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a Crop Sciences, Univ. of Illinois, 1102 S. Goodwin Ave., Urbana, IL 61801 USA
mwander{at}uiuc.edu
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMaterials and methodsResults and discussionSummary and conclusionsREFERENCES |
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ND > CT. Multivariate assessment of soil quality indicated use of NT practices improved the biological and physical condition of the soil (0–15 cm) despite increased consolidation. It also showed that those biological and physical aspects of soils influenced by organic matter were the properties most altered by agronomic practices. Particulate organic matter (POM) was identified as a promising soil quality measure. A next step is to determine the biological and environmental relevance of a refined set of soil quality measures in conjunction with soil processes of regional concern.
Abbreviations: ANOVA, analysis of variance • CR, central region • CT, conventional tillage • ECR, eastern-central region • Infil.1'', the rate of infiltration for 444 mL (1 linear inch) of water • Infil.2'', the rate of infiltration for a second application of 444 mL (1 linear inch) of water • MANOVA, multivariate analysis of variance • MWDD, mean weight dry diameter • MWWD, mean weight wet diameter • ND, nondisturbed • NR, northern region • NT, no-tillage • PC, principal component • PCA, principal component analysis • PEN-5, penetration resistance to 5-cm depth • PEN-15, penetration resistance to 15-cm depth • PMN, potentially mineralizable N • POM, particulate organic matter • SOM, soil organic matter • SR, southern region
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMaterials and methodsResults and discussionSummary and conclusionsREFERENCES |
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Soil quality research differs from some soil management research in that it emphasizes the multifaceted nature of soils and requires that biological, physical, and chemical aspects of the soil be considered simultaneously. Accordingly, many investigations of soil quality have continued in a soil management tradition, exploring the effects of agronomic practices on soil properties. Much of this work has demonstrated that reduced tillage, residue-returning practices like cover cropping and manure application, or the commitment of lands to conservation reserve practices lead to significant shifts in some soil properties included in the minimum data set (Karlen et al., 1994; Franco-Vizicano, 1997; Staben et al., 1997; Wani et al., 1994). These studies are unique in that they provide information about suites of properties collected simultaneously or within a time frame that allows system assessment. They reveal the relatedness, or in some cases, distinction between properties assumed to represent similar or dissimilar facets of the soil (Halvorson et al., 1995). These types of data sets are needed to develop management strategies to apply soil quality information to specific situations and regions (Hussain et al., 1999). Ideally, such studies integrate complex data sets into useful frameworks.
The progress of soil quality research has been hindered because consensus has not yet developed on how to quantify or implement the information resulting from minimum data set collection. Some studies investigating components of the minimum data set have attempted to summarize the overall effects of management on soil quality, as an entity in and of itself. According to Karlen et al. (1997), assessments of management impacts on soil quality require evaluation of the current state of an indicator in comparison with known or desired values. In most cases, scientists have established ranges for only a few of the parameters listed in the minimum data set. Very rarely do they have the kind of data required to establish norms that accurately reflect a soil's inherent productive or environmental filtering potential. This makes it difficult to evaluate soil quality with respect to soil functions and performance criteria, which is the objective of many researching soil quality (Bouma, 1989; Karlen et al., 1997, 1994; Larson and Pierce, 1994).
Many researchers have drawn upon the literature to establish relationships between soil properties and pedotransfer functions (Larson and Pierce, 1994) or soil performance functions (Karlen et al., 1994; Harris et al., 1996). While efficient, aspects of function and index-based approaches do not readily lend themselves to traditional statistical analyses that might be used to identify indicators that are most sensitive to or are having the largest impact on soil quality. Multivariate statistical approaches such as PCA may be an appropriate first step toward soil quality assessment within regions and cropping systems. This approach provides a nonsubjective means to extract and weight information in complex univariate data sets. Examples of this approach can be found in the soils literature (Drinkwater et al., 1996; Halvorson et al., 1995). Such an approach might identify parameters that merit the development and tailoring of locally relevant, function-based soil quality measures.
In this study we attempt to show how PCA can be used to evaluate complex interactions between soil management practices and the soil properties contributing to soil quality in Illinois. Currently, NT practices are advocated as one of the key means through which soil quality and soil organic matter (SOM) can be maintained (Karlen and Cambardella, 1996); however, use of NT practices does not increase SOM levels in all soils. The ability of NT practices to increase SOM sequestration has been reported to be limited in poorly drained soils (Paustian et al., 1997), in cooler climates where the impacts of tillage on SOM decay are minimized (Angers et al., 1997), and where erosion rates are low (Alvarez et al., 1998). Relatively little research has been carried out on NT practices in central and northern Illinois because the adoption of these practices has not been widespread until recent years (Conservation Technology Information Center, 1995). Recent studies suggest that the use of NT practices is generally increasing SOM stratification in central and northern Illinois and reveal that the effects of tillage practices on SOM sequestration are inconsistent (Wander et al., 1998). The effect of changing tillage practices on soil quality is unclear.
This study was a first step toward identifying how the concept of soil quality could be meaningfully applied in Illinois. Our objectives were to determine whether recent adoption of NT practices in the region had generally altered soil quality and to screen potential soil quality indices by establishing norms for and assessing the effects of region and of tillage practices on potential minimum data set properties.
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Materials and methods |
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TOPABSTRACTINTRODUCTIONMaterials and methodsResults and discussionSummary and conclusionsREFERENCES |
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Soil respiration rates were estimated using a modification of Doran's soil test kit procedure (Sarrantonio et al., 1996). We installed 15-cm-diameter rings 8 cm deep, sealed the cylinder with a tin lid, inserted a thermometer into the soil, and shaded the chamber. After 20 min, temperature was recorded and the head space gas was mixed by pumping an attached 100-mL syringe up and down five times. After this, a 100-mL sample was withdrawn. The needle was then placed into a 5-mL evacuated container, which was vented with a second needle. The contents of the 100-mL syringe were then expelled through the evacuated container. As soon as the syringe was emptied, both needles were quickly removed from the septum. The vials were kept cool during transport to the lab. Once back in the lab, 0.5-mL subsamples were injected manually into a Varian gas chromatograph (Varian Instruments, Palo Alto, CA) equipped with a Poropack 80/40 column and a thermal conductivity detector. Respiration rates were adjusted in accordance with soil temperature.
In 1995, the 15-cm ring was used to determine infiltration rates after soil respiration measurements were complete. Our method was a modification of Doran's test kit method (Sarrantonio et al., 1996). Because of sampling constraints, we did not presaturate the soil before measurement. We lined the ring with plastic wrap, applied 450 mL of deionized H2O, and measured the distance from the top of the ring to the water surface. We then removed the plastic wrap to begin wetting and recorded the time required for all water to infiltrate. After determination of the rate of infiltration for 1 linear inch of water (Infil.1''), we applied a second 444 mL in the same fashion and recorded the infiltration rate again (Infil.2'').
Aggregates were characterized using a 10-cm3 sample collected in 1995 from the top of the profile using a spade. Soil was passed through a 25-mm sieve in the field and transferred into a paper bag that was then placed inside an individual cardboard box to prevent crushing. Once in the lab, boxes were opened and placed on a wire table in a well-ventilated greenhouse to dry. Aggregate mean weight dry diameter (MWDD) was determined by placing dry soil (200-g increments) on a stack of four sieves with 16-, 8-, 4-, 2-, 1-mm openings and then shaking soil for 2 min with a Ro-tap sieve (W.S. Tylor, Inc., Mentor, OH). Wet-aggregate stability was determined using 20-g samples of 2- to 4-mm dry aggregates placed on the top of a stack of two sieves (1 and 0.25 mm). The screens were lowered to wet the base of the topmost sieve to allow aggregates to become completely wet (10 min) by capillary action. The aggregates were then sieved for 2 min, lowering and then raising the sieves with stroke length of 27 mm and a frequency of 35 strokes min-1 using a custom-made sieving machine. Soil retained on each sieve was transferred to a container, dried, and weighed. Both MWDD and mean weight wet diameter (MWWD) were computed according to Youker and McGuiness (1957).
In 1996, penetration resistance to 5- and 15-cm depths (PEN-5 and PEN-15) was determined using a dynamic cone penetrometer (J.E. Herrick and T.L. Jones, 1997, personal communication). We recorded the number of strikes required to drive a cone (30° internal angle) with a 20.3-mm-diameter base 5 and 15 cm into the soil by dropping a 2-kg hammer along a sliding shaft 40 cm to contact a strike plate. Bulk density was determined on samples from the top 30 cm that were collected using a 6.5-cm diameter splitable core sampler (Forestry Supply, Jackson, MS). The corer was extracted and carefully opened. Only samples that completely filled the entire volume of the sampler were used. The core was divided into 0- to 5-, 5- to 15-, and 15- to 30-cm sections that were transferred to plastic bags. Soil was kept shaded and on ice for transport to the lab and processed within 24 h of returning to the lab. The entire moist mass was recorded before field-moist soil was manually homogenized. A subsample was removed, its soil moisture content was determined gravimetrically. That moisture content was used to compute bulk density of the entire soil mass. A portion of the moist soil was air dried for measurements not requiring fresh samples.
In 1996, potentially mineralizable N (PMN) was determined by anaerobic incubation after Waring and Bremner (1964). A field-moist soil sample (6 g) was placed in a 50-mL centrifuge tube, saturated with 10 mL of deionized water, and incubated at 40°C for 7 d. Then, 40 mL of 0.625 M K2SO4 was added to give a final concentration of approximately 0.5 M. The tube was shaken for 1 h on a reciprocating shaker. The supernatant was filtered (Whatman no. 42). The identical method, excluding the incubation step, was performed on a separate sample. Ammonium was determined colorimetrically (read at 650 nm) by the indophenol blue method as modified for microplate analyses (Sims et al., 1995). The PMN was determined as the difference between the NH4 recovered from the incubated soil and the NH4 recovered from the nonincubated soil. Inorganic NO3 and NH4 recovered from the nonincubated sample were summed to estimate available N. With Devarda's alloy, NO3 was reduced to NH4, which was then quantified using the indophenol blue method.
Carbon in the soil microbial biomass (biomass C) was estimated in both years by the chloroform fumigation extraction method (Wu et al., 1990). A 10-g sample of field-moist soil was placed in a 30-mL centrifuge tube, transferred to a desiccator and fumigated with ultra-pure chloroform for 24 h in the dark. After chloroform removal, 30 mL of 0.5 M K2SO4 was added to the tube; this was shaken for 1 h on a reciprocating shaker and then supernatant was filtered (Whatman no. 42). Carbon recovered in the extract was determined with a Dohrman DC-80 C analyzer (Rosemont Analytical, Santa Clara, CA). The same method, excluding the fumigation step, was performed on a separate sample. Chloroform-labile C (biomass C) was calculated as the amount of dissolved organic C recovered from the fumigated soil less the amount of dissolved organic C (soluble C) recovered from the nonfumigated soil.
Air-dried soil was ground to pass a 2-mm sieve. Dry soil was extracted with Mehlich III extracting solution (3 g soil:30 mL) for determination of available P and base cations (Ca, Mg, and K) (Tran and Simard, 1993). Soil was shaken for 5 min and filtered through Whatman no. 42 filter paper. The Murphy and Reily (1962) procedure was modified for microplate analyses of extractable P. Determinations were based on absorbance at 880 nm. Data were compared against a standard soil extract and a standard curve 0 to 100 mg L-1 soil made from 1 M potassium phosphate stock. Base cations (Ca, Mg, and K) were determined using atomic absorbance measured with a Perkin Elmer-360 (Perkin Elmer Corp., Norwalk, CT). Samples were compared against working and certified standards and against an in-house soil standard. Soil pH (1:1 soil/water) was determined with an Orion pH electrode (Orion Research Inc., Beverly, MA). After reading pH, 9 mL of deionized water was added to the sample that was then shaken and centrifuged. Conductivity was read with an Orion 160 conductivity meter after calibration with 0.1 M NaCl standard and temperature correction.
Particulate organic matter was isolated from air-dried soils by dispersion of 20-g samples in 20 mL of 5% Na-hexametaphosphate. Liberated POM was collected on, and dried in, 53-mm-opening polycarbonate mesh (Gilson, Columbus, OH). This method is similar to that used by Gregorich and Ellert (1993). The POM and soil samples were ground with a disk mill to a powdery consistency. Total N and organic C contents of POM and whole soils were determined by dry combustion, according to Nelson and Sommers (1982), with a Carlo Erba NA 1500 C/N analyzer (Carlo Erba, Milan, Italy). Free carbonates were removed by destruction with sulfurous acid prior to analysis of total soil N and soil organic C contents. The dry mass of POM per kilogram of soil was multiplied by C and N concentrations to determine POM C and POM N per kilogram of soil.
Statistical analysis was conducted with the SAS UNIVARIATE, GLM, and PRINCOMP procedures (SAS Institute, 1994). Each year–field combination was considered an environment as suggested by Carmer et al. (1989), and environments were considered random within blocks. Simultaneous observations of 22 variables were taken from each experimental unit. These variables were checked for multivariate normality and homogeneity of covariance matrices. Variables were grouped into chemical, physical, and biological categories. Multivariate statistical analysis was conducted in two steps as suggested by Hatcher and Stepanski (1996). Multivariate analysis of variance (MANOVA) was the first step used to determine whether there were significant inherent (regional) or management (tillage) effects on at least one of the physical, chemical, and biological variables assessed. After this criteria was met, analysis of variance (ANOVA) of individual parameters was run on all the parameters. The obtained Wilk's lambda and the F statistics derived from Wilk's lambda were reviewed to test the null hypothesis of no overall treatment effect. The second step consisted of interpreting the univariate ANOVAs. Those variables for which the tillage F statistic was significant at P < 0.06, and that had CVs <40 (Hatcher and Stepanski, 1996), were retained for further analyses. Both MANOVA and ANOVA models identified significant tillage x depth interactions for several parameters where data were collected from multiple depths (data was not shown). For these factors, means comparisons of main effects were based on weighted depth averages expressed on a mass basis. Treatment means separations were interpreted from the LSD results. All retained physical, chemical, and biological variables were then used in PCA for further screening. Values used for PCA represented a single depth or point location or were weighted means representing the 0- to 15-cm depth. The number of components was determined by the eigenvalue-one criterion (Kaiser, 1960) and scree test (Cattell, 1966). All meaningful loadings (i.e., >0.40) were included in the interpretation of the PC. Principal components that explained more than 5% of the total variance were considered to be significant. Tillage and region effects on soil quality were assessed using ANOVA of significant PC scores.
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Results and discussion |
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TOPABSTRACTINTRODUCTIONMaterials and methodsResults and discussionSummary and conclusionsREFERENCES |
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CR NR ECR and was greater in the SR and CR than in the ECR. Tillage effects on MWWD were also significant (ND > NT > CT). Aggregate MWDD was greater in the NR than in all other areas and was greater in cropped than in ND soils. We suspect regional differences between MWDD reflect the positive relationship between SOM contents and macroaggregate abundance (Yang and Wander, 1998), while tillage-based comparisons reflect cloddiness (Chepil, 1942). Even though Infil.2'' was marginal with respect to R2 and CV values, this variable was retained because tillage effects were very highly significant. The Infil.2'' was significantly lower in the NT than in the ND or CT soils (Table 2). These results are consistent with Reynolds et al. (1996), who found NT soils to be less permeable and less aerated than CT soils 5 yr after NT practices were adopted. In their study and in ours, both total porosity, determined indirectly in our case from bulk density, and the likelihood of lateral flow were greater in CT soils loosened by recent tillage. Residue coverage was greater in the SR than in CR, ECR, and NR fields (Table 2). This may have been associated with the production of wheat (Triticum aestivum L.) during the previous season in some of the SR fields. Percentage of residue cover, which was more sensitive to tillage (P < 0.0001) than regional (P < 0.079) effects, was greater in the ND than NT soils and greater in the NT than CT soils. Of all the physical parameters, residue coverage, MWWD, and bulk density had the highest overall R2 values and lowest CVs (Table 1). Aggregate MWDD had intermediate R2 and CV values, while the two penetration (PEN-5 and PEN-15) and infiltration (Infil.1'' and Infil.2'') variables were the least affected by main effects and more variable.
Chemical Parameters
Total organic C and N varied significantly among regions, tillage treatments, and depths (Table 1). The C and N contents of SR soils was lower than in the other regions (Table 2). The C and N contents of ND soils were greater than NT and CT soils, which did not differ when treatments were averaged across the 0- to 15-cm depth. Similar results were obtained when comparisons were made on a volumetric and equivalent mass basis (data not shown). Total C and N concentrations were greater in the 0- to 5- than in the 5- to 15-cm depths. Extractable P was greater in the CR and ECR than in NR or SR. This is consistent with known regional differences in soil P supply (Hoeft et al., 1994). Phosphorus levels were greater in CT than either NT or ND soils, which had identical P test levels. Phosphorus was concentrated in surface soils, with levels in the top 5 cm 54% greater than in the 5- to 15-cm depth. Despite differences, all P test values fell within desirable agronomic ranges in all cases (Hoeft et al., 1994). Extractable K was lower in the SR than in other regions and was greater in ND than in cropped soils. As was the case for P, K was concentrated in the surface 5 cm. Available N was significantly influenced by tillage and depth and, to a lesser extent, by region (Table 1); the magnitude of these differences was insignificant in agronomic terms (Table 2). Calcium concentrations were ranked: CR ECR > NR > SR. Soil pH was affected by region but not by tillage or depth: NR = CR > ECR > SR (Tables 2 and 3)
. The magnitude of these differences was small in agronomic terms. Electrical conductivity was significantly lower in the SR than in other regions, lower in CT than in ND soils, and lower in subsurface than in surface depths (Table 2).
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40%) were among the highest in this category.
Biological Parameters
The POM C and N, PMN, and biomass C were not affected by region but did vary significantly among tillage treatments and soil depths (Table 1). The POM C and N concentrations were greater in ND than in cropped soils and in surface than in subsurface soils. The PMN was greater in ND than in cropped soils and was more concentrated in the 0- to 5- than the 5- to 15-cm depth (Table 2). Microbial biomass C was greater in the ND than in the NT and CT soils and in surface than subsurface soils (Table 2). Soil respiration rate varied significantly among regions and tillage types. Rates were ranked: NR > ECR SR > CR, and were greater in ND than in cropped soils.
All biological parameters except soluble C were significantly impacted by tillage, when applicable, by depth but not by region (Table 1). Measures in this category had R2 values >59% even though they had high variability (all CVs were >28%). Among the biological measures, POM C and N had the highest model R2 values even though the associated CVs were 44 and 59%, respectively. The CV of biomass C was the lowest among the biological parameters and its R2 value ranked third after POM C and N. Respiration rate had relatively low CV and an intermediate R2 value, while PMN was the most variable
and was the least sensitive of the retained biological parameters.
Principal Component Analysis of Physical Parameters
Following univariate analysis, retained parameters were assessed using PCA. This was done to analyze parameters by category as uncorrelated variables. The PCA output for the physical, chemical, and biological categories is summarized in Table 3. Only PCs with eigenvalues >1 that explained at least 5% of the total variance were retained for interpretation. Sixty-eight percent of the variance (proportion) was explained by the first two PCs. Both tillage and region had significant effects on physical parameter PC1 and PC2 (ANOVA not shown). The PC1 explained 40% of the variance and five parameters that had significant loadings on PC1 (Table 3). Bulk density, MWDD, and Infil.2" were negatively weighted and were contrasted with MWWD and residue.
According to Harris et al. (1996), soil quality decreases with increasing bulk density and increases with aggregate strength and residue cover. If MWDD represents cloddiness, then seedbed quality would be diminished by increased values (Chepil, 1942). In Illinois, where water-saturated soils are susceptible to erosion and hinder crop production, soil quality is diminished by increasing time required for water to infiltrate (Harris et al., 1996). We concluded that soil physical condition increased with PC1 scores. Among regions, PC1 ranked: CR NR ECR SR (Table 4)
. Only the difference between CR and SR was significant. There was a difference between the PC1 scores of the ND and cropped soils, indicating the ND soil's physical condition was better than that of the cropped soils (Table 4). Differences between the NT and CT soil's PC1 scores were not significant.
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Principal Component Analysis of Chemical Parameters
There were four chemical parameter PCs with significant loadings that collectively explained 80% of the total variance (Table 3). The PC1 scores increased with organic C, with total N and K, and with conductivity (Table 3) and were significantly less in the SR than other areas (Table 4). The chemical category PC1 scores of the tillage treatments differed significantly: ND > NT > CT. Soils with high PC2 scores had relatively high conductivity and total N and had low Ca levels (Table 3). The PC2 scores were higher in the SR than other areas. The tillage treatments were ranked: NT CT > ND. Chemical PC3 scores reflected extractable P, K, and Ca levels, while PC4 scores reflected pH and conductivity (Table 3). Soils in ECR and CR had higher PC3 scores than soils in the NR or SR. Southern soils also had the lowest PC4 scores.
Principal Component Analysis of Biological Parameters
All five retained biological measures were clustered together and there was a sharp drop in eigenvalues from PC1 to PC2 (Table 3). Only PC1 was statistically significant and it explained 70% of the total variance. All variables had positive scores that were >70 (Table 3). According to Harris et al. (1996), available N, PMN, soil respiration, and biomass C affect the environmental and production functions of soil quality. Such measures can be used to assess soil metabolic and nutrient supply potential (Doran and Parkin, 1996; Duxbury and Nkambule, 1996). Measures of POM have been positively associated with nutrient retention and supply and soil physical condition (Elliott and Coleman, 1988; Wander et al., 1994). Soils with higher PC1 scores, which had higher biological activity (i.e., respiration and biomass C) or labile C (i.e., available N, PMN, POM C and N), could be argued to have greater biological condition. There were no regional differences in soil biological condition (as assessed by PC1) (Table 4). Tillage significantly affected the biological category's PC1 scores (Table 4). Biological activity and labile C were significantly greater in the ND than in the NT and CT soils and did not differ among the cropped soils.
Principal Component Analysis of Overall Soil Quality
The relative significance of data set parameters and of overall soil quality was assessed using PCA of the 20 retained variables. There were five significant PCs (Table 5)
that together explained 73% of the total variance. Information obtained from the analysis of all the variables is similar to that obtained from the three single-category analyses, with the advantage that information is ranked with respect to the degree of variable importance. In general, PC1, which accounted for 39% of the total variance, contrasted parameters associated with organic matter content and quality and soil biological activity with measures that reflected soil physical condition (Table 5). Twelve parameters had significant loadings on PC1. Listed in order of decreasing significance are the ten positively weighted (POM C, MWWD, POM N, total N, organic and biomass C, residue, respiration, PMN, and conductivity) and two negatively weighted (bulk density and MWDD) parameters. This indicates that organic matter and the biological and physical aspects of soil quality were the most sensitive indicators of soil quality considered by this study. Tilth aspects of soil quality (Singh et al., 1992) increased with PC1 scores. There were no significant regional effects on PC1 (Table 6
and Fig. 2b)
. Nondisturbed soils had significantly higher PC1 scores than NT soils, which, in turn, had higher scores than CT soils.
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Significant PC3, PC4, and PC5 loadings, which explained 8, 7, and 6% of the total variance, respectively, were associated with chemical parameters (Table 5). The PC3 scores reflected Ca, K, and P levels. Soils with high PC4 scores had relatively high available N contents and slow infiltration rates. The PC5 reflected pH, Ca, and conductivity levels. Soils with high PC3 and PC5 scores were relatively fertile (scores increased with K, P, Ca, conductivity, and pH). The PC3 scores were significantly greater in the ECR and CR than in the NR and were greater in the NR than in the SR. The PC4 was significantly greater in the SR than in the CR region and was greater in NT than ND soils. As was true for PC3, PC5 was significantly lower in the SR than in all others and was not affected by tillage.
Assessing Soil Quality Status
The multivariate data set reflects the complex effects of management on soil properties in Illinois. Results from MANOVA and PCA of the physical, chemical, and biological data categories and of the overall data set were consistent. Both ANOVA and PC analyses of the physical parameters suggest the physical condition of soil in the SR was lower than the other regions. The ANOVA results determined that bulk density, penetration resistance, MWWD, MWDD, Infil.2'', and residue cover were affected by tillage practices. Use of PCA to summarize these effects indicated the physical condition of the ND soils was superior to the condition of the cropped soils and that the NT soils were relatively consolidated (PC2). Both the ANOVA results and PCA of chemical parameters identified aspects of soil fertility status that were lower in the SR than in the other regions. Both ANOVA results (total organic C and N, P, K, available N, and conductivity) and PCA indicated that tillage treatments had no consistent effect on soil's mineral fertility status. The more even distribution of the chemical category's information among retained PCs made interpretation of this data more difficult than that derived from PCA of the physical or biological parameters. The ANOVA and PCA of the biological parameters indicated inherent soil characteristics had less influence on soil biological condition than management practices and that agricultural use of soils decreased biological activity (respiration, and biomass C) and labile C (POM C, POM N, PMN).
The complex effects of tillage practices on soil characteristics were effectively summarized by PCA of the overall data set. The PC1 scores suggested that even though use of NT practices has not increased organic C or total N contents, NT practices have enhanced tilth and SOM-dependent properties when compared with CT soils. However, subtle increases in total organic C and total N, POM C and N, aggregate strength, and biological activity were generally accompanied by increased soil consolidation (PC2). Our study assessed soil condition 5 to 10 yr after the adoption of NT practices. Changes in soil characteristics continue to occur for decades after conversion to NT (Dick et al., 1991); however, the most dramatic changes are said to occur within the first decade of practice adoption (Dick, 1983; Campbell et al., 1995). The long-term effects on NT practices will depend on whether and in what form NT practices persist in the region.
Screening Measures
The data were used to screen potential indices of soil quality by identifying the effects of tillage and region on physical, chemical, and biological soil properties that are candidates for inclusion in a soil quality minimum data set. The MANOVA and ANOVA revealed the general effects of agricultural use of soils and of NT practices on individual parameters, while PCA of the overall data set identified which variables differed the most and which were the most informative, redundant, and unique.
Particulate organic matter (POM C) was the most highly weighted variable loading on PC1 in both the biological category and overall data set assessments. According to these PCA results, POM C was the most sensitive measurement of treatment differences. Even though many have asserted that POM is a promising index of organic matter contributions to soil and organic matter quality (Gregorich et al., 1994; Sikora et al., 1996; Wander et al., 1994), its value as an index has not been proved. Based on our statistical results, POM C was determined to be the most promising soil quality indicator.
Principal component analysis of biological parameters suggested that these measures were fairly redundant. This was supported by ANOVA output that indicated biomass C, PMN, and respiration rates, and MWWD, and POM C and N, were similarly affected by main effects. Biomass C and respiration rates, which had relatively low CVs, were more promising measurements of soil quality than PMN. Our use of field means may have influenced measurement sensitivity. Measurements like pH and available N also had low CVs when considered on a large scale but were insensitive to treatment effects; these measures were more variable when considered within fields (data not shown). Soil respiration rates were difficult to assess in a meaningful way in a field-to-field context because of regional effects that were influenced by sampling dates.
Principal component analysis of the physical parameter category and overall data sets linked MWDD and bulk density and contrasted these with residue cover and MWWD. The variability and R2 values of these physical parameters could be used to determine whether MWDD or bulk density on the one hand, or MWWD or residue cover on the other, might be left out of the data set without sacrificing information. The MWDD has not been widely recommended as a component of minimum data sets. Simultaneous characterization of MWDD, which is relatively inexpensive and easy to quantify, along with better-understood measures, provided valuable information about the less-understood parameter. In the PCA of both the physical category and overall data sets, penetration resistance was the only significant variable loading on PC2. This suggests the information provided by the impact penetrometer was relatively unique and not supplied by other measures, like bulk density, which might have been assumed to provide duplicative information.
The PCA of the overall data set identified which of the physical, chemical, or biological parameters were most affected by inherent soil characteristics and management. Loadings on PC1, which explained three times the variance explained by PC2, show that SOM and biological and physical parameters were most affected by treatments and that tillage effects were more influential than regional factors. This lends credence to arguments which assert that nonchemical aspects of soils have been significantly affected by agricultural use and that overlooked biological (Doran and Parkin, 1994) and physical (Grossman et al., 1995) properties are the most sensitive indicators of system condition. The comparatively high ranking of SOM-dependent chemical, biological, and physical measurements indicates they are able to differentiate between treatments but does not necessarily prove they have the greatest impact on soil performance. Traditional measures of fertility, which had lower CVs and did not differ among tillage treatments, may have varied less among fields because targets for parameter values and guidelines to maintain test levels have been established. Inorganic nutrients and pH are highly managed because of their importance for crop production.
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Summary and conclusions |
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TOPABSTRACTINTRODUCTIONMaterials and methodsResults and discussionSummary and conclusionsREFERENCES |
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ACKNOWLEDGMENTS |
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Received for publication January 8, 1998.
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TOPABSTRACTINTRODUCTIONMaterials and methodsResults and discussionSummary and conclusionsREFERENCES |
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M. S. Grigera, R. A. Drijber, K. M. Eskridge, and B. J. Wienhold Soil Microbial Biomass Relationships with Organic Matter Fractions in a Nebraska Corn Field Mapped using Apparent Electrical Conductivity Soil Sci. Soc. Am. J., August 3, 2006; 70(5): 1480 - 1488. [Abstract] [Full Text] [PDF]
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L. A. Sherrod, G. A. Peterson, D. G. Westfall, and L. R. Ahuja Soil Organic Carbon Pools After 12 Years in No-Till Dryland Agroecosystems Soil Sci. Soc. Am. J., August 25, 2005; 69(5): 1600 - 1608. [Abstract] [Full Text] [PDF]
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W. K. Jung, N. R. Kitchen, K. A. Sudduth, R. J. Kremer, and P. P. Motavalli Relationship of Apparent Soil Electrical Conductivity to Claypan Soil Properties Soil Sci. Soc. Am. J., May 6, 2005; 69(3): 883 - 892. [Abstract] [Full Text] [PDF]
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D. Ginting and B. Eghball Nitrous Oxide Emission from No-Till Irrigated Corn: Temporal Fluctuation and Wheel Traffic Effects Soil Sci. Soc. Am. J., May 6, 2005; 69(3): 915 - 924. [Abstract] [Full Text] [PDF]
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S. S. Andrews, D. L. Karlen, and C. A. Cambardella The Soil Management Assessment Framework: A Quantitative Soil Quality Evaluation Method Soil Sci. Soc. Am. J., November 1, 2004; 68(6): 1945 - 1962. [Abstract] [Full Text] [PDF]
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K. P. Fabrizzi, A. Moron, and F. O. Garcia Soil Carbon and Nitrogen Organic Fractions in Degraded vs. Non-Degraded Mollisols in Argentina Soil Sci. Soc. Am. J., November 1, 2003; 67(6): 1831 - 1841. [Abstract] [Full Text] [PDF]
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T. M. Nissen and M. M. Wander Management and Soil-Quality Effects on Fertilizer-Use Efficiency and Leaching Soil Sci. Soc. Am. J., September 1, 2003; 67(5): 1524 - 1532. [Abstract] [Full Text] [PDF]
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G. Sparling, R. L. Parfitt, A. E. Hewitt, and L. A. Schipper Three Approaches to Define Desired Soil Organic Matter Contents J. Environ. Qual., May 1, 2003; 32(3): 760 - 766. [Abstract] [Full Text] [PDF]
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