Open AccessArticle

Comparing the Soil Management Assessment Framework to the Haney Soil Health Test Across Managed Agroecosystems

Xucheng Hu

^1,*

Megan B. Machmuller

Steve W. Blecker

³,

Cassidy M. Buchanan

²,

Ian B. Aksland

²,

Alexandra G. Firth

and

James A. Ippolito

^1,2,*

School of Environment and Natural Resources, The Ohio State University, Columbus, OH 43210, USA

Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA

Agricultural Experiment Station, Colorado State University, Fort Collins, CO 80523, USA

Authors to whom correspondence should be addressed.

Agronomy 2025, 15(3), 643; https://doi.org/10.3390/agronomy15030643

Submission received: 22 January 2025 / Revised: 1 March 2025 / Accepted: 2 March 2025 / Published: 4 March 2025

(This article belongs to the Section Soil and Plant Nutrition)

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Figure 1
Pearson correlation coefficient diagram of the soil health indicators and overall soil health scores (SHSs) from the Soil Management Assessment Framework (SMAF) and Haney Soil Health Tool (HSHT). Significant levels of p < 0.05, p < 0.01, and p < 0.001 are shown according to the correlations between each indicator. SM = soil moisture, Bd = bulk density, WSA = water-stable aggregates, EC = electrical conductivity, MBC = microbial biomass carbon, PMN = potentially mineralizable nitrogen, SOC = soil organic carbon, BG = β-glucosidase activity, SHS (S) = Soil Management Assessment Framework overall soil health score, CO2-C = Solvita 24 h CO2-C burst, WEOC = water-extractable organic carbon, WEON = water-extractable organic nitrogen, SHS (H) = Haney Soil Health Tool soil health score. "> Figure 2
Pathway analysis of Soil Management Assessment Framework (SMAF) and Haney Soil Health Tool (HSHT) indicators. Indicators were selected based on (in)direct significance from the Pearson correlation matrix at p < 0.05. Values above the arrowed lines are standardized pathway coefficients that were used to compare the effect size-independent variables have on the Haney SHS and variables that directly affect the Haney SHS. Bd = bulk density, WSA = water-stable aggregates, EC = electrical conductivity, MBC = microbial biomass carbon, PMN = potentially mineralizable nitrogen, SOC = soil organic carbon, BG = β-glucosidase activity, Solvita CO2-C = Solvita 24 h CO2-C burst, WEOC = water-extractable organic carbon, WEON = water-extractable organic nitrogen, Haney SHS = Haney Soil Health Score. "> Figure 3
Soil-extractable phosphorus and potassium comparison between the Soil Management Assessment Framework (SMAF) and Haney Soil Health Tool (HSHT). (A) Linear regression of H3A- and Mehlich-3-extractable phosphorus. (B) Linear regression of H3A- and Mehlich-3-extractable potassium. "> Figure 3 Cont.
Soil-extractable phosphorus and potassium comparison between the Soil Management Assessment Framework (SMAF) and Haney Soil Health Tool (HSHT). (A) Linear regression of H3A- and Mehlich-3-extractable phosphorus. (B) Linear regression of H3A- and Mehlich-3-extractable potassium. ">

Versions Notes

Abstract

Soil health assessments within managed agroecosystems help to further understand conservation practice efficacy when management practices are altered. In this study, soil health was quantified via the Soil Management Assessment Framework (SMAF) and the Haney Soil Health Test (HSHT) within eight fields (a dryland pasture and seven dryland fields under no-till conditions for various time lengths, cropping system diversity differences, and (in)organic fertilizer use) in Northeastern Colorado. The results across cropping systems were variable when comparing the two frameworks, yet the pasture site received the greatest soil health score (SHS) from both frameworks. Management differences were present for soil physical, chemical, and biological indicators in SMAF, yet the HSHT outcomes show high variability between each field, and the SHS did not align with the understanding of management practices. The HSHT SHSs greatly relied on the single indicator Solvita CO₂-C burst (r = 0.82). The HSHT mineralizable N overestimated N availability and was not correlated to the SMAF 28-day N mineralization (R² < 0.01), and via a pathway analysis, only two SMAF biological indicators (β-glucosidase (BG) and microbial biomass carbon (MBC)) along with bulk density (Bd) correlated to the HSHT. The overall soil health scores between the two frameworks were only moderately correlated (r = 0.48), which was ascribed to the lack of HSHT soil physical and chemical indicators. While the HSHT can still be useful for tracking general trends in soil biological health over time, the SMAF remains the more comprehensive and robust tool for assessing soil health in the studied agroecosystems.

Keywords:

soil health quantification; H3A extractant; dryland agroecosystems

1. Introduction

Soil health (SH) is “the continued capacity of soil to function as a vital living ecosystem that sustains plants, animals, and humans” [1,2]. However, intensive agriculture can negatively affect soil health by accelerating soil degradation. Soil degradation in agroecosystems causes the loss of soil organic matter (SOM), a depletion in soil nutrients, and a reduction in biodiversity, ultimately leading to reduced crop yields [3,4]. Global efforts have been undertaken through the establishment of the Global Soil Partnership, aimed at enhancing the governance of soil globally and promoting sustainable soil management practices [5]. Fortunately, SH in agroecosystems can be restored through multiple conservation management practices (e.g., the use of complex crop rotations, cover crops, conservation tillage or no till, manure application, and/or the introduction of livestock). For example, in reduced tillage systems, SH improvements are reflected as increases in the soil organic carbon (SOC) content, aggregate stability, and labile C and N fractions [6,7]. In addition, reducing tillage and crop rotations can increase soil microbial properties (e.g., microbial biomass), which affect biological SH indicators and overall SH health [8,9,10]. Thus, soil management is not merely performed to enhance crop yields that satisfy increasing future global food demands but to concomitantly sequester C to mitigate climate change and enhance ecosystem properties to promote sustainable environments [11,12,13].

Soil health assessments should use a quantitative approach to differentiate between inherent and dynamic soil properties, ultimately providing insights for land management practices [14]. However, SH cannot be reflected solely on one indicator; rather, SH quantification should use a comprehensive measurement of soil physical, chemical, and biological properties. Thus, SH frameworks are developed based on quantifying multiple SH metrics, interpreting the result in a numerical format representing the capacity of the soil to function and identifying related management recommendations to further improve SH. The beauty of SH frameworks lies in identifying soil functionality in relation to soil physical, chemical, and biological properties, which goes beyond crop production to ecosystem services that affect biodiversity, water quality, the climate, animals, and ultimately human health and well-being.

One such soil health quantification tool is the Soil Management Assessment Framework (SMAF [15]), developed following a three-step process: (1) soil indicator selection; (2) soil indicator interpretation; and (3) the integration of indicator scores into SH indices. Ten SH indicators in the SMAF include bulk density (Bd), water-stable aggregates (WSAs), β-glucosidase activity (BG), microbial biomass carbon (MBC), potential mineralizable nitrogen (PMN), soil organic carbon (SOC), pH, electrical conductivity (EC), and extractable P and K. Soil clay content, soil texture, the crop(s)/plant(s) present, and inherent site characteristics (e.g., soil taxonomy and climatic conditions) are also utilized in SMAF as overarching indicators contributing to SH. Several studies have shown the ability of SMAF to identify SH variations across diverse agriculture management practices [6,16,17,18,19] and soil amendment applications [20,21,22].

Another SH quantification tool is the Haney Soil Health Test (HSHT or Haney test [23]). The goal of the HSHT is to quantify SH related to the soil N and C cycles. Water-extractable organic carbon (WEOC), water-extractable organic nitrogen (WEON), and the Solvita 24 h CO₂-C burst are used as indicators for the algorithms used to calculate the HSHT score [24,25,26]. Additionally, but not yet incorporated in the HSHT score, is the Haney, Haney, Hossner, and Arnold (H3A) extractant developed to determine plant-available N, P, and K and to provide estimations for fertilizer applications [27,28].

The utility and reproducibility of SMAF have been shown through various studies, but SMAF is not a fast and convenient tool for SH quantification. The HSHT can be performed quickly and has shown the ability to discern differences between agroecosystem management practices [29], but studies have found high variability and less reproducibility with the HSHT for research purposes [30,31,32]. As an economical and convenient tool designed for producers to track SH changes over time, the HSHT might be good enough. Still, the HSHT may not provide the level of detail needed by scientists and practitioners to explain to producers why and how soil health is indeed changing.

Comparisons between individual SH frameworks may be helpful to identify important SH indicators, to further understand what each framework is actually providing in terms of output, and to transfer this information to producers. Several studies [29,32,33] have compared the Comprehensive Assessment of Soil Health (CASH [34]) to the HSHT. However, based on our knowledge, there are no published reports comparing the SMAF to the HSHT for SH quantification when using the same sets of soils. Thus, the objectives of this study were to the following: (1) to use the SMAF as a scientific tool for comparing soil health scores (SHSs) across different management practices in seven cropping fields and one pasture; (2) to use HSHT to compare SHSs in the same agroecosystems; and (3) to compare SMAF to the HSHT to understand what correlations can be made between the two. The overarching goal of this study was to provide a deeper level of HSHT understanding to producers and other stakeholders.

2. Materials and Methods

2.1. Site Description

Eight sites from four different producers in eastern to Northeastern Colorado were selected. All sites were under semi-arid climatic conditions, with low and high temperatures ranging from approximately −8 to 32 °C and receiving between 430 to 480 mm of mean annual precipitation. In addition, all fields selected were in dryland cropping systems with similar soil textures (loams, silt loams, and clay loams). Almost all fields (seven of eight) were under no-till conditions but for various lengths of time, while one field was only disturbed by placing a blade horizontally and ~5 to 6 cm below the soil surface several months prior to planting wheat in the rotation; this was performed to create a capillary break between the lower and upper soil to help reduce evaporative losses. Various cropping rotations were used, ranging from relatively usual for Eastern Colorado (i.e., wheat–corn–fallow rotations) to highly unusual for this region (7 to 8 different crops in rotation over the last 20 years). One field had been in pasture for at least 70 years, five production fields utilized inorganic fertilizer applications, and two production fields utilized organic fertilizer applications in the form of a homemade compost extract. General field characteristics and management practices of each field are shown in Table 1.

2.2. Soil Sampling and Processing

A Giddings hydraulic probe with a 150 cm long and 5 cm diameter stainless-steel coring tube was used to collect soil samples from all sites in July 2022. Six locations were randomly selected from within each site, and within a 5 m radius around the location, approximately 10 soil cores were obtained from the 0–15 cm depth, placed into a bucket, gently mixed, then transferred into a 3.8 L Ziploc bag and stored at 4 °C until returned to the lab. An additional soil core was collected, immediately weighed, and then oven-dried at 105 °C for 48 h to determine the soil moisture content and Bd. After returning the composite sample to the lab, the sample was stored at 4 °C and then, as soon as possible, passed through an 8 mm sieve. For SMAF, the following was conducted: (1) approximately 150 g of field-moist 8 mm-sieved soil was stored at 4 °C; (2) approximately 300 g of the 8 mm field-moist soil was passed through a 2 mm sieve and then air-dried; and (3) the remainder of the 8 mm-sieved soil was air-dried, all for subsequent SMAF soil indicator analyses. For the HSHT, only the 2 mm air-dried soil was required.

2.3. Soil Health Laboratory Analysis

2.3.1. The Soil Management Assessment Framework

The SMAF uses on-site characteristics, the crop(s) present, climatic variability, and a set of soil characteristics to quantify SH. SMAF has been used extensively in Colorado (e.g., Trimarco et al. [19]), and the reader is pointed to this literature for additional SMAF information.

Physical, Chemical, Nutrient, and Biological Indicators and Scores of SMAF

Besides the determination of clay content via the hydrometer method [36], SMAF utilizes physical (Bd and WSA); chemical (pH and EC); nutrient (extractable P and K); and biological (MBC, PMN, SOC, and BG) indicators to derive individual indicator scores based on “more is better”, “less is better”, or “somewhere in the middle is better” algorithms (see Andrews et al. [15] for additional details). SMAF then utilizes those individual indicator scores to derive weighted physical, chemical, nutrient, and biological indices. Finally, SMAF combines all soil health index scores into an overall weighted soil health score. All SMAF scores are based on a scale from 0 to 1, with 0 being the “worst” and 1 being the “best” in terms of soil health. For brevity’s sake, all indicator analysis methods have been previously published by our group (see Buchanan and Ippolito [20]; Trimarco et al. [19]). The Olsen extraction method [37] was used on the two calcareous sites (C-north and C-south) for the determination of plant-available P and K soil concentrations, similar to our previously published work. Different from our previous work, the remaining six sites had acidic pH conditions, and thus, the Mehlich-3 extraction method [38] was used to determine plant-available P and K concentrations.

2.3.2. The Haney Soil Health Test

Solvita CO₂-C Burst

A 24 h CO₂ burst was measured according to Haney et al. [28]. Forty g of air-dried 2 mm-sieved soil was placed into a 50 mL disposable beaker containing a Whatman #2 filter paper covering four 6.35 mm holes in the base. The beaker was then placed into a Solvita (Solvita, Mount Vernon, ME, USA) standard CO₂ burst jar, with 25 ml of deionized (DI) water added to the bottom of the jar for re-wetting the soil via capillary action [28]. A Solvita CO₂ probe was placed on the top of the beaker, the jar lid was tightened, and the jar was incubated at 25 °C for 24 h. After 24 h, the lid was removed, the CO₂ probe was removed and then read colorimetrically for CO₂ concentration (ppm) using a Solvita Digital Color Reader in CO₂-Lo mode.

Water-Extractable Organic Carbon and Organic Nitrogen

The WEOC and WEON were determined, according to Haney et al. [23,25], by placing 4.0 g of air-dried 2 mm-sieved soil into a 50 mL centrifuge tube and by adding 40 mL of DI H₂O. The samples were then shaken for 10 min, followed by centrifugation for 5 min at 4000 rpm. The supernatant was removed, passed through a 0.45 μm nylon syringe filter, and then analyzed using a Shimadzu TOC-LCPH/CP (Shimadzu Scientific Instruments, Inc., Kyoto, Japan) for WEOC and water-extractable total nitrogen (WETN) concentrations. Extracts for water-extractable inorganic N (WEIN; NO₃-N + NH₄-N) concentrations were treated with reagents to determine NO₃-N (V reduction [39]) or NH₄-N (salicylate [40]) colorimetrically using a microplate reader (Synergy H1, BioTek Instrument, Winooski, VT, USA) at 540 or 650 nm, respectively. The WEON was determined by the difference between WETN and WEIN.

Soil Health Score Calculation

The HSHT uses the Solvita CO₂-C, WEOC, and WEON concentrations (Haney et al. [23]) to determine the HSHT score, calculated via Equation (1)).

H S H T s c o r e = \frac{S o l v i t a {C O}_{2} - C}{10} \times \frac{W E O C}{100} \times \frac{W E O N}{10}

(1)

HSHT scores typically range from 0 to 50, and the higher the number, the “better” the soil health [41]. However, the HSHT score can exceed 50 in some cases with optimum soil biochemical conditions, with sites scoring >50 likely approaching a maximum SH potential [23]. For the current work, we considered 50 as the upper limit for the HSHT score for comparison to SMAF.

The Haney test [23] accounts for site-specific precipitation to estimate mineralizable N according to Equation (2):

N_{m i n} (k g h a^{- 1}) = \frac{W E O C}{S o l v i t a C O_{2} - C} \times W E O N \times n \times 2.24

(2)

where n is the site-specific factor used in the Haney test that equals growing season rainfall events that are greater than 2.54 cm (presented in Table 1). Precipitation that could have been used by the current crop was considered to have fallen between 1 January and 8 July 2022, based on daily precipitation data obtained from the Colorado Climate Center [35]. The Colorado Climate Center website data were collected by selecting “Data Access”; then “Daily, Monthly, and Annual”; and then choosing the specific Colorado Climate Center station (Table 1). Next, under “Data options,” the chosen output type was “csv”, and under “daily data”, the start date selected was 1 January 2022, the end date selected was 8 July 2022, and the precipitation box was checked. The generated .csv data were opened in Excel and were sorted from highest to lowest precipitation value in order to identify the n value for each site. Each daily precipitation event that was greater than 2.54 cm accounted for 1 in the n value, with the maximum n value being 4. The n value for sites J-30, J-10, R-pasture, R-D7, and R-D17 equaled 1, while the n value for sites B, C-south, and C-north equaled 4 (Table 1).

H3A-Extractable Nutrients

The H3A-2 extractant [26], modified from Haney et al. [27], was used to extract soil P and K for use in the HSHT but does not play a role in the overall HSHT SHS calculation. For the sake of discussion, we will refer to this extractant as H3A. The H3A extractant was made with 2 g L⁻¹ of lithium citrate, 0.6 g L⁻¹ of citric acid, 0.4 g L⁻¹ of malic acid, and 0.4 g L⁻¹ of oxalic acid. Four grams of soil was shaken with 40 mL of an H3A solution for 10 min, then centrifuged for 5 min at 4000 rpm before passing the liquid through a 0.45 μm syringe filter. The H3A-extractable P (H3A-P) and K (H3A-K) were analyzed by inductively coupled plasma–atomic emission spectrometry (ICP-AES).

2.4. Statistical Analysis

Statistical analyses were performed using RStudio version 4.3.1 [42]. A one-way analysis of variance (ANOVA) was used with a significant level of p < 0.05 in order to compare the different land use and management impacts on the SH indicators, indicator scores, and overall SH scores between fields. If significance was shown by the ANOVA, a post hoc Tukey Honest Significant Difference (HSD) test for a pairwise mean comparison analysis was performed using the agricolae package (version 1.3-7) in R. For all statistical analyses, data were tested for normality using a Shapiro–Wilk test and by visually inspecting QQ plots to see if points (and especially the tails) fall on or off the line. If the QQ-plot tails of the distribution deviated from the line, mathematical transformations were performed on the data, including square root, logarithm, and reciprocal to meet the assumptions of normality (i.e., tested again via the Shapiro–Wilk test and QQ plots). All data presented in tables and figures are non-transformed, but if needed, the transformation used is indicated in the tables and figures.

Additionally, a Pearson correlation was performed (at significance levels of p < 0.05, p < 0.01, and p < 0.001) to illustrate the relationship between all SMAF and HSHT SH indicators and their overall SHSs via the metan package (version 1.18.0) in R. Furthermore, a pathway analysis was conducted based on the significant correlations (i.e., p < 0.05) in the correlation coefficient matrix between the indicators from SMAF and HSHT using the lavaan package (version 0.6-19) in R. A pathway analysis conceptual model was built based on setting the Haney SHS as the response variable and how it was affected by individual indicators from SMAF and HSHT representing the overall contributions to the overall HSHT SHS. The best-fit model was evaluated based on the root mean square error of approximation (RMSEA), comparative fit index (CFI), and Chi-square test (χ²). When present, significant correlations between SMAF and HSHT indicators are represented by two-headed arrows, while indicators that had a direct correlation as predictor variables of the Haney SHS are represented as single-headed arrows. Standardized pathway coefficients (β) were used to determine the contribution of the individual independent variables on the Haney SHS predictor variable.

3. Results and Discussion

3.1. SMAF-Associated Soil Health Indicators, Indicator Scores, and SHSs

Soil health indicators from SMAF, along with indicator scores, are presented in Table 2 and Table 3, respectively. The subsection discussions below focus on SMAF physical, chemical, biological, and nutrient indicators and scores.

3.1.1. Physical Indicators and Scores

Bulk density and WSA account for the physical SH properties in SMAF. Although there were no statistical differences in Bd between the sites, the SMAF Bd indicator score did show differences. These differences were likely a result of site-specific parameters, such as soil mineralogy and texture embedded in the SMAF scoring functions [15] (see texture class in Table 1). The Bd values in the current study were similar to those found in other published studies from eastern CO (e.g., [21], where Bd = 1.20 to 1.38 g cm⁻³ in a wheat–fallow rotation, [19,43] in managed, grazed systems, where Bd = 1.15 to 1.59 and 1.41 to 1.70, respectively) as well as unpublished Eastern Colorado studies (wheat–fallow and wheat–corn–fallow rotations, where Bd = 1.00 to 1.70; organic agroecosystems, where Bd = 1.35 to 1.51; a Conservation Reserve Program location, where Bd = 1.11 to 1.61; and a native prairie, where Bd = 1.03 to 1.38; Ippolito, personal communication).

Water-stable aggregates are often inversely impacted by tillage intensity [44] due to tillage-inducing disturbance that breaks up soil macroaggregates [45]. The results from the current study follow this same inverse relationship between disturbance and reduction in WSA. The uncultivated pasture (R-pasture) showed one of the highest WSA percentages (61.1%), while the slightly disturbed site (B) contained the lowest WSA (21.8%). These differences were likely due to the lack of tillage for at least 70 years at the R-pasture site, while the B site was disturbed by placing a blade horizontally and ~ 5 to 6 cm below the soil surface several months prior to planting wheat in the rotation. Similarly, the WSA indicator scores reflected one of the greatest values associated with the R-pasture (1.00), while the lowest value was associated with the B site (0.59). It has been previously noted that WSA scores above 0.90 may be interpreted as being almost optimal for WSA [46]. All sites in the current work, except for the B site, met this criterion.

3.1.2. Chemical Indicators and Scores

Soil pH and EC account for the chemical SH properties in SMAF. Soil pH is a major SH indicator in that it controls crucial biochemical reactions in soils. Soil pH algorithms in SMAF are tailored towards specific crops, attempting to align optimum soil conditions at the species level [15]. A significant difference in soil pH was found between fields. These differences could have been due to inorganic N-based fertilizer applications over time to the J, R, and B sites as compared to compost extract being applied to both C sites. Depending on the inorganic N fertilizer source (i.e., a urea- or ammonia/ammonium-based one), at relatively high initial soil pH values (like those inherently found in Eastern Colorado), N volatilization may occur, leaving a free H⁺ behind and eventually leading to soil acidification. In addition, if N-based fertilizers are applied and a subsequent crop failure occurs (which occurs, with some frequency, in rain-fed semi-arid dryland agroecosystems), soil NO₃-N may accumulate and contribute to soil pH reductions. These issues have been documented in semi-arid agroecosystems similar to Colorado [47]. The SMAF algorithm for soil pH follows a “somewhere in the middle is best” approach, with optimal pH being approximately 6.5 and with the algorithm tailing off as the pH decreases or increases from 6.5. Thus, and as a consequence, soils containing CaCO₃ showed a lower soil pH indicator score compared to those fields that had a near-neutral to slightly acidic soil pH.

Electrical conductivity is a proxy of soil salinity, or the amount of soluble salts present in soil. Dryland agroecosystems are more susceptible to soil salinization due to high evapotranspiration rates leaving salts behind on or near the soil surface. Elevated EC values can affect plant growth and ultimately cause economic loss to the producer [48,49]. Even though differences were observed in EC values among the various fields, the values were approximately an order of magnitude lower than the crop EC threshold values present in SMAF. This, in turn, led to all sites receiving EC indicator scores of 1.00.

3.1.3. Biological Indicators and Scores

Soil organic C, MBC, PMN, and BG account for the biological SH properties in SMAF. Soil organic carbon is the fundamental food source for soil microbes that drive soil biogeochemical reactions and nutrient cycling and is considered the foremost among all SH indicators [50,51]. Soil organic C content differences existed between all sites, yet SOC concentrations were considered relatively low in SMAF, leading to significantly different yet low SOC scores between sites. With respect to SOC content differences, R-pasture contained among the greatest SOC content, which is logical given that this site has been in pasture and has remained undisturbed for over 70 years. Differences among cultivated fields were more difficult to separate, with the data suggesting that regardless of the cropping system or level of diversity, SOC contents tended to be equal. This result is similar to that of Liptzin et al. [52], who showed that cropping rotation diversity had no effect on SOC concentrations across North American cropping systems.

Soil MBC quantifies the living soil microbial biomass and is sensitive to management practices, such as tillage and crop rotations [53] or management practices that affect soil–water relations (e.g., Bian et al. [54]). Differences existed across almost all cultivated sites, yet tillage alone cannot explain the differences, as almost all sites were under no-till conditions. And, as sites R-D7 and R-D17 had the most complex cropping rotations of all cultivated fields, yet had some of the lowest MBC concentrations, cropping rotations not only cannot explain differences in MBC, but these findings contradict those of Moore et al. [53]. One potential explanation for differences in MBC may be due to the addition of compost extracts (at C-north and C-south) that may have provided more labile carbon as a food source for microbes and the presence of a perennial system (R-pasture) where a potential multitude of organic root exudates are promoting microbial populations [55,56]. Another potential explanation for differences in MBC may be due to the moisture content at and near the time of initial soil sampling. Table 1 shows that the C-north site had the greatest moisture content and correspondingly the greatest MBC concentration as compared to other sites, with this supported by a significant correlation between soil moisture content and MBC (p < 0.001; Figure 1). This contention is also supported by Tomar and Baishya [57], who showed that MBC was positively correlated with soil moisture. Although the concept of increased moisture content relating to increased MBC did not hold entirely true across all sites studied, this relationship should be looked at further in future studies. It is also important to note that the J-10 and J-30 sites tended to have the lowest MBC values. Although these sites are under no-till conditions, crop residues are removed (i.e., not returned to the system), and concomitantly, this management practice led to the lowest soil moisture contents at the time of sampling, both of which likely led to reduced MBC values.

Soil PMN measures the stock of organic N in the soil that can be mineralized to inorganic forms that are readily available for plant uptake, which also could offset and thus reduce N fertilizer inputs [58]. Positive correlations were observed between PMN and MBC and between PMN and SM (both p < 0.001; Figure 1), which supports N mineralization being driven by the soil microbial population [59]. Similar to MBC, the two sites that have had long-term organic fertilizer applications (C-north and C-south) tended to contain more PMN as compared to fields using inorganic fertilizers. Similar results were also observed by Zhang et al. [60], where organic N inputs facilitated the mineralization process typically associated with labile organic N. In contrast, the J-10 and J-30 sites tended to have the least amount of PMN, likely due to a reduction in crop residue inputs. It is important to note that the greatest cropping rotation diversity (i.e., R-D7 and R-D17 fields) tended to have PMN concentrations that were in the middle to low range among all sites. Similarly, Liptzin et al. [61] showed that increases in cropping rotation diversity had a negative effect on PMN across North American cropping systems.

Beta-glucosidase, an extracellular enzyme produced by microbes, is a biological SH indicator that plays a crucial role in the degradation of complex carbohydrates into simple sugars, the form that microbes readily consume [62]. Beta-glucosidase activity has been shown to be sensitive to management practices [63] and soil moisture content [57,64]. Differences in BG activity were relatively similar to the difference observed in MBC across sites. In fact, BG correlated well with MBC, SOC (both p < 0.001), and soil moisture (p < 0.05; Figure 1). However, all BG indicator scores were relatively low in this study, likely due to a drought through the growing season, with similar results reported by Buchanan and Ippolito (2021) [20] and Ippolito et al. (2021) [21] from sites under semi-arid dryland conditions that have experienced drought over time.

3.1.4. Nutrient Indicators and Scores

Soil-extractable P and K concentrations account for the nutrient SH properties in SMAF, with differences existing between sites. Regardless of the differences, all sites had sufficient extractable soil P concentrations for crops raised in Colorado, such as corn and wheat, based on in-state fertilizer recommendation guidelines [65,66]. Furthermore, the soil-extractable P and K indicator scores were almost all optimal (i.e., between 0.92 and 1.00), further suggesting that adequate P and K concentrations were present to support plant growth while potentially minimizing P loss through runoff and erosion [15,67].

3.1.5. Overall Soil Health Scores

Data associated with SMAF physical, chemical, biological, nutrient, and overall SH scores are shown in Table 4. The soil physical health scores (driven by a combination of Bd and WSA) were significantly different across sites, yet trends in management practice were difficult to fully separate; R-pasture and the B site (with slight tillage) had the highest and lowest physical soil health values, respectively. The soil chemical health scores (a combination of pH and EC) were lower in the C-south and C-north locations, driven primarily due to an elevated soil pH at these sites compared to other locations (a potential fallacy of the pH scoring function in SMAF relative to agricultural sites inherently high in pH; this should be addressed within SMAF in the future). The soil biological health scores (the average score of SOC, MBC, PMN, and BG) mainly suggested that the perennial pasture system (R-pasture) and the addition of organic fertilizers (C-south and C-north) had approximately the greatest scores as compared to other cropping systems. Crop rotation diversity (R-D7 and R-D17) was not found to improve the biological soil health condition within these fields. In addition, no-till conditions, when crop residue is removed (J-10 and J-30), led to among the lowest biological soil health scores, potentially attributed to a reduction in fresh organic C inputs as compared to other locations. This contention should be examined in the future using SOC fractionation techniques. Although significant differences existed in the soil nutrient health scores (driven by extractable P and K), separating differences between locations was difficult. Finally, the SMAF overall SHS showed only a significant difference between the pasture as compared to the other cropping fields. Similar literature results have supported pasture systems typically having greater SH from physical, chemical, and biological aspects [68,69,70,71]. Equally important, these data reveal that in terms of overall soil health outcomes, the way producers manage these agroecosystems under (mostly) no-till conditions leads to the same endpoint: equal overall soil health.

3.2. Haney Test

All indicators associated with the HSHT, including the SHS score, are presented in Table 5. The R-pasture site resulted in the greatest Solvita CO₂-C value and was significantly different from several other cropping fields. Although statistical differences were observed among the cropping fields, they did not correspond to the conservation practices implemented in this study (e.g., no tillage and organic fertilizer applications). The Solvita CO₂-C did not link with management, likely due to a complexity of factors (e.g., ammoniacal N fertilizer use, soil texture, and slope position) that affected soil respiration [72]. The Solvita CO₂-C was also unsurprisingly correlated with SMAF biological SH indicators (i.e., MBC, BG, and PMN; Figure 1). Although not shown in this study, Adhikari et al. [72] concluded that soil C mineralization was highly responsive to soil chemical properties, such as pH and EC, that might potentially impact the Solvita CO₂-C readings. However, SMAF chemical indicator (pH and EC) results do not corroborate this contention (i.e., no significant correlation between these factors; Figure 1).

Water-extractable C and N are measurements associated with microbial activity [25]. However, WEOC and WEON not only failed to interpret the treatment differences among fields but almost always failed to correlate with SMAF biological indicators, except for the case of WEON and BG (p < 0.05; Figure 1). Correspondingly, among the limited research reporting WEOC and WEON, researchers have found issues using these two SH indicators. Chahal and Van Eerd [30] indicated that WEOC did not show a significant correlation with SOC or Solvita CO₂-C values. In addition, Singh et al. [72] showed that WEOC concentrations did not vary between different cultivation practices, and Singh et al. [29] reported that WEOC and WEON did not change with different tillage management practices. These two fractions only represent a small portion of the entire SOM pool and, interacting widely with various soil properties, likely give rise to uncertainties that lead to a lack of correlations across fields [73].

The R-pasture site had among the greatest Haney SHS measurements as compared to other fields, similar to the SMAF overall SHS. And although differences between sites were more varied with respect to the Haney overall SHS, they fell somewhat in line with the SMAF overall SHS. In fact, the correlation between both SHSs was significant (p < 0.001; Figure 1). However, different from SMAF, the HSHT only utilized three indicators to quantify the overall SHS (Equation (1)). Of those three indicators, the Haney overall SHS was highly correlated to the Solvita CO₂-C (p < 0.001) while less correlated with WEOC (p < 0.01) and WEON (p < 0.01) (Figure 1), suggesting the HSHT SHS is heavily dependent on the Solvita CO₂-C burst in these dryland agroecosystems; similar findings have been reported by others (Yost et al. [74], Chu et al. [31], and Sherbine et al. [75]). The correlation between the HSHT and the CO₂-C burst in and of itself is not useful for explaining to producers, in more detail, what is occurring belowground.

Pathway analyses (Figure 2) between the HSHT overall SHS and other indicators determined via SMAF may help to explain to producers what the HSHT is describing. Pathway analyses were also used to make preliminary sense of connections between the HSHT and other indicators, with the hope that this approach can be utilized on larger, more robust datasets in the future. Based on the pathway analysis model, BG, MBC, and Bd (β = 0.06, 0.05, and −0.02, respectively) were the only three SMAF indicators that directly correlated to the HSHT overall SHS. Additionally, MBC and PMN from SMAF also made indirect contributions to the HSHT overall SHS via the Solvita CO₂-C burst. Bulk density negatively contributed to the HSHT overall SHS score, which might be explained by slight soil compaction that reduces microbial populations and activity. Despite this, Bd presented less contributions to the HSHT overall SHS compared to the other two SMAF biological indicators. Three indicators from SMAF (PMN, EC, and pH) showed only indirect paths contributing to the HSHT overall SHS via Solvita CO₂-C and WEON. Unfortunately, as a means to interpret the Haney soil health score for producers, although interesting, pathway analyses between SMAF and Haney did not provide greater utility. This pathway analysis suggests to these producers that the HSHT focuses too much attention on the Solvita CO₂-C burst to quantify SH, when in fact the HSHT is only quantifying biological SH in these systems.

The HSHT utilized the H3A extract for soil nutrient (e.g., P and K) availability determination. H3A-extractable P and K concentrations both showed significant differences across sites (Table 5). The ranking was similar to each other for most sites except for C-south, which showed the lowest H3A-extractable P and K concentrations (i.e., similar to SMAF).

The HSHT Nmin indicated that sites B, C-south, and C-north resulted in the greatest rates, with all other sites being similar to one another yet lower than B, C-south, and C-north. Despite the significance of the Nmin rate across the fields, all values calculated in HSHT by Equation (2) were one to two orders of magnitude greater than the SMAF determined PMN, suggesting no need for additional N fertilizer application. Furthermore, the HSNT Nmin concentrations did not correlate with the SMAF PMN concentrations (R² <0.01). Similar findings outside of dryland agriculture have shown that the HSHT Nmin amount exceeded the soil supply of organic N (in Northwest U.S. [75]). In Southeast U.S., HSHT Nmin did not respond to a variety of tillage, cover crops, and N fertilizer treatments [76]. And in Midwest U.S., the HSNT Nmin was not correlated with the economically optimum N rate [74].

3.3. Soil Nutrients’ Comparison Between SMAF and HSHT

To understand the relationship between SH extractants used for P and K, comparisons between P and K concentrations from SMAF (Mehlich-3 only for this discussion; C-south and C-north sites were excluded) and HSHT (H3A) are shown in Figure 3. The SMAF- and H3A-extractable P and K were correlated (R² = 0.75 and 0.73, respectively). However, the H3A-extractable P and K were, on average, 37% and 63% lower than the M3-extractable P and K, respectively. This makes sense, as the H3A extractant was designed to mimic root exudates, theoretically extracting biologically available nutrients that plants might encounter [23]. Furthermore, the H3A extractant is not as harsh as a chemical extractant as Mehlich-3. A poor to a complete lack of correlation between H3A and single- or multi-element extractants (e.g., Mehlich-1 and Mehlich-3) have been previously reported [31,77,78].

3.4. Limitations of SMAF and HSHT and Future Directions

One limitation of SMAF is related to the intensive laboratory analysis, costly laboratory supplies, and analytical instruments, which introduces barriers for the commercialization of this SH quantification tool. Given that the HSHT relies heavily on the Solvita CO₂-C burst and that the Solvita CO₂-C burst was correlated to SMAF MBC, BG, and PMN (Figure 1), replacing these three SMAF soil health indicators with the simple Solvita CO₂-C burst may be justified for commercial analytical purposes. Certainly, more research is needed in this area to assess the applicability of the regional and soil-inherent property distinctions that may affect the representation of the Solvita CO₂-C burst values.

Due to the complexity of soil ecosystems and interconnectivity with physical, chemical, and biological processes, SH assessments should not issue conclusions based solely on an individual SH indicator (e.g., Solvita CO₂-C burst in HSHT) [79], which appears to be the case with the HSHT in these dryland agroecosystems. Soil physical and chemical indicators are not included in the HSHT SHS, creating a potential pitfall in predicting SH. In the context of this study, the HSHT SHS appears to be affected by reductions in Bd and increases in MBC and BG, and thus, management practices that would enhance these soil indicators may improve the HSHT SHS results in Eastern Colorado dryland agroecosystems. However, any improvement regarding these three soil indicators suggests a change in biological soil health, which likely is reflected in the CO₂ burst, along with reductions in compaction that likely affect microbial activity. Further research is obviously needed in order to interpret the HSHT SHS for producers in this and other regional, national, and global agroecosystems.

4. Conclusions

The objective of this study was to compare two logistically different SH frameworks (SMAF versus HSHT) to quantify the SH status pertaining to different management practices in dryland agroecosystems (i.e., pasture and crop fields). Both frameworks showed that a pasture system had the “best” overall SH as compared to cultivated systems. Although no difference was present in the SMAF overall SH score when comparing between cultivated fields, the physical, chemical, and biological indicator scores indicated differences in line with various management practices; slight tillage was distinguished by lower soil physical health in SMAF; improvements in SMAF biological indicators likely resulted from the use of organic as compared to inorganic fertilizers; the effect on crop rotation and diversity was not evident via SMAF, similar to results of previous studies. The results from the HSHT show greater variability among the studied fields and did not follow expected outcomes based on previously published literature. The above results suggest that as an SH quantification tool, the HSHT is not as robust as SMAF; the HSHT relies almost exclusively on the Solvita CO₂ burst (a measure of only soil biological status) for the studied fields. The soil nutrient assessment suggested that the H3A extracted less P and K compared to Mehlich-3. A comparison between SMAF and HSHT N mineralization suggested that the HSHT Nmin was not correlated with the SMAF 28-day PMN laboratory incubation, and yet more concerning was that the HSHT Nmin appeared to severely overestimate the amount of mineralizable N in the agroecosystems studied. The findings suggest that HSHT is not a robust tool for overall SH determination. The HSHT has a major connection to the Solvita CO₂ burst for quantifying soil biological health, has only a slight indirect connection to soil physical health (e.g., Bd), and has no connection to soil chemical health in the agroecosystems studied. A more holistic approach to SH assessment should be used as the baseline in SH research to provide authentic conclusions and recommendations for producers.

Author Contributions

Conceptualization, X.H., M.B.M., S.W.B., C.M.B., I.B.A., A.G.F., and J.A.I.; methodology, J.A.I.; validation, J.A.I.; formal analysis, X.H, C.M.B., and J.A.I.; investigation, X.H., M.B.M., S.W.B., C.M.B., I.B.A., A.G.F., and J.A.I.; resources, X.H., M.B.M., S.W.B., C.M.B., I.B.A., A.G.F., and J.A.I.; data curation, X.H., C.M.B., and J.A.I.; writing—original draft preparation, X.H. and J.A.I.; writing—review and editing, X.H., M.B.M., S.W.B., C.M.B., I.B.A., A.G.F., and J.A.I.; visualization, J.A.I.; supervision, J.A.I.; project administration, X.H., M.B.M., S.W.B., C.M.B., I.B.A., A.G.F., and J.A.I.; funding acquisition, J.A.I. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by The Colorado Department of Agriculture, grant number CDA 202200003060.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.

Acknowledgments

We would like to acknowledge Ryan Taylor (deceased) for his dedication and passion to the field of soil science and soil health and for being instrumental in helping our team obtain funding for this project.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:

Bd	Bulk density
BG	β-glucosidase
CASH	Comprehensive Assessment of Soil Health
Solvita CO₂-C	Solvita 24 h CO₂-C burst
EC	Electrical conductivity
HSHT	Haney Soil Health Test
H3A	Haney, Haney, Hossner, and Arnold extractant
MBC	Microbial biomass carbon
Nmin	Estimated nitrogen mineralization
PMN	Potential mineralizable nitrogen
SH	Soil health
SHS	Soil health score
SM	Soil moisture
SMAF	Soil Management Assessment Framework
SOC	Soil organic carbon
SOM	Soil organic matter
WEOC	Water-extractable organic carbon
WEON	Water-extractable organic nitrogen
WSA	Water-stable aggregates

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Figure 1. Pearson correlation coefficient diagram of the soil health indicators and overall soil health scores (SHSs) from the Soil Management Assessment Framework (SMAF) and Haney Soil Health Tool (HSHT). Significant levels of p < 0.05, p < 0.01, and p < 0.001 are shown according to the correlations between each indicator. SM = soil moisture, Bd = bulk density, WSA = water-stable aggregates, EC = electrical conductivity, MBC = microbial biomass carbon, PMN = potentially mineralizable nitrogen, SOC = soil organic carbon, BG = β-glucosidase activity, SHS (S) = Soil Management Assessment Framework overall soil health score, CO₂-C = Solvita 24 h CO₂-C burst, WEOC = water-extractable organic carbon, WEON = water-extractable organic nitrogen, SHS (H) = Haney Soil Health Tool soil health score.

Figure 2. Pathway analysis of Soil Management Assessment Framework (SMAF) and Haney Soil Health Tool (HSHT) indicators. Indicators were selected based on (in)direct significance from the Pearson correlation matrix at p < 0.05. Values above the arrowed lines are standardized pathway coefficients that were used to compare the effect size-independent variables have on the Haney SHS and variables that directly affect the Haney SHS. Bd = bulk density, WSA = water-stable aggregates, EC = electrical conductivity, MBC = microbial biomass carbon, PMN = potentially mineralizable nitrogen, SOC = soil organic carbon, BG = β-glucosidase activity, Solvita CO₂-C = Solvita 24 h CO₂-C burst, WEOC = water-extractable organic carbon, WEON = water-extractable organic nitrogen, Haney SHS = Haney Soil Health Score.

Figure 3. Soil-extractable phosphorus and potassium comparison between the Soil Management Assessment Framework (SMAF) and Haney Soil Health Tool (HSHT). (A) Linear regression of H3A- and Mehlich-3-extractable phosphorus. (B) Linear regression of H3A- and Mehlich-3-extractable potassium.

Table 1. Approximate location, soil textural class, disturbance intensity, crop rotations, current crop when soil was sampled in July 2022, fertilizer applications, soil moisture content, Colorado Climate Center station, and Haney test n values determined from Colorado Climate Center [35] station with daily precipitation events of > 2.54 cm between 1 January and 8 July 2022.

Sites	Approximate Colorado Location	Soil Texture	Disturbance Intensity	Cropping Rotation	Crop In Field When Sampled in July 2022	Fertilizer Applications (from 2020–2022)	Soil Moisture at Time of Sampling (%)	Colorado Climate Center Station	Haney Test n Value
J-30	Julesburg	Loam	30-year no till with crop residue removed	Winter wheat–corn–sunflower–millet	Corn	2020: 18 kg of urea ha⁻¹	7.8	057515 Sedgwick 5 S	1
						2021: 18 kg of Mono-Ammonium Phosphate ha⁻¹; 23 kg of P₂O₅ ha⁻¹ and 168 L of UAN ha⁻¹
						2022: 290 L of UAN ha⁻¹; 47 L of (NH₄)₃PO₄ ha⁻¹
J-10	Julesburg	Loam	10-year no till with crop residue removed	Winter wheat–corn–sunflower–millet	Corn	2020: 18 kg of urea ha⁻¹	7.8	057515 Sedgwick 5 S	1
						2021: 18 kg Mono-Ammonium Phosphate ha⁻¹; 23 kg of P₂O₅ ha⁻¹; 168 L of UAN ha⁻¹
						2022: 290 L of UAN ha⁻¹; 47 L of (NH₄)₃PO₄ ha⁻¹
R-pasture	Haxtun	Loam	70-year pasture	Continuous pasture	Pasture	NFA *	12.3	054082 Holyoke	1
R-D7	Haxtun	Loam	20-year no till	7 different crops in rotation since 2000 ^†	Camelina	2020: 87 kg of urea ha⁻¹; 15 kg of P₂O₅ ha⁻¹	10.3	054082 Holyoke	1
						2021: 80 kg of urea ha⁻¹; 15 kg of P₂O₅ ha⁻¹
						2022: 37 kg of urea ha⁻¹; 27 kg of P₂O₅ ha⁻¹
R-D17	Haxtun	Loam	20-year no till	8 different crops in rotation since 2000 ^‡	Corn	2020: 37 kg of urea ha⁻¹; 15 kg of P₂O₅ ha⁻¹	10.4	054082 Holyoke	1
						2021: 28 kg of urea ha⁻¹; 15 kg of P₂O₅ ha⁻¹
						2022: 28 kg of urea ha⁻¹; 15 kg of P₂O₅ ha⁻¹
B	Burlington	Silt Loam	Minimum till with subsurface tillage ~5–6 cm below the surface during fallow	Winter wheat–corn–fallow	Corn	2020: NFA	25.0	051121 Burlington	4
						2021: 116 kg of anhydrous ammonia ha⁻¹; 22 kg of P₂O₅ ha⁻¹
						2022: 116 kg of anhydrous ammonia ha⁻¹; 22 kg of P₂O₅ ha⁻¹
C-south	Siebert	Clay Loam	23-year no till	Corn–sunflower–fallow–cereal rye	Corn	2020: 75 L of compost extract ha⁻¹	17.0	051121 Burlington	4
						2021: 75 L of compost extract ha⁻¹
						2022: 75 L of compost extract ha⁻¹
C-north	Siebert	Silt Loam	25-year no till	Winter wheat–corn–sunflower–millet	Corn	2020: 75 L of compost extract ha⁻¹	23.0	051121 Burlington	4
						2021: 75 L of compost extract ha⁻¹
						2022: 75 L of compost extract ha⁻¹

^† Cropping rotation for R-D7 (hay millet, hay millet, fallow, winter wheat, hay millet, fallow, winter wheat, fallow, fallow, winter wheat, proso millet, fallow, winter wheat, proso millet, fallow, winter wheat, proso millet, yellow peas, winter wheat, buckwheat, garbanzo beans, winter wheat, camelina). ^‡ Cropping rotation for R-D17 (winter wheat, corn, fallow, winter wheat, sunflower, fallow, winter wheat, fallow, winter wheat, proso millet, fallow, winter wheat, proso millet, fallow, winter wheat, milo, fallow, winter wheat, proso millet, blackeyed peas, oats, buckwheat, corn). * NFA = no fertilizer application.

Table 2. Soil Management Assessment Framework (SMAF) soil health indicator mean values from the investigated fields. The values inside the parentheses represent the standard error of the mean (n = 6). Lowercase letters, within a given column for either indicators or indicator scores, represent significant differences between means, as determined using a Tukey-adjusted pairwise comparison test at significant values of p < 0.05 from ANOVA and are shown in bold. All data represent raw values. The transformations used are presented when the data were not normally distributed.

Sites		Physical Indicators		Chemical Indicators		--------------- Biological Indicators ---------------				Nutrient Indicators ^‡
	Clay	Bd ^†	WSA	pH	EC	SOC	MBC	PMN	BG	Extractable P	Extractable K
	(%)	(g cm⁻³)	(%)		(dS m⁻¹)	(%)	(mg kg⁻¹)	(mg kg⁻¹)	(mg pnp^a kg⁻¹ soil h⁻¹)	(mg kg⁻¹)	(mg kg⁻¹)
J-30	22	1.48 (0.03)	58.8 (6.6) ab	5.87 (0.1) cd	0.19 (0.04) c	0.84 (0.27) ab	18.9 (6.9) f	5.28 (1.04) c	34.2 (7.1) de	62.5 (10.9) a	737 (43) b
J-10	20	1.42 (0.05)	44.0 (2.51) b	6.03 (0.12) bcd	0.36 (0.04) ab	0.99 (0.22) ab	24.6 (6.7) ef	6.83 (1.85) bc	61.0 (7.1) bcd	44.7 (9.8) ab	770 (36) ab
R-pasture	18	1.40 (0.03)	61.1 (2.4) a	6.41 (0.22) bc	0.17 (0.04) c	1.79 (0.27) a	207 (26.7) ab	16.9 (0.99) a	109 (23.0) ab	53.3 (4.2) a	550 (68) bc
R-D7	19	1.50 (0.07)	46.8 (4.2) ab	6.79 (0.27) b	0.20 (0.02) bc	1.34 (0.36) ab	62.8 (11.6) de	8.10 (0.75) bc	76.6 (6.8) abc	31.6 (5.4) abc	609 (43) bc
R-D17	19	1.46 (0.04)	43.6 (2.3) b	5.43 (0.07) d	0.55 (0.04) a	0.95 (0.06) b	65.6 (4.4) de	13.6 (0.84) ab	29.8 (4.4) e	55.7 (7.5) a	639 (35) b
B	26	1.43 (0.05)	21.8 (2.3) c	6.05 (0.28) bcd	0.30 (0.05) bc	1.26 (0.04) ab	73.3 (12.1) cd	18.5 (1.56) a	47.2 (13.1) cde	52.3 (9.4) a	1010 (48) a
C-south	26	1.63 (0.07)	56.9 (3.1) ab	7.95 (0.28) a	0.16 (0.02) c	0.96 (0.13) ab	145 (16.3) bc	13.2 (2.96) ab	55.8 (7.5) bcde	14.5 (3.2) c	386 (65) c
C-north	32	1.46 (0.03)	47.0 (1.6) ab	8.10 (0.17) a	0.23 (0.01) bc	1.64 (0.14) a	255 (30.0) a	15.6 (1.13) a	149 (6.0) a	20.6 (3.9) bc	980 (70) a
ANOVA p-Value	ND ^¥	0.074	<0.001	<0.001	<0.001	0.022	<0.001	<0.001	<0.001	<0.001	<0.001
Transformation		NT ^¶	NT	SQRT	SQRT	NT	SQRT	NT	log10	SQRT	NT

^† Bd = bulk density, WSA = water-stable aggregates, EC = electrical conductivity, MBC = microbial biomass carbon, PMN = potentially mineralizable nitrogen, SOC = soil organic carbon, BG = β-glucosidase activity. ^‡ Extractable P and K were determined using Mehlich-3 extraction for fields J-30, J-10, R-pasture, R-7, R-17, and B, while Olsen extraction was used for fields C-south and C-north, based on soil pH values. ^¥ ND = not determined. ^¶ NT = no transformation; SQRT = square root.

Table 3. Soil Management Assessment Framework (SMAF) soil health indicator scores from the investigated fields. The values inside parentheses represent the standard error of the mean (n = 6). Lowercase letters, within a given column for either indicators or indicator scores, represent significant differences between means, as determined using a Tukey-adjusted pairwise comparison test at significant values of p < 0.05 from ANOVA and are shown in bold. All data represent raw values. The transformations used are presented when the data were not normally distributed.

Sites	Physical Indicator Sores		Chemical Indicator Scores		---------------- Biological Indicator Scores ----------------				Nutrient Indicator Scores
	Bd ^†	WSA	pH	EC	SOC	MBC	PMN	BG	Extractable P	Extractable K
J-30	0.61 (0.08) abc	0.99 (0.01) a	0.97 (0.01) a	1.00 (0.00)	0.14 (0.11)	0.03 (0.00) b	0.18 (0.06) c	0.04 (0.00) de	1.00 (0.00) a	1.00 (0.00) a
J-10	0.72 (0.07) ab	0.93 (0.02) a	0.98 (0.01) a	1.00 (0.00)	0.16 (0.10)	0.04 (0.00) b	0.40 (0.15) bc	0.05 (0.01) bcde	1.00 (0.00) a	1.00 (0.00) a
R-pasture	0.81 (0.05) a	1.00 (0.00) a	0.89 (0.06) a	1.00 (0.00)	0.45 (0.28)	0.44 (0.09) a	0.98 (0.01) a	0.13 (0.05) ab	1.00 (0.00) a	1.00 (0.00) a
R-D7	0.52 (0.10) abc	0.93 (0.03) a	0.94 (0.03) a	1.00 (0.00)	0.30 (0.34)	0.07 (0.01) c	0.41 (0.08) bc	0.06 (0.01) abc	0.94 (0.03) ab	1.00 (0.00) a
R-D17	0.68 (0.07) abc	0.93 (0.02) a	0.91 (0.01) a	1.00 (0.00)	0.12 (0.03)	0.07 (0.00) b	0.92 (0.03) a	0.03 (0.00) e	1.00 (0.00) a	1.00 (0.00) a
B	0.55 (0.10) abc	0.59 (0.07) b	0.95 (0.02) a	1.00 (0.00)	0.17 (0.03)	0.07 (0.02) b	0.99 (0.01) a	0.04 (0.01) cde	1.00 (0.00) a	1.00 (0.00) a
C-south	0.40 (0.11) bc	0.98 (0.02) a	0.70 (0.08) b	1.00 (0.00)	0.21 (0.12)	0.38 (0.12) a	0.77 (0.13) ab	0.06 (0.01) bcd	0.92 (0.04) b	0.99 (0.00) b
C-north	0.35 (0.03) c	0.92 (0.03) a	0.67 (0.05) b	1.00 (0.00)	0.26 (0.12)	0.39 (0.08) a	0.93 (0.03) a	0.15 (0.01) a	1.00 (0.00) a	1.00 (0.00) a
ANOVA p-Value	0.0011	<0.001	<0.001	0.446	0.098	<0.001	<0.001	<0.001	0.0021	0.033
Transformation	SQRT	NT ^¶	NT	NT	log10	NT	NT	Reciprocal	NT	NT

Table 4. Soil Management Assessment Framework (SMAF) overall soil health score (SHS) values from the investigated fields. The values in the parentheses present the standard error of the mean (n = 6). Lowercase letters, within a given column, represent significant differences between means as determined using a Tukey-adjusted pairwise comparison test at significant values of p < 0.05 from ANOVA and are shown in bold. All data represent raw values. The transformations used are presented when the data were not normally distributed.

Sites	Physical SHS	Chemical SHS	Biological SHS	Nutrient SHS	Overall SHS
J-30	0.80 (0.04) ab	0.99 (0.01) a	0.10 (0.03) e	1.00 (0.00) a	0.60 (0.01) b
J-10	0.82 (0.03) ab	0.99 (0.01) a	0.16 (0.04) de	1.00 (0.00) a	0.62 (0.02) b
R-pasture	0.90 (0.03) a	0.97 (0.03) a	0.42 (0.06) a	1.00 (0.00) a	0.77 (0.03) a
R-D7	0.72 (0.05) abc	0.97 (0.02) a	0.20 (0.02) cde	0.97 (0.01) ab	0.62 (0.01) b
R-D17	0.80 (0.04) ab	0.95 (0.01) a	0.28 (0.01) bcd	1.00 (0.00) a	0.67 (0.01) b
B	0.57 (0.06) c	0.95 (0.01) a	0.31 (0.01) bcd	1.00 (0.00) a	0.63 (0.01) b
C-south	0.69 (0.06) bc	0.85 (0.04) b	0.29 (0.03) abc	0.96 (0.02) b	0.64 (0.02) b
C-north	0.64 (0.02) bc	0.83 (0.02) b	0.36 (0.06) ab	1.00 (0.00) a	0.67 (0.01) b
ANOVA p-Value	<0.001	<0.001	<0.001	0.0019	<0.001
Transformation	NA	NA	NA	NA	SQRT ^¶

^¶ SQRT = square root.

Table 5. Means of Haney Soil Health Tool (SHST) indicators (CO₂-C, WEOC, and WEON); soil health scores (SHSs); and nutrients (H3A-P, H3A-K, and Nmin) of the investigated fields. The values in parentheses present the standard error of the mean (n = 6). Lowercase letters, within a given column represent significant differences between means as determined using a Tukey-adjusted pairwise comparison test at significant values of p < 0.05, from ANOVA and are shown in bold. All data represent raw values. The transformations used are presented when the data were not normally distributed.

Sites	CO₂-C ^†	WEOC	WEON	Overall SHS	H3A-P	H3A-K	Nmin
	mg kg⁻¹	mg kg⁻¹	mg kg⁻¹		mg kg⁻¹	mg kg⁻¹	kg ha⁻¹
J-30	26.0(1.7) b	162.8(5.5) a	38.6(1.2) a	16.4(1.4) ab	35.2(5.4) a	294(21) ab	551(37) b
J-10	31.8(2.4) ab	136.0(11.4) ab	32.5(0.7) a	14.4(2.2) abc	27.8(6.4) ab	300(13) ab	312(18) b
R-pasture	68.2(12.1) a	119.4(8.0) bc	35.2(0.9) a	28.7(5.5) a	25.9(3.5) ab	172(13) de	163(29) b
R-D7	35.7(7.9) ab	93.8(4.4) c	39.3(5.7) a	12.9(2.7) bc	23.7(3.4) ab	214(21) cd	280(60) b
R-D17	29.4(5.1) b	102.5(1.9) bc	19.4(4.7) b	6.0(1.8) c	36.5(4.8) a	261(10) bc	169(47) b
B	38.0(8.1) ab	134.0(7.3) ab	33.6(1.6) a	16.9(3.1) ab	38.9(10.4) ab	347(18) a	1332(249) a
C-south	23.1(4.2) b	103.8(8.5) bc	35.3(0.6) a	8.9(2.2) bc	13.1(2.3) b	122(15) e	1573(235) a
C-north	31.7(3.8) b	135.4(8.5) ab	38.3(0.6) a	16.8(3.1) ab	35.9(8.4) ab	244(13) bc	1561(198) a
ANOVA p-Value	0.002	<0.001	<0.001	<0.001	0.025	<0.001	<0.001
Transformation	log	NA	NA	SQRT ^¶	SQRT	NA	NA

^† CO₂-C = Solvita 24 h CO₂-C burst, WEOC = water-extractable organic carbon, WEON = water-extractable organic nitrogen, SHS = soil health score, H3A-P = H3A-extractable phosphorus, H3A-K = H3A-extractable potassium, Nmin = estimated nitrogen mineralization. ^¶ SQRT = square root.

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Hu, X.; Machmuller, M.B.; Blecker, S.W.; Buchanan, C.M.; Aksland, I.B.; Firth, A.G.; Ippolito, J.A. Comparing the Soil Management Assessment Framework to the Haney Soil Health Test Across Managed Agroecosystems. Agronomy 2025, 15, 643. https://doi.org/10.3390/agronomy15030643

AMA Style

Hu X, Machmuller MB, Blecker SW, Buchanan CM, Aksland IB, Firth AG, Ippolito JA. Comparing the Soil Management Assessment Framework to the Haney Soil Health Test Across Managed Agroecosystems. Agronomy. 2025; 15(3):643. https://doi.org/10.3390/agronomy15030643

Chicago/Turabian Style

Hu, Xucheng, Megan B. Machmuller, Steve W. Blecker, Cassidy M. Buchanan, Ian B. Aksland, Alexandra G. Firth, and James A. Ippolito. 2025. "Comparing the Soil Management Assessment Framework to the Haney Soil Health Test Across Managed Agroecosystems" Agronomy 15, no. 3: 643. https://doi.org/10.3390/agronomy15030643

APA Style

Hu, X., Machmuller, M. B., Blecker, S. W., Buchanan, C. M., Aksland, I. B., Firth, A. G., & Ippolito, J. A. (2025). Comparing the Soil Management Assessment Framework to the Haney Soil Health Test Across Managed Agroecosystems. Agronomy, 15(3), 643. https://doi.org/10.3390/agronomy15030643

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