Enhancing global soil health will improve humankind's capacity to maintain or increase crop yield, achieve better yield stability, reduce purchased input costs, and enhance critical ecosystem services. In contrast to soil quality efforts during the 1990s and early 2000s, a major driver of soil health projects from 2011 to 2020 has been investment by private industry. The soil health partnership has focused on using science and data to work directly with farmers to adopt practical agricultural practices including cover crops, conservation tillage, and advanced nutrient management to improve the economic and environmental sustainability of the farm. Soil health indicator measurements, when coupled with an available assessment framework, complement soil erosion tools as they can directly and more definitively detect less advanced symptoms of soil health degradation across diverse management systems. The chapter also presents an overview on the key concepts discussed in this book.

Chapter 5 Soil Health Assessment of Agricultural Lands Diane E. Stott, Diane E. StottSearch for more papers by this authorBrian Wienhold, Brian WienholdSearch for more papers by this authorHarold van Es, Harold van EsSearch for more papers by this authorJeffrey E. Herrick, Jeffrey E. HerrickSearch for more papers by this author Diane E. Stott, Diane E. StottSearch for more papers by this authorBrian Wienhold, Brian WienholdSearch for more papers by this authorHarold van Es, Harold van EsSearch for more papers by this authorJeffrey E. Herrick, Jeffrey E. HerrickSearch for more papers by this author Book Editor(s):Douglas L. Karlen, Douglas L. KarlenSearch for more papers by this authorDiane E. Stott, Diane E. StottSearch for more papers by this authorMaysoon M. Mikha, Maysoon M. MikhaSearch for more papers by this author First published: 09 July 2021 https://doi.org/10.1002/9780891189817.ch5Book Series:ASA, CSSA, and SSSA Books AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Summary This chapter provides a brief overview of soil health assessment and summarizes the development of the indexes for assessing soil health. An assessment framework facilitates comparison of results from similar soils and production environments to determine where a field may be on a soil health continuum. The primary soil health indicator for quantifying the water entry function is the infiltration rate, which has secondary effects reflected by surface crusting, surface roughness, soil macroporosity, and crop residue cover. Most soil health assessment frameworks utilize spreadsheets, but Agroecosystem Performance Assessment Tool is a software tool developed to assess relative soil management effects on multiple indicators of agricultural sustainability, including soil health. The comprehensive assessment of soil health framework, initially known as the Cornell Assessment of Soil Health was developed at Cornell University and designed to offer a comprehensive set of soil measurements to support soil and crop management decisions and associated applied research. Soil Health Series: Volume 1 Approaches to Soil Health Analysis RelatedInformation

Chapter 2 Evolution of the Soil Health Movement Douglas L. Karlen, Douglas L. KarlenSearch for more papers by this authorMriganka De, Mriganka DeSearch for more papers by this authorMarshall D. McDaniel, Marshall D. McDanielSearch for more papers by this authorDiane E. Stott, Diane E. StottSearch for more papers by this author Douglas L. Karlen, Douglas L. KarlenSearch for more papers by this authorMriganka De, Mriganka DeSearch for more papers by this authorMarshall D. McDaniel, Marshall D. McDanielSearch for more papers by this authorDiane E. Stott, Diane E. StottSearch for more papers by this author Book Editor(s):Douglas L. Karlen, Douglas L. KarlenSearch for more papers by this authorDiane E. Stott, Diane E. StottSearch for more papers by this authorMaysoon M. Mikha, Maysoon M. MikhaSearch for more papers by this author First published: 09 July 2021 https://doi.org/10.1002/9780891189817.ch2Book Series:ASA, CSSA, and SSSA Books AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Summary Soil Health, during the second decade of the 21st Century, has become a familiar term to both rural and urban audiences. Advocates for the care and wise use of soil have been warning humankind since before the common era that soil is the foundation for everything we do or share. Soil health is built upon a solid foundation reflecting numerous agronomic and soil science publications and advancements in knowledge. Soil quality activities around the world expanded rapidly during the early 1990s, driven in part by increasing recognition of the role soils had in buffering and mitigating factors affecting environmental quality. The philosophical debates gradually waned and many soil quality proponents quietly moved forward emphasizing soil health which originally had slightly different approaches and priorities than soil quality, but overall were very similar concepts, appropriate for assessing biological production and environmental protection. Soil Health Series: Volume 1 Approaches to Soil Health Analysis RelatedInformation

Much attention has been paid to the effects of multiple soil conservation and soil health practices on the mean yield of the subsequent crop. Much less research has focused on the variability of crop yields over time or space. Yield stability reported in standard deviation, mean absolute deviation, or coefficient of variation can be an important measure of risk for producers. Risk reduction has economic value, and understanding the effect of tillage and other soil conservation practices on yield risk is relevant to farm financial management and crop insurance risk assessment. We used data from test plots in a corn (Zea mays L.)-soybean (Glycine max L.) rotation, spanning from 2003 to 2011 to assess differences in yield stability over time and space. In this experiment, each plot was randomly assigned to a treatment of no-till with no cover crop (NTNC), no-till with an annual ryegrass (Lolium multiflorum Lam.) cover crop (NTCC), or a control group using conventional tillage with no cover crop (CTNC). The statistical analysis made three relevant comparisons: (1) NTCC versus NTNC, (2) NTNC versus CTNC, and (3) NTCC versus CTNC. The analysis also included separating temporal and spatial variation using a time-first approach from the literature, followed by testing for differences between groups. We employed a standard deviation ratio test, Levene's test, and coefficient of variation t-test. Additionally, analysis of temporal volatility was conducted using ordinary least squares regression and associated t-tests in a method similar to a stock beta, a technique commonly accepted in finance to measure the volatility of an investment. We propose this as a new method in analyzing the temporal volatility in crop yields. We found that no-till reduced average temporal yield variation in corn, and that cover crops reduced average spatial variation in corn. These results were robust over multiple statistical tests. Using the beta coefficient methodology proposed in this paper, we found in both corn and soybeans that NTNC and NTCC had lower temporal yield volatility relative to a benchmark yield from the CTNC group. However, the beta coefficients were, in most cases, not statistically significant. The results of this study suggest that both no-till and cover crops may help reduce yield risk for Midwestern farmers while reducing soil and nutrient loss.

In recent years, a broad stakeholder base within the agricultural sector and among the public has become aware of the critical importance of healthy soils, spurred by public awareness campaigns and workshops. As we continue to grapple with a changing climate and more extreme weather events, regenerating the health and proper functioning of our nation's, and indeed world's, soil resource will markedly improve the capacity of soil to maintain or increase yield and yield stability, lower input costs, and contribute to other ecosystem services. This is true not only for croplands but also for pastures and native rangelands, orchards, and forests. To aid in moving forward initiatives to help farmers and ranchers improve the soil resource base, the USDA Natural Resources Conservation Service (NRCS) has created a new Soil Health Division (SHD). Personnel distributed across the country will facilitate soil health technical training and education for stakeholders, work with partners to standardize soil health assessments, promote soil health management systems as part of the conservation planning process, and facilitate implementation and long-term adoption of soil health management systems on our nation's agricultural lands. The new division will leverage skills, resources, technology, and partnerships to achieve these goals.

Conversion from furrow to sprinkler irrigation is a recommended conservation practice for improved water-use efficiency (and erosion control), but effects on soil quality indicators are unknown. Several soil quality indicators were therefore quantified within a northwestern United States Conservation Effects Assessment Project (CEAP) watershed after changing from long-term furrow to sprinkler irrigation. Four on-farm sites were identified where producers were growing irrigated barley (Hordecum vulgare L.) using both irrigation practices. Climate, soil type, and management were similar between sites. Soil samples were collected from the upper and lower ends of furrow irrigated fields at three in-field positions (bed, shoulder, and furrow); fields converted to sprinkler irrigation were sampled where the upper and lower ends were when the field was furrow irrigated. Soil quality indices (physical, chemical, biological, nutrient, and overall) were computed using the Soil Management Assessment Framework (SMAF). Regardless of in-field position, furrow irrigated field bottoms had higher soil quality index scores than field tops because of long-term erosional deposition. Within sprinkler irrigated fields, soil quality indices for field tops and bottoms showed minimal differences. Overall, when all sampling locations and in-field positions were combined, soil quality was similar for both irrigation methods. However, as compared with furrow irrigation, sprinkler irrigation had greater soil quality indices in the field tops, suggesting that sprinkler irrigation improved soil quality of historically eroded furrow irrigated fields.

Sustaining the productive capacity of soils has raised interest in the maintenance of soil organic matter through management practices and use of crop residues. While the impact of management practices has been studied, little is done to understand how the charateristics of the residue itself impact the decomposition at the soil surface. This study relates the chemical composition and the surface area of the aboveground residue to the decomposition rates for three cultivars each of three crops: cotton, peanut and sorghum. The rates were determined by mass loss. Change in the residue specific surface area to mass loss was also measured. Findings show that after 14 days, the aboveground residue for the three crops were from the most rapid loss to the slowest: cotton (43%) > peanut (32%) > sorghum (24%). Changes in the specific surface area-to-mass ratio were from the slowest to the most rapid loss: cotton (1.60×10-4) > peanut (1.50×10-4> sorghum (1.20×10-4). Since varietal differences within crops have led to variation in decomposition rates, cultivars with slower decaying residues might be recommended for C sequestration and for erodible lands in semi-arid zones of the Sahel. Likewise, crop residues with faster decomposition rates can be recommended for soil fertility improvement. Key words: Decomposition rate, crop type, crop residue, chemical composition, specific surface area-to-mass ratio.

Soil quality is a critical link between land management and water quality. We aimed to assess soil quality within the Cedar Creek Watershed, a pothole- dominated subwatershed within the St. Joseph River watershed that drains into the Western Lake Erie Basin in northeastern Indiana. The Soil Management Assessment Framework (SMAF) with 10 soil quality indicators was used to assess inherent and dynamic soil and environmental characteristics across crop rotations, tillage practices, and landscape positions. Surface physical, chemical, and nutrient component indices were high, averaging 90, 93, and 98% of the optimum, respectively. Surface biology had the lowest component score, averaging 69% of the optimum. Crop rotation, tillage, and landscape position effects were assessed using ANOVA. Crop selection had a greater impact on soil quality than tillage, with perennial grass systems having higher values than corn (Zea mays L.) or soybean [Glycine max (L.) Merr.]. Furthermore, soybean rotations often scored higher than corn rotations. Uncultivated perennial grass systems had higher overall soil quality index (SQI) values and physical, chemical, and biological component values than no-till or chisel–disk systems. Chisel–disk effects on overall and component SQI values were generally not significantly different from no-till management except for a few physical indicators. Toe-slopes had higher physical, biological, and overall SQI values than summit positions but toe-slope values were not significantly different from those of mid-slope positions. This work highlights the positive effects of perennial grass systems, the negative effects of corn-based systems, and the neutral effects of tillage on soil quality.

The Conservation Effects Assessment Project (CEAP). was initiated in 2002 to quantify the potential benefits of conservation management practices throughout the nation. Within the Central Claypan Region of Missouri, the Salt River Basin was selected as a benchmark watershed for soil and water quality assessments. This study focuses on two objectives: (1) assessing soil quality for 15 different annual cropping and perennial vegetation systems typically employed in this region, and (2) evaluating relationships among multiple measured soil quality indicators (SQIs). Management practices included annual versus perennial vegetation, and varying grass species composition (cool-season versus warm-season), tillage intensity (no-till versus mulch-till), biomass removal, rotation phase, crop rotation (corn [Zea mays L.]-soybean [Glycine max L. Merr] versus corn-soybean-wheat [Triticum aestivum L.]) and incorporation of cover crops into the rotation. Soil samples were obtained in 2008 from 0 to 5 cm (0 to 2 in) and 5 to 15 cm (2 to 6 in) depth layers. Ten biological, physical, chemical, and nutrient SQIs were measured and scored using the Soil Management Assessment Framework (SMAF). Across SQIs, biological and physical indicators were the most sensitive to management effects, reflecting significant differences in organic carbon (C), mineralizable nitrogen (N), beta-glucosidase, and bulk density. In the 0 to 5 cm layer, perennial systems demonstrated the greatest SMAF scores, ranging from 93% to 97% of the soil's inherent potential. Scores for annual cropping systems ranged from 78% to 92%: diversified no-till, corn soybean wheat rotation with cover crops (92%) > no-till, corn-soybean rotation without cover crops (88%) > mulch-till corn-soybean rotation without cover crops (84%). Conversely, in the 5 to 15 cm layer, no-till cropping systems scored lower for overall soil function (58% to 61%) than mulch-till systems (65% to 66%). In the 0 to 5 cm layer, biological soil quality under the diversified no-till system with cover crops was 11% greater than under no-till without cover crops, and 20% greater than under mulch-till without cover crops. The effect of rotation phase was primarily reflected in 64% lower mineralizable N following corn relative to soybean. Additionally, soil nutrient function was significantly affected by biomass removal. The results of this study demonstrate that the benefits of conservation management practices extend beyond soil erosion reduction and improved water quality by highlighting the potential for enhanced soil quality, especially biological soil function. In particular, implementing conservation management practices on marginal and degraded soils in the claypan region can enhance long-term sustainability in annual cropping systems and working grasslands through improved soil quality.

This chapter focuses on physical and chemical indicators of soil quality as related to soil erosion by water. It illustrates a procedure that can be tailored to site-specific situations and used to quantify soil quality impacts, even when tradeoffs between short-term economics vs. long-term sustainability, or water erosion vs. deep percolation of chemicals are considered. The chapter proposes a framework for evaluating soil quality. The framework and procedure are demonstrated using information collected from an alternative and conventional farm in central Iowa. The framework may be useful as a model for developing quantitative assessments of soil quality at various scales of evaluation. The Water Erosion Prediction Project significantly changed soil erosion prediction technology that is available for use in soil and water conservation planning and assessment. Soil with high quality must accommodate water entry, facilitate water transfer and absorption, resist physical degradation, and sustain plant growth.

Quantitative relationships between soil color and organic matter content are only poorly understood, but they are of considerable practical importance in mapping and classifying soils, interpreting soil properties, and in designing sensors for agricultural equipment. We studied the color-organic matter relationships for Ap horizons from Indiana and Illinois soils to test the hypothesis that Munsell value and organic matter content are more closely related for soils occurring together in soil landscapes than for soils over a wide geographic region. Two sample sets were collected. Sample set 1 consisted of 105 Ap horizons from throughout Indiana, while set 2 consisted of 10 to 15 Ap horizons from each of 16 landscapes in Indiana and Illinois. Organic matter content was determined by dry combustion, and Munsell colors of both moist and dry samples were calculated from reflectance spectra. The relationship between Munsell value and organic matter content: (i) was poor for Indiana soils statewide (sample set 1), (ii) was predictable (r 2 > 0.9) within soil landscapes if soil textures did not vary widely, (iii) was linear within landscapes with silty and loamy textured soils but was curvilinear within landscapes with sandy-textured soils, (iv) was similar among landscapes having the same soil textures and parent materials, and (v) was not predictable if soil texture varied widely (sands vs. silts and loams) within the landscape. In a separate study, we measured the colors of various organic and inorganic fractions from the Ap horizons of four Indiana soils. The purified humic acid content was about 15 times the purified fulvic acid content for all four soils. The black humic acid, which masked the yellowish brown color of the fulvic acid, was responsible for the dark color of the soil organic matter.

The Soil Conditioning Index (SCI) and Soil Management Assessment Framework (SMAF) are two different but complementary methods for evaluating soil quality. Both tools have been widely used, but little is known regarding how they compare and if they provide similar results when the same agricultural management practices are compared. This SCI and SMAF soil quality index (SQI) comparison was conducted on the Fort Cobb Reservoir Experimental Watershed (FCREW) in Oklahoma. Forty-one loamy and sandy surface soil sites were sampled on the FCREW under (i) annual cropping with conventional tillage (conventional), (ii) annual cropping with either conservation tillage or no-till (conservation), (iii) cropland that had been converted to perennial grass (managed grass), and (iv) native grass. The SCI and SMAF SQI gave similar assessments, indicating that soil quality within conventional and conservation systems was similar but lower than in either managed or native grassland systems. Simple comparisons of soil properties by textural class showed no significant effects on most soil quality indicator values, and although texture by management subgroups was examined, no clear relationships were detected because of the limited number of sampling sites. The SMAF and SCI indicators and scores were correlated in tilled systems with limited vegetative cover but not for no-till or forage-based pasture or grassland systems that were in place for at least 10 yr. The SMAF provided more resolution when evaluating agroecosystem management effects on soil quality, including soil organic C enrichment, especially within forage-based systems. Recognizing that the SMAF currently does not account for soil loss, we conclude with recommendations for improving the tool, particularly for tilled systems.

What is a resilient, healthy soil? A resilient soil is capable of recovering from or adapting to stress, and the health of the living/biological component of the soil is crucial for soil resiliency. Soil health is tightly coupled with the concept of soil quality (table 1), and the terms are frequently used interchangeably. The living component of soil or soil biota represents a small fraction (<0.05% dry weight), but it is essential to many soil functions and overall soil quality. Some of these key functions or services for production agriculture are (1) nutrient provision and cycling, (2) pest and pathogen protection, (3) production of growth factors, (4) water availability, and (5) formation of stable aggregates to reduce the risks of soil erosion and increase water infiltration (table 2). Soil resources and their inherent biological communities are the foundation for agricultural production systems that sustain the human population.

Our objective is to provide an optimistic strategy for reversing soil degradation by increasing public and private research efforts to understand the role of soil biology, particularly microbiology, on the health of our world’s soils. We begin by defining soil quality/soil health (which we consider to be interchangeable terms), characterizing healthy soil resources, and relating the significance of soil health to agroecosystems and their functions. We examine how soil biology influences soil health and how biological properties and processes contribute to sustainability of agriculture and ecosystem services. We continue by examining what can be done to manipulate soil biology to: (i) increase nutrient availability for production of high yielding, high quality crops; (ii) protect crops from pests, pathogens, weeds; and (iii) manage other factors limiting production, provision of ecosystem services, and resilience to stresses like droughts. Next we look to the future by asking what needs to be known about soil biology that is not currently recognized or fully understood and how these needs could be addressed using emerging research tools. We conclude, based on our perceptions of how new knowledge regarding soil biology will help make agriculture more sustainable and productive, by recommending research emphases that should receive first priority through enhanced public and private research in order to reverse the trajectory toward global soil degradation.

Re-establishing deforested ecosystems to pre-settlement vegetation is difficult, especially in ecotonal areas, due to lack of knowledge about the original physiognomy. Our objective was to use a soils database that included chemical and physical parameters to distinguish soil samples of forest from those of savannah sites in a municipality located in the southeastern Brazil region. Discriminant analysis (DA) was used to determine the original biome vegetation (forest or savannah) in ecotone regions that have been converted to pasture and are degraded. First, soils of pristine forest and savannah sites were tested, resulting in a reference database to compare to the degraded soils. Although the data presented, in general had a high level of similarity among the two biomes, some differences occurred that were sufficient for DA to distinguish the sites and classify the soil samples taken from grassy areas into forest or savannah. The soils from pastured areas presented quality worse than the soils of the pristine areas. Through DA analysis we observed that, from seven soil samples collected from grassy areas, five were most likely originally forest biome and two were savannah, ratified by a complementary cluster analysis carried out with the database of these samples. The model here proposed is pioneer. However, the users should keep in mind that using this technology, i.e., establishing a regional-level database of soil features, using soil samples collected both from pristine and degraded areas is critical for success of the project, especially because of the ecological and regional particularities of each biome.

In-field measurements of direct soil greenhouse gas (GHG) emissions provide critical data for quantifying the net energy efficiency and economic feasibility of crop residue-based bioenergy production systems. A major challenge to such assessments has been the paucity of field studies addressing the effects of crop residue removal and associated best practices for soil management (i.e., conservation tillage) on soil emissions of carbon dioxide (CO2), nitrous oxide (N2O), and methane (CH4). This regional survey summarizes soil GHG emissions from nine maize production systems evaluating different levels of corn stover removal under conventional or conservation tillage management across the US Corn Belt. Cumulative growing season soil emissions of CO2, N2O, and/or CH4 were measured for 2–5 years (2008–2012) at these various sites using a standardized static vented chamber technique as part of the USDA-ARS's Resilient Economic Agricultural Practices (REAP) regional partnership. Cumulative soil GHG emissions during the growing season varied widely across sites, by management, and by year. Overall, corn stover removal decreased soil total CO2 and N2O emissions by -4 and -7 %, respectively, relative to no removal. No management treatments affected soil CH4 fluxes. When aggregated to total GHG emissions (Mg CO2 eq ha−1) across all sites and years, corn stover removal decreased growing season soil emissions by −5 ± 1 % (mean ± se) and ranged from -36 % to 54 % (n = 50). Lower GHG emissions in stover removal treatments were attributed to decreased C and N inputs into soils, as well as possible microclimatic differences associated with changes in soil cover. High levels of spatial and temporal variabilities in direct GHG emissions highlighted the importance of site-specific management and environmental conditions on the dynamics of GHG emissions from agricultural soils.

The Cropland Conservation Effects Assessment Project (CEAP) was initiated in the USA to provide a scientific basis for assessing effectiveness of conservation practices on water and soil quality. In 2006, sampling was initiated within a number of USDA-ARS experimental watersheds to measure and assess management impacts on near-surface (0-5 cm) soil quality indicators. Here, we focus on soil organic carbon (SOC) content because of its influence on key soil quality indicators. The sampling schemes for each of the 12 locations (< 1,500 samples) in the states of Georgia, Iowa, Indiana, Maryland, Missouri, Mississippi, New Hampshire, Ohio, Oklahoma and Texas, were designed to address individual objectives. We used the Soil Management Assessment Framework (SMAF) to score the measured data so that climate and inherent soil properties would be taken into account. The SOC-SMAF scoring algorithms uses a more-is-better model reflecting SOC concentrations associated with high productivity and minimal environmental impact. Interactions include soil type, climate, and management practices such as tillage and crop rotation, which influence SOC content at each sampling site. Measured SOC contents ranged from 3.0 to 21.7 g kg(-1) and SMAF-SOC scores ranged from 0.09 to 1.00, where 1.00 is an optimum level of SOC with regard to most soil functions. This assessment showed that SOC evaluations need to be soil-and site-specific because many factors, including environmental influences and inherent soil characteristics, influence SOC levels.

Corn's (Zea mays L.) stover is a potential nonfood, herbaceous bioenergy feedstock. A vital aspect of utilizing stover for bioenergy production is to establish sustainable harvest criteria that avoid exacerbating soil erosion or degrading soil organic carbon (SOC) levels. Our goal is to empirically estimate the minimum residue return rate required to sustain SOC levels at numerous locations and to identify which macroscale factors affect empirical estimates. Minimum residue return rate is conceptually useful, but only if the study is of long enough duration and a relationship between the rate of residue returned and the change in SOC can be measured. About one third of the Corn Stover Regional Partnership team (Team) sites met these criteria with a minimum residue return rate of 3.9 ± 2.18 Mg stover ha−1 yr−1, n = 6. Based on the Team and published corn-based data (n = 35), minimum residue return rate was 6.38 ± 2.19 Mg stover ha−1 yr−1, while including data from other cropping systems (n = 49), the rate averaged 5.74 ± 2.36 Mg residue ha−1 yr−1. In broad general terms, keeping about 6 Mg residue ha−1 yr−1 maybe a useful generic rate as a point of discussion; however, these analyses refute that a generic rate represents a universal target on which to base harvest recommendations at a given site. Empirical data are needed to calibrate, validate, and refine process-based models so that valid sustainable harvest rate guidelines are provided to producers, industry, and action agencies.

Soil quality (SQ) assessment is a proactive process for evaluating soil and crop management effects on biological, chemical, and physical indicators of soil health. Our objectives were to evaluate several SQ indicators within five Agricultural Research Service (ARS) experimental watersheds (WS) and determine if those indicators were affected by manure, tillage, or crop rotation histories. Ten soil quality indicators were measured within each of 600 0 to 5 cm (0 to 2 in) depth and 398 5 to 15 cm (2 to 6 in) depth increment samples, evaluated statistically, and then scored using the Soil Management Assessment Framework. Except for soil organic carbon (C) at both depth increments or microbial biomass C and β-glucosidase within the 5 to 15 cm increment, the indicators showed significant WS differences. Except for surface soil-test phosphorous (P), Soil Management Assessment Framework indicator scores and overall soil quality index values also showed significant (p ≤ 0.05) WS differences. Microbial biomass C was significantly affected by crop rotation at both sampling depths and by WS within the surface 5 cm. β-glucosidase was significantly affected by all four factors (WS, manure, tillage, and crop rotation) and their interactions within the 0 to 5 cm increment. The water-stable macroaggregate indictor within the 0 to 5 cm increment and within the 5 to 15 cm increment, however, were not significantly different for the tillage and manure application treatments, respectively. Our study showed that the ARS Conservation Effects Assessment Project (CEAP) watersheds provided a moderately controlled example that watershed-scale monitoring of soil quality is feasible and should be used to monitor soil health and/or conservation program effectiveness.