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a USDA-ARS, Natl. Soil Tilth Lab., 2150 Pammel Dr., Ames, IA 50011-4420
b North Central Tech. College, 1000 W. Campus Dr., Wausau, WI 54401
c USDA-NRCS, 200 E. Northwood St. Suite 410, Greensboro, NC 27401
d Dep. of Agric. Econ.
e Dep. of Agron., Iowa State Univ., Ames, IA 50011-1070
* Corresponding author (Karlen{at}nstl.gov)
Received for publication April 4, 2005.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSNOTESRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Abbreviations: BD, bulk density • C, corn • CSP, Conservation Security Program • ISU, Iowa State University • Om, oat/meadow • M, meadow • MB-C, microbial biomass carbon • Sb, soybean • SMAF, Soil Management Assessment Framework • SQI, soil quality index • TN, total nitrogen • TOC, total organic carbon • WSA, water-stable macro-aggregation
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSNOTESRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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The merits of extended crop rotations that include forage or pasture crops have been debated for centuries (Karlen et al., 1994). Key benefits include increased C retention in the surface horizon and a more even distribution of labor needs and risk due to climate or market conditions than those involving only grain or fiber crops (Magdoff and van Es, 2000). Despite those benefits, the infrastructure developed and devoted to corn and soybean has resulted in a 500% increase in harvested area and 800% increase in soybean production between 1950 and 2003 (USDA-NASS, 2004). During that same period, oat production declined by 90%, and although hay production increased because of better yields, the land area devoted to it decreased more than 15%. Expansion of the simplified corn–soybean system has tremendous economic and world trade benefits because of the many products and materials developed from those crops, but what impact has it had on soil resources, water quality, biodiversity, wildlife corridors, and rural communities?
Some would argue that externalities of agricultural development are impossible to quantify and that the loss in crop diversity is no different now than during the 1950s when an average of 10 Wisconsin dairy farms went out of business each day for an entire decade (Apps, 1998). Others emphasize that to help shape a sustainable future, it is important to know where we are and how we got here (Flora, 2000; Randall, 2003).
Effects of extended crop rotations on soil quality have been discussed in general terms (Karlen et al., 1994) and specifically with regard to aggregate size distribution and stability (Kay, 1990; Robinson et al., 1994) and other indicators (e.g., yield, profit, N balance, etc.), but not with regard to soil quality per se. We acknowledge that soil quality assessment is in its infancy and that several valid concerns regarding its potential use and deficiencies have been raised (Letey et al., 2003; Sojka et al., 2003; Sojka and Upchurch, 1999). However, because shortsighted or improper soil and crop management decisions can lead to resource degradation and deleterious changes in soil function, tools and methods to assess and monitor soil quality are needed.
Soil quality effects of alternative vegetable production systems in northern California were quantified using the Soil Management Assessment Framework (SMAF) to combine various biological, chemical, and physical indicator measurements for an overall assessment (Andrews et al., 2002a, 2004). It was also used for an on-farm assessment of supplemental C management practices in California's Central Valley where it scored an established organic farming system higher than an adjacent conventional system (Andrews et al., 2002b). Recently, the SMAF was used to evaluate long-term cropping system studies at eight locations in the Great Plains (Wienhold et al., 2006). They found that reducing tillage and the incidence of fallow combined with more diversified crop rotations improved soil function (i.e., supporting plant growth, providing a reservoir for essential plant nutrients, storing and purifying water as it passes through the soil, and providing a site for biological activity such as decomposing and recycling of plant and animal materials) throughout the Great Plains.
The SMAF focuses on dynamic soil quality, which refers to the effects of human use and management (e.g., crop rotations) on soil functions, in contrast to inherent soil quality, which is determined by the five soil-forming factors (Jenny, 1941) and emphasizes land use suitability (Andrews et al., 2004). Soil quality and its assessment is soil and site specific and depends on factors including its inherent capabilities, environmental influences such as temperature and precipitation, intended land use, and management goals. It is not intended to detract from the importance of soil taxonomy, but rather uses taxonomy as a foundation for assessment (Karlen et al., 2003). For example, optimum levels of soil organic matter (and all other properties) will differ depending on how and where the soil formed. Similarly, the soil functions, properties, and processes necessary for a road bed or to support a physical structure are not the same as those needed to produce crops. Furthermore, the functions and properties for benign land application of animal waste are not identical to those for maximum crop production—even within the same field or for the same crop. There is no single target or optimum soil quality standard for the U.S. or anywhere in the world (Andrews et al., 2004). Each site is independent and unique although high SQI values are always considered better than low values because they indicate better soil functioning.
Soil quality is thus based on a series of thresholds defined by limiting factors and user needs. Its assessment depends on indicators, defined as those soil properties and processes that have greatest sensitivity to changes in soil function. Indicators should correlate well with ecosystem processes, integrate soil properties and processes, be accessible to many users and sensitive to management and climate, and if possible, be components of existing databases (Doran and Parkin, 1996). Indicator groups or minimum data sets must be sufficiently diverse to represent biological, chemical, and physical properties and processes of complex systems. Currently, the SMAF has 11 scored indicators with several more (>20) being proposed, evaluated, or having preliminary scoring curves (Fig. 1 ). As evidenced by the initial case studies (Andrews et al., 2002a, 2002b, 2004), the SMAF uses quantitative laboratory analyses, provides site-specific interpretations, and can lead to a better understanding of how management affects a specific soil resource with respect to multiple endpoints that are outcomes driven by landowner/operator or societal goals (e.g., productivity and environmental quality). Proponents of SMAF suggest that it addresses most misgivings and misconceptions among those who have reservations regarding the soil quality concept (e.g., Sojka et al., 2003), but additional verification studies and subsequent improvements are needed.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSNOTESRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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12000 to 14000 yr) with 0 to 3% slope and mean annual air temperature and precipitation of 7.8°C and 762 mm, respectively (Prior, 1991). With appropriate drainage, the Webster soil is highly productive and is one of the most important row crop production soils in the Midwest, especially for corn and soybean. Seven crop sequences have been evaluated since the experiment's inception although some changes have been made over time (Table 1). The meadow (forage legumes), whether seeded alone or with oat, has been a mixture of alfalfa and red clover (Trifolium pratense L.). No hay was harvested during the seeding year following oat harvest although in years when weed problems were severe, the oat was occasionally harvested for hay instead of grain. For rotations with 1-yr meadow, the legume mixture was plowed under during the fall of the same year. For rotations with 2 or 3 yr of meadow, two to four hay harvests were taken depending on seasonal weather patterns.
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Nashua Cropping Systems Study
The Nashua cropping systems study was initiated in 1979 on an area with Kenyon (fine-loamy, mixed, superactive, mesic Typic Hapludolls) and Readlyn (fine-loamy, mixed, superactive, mesic Aquic Hapludolls) soils. The Kenyon series consists of deep, moderately well- and well-drained, moderately permeable soils formed on uplands in loamy sediments and the underlying glacial till with slopes ranging from 0 to 9% (USDA-SCS, 1995). The Readlyn series consists of very deep, somewhat poorly drained soils that formed in 35 to 66 cm of loamy sediments and the underlying firm glacial till. Readlyn soils are on slightly convex side slopes on uplands and have slopes ranging from 0 to 5%. Mean annual temperature and precipitation at this site are 8.1°C and 844 mm, respectively.
The Nashua site is located on the Iowan Surface, an area found in northeast Iowa and southeast Minnesota (Prior, 1991). The region is characterized by gently rolling hills with long slopes and low relief. Last glaciated over 500 000 yr ago, the glacial drift has been modified by erosion, especially severe during the Wisconsin glacial period (10500 to 30000 yr ago) when the region was tundra and the Des Moines lobe (Kanawha site) was being formed. Parent materials consist of eroded glacial till surfaces and alluvial and loess deposits. The Kenyon and Readlyn soils are highly productive and important for corn and soybean production.
The crop sequences (Table 1) varied depending on the frequency with which corn was planted, the harvest system for corn, and the presence of alternative grain (soybean or oat) or forage crops. The alfalfa was always seeded with oat. No hay was harvested during the seeding year, and three harvests were made in the second year. The crop sequence treatments were replicated three times (in a completely randomized, split-plot design). To grow all crops of each sequence every year, each crop sequence block was subdivided to accommodate each crop in the rotation. Thus, Treatments 1, 2, and 7, which were continuously cropped, have only one plot per block; Treatment 2 has two plots; Treatment 4 has three; and Treatments 5 and 6 each have four plots, for a total of 16 plots per block. The 16 plots are further divided into four smaller plots to accommodate the N fertilization treatments. Nitrogen fertilizer treatments were applied only to corn, using urea at rates of 0, 90, 180, and 270 kg N ha–1 and incorporated by disking immediately after application in the spring.
Tillage for all rotations consisted of fall chisel plowing and spring disking for corn or alfalfa residues and spring disking for soybean residues. Phosphorus and K fertilizers were applied as needed to maintain optimum soil-test levels. Herbicides and cultivation were used for weed control as needed. The type of herbicides used, as well as corn hybrid and soybean, oat, or alfalfa varieties, have varied over time but followed ISU recommendations. All crop rotations receiving either 0 or 180 kg N ha–1 fertilization rates were sampled for this study.
Nashua Organic Study
The Nashua organic study (Table 1) was established in 1977 on the same ISU research farm as the Nashua cropping systems study to demonstrate conventional and organic farming practices (Chase and Duffy, 1991). The initial study consisted of six plots: each phase of a corn–oat/meadow–meadow (COmM) rotation (three plots), continuous corn (one plot), and both phases of a corn and soybean rotation (two plots). The COmM rotation was fertilized with bovine manure, while the other two received ISU-recommended rates of fertilizer and pesticide as needed. Three additional plots were added in 1981 to accommodate another COmM rotation that did not receive any manure, pesticides, or commercial fertilizer.
Lancaster Cropping Systems Study
The Lancaster experiment was located on Rozetta (fine-silty, mixed, superactive, mesic Typic Hapludalfs) soil. The Rozetta series consists of very deep well-drained soils formed in loess on uplands (USDA-SCS, 1961). Permeability is moderate, and slopes range from 0 to 25% because the area was not glaciated 12000 to 15000 yr BP. Mean annual temperature and precipitation are 10.6°C and 914 mm, respectively. The site is located in the driftless area of Major Land Resource Area (MLRA) 105 found in southwest Wisconsin, southeast Minnesota, northeast Iowa, and northwest Illinois (USDA-SCS, 1981). The deep, rugged valleys and karst topography that characterize this 16.2 million ha region were carved into the sedimentary bedrock of a Paleozoic plateau by glacial runoff. The productive silt loam and loam soils were formed primarily from a deep loess layer overlying limestone, dolomite, sandstone, and shale bedrock (Prior, 1991). The steeply sloping land has very high erosion potential if not properly managed. Crop rotations, especially those with high residue production and including perennial crops like alfalfa, are important for comprehensive soil and crop management programs within this region.
The crop sequence and N study (Table 1) was initiated in 1967 as a cooperative experiment (NC-157) among the University of Illinois, ISU, University of Minnesota, and the University of Wisconsin to determine the crop production response to crop rotation on a typical upland soil within MLRA 105. A randomized complete block design with two replications of 21 treatments was established to test the rotation effect by having each phase of every rotation represented each year. The crop sequence plots were split to accommodate four N rate treatments, but only two (0 and 112 kg N ha–1) were sampled for this study. Potassium and P fertilizer were applied at standard rates based on periodic soil testing (Mallarino and Rueber, 2001).
Sampling Strategy and Protocol
To control the overall size of this study, soil samples from the Kanawha, Nashua, and Lancaster cropping system sites were generally collected only from the plots planted to corn in 1997 and then only from two of the four N rates (0 and 90, 0, 180, or 0 and 112 kg N ha–1, respectively). At Nashua and Lancaster, soil samples were also collected from the continuous soybean (SbSb) and continuous meadow (MM) plots. For the Nashua organic site (Table 1), samples were collected from the corn, soybean, and meadow phases of the rotations. However, this site was established as a nonreplicated demonstration project; therefore, pseudo-replication was achieved by dividing each treatment into three sections and collecting composite samples from each. The sampling strategy for each location resulted in data sets with an unequal number of replicates or pseudo-replicates (Table 2). Nevertheless, each set was still hypothesized to have completely random (i.e., sampling error) effects associated with the actual field replications, a hypothesis tested and verified as described in the statistical methods section.
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Laboratory Analyses
Field-moist samples were weighed and then pushed through an 8-mm-diam- sieve. The soil water content was determined gravimetrically by oven-drying a subsample overnight at 105°C. Bulk density was estimated using the volume (121 cm3) of soil associated with the five-core samples, the weight of soil, and the water content measurements (Arshad et al., 1996; Blake and Hartge, 1986). Aggregate stability (WSA) was assessed for 8-mm-sieved, air-dried samples and expressed as a percentage of the total soil that was greater than 250 µm in diameter after sieving in water (Cambardella and Elliott, 1993). Microbial biomass C was measured by fumigation and direct extraction with 0.5 M K2SO4 on 8-mm-sieved field-moist samples (Tate et al., 1988). Organic C in fumigated and nonfumigated extracts was measured using a Dohrmann DC-180 carbon analyzer1 (Rosemount Analytical Services, Santa Clara, CA), and biomass C was calculated using a correction factor (k = 0.33) (Sparling and West, 1988).
Another subsample was pushed through a 2-mm-diam. sieve, air-dried, and stored at room temperature before analysis for TOC, TN, pH, and Mehlich III (Mehlich, 1984) extractable P and K. A subsample was pulverized and analyzed by dry combustion for TOC and TN using a Carlo-Erba NA1500 NCS elemental analyzer (Haake Buchler Instruments, Paterson, NJ). Phosphorous and K concentrations in the extracts were determined using an inductively coupled plasma–atomic emission spectrograph (ICP–AES). Soil pH was measured using a 1:2 soil-to-water ratio (Watson and Brown, 1998).
Soil Quality Assessment
The SMAF (Andrews et al., 2004) was used to calculate SQI values for each rotation at every experimental site. The measured indicator values were transformed with nonlinear scoring curves that had been constructed using CurveExpert v. 1.3 shareware (http://curveexpert.webhop.biz/; verified 25 Jan. 2006). The y axis for each curve provided a unitless value ranging between 0 and 1 reflecting the estimated performance of that indicator with regard to the critical soil functions. The x axis reflected the site-specific, expected range for each soil indicator. The shape of each scoring curve (Fig. 1) was either a bell-shaped curve ("midpoint optimum"), a sigmoid curve with an upper asymptote ("more is better"), a sigmoid curve having a lower asymptote ("less is better"), or some combination of curves linked with logic statements. For each study, inflection points of the scoring curves were adjusted for each site based on inherent environmental factors, such as organic matter class (taxonomic suborder), texture, climate, sampling time, mineralogy, region, slope, and analytical method for P (Andrews et al., 2004). The important soil functions (optimizing nutrient cycling, optimizing partitioning and storage of water, providing for good plant root growth, and providing filtering and buffering to minimize leaching and runoff of N and P) and their relationships to soil quality indicators were selected based on the literature and agronomic experience with extended crop rotations. Further information on the theory and development of the SMAF can be found in Andrews et al. (2004).
For this study, six soil quality indicators (BD, WSA, pH, TOC, MB-C, and P) were used to compute index values. Scoring curves for penetration resistance, extractable K, and TN are under development but have not been completed or rigorously tested. The scored indicator values were summed to create an additive index of soil quality for each rotation, assuming that higher scores (maximum value = 6.0) indicated better soil quality. An equal weighting (i.e., 1.0) was given to each indicator because there was no scientific or other reason (e.g., policy or regulatory) to justify variable weighting.
Profitability Assessment
Profit associated with the various crop rotations was computed by estimating the variable and fixed costs of production as outlined by Duffy and Smith (2002) and subtracting them from the potential income calculated using the actual yields for the 20-yr period before sampling and the average crop prices for those years from the NASS (National Agricultural Statistics Service) database. The potential impact of government support payments on cropping system choices was considered, but we chose to not use them for this analysis. Organic premiums were not used in the calculations associated with that study because the site was not certified. Actual costs for corn, corn silage, soybean, oat, and alfalfa production were gathered from several sources including annual Iowa Farm Business Association record summaries; production and cost data from the Economics, Agricultural and Biosystems Engineering and Agronomy departments at ISU; and a survey of selected agriculture cooperatives around the state. The cost estimates are representative of the average costs for medium and large farms in central Iowa and elsewhere in the state where yields and cultural practices are similar. Actual operations and input costs, including land and labor, were used if available, but if not, estimated costs were used (Duffy and Smith, 2002).
The owner/operator, family, or permanent hired personnel supply most labor on Iowa farms, so that cost, based on a wage rate of US $8.00 h–1, was treated as a fixed expense. The hours per crop-acre include not only the fieldwork but also time for maintenance, travel, and other activities related to crop production. The land charge was based on rent equivalent while equipment costs reflect a fleet consisting of both new and used machinery. Costs for machine operations were based on the 1998 Crop Production Practices Survey conducted by the Iowa Agricultural Statistics Service because those numbers reflected the economic situation when the soil samples were collected.
Statistical Methodology
This assessment of crop rotation (treatment) and N-rate (N_R) effects on soil quality and profitability used existing long-term field studies where the number of replicates or pseudo-replicates were unequal (Table 2). Therefore, each soil quality indicator variable for each location was examined separately with ANOVA procedures using SAS software (SAS Inst., 1990). First, a univariate distribution for each variable was determined using multiple exploratory tools including normality tests like the Shapiro-Wilk W statistic and symmetry transformation plots and statistics (Friendly, 1991). Appropriate transformations for meeting the ANOVA assumptions of normality and heteroskedasticity were performed before further analysis. All transformed data were converted back to original units for presentation in the tables and further discussion.
The next step for each variable was to examine the significance of the block as a random effect under the long-term design with a mixed-model procedure (Littell et al., 1996). No variable was found to have a significant block effect (data not shown), so the plots from each site were considered as samples in a completely randomized design. An ANOVA for each variable was computed with a two-way treatment structure and standard residual analyses. Planned contrasts among the various rotations were then evaluated.
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RESULTS AND DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSNOTESRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Penetration resistance (data not presented) showed few significant differences among the rotations at any of the sites, probably because the measurements were taken only within the top 15 cm of the soil profile. The average resistance for all rotations at all sites was between 100 and 200 kPa. Volumetric water content, which can significantly affect penetration resistance (Arshad et al., 1996), averaged 0.32, 0.31, 0.25, and 0.32 cm3 cm–3 at the Lancaster, Kanawha, Nashua, and Nashua organic sites, respectively. There was a significant (P 
0.02) decrease in penetration resistance as volumetric water content increased at Lancaster, but overall predictability was low (R2 = 0.12), and relationships at the other locations were not statistically significant.
Samples from the Kanawha site showed significant differences (P 0.1) in BD, pH, TOC, and P (Table 3). Bulk density was approximately 7% higher in continuous row crop treatments (CC or CSb rotation) than in samples from corn grown as part of a more extended crop rotation. Soil pH was significantly lower for the COmMM rotation than for the continuous row crops (5.6 vs. 6.8), perhaps because long-term hay harvest had removed more bases (K, Ca, and Mg) than harvesting only the grain. Total organic C was 8.6 g kg–1 higher for the COmMM rotation reflecting less tillage and cycling C and N from the nodules and forage root system. Average extractable P concentrations were 9 mg kg–1 lower in rotations including multiple years of hay harvest (CCOmM or COmMM) than in continuous grain or rotations where oat was harvested but the legume was plowed down as green manure (CC, CSb, and CCCOm). We suggest greater P removal with the forage than with grain as the cause. Contrast analyses also showed significant differences between row crop treatments and extended rotations that included oat and forage crops (Table 3). Between the two sampled N rates, pH and P were both slightly lower (0.3 unit and 14 mg kg–1, respectively) for the 80 kg ha–1 treatment than for the 0 N control, probably due to increased grain yield, nutrient removal, and acidification associated with higher N fertilizer rate.
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12 000 to 15 000 yr BP) and is characterized by steep hills and deep, rugged valleys (USDA-SCS, 1981). Those inherent conditions presumably resulted in greater soil erosion before the establishment of the cropping system study, and therefore TOC values were much lower. During the 30 yr that the cropping systems studies were conducted, annual C inputs (i.e., substrate for the microbial communities) were presumably similar because all three sites were located at similar latitude (Fig. 2), with similar climatic conditions and similar crop sequences. Those similarities would thus account for the similar amounts of MB-C (i.e., 500 to 800 mg C kg–1 soil), but the ratios differ because of inherent differences in TOC.
Soil Quality Assessment
After evaluating individual indicator data from each experimental site, the values were scored using the SMAF (Andrews et al., 2004). This mathematical framework is sensitive to factors such as soil type, crop, and climate within and among studies and locations, with changes in the scoring curves based on inherent soil properties being derived by shifting inflection points and expected indicator ranges based on the best available information. For example, a TOC value of 20 g kg–1 for an Ultisol would receive a score of 1.0, indicating full functioning of any processes influenced by TOC. However, that same value (20 g kg–1) in a Midwestern Mollisol would be scored below the threshold level (<0.5), indicating a degraded condition with probable impairment of soil functions influenced by TOC. Similarly, a soil-test P value of 150 mg kg–1 would receive a score of 1.0 for soils with 0 to 2% slope, approximately 0.75 for soils with 4 to 8% slope, and less than 0.1 for soils with more than 16% slope because of the potential for P loss through surface runoff or erosion on the more sloping soils.
The scoring curves used within the SMAF are adjusted for each indicator to make them site specific. When using the SMAF, information such as location, crop sequence, dominant soil series, when samples were collected, and how they were analyzed is entered by the user and used to automatically adjust the scoring curves. Thus, to model the associations among indicators, soil function, and controlling factors, one must have knowledge of (or make assumptions about) not only the appropriate curve shape (generally midpoint optimum, more is better, or more is worse, depending on the indicator's relationship to ecosystem function), but also the expected direction of change in curve inflections as major controlling factors change. For example, as temperature and precipitation increase, expected TOC values will decrease due to increased decomposition rates. This will result in a shift to the left (i.e., lower values get higher scores because that is the best possible for those inherent conditions) in the algorithm's inflection points (Andrews et al., 2004).
The final step associated with soil quality assessment is to combine all the available indicators into an overall SQI. This is an optional step since scored values for each indicator can and should be examined for their relative importance, but it offers the potential to integrate all of the indicator scores into a single, additive index value. The SQI is considered to be the overall assessment of soil quality. The integrated value, however, will be most relevant for evaluating dynamic or management-associated effects such as summarizing changes at the same location over time or comparing different management practices at a single field site because of the subtle differences in scoring curves based on inherent soil properties.
A comparison between the Kanawha and Nashua sites illustrates several points relative to soil quality assessments of long-term crop rotations (Tables 3 and 4). First, there is a distinct soil difference. Both have a loam texture, but TOC, WSA, and MB-C are all lower on the Till Plain soil (Nashua) than for the Des Moines lobe (Kanawha). Bulk density at Nashua was generally higher, reflecting more frequent tillage associated with row crops and lower TOC than at Kanawha.
Using the SMAF with Kanahwa data, BD and TOC showed scored differences (P 0.1) among crop rotations with the lowest values being for the continuous corn (0.895 and 0.771) and CSb rotations (0.931 and 0.744), respectively. The 4-yr rotations with 1, 2, or 3 yr of oat/meadow or meadow had average values of 0.976, 0.978, and 0.990 for BD and 0.817, 0.854, and 0.926 for TOC, respectively. The nonscored data also showed differences (P < 0.05) for pH and P, but they were not significant when scored. Andrews et al. (2004) suggested that this pattern (i.e., significant unscored but nonsignificant scored values) occurs when measured differences are not significant with regard to soil function. With regard to soil pH and extractable P at the Kanawha site (Table 3), the scored differences were not significant because the values were within ranges acceptable for soil function, particularly crop productivity and environmental protection. When combined into an overall SQI, the continuous corn and CSb rotations also had lower values (5.50 and 5.53, respectively) than the 4-yr rotations, which averaged 5.75.
Scored values for WSA, TOC, and MB-C showed significant differences in the long-term cropping systems study at Nashua (Table 7). Continuous soybean had the lowest WSA score (0.743), which agrees with conclusions reached by others (e.g., Power, 1990) that low residue production by soybean often reduces soil aggregation. Lower TOC scores were recorded for the continuous corn grain (0.381), continuous soybean (0.394), and 2-yr (0.384 for CCSb) or 4-yr (0.373 for CCCSb) rotations than for the other cropping systems, which (excluding continuous corn silage) averaged 0.460. The relatively high TOC score for the continuous corn silage treatment (0.520) was not anticipated but coincides with a higher observed value. The difference for MB-C score was caused by the slightly lower value (0.983) for the continuous corn grain treatment. However, MB-C scores were very high for all rotations at this site, and from a management perspective, this statistical result was not very important. We suggest that any score above 0.95 can be considered to be nearly optimum with regard to soil function.
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For the Lancaster site, TOC, WSA, and MB-C were different (P < 0.01), with continuous corn having lower scored values (0.353, 0.250, and 0.353, respectively) than the continuous meadow (0.586, 0.422, and 0.586, respectively) or than mean values associated with the 2-yr (0.397, 0.329, and 0.397, respectively) or 4-yr (0.453, 0.333, and 0.453, respectively) rotations (Table 8). The same response pattern was evident for the overall SQI (4.48, 4.54, 4.72, and 4.92) for the continuous corn, 2-yr rotations, 4-yr rotations, and continuous meadow, respectively.
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0.05) at the Lancaster and Nashua organic locations (Table 9). For the Kanawha and Nashua cropping system sites, measured pH and Mehlich P showed significant differences, but scored values did not. This occurred because both pH and soil-test P were near optimum for crop production, and P levels were below those expected to have adverse environmental effects. These results were similar to those found in Northern Great Plains cropping systems (Wienhold et al., 2006), suggesting that extended rotations have a positive impact on soil quality and result in higher SQI values because of improved ecosystem functions.
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CONCLUSIONS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSNOTESRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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ACKNOWLEDGMENTS |
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NOTES |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSNOTESRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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REFERENCES |
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