effects of alternative management actions on agroecosystems, but land managers lack tools to effectively use this information when making management decisions (Park and Cousins 1995). In addition, sustainable agroecosystem management requires consideration of economic and social as well as environmental goals, thereby requiring increased inputs of management effort and skill (Mad- den 1990, Edwards et al. 1993). To support these needs, scientists must develop innovative, multi-objective decision tools with which to evaluate management alternatives (El-Swaify and Yakowitz 1998). Science-based soil quality indices (SQIs) provide the necessary in-

Manuscript received 10 January 2000; revised 31 August 2000; accepted 21 November 2000.

' Present address: USDA-ARS National Soil Tilth Labo- ratory, 2150 Pammel Drive, Ames, Iowa 50011 USA. E-mail: [email protected]

tegration of information for land managers to facilitate informed decisions about complex issues such as agroecosystem management.

While the concept of integrative indices has been in use for years to monitor water quality (Karr 1981), Larson and Pierce (1991) were among the first to apply this idea to soil ecosystems. The soil quality concept integrates soil biological, chemical, and physical attributes to assess a soil's capacity to function (Larson and Pierce 1991, Doran and Parkin 1994, Karlen et al. 1997). Soil quality indices apply concepts of soil ecology to assess the sustainability of soil ecosystem management by effectively combining a variety of information for multi-objective analysis (Karlen and Stott 1994, Herrick 2000). Because indices are specifically designed to compare management practices (e.g., Kar- len et al. 1994, 1996, Hussian et al. 1999, Wander and Bollero 1999), SQIs can become part of an adaptive- management program (sensu Walters 1986) for sustainable soil ecosystem management.

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Ecological Applications Vol. 11, No. 6

1574 SUSAN S. ANDREWS AND C. RONALD CARROLL

Choosing the appropriate soil attributes to include in an index must include consideration of soil functions (Harris et al. 1996, Karlen et al.1997) and management goals that are both site and use specific (Christensen et al. 1996). Furthermore, when management goals focus on sustainability rather than just crop yields, an SQI can be viewed as one component of a compre- hensive, hierarchical agroecosystem management evaluation system. The indicators and index outcomes should reflect not only soil function but also aspects of the larger agroecosystem performance as delineated by goals for sustainable management.

In this paper we focus on development of a soil quality index using multiple constraints defined by management goals. We chose to use poultry-litter management in Georgia as a case study because animal waste management is a growing concern in the United States (Sims 1995), and Georgia's poultry industry represents > 15% of total U.S . production (NASS 1999). "Poultry litter" refers to the manure generated during broiler production (poultry raised for meat) combined with the bedding material (consisting of wood shavings, saw- dust, peanut shells, or other absorbent materials) and soil that is removed from the poultry house "dirt" floor during cleanout. Litter management problems include overapplication, lack of quality standards (as poultry management practices greatly affect litter nutrient content; Sims 1995), and high transport costs (Bosch and Napit 1992). Geographical concentration within the southeastern United States, with 83% of U.S. broiler production in 14 states from Pennsylvania to Texas (NASS 1999), exacerbates the transportation problem. Traditional land-application methods for managing poultry litter have led to nitrate contamination of groundwater, phosphate contamination of surface wa- ters (Sharpley et al. 1993), and heavy-metal contamination (originating from feed additives) of soils and the plants produced on them (Sims 1995). Alternative litter management systems could reduce soil degra- dation and improve environmental quality. One alternative management strategy is composting, an aerobic, thermophilic, biological treatment that accelerates de- composition of the litter, to create a more stable, value- added product (Carr et al. 1995). The use of site-specific soil quality indices may help poultry producers evaluate which litter management practices are the most sustainable for their situation.

Our objective was to create a transferable framework or general approach for choosing a minimum data set to evaluate soil quality from a much larger data set of plausible physical, chemical, and biological factors. Through the use of multivariate statistical techniques, we attempt to minimize disciplinary bias and maximize transferability of the index method. We illustrate SQI development and site transferability using two case study sites with varying limiting factors, primarily based on differences in soil order.

MATERIALS AND METHODS

Study systems

Experimental data collected from two tall fescue (Festuca arundinacea) pastures managed by Georgia Agricultural Experiment Stations in northwest and northeast Georgia (USA) provided the information needed for index development. We used two locations to compare the indexing approach on contrasting soil orders: Alfisols and Ultisols. The Alfisol site, near Cal- houn, Georgia, was located in the Southern Appala- chian Ridges and Valleys region on a Conasauga silt loam (fine, mixed, thermic, Typic Hapludalf). The Ul- tisol site, near Farmington, Georgia, was in the Pied- mont region on a Cecil sandy loam (clayey, kaolinitic, Typic Kanhapludult). The sites had similar land-use histories. The Alfisol site was cut for hay and the Ul- tisol site was in pasture at least five years prior to the experiment. Neither site had litter or compost applied during that time. Forage at the Alfisol site continued to be cut and harvested several times each year during the experimental period from 1992 to 1995. No harvests or grazing occurred after 1992 at the Ultisol site. The experimental design at both locations was a ran- domized complete block with four blocks and six soil- amendment treatments. Amendments were added in a split application, Spring and Fall, 1992. Experimental treatments (application totals) consisted of surface-applied poultry (broiler) litter (PL) applied at two rates of 220 and 1845 kg N/ha, surface-applied composted broiler litter (CL) at two rates of 950 and 1845 kg N/ha, a surface-applied ammonium nitrate treatment providing 100 kg N/ha, and a control. The synthetic- fertilizer plots also received 13 kg P/ha and 33 kg K/ ha as triple super phosphate and potassium chloride, respectively (Tyson 1994). Composted litter for the CL treatment was commercially composted using wood chips as a carbon source in open windrows for 6 wk until reduced temperatures were observed. The high application rates of fresh and composted poultry litter reflected the high end of typical land applications for litter disposal, while the low rates were more representative of recommended application rates based on potentially available N (Sims 1987) in each amendment. The synthetic-fertilizer treatment represented recommended fertilizer rates for fescue. No supple- mental nutrients were added to either fresh or composted litter treatments. Slight differences in the actual nutrient application rates for fresh and compost treatments between sites were caused by variations in N and water content (Tyson 1994).

Laboratory analyses

We sampled these sites three years after amendment application to evaluate residual effects. Soil cores were taken for 0 to 5 cm and 5 to 15 cm depths at both sites. Samples were stored at 4°C for no longer than 10 d before analysis. We used results for the 0-5 cm samples



TABLE 1. Potential soil quality indicators assayed at Calhoun and Farmington, Georgia, USA, in 1995.

Variable Method Reference as indicatort

t Citation suggests the use of the assay as a soil quality indicator. :: Aluminum, arsenic, cadmium, copper, lead, nickel, potassium, sodium, and zinc. § Inductively coupled plasma spectrophotometry.

Cation exchange capacity. [ Calhoun site only.

December 2001 DESIGNING A SOIL QUALITY ASSESSMENT TOOL 1575

Physical

Soil moisture gravimetric

lSminusO.lbar

soil core

wet sieve

Doran and Parkin (1994), Lowery et al. (1996)

Larson and Pierce (1991), Lowery et al. (1996); Olson et al. (1996)

Larson and Pierce (1991), Doran and Parkin (1994), Arshad et al. (1996), Karlen et al. (1996)

Harris et al. (1996), Arshad et al. (1996), Karlen et al. (1996)

Available H20

Bulk density

Aggregate stability

Chemical

Total carbon

Total nitrogen

Soluble C

Mineral N

Extractable P

dry combustion

dry combustion

K2SO4 extraction

K2SO4 extraction

anion exchange

double acid/ICP§

in water and CaCl2

ammonlum

Larson and Pierce (1991), Doran and Parkin (1994), Elliot et al. (1994), Sikora and Stott (1996)

Doran and Parkin (1994), Sikora and Stott (1996)

Larson and Pierce (1991)

Doran and Parkin (1994) Allan and Killorn (1996), Harris et al. (1996), Karlen et al . (1996)

Harris et al. (1996)

Micronutrients::

pH

CECIg

Cihacek et al. (1996), Olson et al. (1996)

Doran and Parkin (1994), Karlen et al. (1996), Smith and Doran (1996)

Larson and Pierce (1991), Olson et al. (1996)

Biological

Microbial C

Microbial N

Soil respiration

Urease

fumigation extraction

persulfate digest

NaOH traps

urea

Fauci and Dick (1994), Gregorich et al. (1994), Turco et al. (1994), Rice et al. (1996)

Doran and Parkin (1994), Fauci and Dick (1994), Harris et al. (1996), Rice et al. (1996)

Doran and Parkin (1994), Harris et al. (1996), Sarrantonio et al. (1996)

Dick (1994) Dick et -al. (1996)

Dick -(1994) Dick et al. (1996)

Bongers (1990), Linden et al. (1994), Blair et al. (1996)

Phosphatase

Nematodes1[

p-nitrophenol

functional groups

only for the index because not all of the laboratory analyses were performed on the deeper layer and we wanted to include the largest possible number of potential indicators. For the Alfisol site, most treatment differences were observed in this shallow layer only, due to the surface application of amendments and ap- parently little downward movement of nutrients in the soil profile. At the Ultisol site, significance of results between the two depths was nearly identical (Andrews 1998).

Thirty-eight separate variables were measured in three categories chemical, biological, and physical- at both sites (Table 1). Chemical variables included total organic carbon (TOC) (Nelson and Sommers 1996); total nitrogen (N) (Bremner and Mulvaney 1982); extractable macronutrients: soluble carbon (C) and mineral N (Mulvaney 1996), anion exchange resin

P (Nuernberg 1994); extractable micronutrients and heavy metals: aluminum, arsenic, calcium, cadmium, copper, lead, nickel, potassium, sodium, and zinc (Amacher 1996); pH (Thomas 1996); and cation exchange capacity (CEC) (Sumner and Miller 1996). The biological attributes were microbial biomass C (MBC) (Sparling and Ross 1993), microbial biomass N (Ca- brera and Beare 1993), and microbial quotient (MBC/ TOC) (Gregorich et al. 1994); two soil enzyme activities: non-buffered modifications of urease (Kandeler and Gerber 1988) and phosphatases (Tabatabai and Bremner 1969); soil respiration (Anderson 1982) and the respiratory quotient (respired CO2/MBC) (Grego- rich et al. 1994). The physical measures included soil moisture (Gardner 1986); plant available water holding capacity (Klute 1986); bulk density (Blake and Hartge 1986); and water-stable aggregates (Beare et al. 1994).



Ecological Applications Vol. 1 1, No. 6 1576 SUSAN S. ANDREWS AND C. RONALD CARROLL

In addition, we enumerated nematode functional groups at the Alfisol site (Neher et al. 1995).

Minimum data-set formulation

Defining goals. The first step in evaluating soil quality, as with any ecosystem-management practice, is the identification of management goals (or soil functions) specific to the site in question (Christensen et al. 1996, Meyer and Swank 1996). We assumed the goals to be those of a poultry producer maintaining pastureland as a disposal site for poultry litter, a dominant litter-management and land-use practice in the region. The specific management criteria were to maximize litter disposal, maximize net revenue, and minimize environmental risks. End-point measures identified as proxies for these goals later served as dependent variables to validate the minimum data set (MDS) for each site.

We reported the disposal goal in terms of kilograms of dry litter applied per hectare. We used a conversion factor adapted from Safley and Safley (1991) to represent compost as an equivalent litter volume. Fescue yield (as kilograms of dried biomass per hectare) represented the economic goal because yield is directly proportional to net revenue by the equation (yield x selling price)-production costs = net revenue. How- ever, yield data were available for only the first year after amendment application for the Ultisol site. We used average yield from four harvests during the 1992 growing season for both sites. At the Alfisol site, yield data were available for the second year as well. So, we calculated a measure of yield stability using the interquartile range (Cressie 1993) from eight harvests over two growing seasons at the Alfisol site only.

Environmental risks to be minimized included P runoff and soil heavy-metal contamination. No runoff data were available, so we used anion-exchange resin-extractable P04-P (resin P) at the 0-15 cm depth three years after application to represent potential runoff of soluble P. We justify this because the experimental sites, having 1-2% slopes, exhibit few problems with runoff and thus retained much of this very soluble P fraction over the 3-yr period. However, there were significant differences in soluble P between management treatments in the lower depth at the Ultisol site, which suggests some downward movement of this P fraction (Andrews 1998). Heavy metals are well-known con- taminants of poultry litter (Sims 1995). We chose arsenic, added to poultry feed as a fungicide, to serve as a proxy for all metals.

Although nitrate leaching is known to be an environmental consequence of litter application, we did not address this risk because sampling for this study occurred three years after amendment application. In an Ultisol under fescue pasture, Adams et al. (1994) found that the majority of excess nitrate N leached through the soil profile during the winter months in the first year after litter application. Further, many studies have

shown that from 60% to 90% of the organic N fraction of litter mineralizes during the fist year. Of the re- maining organic N, only 5% mineralizes in each of the next two years (e.g., Pratt et al. 1973, USDA 1979, Sims 1987) creating a relatively small fraction of potentially leachable N. No longer term mineralization studies are available for composted litter. However, published studies show that the mineral-N fraction is smaller and the N mineralization rates of the organic- N fraction are lower for composted litter than for fresh litter (Tyson and Cabrera 1993, Brinson et al. 1994). Based on these reports, our sampling in the third growing season after application, and our finding of no significant differences between treatments in nitrate N be- low the soil surface (to 15 cm depth) (Andrews 1998), we concluded that any excess N had already leached through the profile. Therefore, nitrate N was not included as a management variable (but was considered as an MDS component).

Data screening. We reduced the data to a minimum data set (MDS) of soil quality indicators through a series of uni- and multivariate statistical methods using JMP software (SAS Institute, Cary, North Carolina, USA). For each site, nonparametric statistics (Kruskal- Wallis x2) were used to identify indicators with significant treatment differences. We chose this nonparametric method because it did not require assumptions of normality and homoscedasticity, thereby avoiding any need to transform the data. Only variables with significant differences between treatments (P < 0.05) were chosen for the next step in MDS formation.

Choosing representative variables. We then performed standardized principal-components analysis (PCA) for each statistically significant variable, sepa- rately for each site. There are several strategies for using PCA to select a subset from a large data set; the one described here is similar to that described by Dun- teman (1989). We assumed that principal components receiving high eigenvalues and variables with high factor loadings best represent system attributes, and examined only the principal components that explained at least 5% of the variation in the data (Wander and Bollero 1999) up to 85% of the cumulative variation. Within each principal component (PC), only highly weighted factors, i.e., those with absolute values within 10% of the highest weight, were retained for the MDS.

Reducing redundancy. To reduce redundancy and rule out spurious groupings among the highly weighted variables within each PC, we used multivariate correlation coefficients to determine the strength of the relationships among variables. Well-correlated variables (>0.70) were considered redundant and candidates for elimination from the data set. To choose variables within well-correlated groups, we summed the absolute values of the correlation coefficients for these variables. We assumed that the variable having the highest correlation sum best represented the group. The choice among well-correlated variables could also be




based on practicality (i.e., ease of sampling, cost, and interpretability). Converselys any uncorrelated, highly weighted variables were considered important and, therefore, retained in the MDS.

We checked for the possibility of further reduction in the number of variables in the MDS by performing a forward stepwise regression of the indicated variables against the higher order, management-goal variables. If any of these variables remained unselected after stepwise regression against each goal, it was eliminated from the MDS.

MDS validation. We ran multiple regressions using the final MDS components as the independent variables and each management-goal attribute as a dependent variable. These regressions served to check the MDS representation of management system goals.

Creation of the soil quality index

After the MDS selection process, we transformed each of the MDS variables for inclusion in an additive soil quality index (SQI). Our SQI approach is similar to that used by Karlen and Stott (1994) and recommended by Seybold et al. (1997). Each variable was transformed or standardized to a value between 0 and 1 using scoring functions (Karlen and Stott 1994). These scoring functions are used widely in economics as utility functions (Norgaard 1994), in multi-objective decision and management sciences as preference functions (Miller 1970, Keeney and Raiffa 1976), and in systems engineering as a tool for modeling (Wymore 1993).

In the index, each MDS variable data point was transformed using a nonlinear scoring function with y-axis ranging from 0 to 1 and the x-axis representing a range of site-dependent scores for that variable. For simplic- ity, the scoring function (x-axis) ranges were set for most variables to within 5% of the observed range across all treatments for each site. The actual shape of the decision function, either a sigmoid curve with an upper asymptote, a sigmoid curve with a lower asymptote, or some variation on a bell-shaped curve, was indicator dependent. For example, total N and extractable Ca were assigned upper asymptote or "more is better" functions and bulk density a lower asymptote, the "less is better" function. Soil pH, nitrate-N, extractable Zn, and plant available water-holding capacity (AWC) were assigned midpoint optimums, or bell- curve functions (after Karlen et al. 1994). This assign- ment of scoring functions, both curve shape and x-axis range, assumed value judgments on the part of the user. Although we attempted to make the index as objective as possible, values and preferences are inherent in any decision-making process (Keeney 1992). We used the defined management goals, environmental-protection goals, and plant resource requirements as the values to determine scoring-function shapes.

Once transformed, the MDS variable scores for each observation were simply added in a cumulative SQI so

that higher scores meant greater soil quality, in accord with our scoring method above. We calculated means, standard deviations, Student's t at (x = 0.05, and AN- OVA for each treatment's SQI score at each site.

Fine-tuning.-After analysis of the SQI values, we performed a second SQI calculation using an adjusted MDS to include resin P in the MDS. Because P runoff is a major environmental concern in this region, the scoring for this indicator was from -1 to 1 to include a penalty for excess P applications. Results of this environmentally biased index were compared with those from the purely statistically chosen index.

RESULTS

MDS formulation

Data screening. The nonparametric x2 test of treatment means revealed that total C and N, soluble C, resin P, extractable A1, As, Ca, Fe, Na, and Zn, pHH2o, pHcacl2, and CEC (cation exchange capacity) had significant differences between amendment treatments at the Alfisol site. No significant differences between treatments at the P < 0.05 level existed for any of the physical or biological assays (data not shown). At the Ultisol site, variables with statistically significant treatment means were identified in all three variable categories. Physical variables that were significantly different among treatments included gravimetric soil water (moist), plant available water-holding capacity (AWC), bulk density, and water-stable aggregates between 53 and 106 Fm (>53 agg.). At the Ultisol site, there were significant effects of the treatments on several chemical variables including NO3-N, resin P, A1, Ca, Na, pHH2o! pHCacl2 and CEC. Microbial biomass C (MBC) and biomass N (MBN) and non-buffered urease production were the biological variables with significantly different treatment means.

Choosing representative variables. In the PCA of variables with significant differences between treatments for the Alfisol site, >88% of the variance in the data was explained by the first three principal components (PCs) (Table 2a). The highly weighted variables under these PCs were total C, total N, resin P, A1, Ca, Na, pHCacl2 and zinc. The Ultisol site (Table 2b) required five principal components to explain 87% of data variance. These PCs included resin P, pHH2o and pHcacl2 A1, Ca, Na, NO3-N, CEC, >53 agg, and AWC as highly weighted variables.

Reducing redundancy.-Using correlation coeffi- cientse we chose calcium as most representative of the PC1 group to be part of the MDS for the Alfisol site (Table 2a). Because the first PC explained such a large percentage of the data variation (73%), we also chose the two variables with the lowest correlation sums, total N and pHCacl2 to represent variation within this group (Table 3a). Additionally, pHCacl2 is a very simple and inexpensive test, while the other tests in this group require approximately the same amount of effort as-



Statistic or variable

a) Alfisol site

Eigenvalue Percentage vari-

ance explained Cumulative per-

centage

Eigenvectors Total C Total N Soluble C Resin P Aluminum Arsenic Calcium Iron Sodium r7-

Llnc pH CaCl2 pH H20 CECT

PC 1 PC2 PC3 PC4 PCS

Notes: Boldface numbers are heavily weighted factors under each principal component (PC) that explains at least 5% of total variation or at least 85% of the total variation in the data (Cumulative percentage).

t Cation exchange capacity. $ Water-stable aggregates between 53 and 106 ,um.

Ecological Applications Vol. 11, No. 6 1578 SUSAN S. ANDREWS AND C. RONALD CARROLL

TABLE 2. Results from the principal-components analysis of statistically significant variables.

9.52

73.27

73.27

0.299 0.290 0.280 0.310

-0.311 -0.271

0.314 -0.207

0.304 0.107 0.290 0.272 0.280

1.06

8.17

81.44

0.227 0.221 0.149 0.067 0.003 0.075 0.045 0.417 0.060

-0.683 -O. 195 -0.289

0.316

0.87

6.68

88.13

0.029 0.013 0.277 0.139 0.056

-0.21 1 -0.067

0.500 0.093 0.633

-0.298 -0.308

0.093

0.65

5.02

93.15

0.258 0.318

-0.071 0.039 0.150 0.542

-0.121 -0.468

0.015 0.248

-0.305 -0.189

0.292

0.31

2.40

95.54

0.329 0.464

-0.574 -0.134

0.041 - 0.269 -0.064

0.252 -0.358

0.125 0.048 0.201 0.030

b) Ultisol site

Eigenvalue Percentage vari-

ance explained Cumulative per-

centage Eigenvectors

Moisture Avail. H20 Bulk density >53 agg.4: NO3-N Resin P Aluminum Calcium Sodium pH H20 pH CaCl2 CEC Microbial C Microbial N Urease

7.74

S1 .60

51.60

0.258 0.178

-0.228 0.093 0.215 0.348

-0.263 0.274 0.259 0.315 0.338 0.185 0.279 0.228 0.290

1.90

12.69

64.29

0.310 -0.006 -0.021 -0.23 1

0.161 -0.007

0.420 -0.412 -0.421 -0.006 -0.025

0.254 0.312 0.353 0.119

1.60

10.67

74.96

0.243 -0.370

0.312 -0.305

0.444 0.007

-0. 147 0.178 0.171

-0.177 -0.081 -0.436

0.127 0.237

-0. 181

1.0 1 0.77

6.74

81.70

0.076 - 0.403

0.524 0.628

-0. 152 0.020

-0.053 -0.013 -0.094

0.156 0.070 0.220 0.047 0.205

-0.072

5.12

86.82

0.128 0.567 0.050 0.551 0.241 0.110 0.156

-0.078 -0.014 -0.276 -0.163 -0.365

0.090 0.058

-0.091

suming the necessary equipment is available. Because zinc was the only highly weighted variable for PC2 and PC3 at the Alfisol site, it was also included in the MDS.

The first principal component for the Ultisol data had three highly weighted variables that were also highly correlated. We chose pHCacl2 to represent this PC in the Ultisol MDS because of its correlation sum and ease of measurement. Calcium had the highest correlation sum of the three highly correlated variables in PC2 at this site. The two important variables for PC3, nitrate N and CEC, were not correlated so both were added to the MDS. Only one variable received a high weighting under PC4. This same variable, agg. >53,

was also highly weighted in the last PC examined. Un- der PC5, agg. >53 was not well correlated with the other heavily weighted variable, AWC, so both were included in the MDS (Table 3b).

Stepwise regression of the preliminary MDS components for the Alfisol site (total N, extractable calcium and zinc, and pHCacl2) using each end-point measure as a dependent variable revealed that all components contributed significantly to the explanation of variation (at the P < 0.05 level) in at least one of the management- goal end points. Stepwise regression using the Ultisol MDS revealed that agg. >53 did not contribute to the interpretation of any of the end-point measures. On that



TABLE 3. Correlation coefficients and correlation sums for highly weighted variables under principal components (PC) with multiple high factor loadings.

a) Alfisol site Total Total Resin

PC1 variables C N P A13+ Ca2+ Na+ PHcacl2

Correlation coefficients Total C 1.00 0.99 0.88 -0.86 0.88 0.86 0.72 Total N 0.99 1.00 0.85 -0.82 0.85 0.81 0.69 Resin P 0.88 0.85 1.00 -0.90 0.91 0.94 0.79 Aluminum -0.86 -0.82 -0.90 1.00 -0.97 -0.89 -0.89 Calcium 0.88 0.85 0.91 -0.97 1.00 0.94 0.89 Sodium 0.86 0.81 0.94 -0.89 0.94 1.00 0.80 CaCl2 pH 0.72 0.69 0.79 -0.89 0.89 0.80 1.00

Correlation sums 6.20 6.01 6.28 6.32 6.43 6.23 5.79

b) Ultisol site PC1 variables Resin P pHH2o PHcacl,

Correlation coefficients Resin P 1.00 0.85 0.94 pH in water 0.85 1.00 0.94 CaCl2 pH 0.94 0.94 1.00

Correlation sums 2.79 2.79 2.87

PC2 variables A13+ Ca2+ Na+

Correlation coefficients Aluminum 1 .00 - 0.92 - 0.84 Calcium - 0.92 1.00 0.97 Sodium -0.84 0.97 1.00

Correlation sums 2.76 2.88 2.81

PC3 variables NO3-N CEC

Correlation coefficients NO3-N 1.00 0.07 CEC 0.07 1.00

PC5 variables AWC >53 agg.

Correlation coeffifficients AWC 1.00 0.22 >53 agg. 0.22 1.00

Notes: The correlation sum is the sum of the absolute value of correlation coefficients for each variable. CEC = cation exchange capacity; AWC = plant available water-holding capacity; >53 agg. = water-stable aggregates between 53 and 106 ,um.

DESIGNING A SOIL QUALITY ASSESSMENT TOOL 1579 December 2001

basis, the water-stable aggregate variable was deleted from the Ultisol MDS.

MDS validation. We performed multiple regressions using the four MDS indicators for the Alfisol site, total N, calcium, zinc, and pHCacl2 as independent variables and the end-point measures representing management goals as dependent variables (Table 4a), it- eratively. Total N was the most significant MDS variable when using litter disposal as the dependent variable (R2 = 0.81). The regression of MDS variables with 1992 yield and with 1992-1993 yield stability had the lowest coefficients of determination (R2 = 0.56 and 0.55, respectively). Zinc contributed the most to these linear relationships between the MDS and both yield measures. The regression using resin P as the dependent variable yielded a significant linear relationship, with total N as the most important independent variable. The arsenic regression resulted in an R2 = 0.74, with pHCacl2 being the most significant variable.

Multiple regressions of the four Ultisol MDS variables, AWC, NO3-N, Ca, and pHCacl29 yielded similar results to the Alfisol data regressions (Table 4b). The MDS regression using litter disposal as the dependent variable had an R2 = 0.85, with pHCacl2 and NO3-N being the sources of the highest P values. Again, the regression of the MDS vs. 1992 yield data had the lowest R2 value at 0.35. The most significant independent variables in the regression using resin P as the dependent variable were pHCacl2 and NO3-N, resulting in R2 = 0.91. Regression of arsenic produced an R2 = 0.75 in which calcium was highly significant.

SQI interpretation

The MDS variables for each treatment were transformed using scoring functions. These scores were then summed for each site. The resulting SQI values indicate that for both sites the compost management alternatives had the best soil quality (Fig. la and b). At the Alfisol



TABLE 4 Results of multiple regressions of the minimum data set (MDS) components using management-goal attributes as dependent variables for each site.

Most significant Goal or function R2 MDS variable(s) P

a) Alfisol MDS: zinc; calcium; total N; pHCac,) Litter disposal 0.81 total N >0.04 Yield (fescue biomass) 0.56 Zn; Ca2+; total N >0.003; >0.007; >0.02 Yield stability (interquartile) 0.55 Zn >0.0007 P runoff potential (resin P) 0.86 total N >0.009 Metal contamination (As) 0.74 PHcacl2 >0.12

b) Ultisol MDS: available water; calcium; nitrate N; ptICaC,2 Litter disposal 0.85 pHCacl7; NO3 c O.000 1; > 0.02 Yield (fescue biomass) 0.35 AWC, NO3 >0.04; >0.05 P runoff potential (resin P) 0.91 pHCac; N°3 C0.0001, >0.0002 Metal contamination (As) 0 75 Ca2+ 2 cO.0001

Ecological Applications Vol. 11, No. 6

1580 SUSAN S. ANDREWS AND C. RONALD CARROLL

site, the two compost treatments and the high litter treatment received significantly higher index values than the other treatments-(P < 0.0001). At the Ultisol site, the two compost application rates had the highest soil quality values. The high litter treatment was significantly higher than the low littex; chemical, or control treatments (P < 0.0001).

Fine-tuning. The adjusted index, including resin P in the ME)S, revealed small changes in the relative rankings of the management treatments (Fig. la and b). These changes still reflect soil quatity but include an added emphasis on off-site environmental effects.

The low compost and high litter treatments received the highest SQI values at the Alfisol site (P < 0.0001). At the Ultisol site, high compost was also included in the significantly higher group (P < 0.0001) but the actual mean value was lower than the tow compost and high litter treatments.

DISCUSSION

MDS validation

The variables identified in the minimum data set (MDS) screening process (xz test) were highly sensitive

b) Ultisol site using PCA-selected MDS * compost high

S compost low

* litter high

z litter low

llm chemical

z contro}

4.0 q a 3.0

2.0

1.0

0.0

c

c

d) Ultisol site using MDS + resin P

4.0 a

2 0 ^ '<>,, ̂ b

1.0 | "'s' | h ^ g

OtO

Litter management treatment

FIG. 1. Soil quality index (SQI) values for poultry-litter management alternatives at two sites with contrasting soil orders. The SQI values in panels (a) and (b) were calculated using the PCA-selected minimum data set (MDS) at each site. The SQI values shown in panels (c) and (d) used the PCA-selected MDS with an additional score for resin-available P. Data are means with 1 se error bars. Different lowercase letters denote a significant difference at (x - 0.05.

a) Alfisol site using PCA-selected MDS 4-0- T t .a

X | t'

> c) Alfisol site using MDS + resin P ce 4.0- a a

0 3.0- ,<,<> ^

Co201 1

.00




to the addition of fresh and composted litter, making them good candidates for an index to describe these systems. Further, we interpreted the high R2 values found for the MDS multiple regressions using end- point measures as dependent variables to mean that these indicators were representative of the identified management goals at both sites. Although the end-point data available to represent sustainable management goals for the MDS validation were not always ideal, any effort to validate an index is important, as most indices are deemed successful simply by their ability to detect differences among the systems studied without regard to the meaning of those differences (Herrick 2000).

The MDS multiple regressions revealed relationships between MDS indicators and end-point measures sup- ported by numerous other studies. For example, the MDS regression using litter disposal as the dependent variable showed total N to be the most significant MDS variable at the Alfisol site. Many studies have shown increases in total N of the surface layer following poultry-waste amendment additions (Jackson et al. 1977, Sharpley et al. 1993, Kingery et al. 1994). Similarly, the regressions with resin P showed the most significant independent variable to be calcium at the Alfisol site and pH at the Ultisol site. Fresh and especially composted poultry litter is high in calcium, which increases soil pH and-buffering capacity (Hue 1992, Andrews 1998). Bioavailable P is solubilized as acidic pH's increase; however, in this case it is probable that high inputs of P have also saturated adsorption sites at the soil surface 0-5 cm layer.

Although most of the MDS regression results sug- gested meaningful relationships between the MDS indicators and management goals, the regressions using yield as a dependent variable did not. The low R2 results for MDS regressions with fescue yield, particularly at the Ultisol site, were likely due to the yield data being from 1992, the year when the amendments were first applied, while all other data were obtained from soil cores sampled in 1995. The R2 result for the regression using 1992-1993 yield stability was nearly identical to that using 1992 yield at the Alfisol site, which may mean that the time period was not long enough to be meaningful.

The two sites in our case study had two of the four MDS variables in common: calcium and pHCacl2. It is likely that the concentration of calcium in compost with its subsequent effect on soil pH drove this occurrence. While the two chosen MDSs were similar, they were not interchangeable. When the MDS components chosen at one site were used as the MDS variables to regress using end-point measures at the other site, R2 values declined (data not shown). We suspect that MDS differences would be greater as differences between baseline soils increase.

Soil quality index (SQI) interpretation

The inherent flexibility of this indexing framework allows users to chose appropriate site-specific scoring functions and acceptable ranges (or use default values provided by experts). Among soil orders, flexibility in scoring, especially x-axis ranges, is essential. An ex- treme example of this would be a comparison of scoring based on inherent differences between an Ultisol (highly weathered, low organic-matter (OM) soils) and a Mollisol (high OM soils formed under grassland): a Mollisol with 2% OM is likely to be highly degraded and should receive a low score while an Ultisol with 2% OM is probably functioning at the high end of its capacity and should rate a high score. Hussain et al. (1999) showed that index scoring could effectively be adapted for different soils and regions. For our case- study sites, we found it necessary to have different scoring-function ranges due to the inherent differences in the two soil orders represented. This avoided unduly low scores for all management systems at the Ultisol site (data not shown).

Another type of flexibility in scoring could be dic- tated by site conditions or management goals. For example, at a site that is known to be underlain by bed- rock, scoring for high levels of soil NO3 (which would normally receive low scores due to potential leaching) might be relaxed since there in little threat of groundwater contamination in this isolated case. Conversely, in an area with very shallow water tables, scoring could include greater penalties for high NO3 levels. This flexibility could, however, lend itself to abuse if this method was ever adopted for regulatory use, by allowing unwarranted devaluing of environmental concerns.

For our case study, the summed MDS indicator scores resulted in the highest soil quality values for the compost treatments at both sites. At the Alfisol site, the high litter treatment showed an equally high soil quality value. The minor site-specific differences in SQI outcomes can be at least partially explained by initial differences in soil pH (Conasauga soil at 5.5 and Cecil soil at 4.5). The pH increases associated with compost treatments had a greater impact at the (lower pH) Ultisol site. We offer this explanation not only because pH was chosen in the MDS for both sites but also because it was much more significant in the regression equations for the low-pH Ultisol site.

Fine-tuning. In the case study, compost applied at the high rate resulted in large quantities of resin P in soils even after three years (Andrews 1998). Although resin P received high weights using PCA at both sites, it was not initially chosen for the MDS in the correlation-sums step (Table 3). The inclusion of resin P in the MDS is warranted as an indicator of potential P runoff, a known environmental problem in these systems. However, when resin P was added to the MDS, SQI relationships changed only marginally. Therefore,



Ecological Applications Vol. 11, No. 6 1582 SUSAN S. ANDREWS AND C. RONALD CARROLL

the original MDS, without resin P, was acceptable for these sites.

This study showed that an indexing framework can effectively be applied to assess poultry-litter management effects on soil quality in different soils. This tool could be used either for side-by-side comparisons, as in our case study, or over time (Larson and Pierce 1991). A major drawback to this framework is the need for a large existing data set for MDS selection. Ad- ditionally, the entire process would likely need to be repeated periodically. This would be especially important after major disturbances, when changes in limiting factors could, in turn, necessitate the use of different indicators for assessment.

Other studies employing similar methods have demonstrated the efficacy of multi-attribute indexing techniques (or parts of the framework) in a variety of systems and scales. Wander and Bollero (1999) used PCA to compare soil quality in tillage and no-till systems in Illinois. Brejda et al. (2001a, b) demonstrated the use of a similar multivariate approach to identify soil indicators (without index development) using regional- scale soils data from four U.S. ecoregions across numerous soil orders. Land-use types included in that work were cropland, Conservation Reserve Program (CRP) land, perennial forages, and woodland and for- ests. Scoring and index techniques, similar to those described for the framework, have been used to compare tillage practices (Karlen and Stott 1994, Hussain et al. 1999), CRP land in the Midwest United States (Karlen et al. 1996), and organic and conventional vegetable production systems in northern California (An- drews et al. 2001). These varied uses suggest that our indexing framework may be applicable not only to different soil types but also to multiple management systems. Using a standardized framework to create an assessment tool, such as the one proposed here, could be a significant advancement for adaptive land-management programs.

Conclusions

We envision this index approach as a tool for adaptive land management for monitoring the effects of management practices on soil functions. In our case studies, the SQI method worked well as a general approach for developing ecologically based, site- and management-specific indices representative of management goals. Relative SQI ranks of the management treatments changed only marginally when the MDS was adjusted to better reflect the environmental threat of P runoff. At both sites, a one-year application of composted litter in moderate levels or fresh litter at higher levels appear to be methods that improve soil quality while maintaining environmental protection and litter- disposal goals.

The use of this approach has the potential to integrate biological, chemical, and physical data for ecological management applications where such integration is of-

ten lacking. We showed the framework for choosing a MDS to be transferable among two soil orders. Addi- tionally, the index framework may work well for other ecosystem management assessments. Comparing index values between systems with varying management practices (e.g., Karlen and Stott 1994) or to baseline (reference) targets would provide ready interpretation for land managers using adaptive-management or restoration techniques. We believe that soil quality indexing may have even broader applications for soil restoration than for agriculture because of the avail- ability of reference systems that do not exist for agroecosystems.

Some form of multicriteria decision making is im- perative to reach solutions that yield the greatest good for the individual, society, and the ecosystem as a whole (Lein 1990, Zublena 1995). This multivariate statistical method effectively selected a minimum data set from large existing data sets; it objectively synthe- sized information and reduced redundancy. Using this indexing approach to compare management alternatives would help land managers choose the management practices that best meet their goals. Certainly, MDS indicators and scoring functions may need to change with differing management, climate, soil type, or even time. However, standard methods for indicator selection and index design would strengthen both com- parability between sites and legal defensibility (Maki 1980), as well as precision and accuracy of interpretation for agroecosystem monitoring and assessment.

ACKNOWLEDGMENTS Funding was provided by grants from the Andrew W. Mel-

lon Foundation and the National Council of State Garden Clubs. Fresh and composted litter was provided by Sargent Nutrients, Inc., Gainesville, Georgia, USA. The authors grate- fully acknowledge S. T. Pierson for design and setup of the experimental plots, M. L. Cabrera for useful discussion throughout this work, and J. Brejda, C. A. Cambardella, J. E. Herrick, and D. L. Karlen for helpful editorial revisions of the manuscript. Any reference to trade names and com- panies in this paper is made for information purposes' only and does not imply endorsement by USDA or the University of Georgia.

LITERATURE CITED Adams, P. L., T. C. Daniel, D. R. Edwards, D. J. Nichols, D.

H. Pote, and H. D. Scott. 1994. Poultry litter and manure contributions to nitrate leaching through the vadose zone. Soil Science Society of America Journal 58: 1206-1211.

Allan, D. L., and R. Killorn. 1996. Assessing soil nitrogen, phosphorus, and potassium for crop nutrition and environmental risk. Pages 123-141 in J. W. Doran and A. J. Jones, editors. Methods for assessing soil quality. Soil Science Society of America, Madison, Wisconsin, USA.

Amacher, M. C. 1996. Nickel, cadmium, and lead. Pages 739-768 in D. L. Sparks, editor. Methods of soil analysis. Part 3. Chemical methods. Soil Science Society of America, Madison, Wisconsin, USA.

Anderson, J. P. E. 1982. Soil respiration. Pages 831-871 in A. L. Page, R. H. Miller, and D. R. Keeney, editors. Meth- ods of soil analysis. Part 2. Second edition. Soil Science Society of America, Madison, Wisconsin, USA.

Andrews, S. S. 1998. Residual effects of raw and composted




poultry litter applied to tall fescue. Pages 18-67 in Sus- tainable agriculture alternatives: ecological and managerial implications of poultry litter management alternatives applied to agronomic soils. Dissertation. University of Geor- gia, Athens, Georgia, USA.

Andrews, S. S., D. L. Karlen, and J. P. Mitchell. 2001. A comparison of soil quality indexing methods for vegetable production systems in northern California. Agriculture, Ecosystems and Environment. In press.

Arshad, M. A., B. Lowery, and B. Grossman. 1996. Physical tests for monitoring soil quality. Pages 123-141 in J. W. Doran and A. J. Jones, editors. Methods for assessing soil quality. Soil Science Society of America, Madison, Wis- consin, USA.

Beare, M. H., P. F. Hendrix, and D. C. Coleman. 1994. Water- stable aggregates and organic matter fractions in conventional and no-tillage soils. Soil Science Society of America Journal 58:777-786.

Blair, J. M., P. J. Bohlen, and D. W. Freckman. 1996. Soil invertebrates as indicators of soil quality. Pages 273-291 in J. W. Doran and A. J. Jones, editors. Methods for assessing soil quality. Soil Science Society of America, Mad- ison, Wisconsin, USA.

Blake, G. R., and K. H. Hartge. 1986. Bulk density. Pages 363-375 in A. Klute, editor. Methods of soil analysis. Part 1. Second edition. Soil Science Society of America, Mad- ison, Wisconsin, USA.

Bongers, T. 1990. The maturity index: an ecological measure of environmental disturbance based on nematode species composition. Oecologia 83: 14-19.

Bosch, D. J., and K. B. Napit. 1992. Economics of trans- porting poultry litter to achieve more effective use as fertilizer. Journal of Soil and Water Conservation 47:342-346.

Brejda, J. J., D. L. Karlen, J. L. Smith, and D. L. Allan. 2000a. Identification of regional soil quality factors and indicators. II. Northern Mississippi Loess Hills and Palouse Prairie. Soil Science Society of America Journal 64:2125- 2135.

Brejda, J. J., T. B. Moorman, D. L. Karlen, and T. H. Dao. 2000b. Identification of regional soil quality factors and indicators. I. Central and Southern High Plains. Soil Sci- ence Society of America Journal 64:2115-2124.

Bremner, J. M., and C. S. Mulvaney. 1982. Nitrogen: total. Pages 595-624 in A. L. Page, R. H. Miller, and D. R. Keeney, editors. Methods of soil analysis. Part 2. Second edition. Soil Science Society of America, Madison, Wis- consin, USA.

Brinson, S. E., Jr., M. L. Cabrera, and S. C. Tyson. 1994. Ammonia volatilization for surface-applied, fresh and composted poultry litter. Plant and Soil 167:213-218.

Cabrera, M. L., and M. H. Beare. 1993. Alkaline persulfate oxidation for determining total nitrogen in microbial biomass extracts. Soil Science Society of America Journal 57: 1007-1012.

Carr, L., R. Grover, B. Smith, T. Richard, and T. Halbach. 1995. Commercial and on-farm production and marketing of animal waste compost products. Pages 485-492 in K. Steele, editor. Animal waste and the land-water Interface. Lewis, New York, New York, USA.

Christensen, N. L., A. M. Bartuska, J. H. Brown, S. Carpenter, C. D'Antonio, R. Francis, J. F. Franklin, J. A. MacMahon, R. F. Noss, D. J. Parsons, C. H. Peterson, M. G. Turner, and R. G. Woodmansee. 1996. The report of the Ecological Society of America committee on the scientific basis for ecosystem management. Ecological Applications 6:665- 691.

Cihacek, L. J., W. L. Anderson, and P. W. Barak. 1996. Link- ages between soil quality and plant, animal, and human health. Pages 9-23 in J. W. Doran and A. J. Jones, editors.

Methods for assessing soil quality. Soil Science Society of America, Madison, Wisconsin, USA.

Cressie, N. A. C. 1993. Statistics for spatial data. John Wiley & Sons, New York, New York, USA.

Dick, R. P. 1994. Soil enzyme activities as indicators of soil quality. Pages 107-124 in J. W. Doran, D. C. Coleman, D. F. Bezdicek, and B. A. Stewart, editors. Defining soil quality for a sustainable environment. Soil Science Society of America, Madison, Wisconsin, USA.

Dick, R. P., D. P. Breakwell, and R. F. Turco. 1996. Soil enzyme activities and biodiversity measurements as integrative microbiological indicators. Pages 247-271 in J. W. Doran and A. J. Jones, editors. Methods for assessing soil quality. Soil Science Society of America, Madison, Wis- consin, USA.

Doran, J. W., and T. B. Parkin. 1994. Defining and assessing soil quality. Pages 3-21 in J. W. Doran, D. C. Coleman, D. F. Bezdicek, and B. A. Stewart, editors. Defining soil quality for a sustainable environment. Soil Science Society of America, Madison, Wisconsin, USA.

Dunteman, G. H. 1989. Principal components analysis. Sage, London, UK.

Edwards, C. A., T. L. Grove, R. R. Harwood, and C. J. P. Colfer. 1993. The role of agroecology and integrated farming systems in agricultural sustainability. Agriculture, Eco- systems & Environment 46:99-121.

Elliott, E. T., I. C. Burke, C. A. Monz, S. D. Frey, K. H. Paustian, H. P. Collins, E. A. Paul, C. V. Cole, R. L. Blevins, W. W. Frye, D. J. Lyon, A. D. Halvorson, D. R. Huggins, R. F. Turco, and M. V. Hickman. 1994. Terrestrial carbon pools in grasslands and agricultural soils: preliminary data from the Corn Belt and Great Plains regions. Pages 179- 191 in J. W. Doran, D. C. Coleman, D. F. Bezdicek, and B. A. Stewart, editors. Defining soil quality for a sustainable environment. Soil Science Society of America, Mad- ison, Wisconsin, USA.

El-Swaify, S. A., and D. S. Yakowitz. 1998. Introduction to multiple, objective decision making for land, water, and envirnmental management. Pages 3-7 in S. A. El-Swaify and D. S. Yakowitz, editors. Multiple objective decision making for land, water, and envirnmental management. Lewis, New York, New York, USA.

Fauci, M. F., and R. P. Dick, 1994. Microbial biomass as an indicator of soil quality: effects of long-term management and recent soil amendments. Pages 229-234 in J. W. Doran, D. C. Coleman, D. F. Bezdicek, and B. A. Stewart, editors. Defining soil quality for a sustainable environment. Soil Science Society of America, Madison, Wisconsin, USA.

Gardner, W. H. 1986. Water content. Pages 494-544 in A. Klute, editor. Methods of soil analysis. Part 1. Second edition. Soil Science Society of America, Madison, Wisconsin, USA.

Gregorich, E. G., M. R. Carter, D. A. Angers, C. M. Monral, and B. H. Ellert. 1994. Towards a minimum data set to assess soil organic matter quality in agricultural soils. Ca- nadian Journal of Soil Science 74:367-385.

Harris, R. F., D. L. Karlen, and D. J. Mulla. 1996. A con- ceptual framework for assessment and management of soil quality and health. Pages 61-82 in J. W. Doran and A. J. Jones, editors. Methods for assessing soil quality. Soil Sci- ence Society of America, Madison, Wisconsin, USA.

Herrick, J. E. 2000. Soil quality: an indicator of sustainable land management? Applied Soil Ecology 15:75-83.

Hue, N. V.1992. Correcting soil acidity of a highly weathered Ultisol with chicken manure and sewage sludge. Com- munications in Soil Science and Plant Analysis 23(2 and 3):241-264.

Hussian, I., K. R. Olson, M. M. Wander, and D. L. Karlen. 1999. Adaptation of soil quality indices and application to



Ecological Applications Vol. 11, No. 6 1584

SUSAN S. ANDREWS AND C. RONALD CARROLL

three tillage systems in southern Illinois. Soil and Tillage Research 50:237-249.

Jackson, W. A., S. R. Wilkinson, and R. A. Leonard. 1977. Land disposal of broiler litter: changes in concentration of chloride, nitrate nitrogen, total nitrogen, and organic matter in a Cecil sandy loam. Journal of Environmental Quality 6:58-62.

Kandeler, E., and H. Gerber. 1-988. Short-term assay of soil urease activity using colorimetric determination of ammonium. Biology and Fertility of Soils 6:68-72.

Karlen, D. L., M. J. Mausbach, J. W. Doran, R. G. Cline, R. F. Harris, and G. E. Schuman.1997. Soil quality: a concept, definition, and framework for evaluation. Soil Science So- ciety of America Journal 61:4-10.

Karlen, D. L., T. P. Parkin, and N. S. Eash. 1996. Use of soil quality indicators to evaluate conservation reserve program sites in Iowa. Pages 345-355 in J. W. Doran and A. J. Jones, editors. Methods for assessing soil quality. Soil Science Society of America, Madison, Wisconsin, USA.

Karlen, D. L., and D. E. Stott. 1994. A framework for evaluating physical and chemical indicators of soil quality. Pag- es 53-72 in J. W. Doran, D. C. Coleman, D. F. Bezdicek, and B. A. Stewart, editors. Defining soil quality for a sustainable environment. Soil Science Society of America, Madison, Wisconsin, USA.

Karlen, D. L., N. C. Wollenhaupt, D. C. Erbach, E. C. Berry, J. B. Swan, N. S. Eash, and J. L. Jordahl. 1994. Crop residue effects on soil quality following 10 years of no-till corn. Soil and Tillage Research 31: 149-167.

Karr, J. R. 1981. Assessment of biotic integrity using fish communities. Fisheries 6:21-27.

Keeney, R. L. 1992. Value-focused thinking. Harvard Uni- versity Press, Cambridge, Massachusetts, USA.

Keeney, R. L., and H. Raiffa. 1976. Decisions with multiple objectives: preferences and value tradeoffs. John Wiley & Sons, New York, New York, USA.

Kingery, W. L., C. W. Wood, D. P. Delaney, J. C. Williams, and G. L. Mullins. 1994. Impact of long-term land application of broiler litter on environmentally related soil properties.JournalofEnvironmentalQuality23:139-147.

Klute, A. 1986. Water retention. Laboratory methods. Pages 635-662 in A. Klute, editor. Methods of soil analysis. Part l. Second edition. Soil Science Society of America, Mad- ison, Wisconsin, USA.

Larson, W. E., and F. J. Pierce. 1991. Conservation and en- hancement of soil quality. Pages 175-203 in J. Dumanski, E. Pushparajah, M. Latham, and R. Myers, editors. Eval- uation for sustainable land management in the developing world. IBSRAM Proceedings number 12. Volume 2. Tech- nical papers. Proceedings of international workshop, 15- 21 September 1991, Chiang Rai, Thailand. International Board for Soil Research and Management, Bangkok, Thai- land.

Lein, J. 1990. Exploring a knowledge-based procedure for developmental suitability analysis. Applied Geography 10: 171-186.

Linden, D. R., P. F. Hendrix, D. C. Coleman, and P. C. J. v. Vliet. 1994. Faunal indicators of soil quality. Pages 91- 106 in J. W. Doran, D. C. Coleman, D. F. Bezdicek and B. A. Stewart, editors. Defining soil quality for a sustainable environment. Soil Science Society of America, Madison, Wisconsin, USA.

Lowery, B., M. A. Arshad, R. Lal, and W. J. Hickey. 1996. Soil water parameters and soil quality. Pages 143-155 in J. W. Doran and A. J. Jones, editors. Methods for assessing soil quality. Soil Science Society of America, Madison, Wisconsin, USA.

Madden, J. P. 1990. The economics of sustainable low-input farming systems. Pages 315-341 in C. A. Francis, C. B. Flora, and L. D. King, editors. Sustainable agriculture in

temperate zones. John Wiley & Sons, New York, New York, USA.

Maki, A. W. 1980. Methods issues in biological monitoring- short-term organization of biological monitoring methods. Pages 203-212 in D. L. Worf, editor. Biological monitoring for environmental effects. Lexington Books, Lexington, Massachusetts, USA.

Meyer, J. L., and W. T. Swank.1996. Ecosystem management challenges ecologists. Ecological Applications 6:738-740.

Miller, J. R.1970. Professional decision-making: a procedure for evaluating complex alternatives. Praeger, New York, New York, USA.

Mulvaney, R. L. 1996. Nitrogen inorganic forms. Pages 1123-1184 in D. L. Sparks, editor. Methods of soil analysis. Part 3. Chemical methods. Soil Science Society of America, Madison, Wisconsin, USA.

NASS [National Agriculture Statistics Service]. 1999. Ag- ricultural statistics. U.S. Department of Agriculture, Wash- ington, D.C., USA.

Neher, D. A., S. L. Peck, J. O. Rawlings, and C. L. Campbell. 1995. Measures of nematode community structure and sources of variability among and within agricultural fields. Plant and Soil 170: 167-181.

Nelson, D. W., and L. E. Sommers. 1996. Total carbon, organic carbon, and organic matter. Pages 961-1010 in D. L. Sparks, editor. Methods of soil analysis. Part 3. Chemical methods. Soil Science Society of America, Madison, Wis- consin, USA.

Norgaard, R. B.1994. Ecology, politics, and economics: finding the common ground for decision making in conservation. Pages 439-465 in G. K. Meffe and C. R. Carroll, editors. Principles of conservation biology. Sinauer As- sociates, Sunderland, Massachusetts, USA.

Nuernberg, N. J. 1994. Movement of surface applied phosphorus fertilizer in a southeastern acid soil. Dissertation. University of Georgia, Athens, Georgia, USA.

Olson, G. L., B. F. McQuaid, K. N. Easterling, and J. M. Scheyer. 1996. Quantifying soil condition and productivity in Nebraska. Methods for assessing soil quality. Pages 357- 369 in J. W. Doran and A. J. Jones, editors. Soil Science Society of America, Madison, Wisconsin, USA.

Park, J., and S. H. Cousins. 1995. Soil biological health and agro-ecological change. Agriculture, Ecosystems & Envi- ronment 56:137-148.

Pratt, P. F., F. E. Broadbent, and J. P. Martin. 1973. Using organic wastes as nitrogen fertilizers. California Agricul- ture 27(6):10-13.

Rice, C. W., T. B. Moorman, and M. Beare. 1996. Role of microbial biomass carbon and nitrogen in soil quality. Pag- es 203-215 in J. W. Doran and A. J. Jones, editors. Methods for assessing soil quality. Soil Science Society of America, Madison, Wisconsin, USA.

Safley, C. D., and J. L. M. Safley. 1991. Economic analysis. of alternative poultry litter compost systems. Report. De- partment of Agricultural and Resource Economics, North Carolina State University, Raleigh, North Carolina, USA.

Sarrantonio, M., J. W. Doran, M. A. Liebig, and J. J. Hal- vorson. 1996. On-farm assessment of soil quality and health. Pages 83-105 in J. W. Doran and A. J. Jones, editors. Methods for assessing soil quality. Soil Science So- ciety of America, Madison, Wisconsin, USA.

Seybold, C. A., M. J. Mausbach, D. L. Karlen, and H. H. Rogers. 1997. Quantification of soil quality. Pages 387- 404 in R. Lal, J. M. Kimble, R. F. Follett, and B. A. Stewart, editors. Soil processes and the carbon cycle. CRC Press, Washington, D.C., USA.

Sharpley, A. N., S. J. Smith, and W. R. Bain. 1993. Nitrogen and phosphorus fate from long-term poultry litter applications to Oklahoma soils. Soil Science Society of America Journal 57: 1131-1137.




Sikora, L. J., and D. E. Stott. 1996. Soil organic carbon and nitrogen. Pages 157-168 in J. W. Doran and A. J. Jones, editors. Methods for assessing soil quality. Soil Science Society of America, Madison, Wisconsin, USA.

Sims, J. T. 1987. Agronomic evaluation of poultry manure as a nitrogen source for conventional and no-tillage corn. Agronomy Journal 79:563-570.

Sims, J. T. 1995. Characteristics of animal wastes and waste- amended soils: an overview of the agricultural and environmental issues. Pages 1-13 in K. Steele, editor. Animal waste and the land-water interface. Lewis Publishers, New York, New York, USA.

Smith, J. L., and J. W. Doran. 1996. Measurement and use of pH and electrical conductivity. Pages 169-185 in J. W. Doran and A. J. Jones, editors. Methods for assessing soil quality. Soil Science Society of America, Madison, Wis- consin, USA.

Sparling, G. P., and D. J. Ross. 1993. Biochemical methods . to estlmate soil microbial biomass: current developments

and applications. Pages 21-37 in K. Mulongoy and R. Merckx, editors. Soil organic matter dynamics and sustainability of tropical agriculture. Wiley-Sayce, New York, New York, USA.

Sumner, M. E., and W. P. Miller. 1996. Cation exchange capacity and exchange coefficients. Pages 1201-1229 in D. L. Sparks, editor. Methods of soil analysis. Part 3. Chemical methods. Soil Science Society of America, Madison, Wis- consin, USA.

Tabatabai, M. A., and J. M. Bremner. 1969. Use of p-nitro- phenyl phosphate for assay of soil phosphatase activity. Soil Biology and Biochemistry 1:301-307.

Thomas, G. W. 1996. Soil pH and soil acidity. Pages 475- 490 in D. L. Sparks, editor. Methods of soil analysis. Part

3. Chemical methods. Soil Science Society of America, Madison, Wisconsin, USA.

Turco, R. F., A. C. Kennedy, and M. D. Jawson. 1994. Mi- crobial indicators of soil quality. Pages 73-90 in J. W. Doran, D. C. Coleman, D. F. Bezdicek, and B. A. Stewart, editors. Defining soil quality for a sustainable environment. Soil Science Society of America, Madison, Wisconsin, USA.

Tyson, S. C. 1994. Fresh and composted broiler litter effects on tall fescue and residual soil nitrate. Thesis. University of Georgia, Athens, Georgia, USA.

Tyson, S. C., and M. L. Cabrera. 1993. Nitrogen mineralization in soils amended with composted and uncomposted poultry litter. Communications in Soil Science and Plant Analysis 24(17&18):2361-2374.

USDA [U.S . Department of Agriculture] . 1979. Animal waste utilization on cropland and pastureland. USDA Uti- lization Research Report number 6. U.S. Department of Agriculture, Washington, D.C., USA.

Walters, C. 1986. Adaptive management of renewable re- sources. Macmillan, New York, New York, USA.

Wander, M. M., and G. A. Bollero. 1999. Soil quality assessment of tillage impacts in Illinois. Soil Science Society of America Journal 63:961 -971.

Wymore, A. W. 1993. Model-based systems engineering: an introduction to the mathematical theory of discrete systems and to the tricotyledon theory of system design. CRC Press, Boca Raton, Florida, USA.

Zublena, J. P. 1995. Characteristics of animal wastes and waste-amended soils: nutrient management planning be- yond the farm boundary. Pages 49-55 in K. Steele, editor. Animal waste and the land-water interface. Lewis, New York, New York, USA.



designing a soil quality assessment tool for sustainable agroecosystem management

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