Introducing the North American project to evaluate soil health ...
21
Received: 16 March 2020 Accepted: 18 March 2020 Published online: 6 May 2020 DOI: 10.1002/agj2.20234 FORUM Introducing the North American project to evaluate soil health measurements Charlotte E. Norris G. Mac Bean Shannon B. Cappellazzi Michael Cope Kelsey L.H. Greub Daniel Liptzin Elizabeth L. Rieke Paul W. Tracy Cristine L.S. Morgan C. Wayne Honeycutt Soil Health Institute, 2803 Slater Road, Suite 115, Morrisville, NC 27560, USA Correspondence Soil Health Institute, 2803 Slater Road, Suite 115, Morrisville, NC 27560, USA. Email: [email protected] Funding information Foundation for Food and Agriculture Research; Samuel Roberts Noble Foundation; General Mills Abstract The North American Project to Evaluate Soil Health Measurements was initiated with the objective to identify widely applicable soil health measurements for evaluation of agricultural management practices intended to improve soil health. More than 20 indicators were chosen for assessment across 120 long-term agricultural research sites spanning from north-central Canada to southern Mexico. The indicators being evalu- ated include common standard measures of soil, but also newer techniques of visible and near-infrared reflectance spectroscopy, a smart phone app, and metagenomics. The aim of using consistent sampling and analytical protocols across selected sites was to provide a database of soil health indicator results that can be used to better understand how land use and management has affected the condition of soil ecosys- tem provisioning for agricultural biomass production and water resources, as well as nutrient and C cycling. The objective of this paper is to provide documentation of the overall design, and methods being employed to identify soil health indicators sensitive across agricultural management practices, pedologies, and geographies. 1 INTRODUCTION There is a growing understanding by farmers, agricultural industry, food and beverage companies, and policymakers that soil management practices need to include goals and mea- sures of long-term environmental sustainability to address contemporary pressures (e.g., climate change, water quality) and to satisfy changing consumer awareness. This awareness Abbreviations: 16S rRNA, 16S ribosomal ribonucleic acid; AWHC, available water holding capacity; CEC, cation exchange capacity; DTPA, diethylenetriaminepentaacetic acid; EC, electrical conductivity; EU, experimental unit; ITS, internal transcribed spacer; K fs , saturated hydraulic conductivity measured in the field; NAPESHM, North American Project to Evaluate Soil Health Measurements; PLFA, phospholipid fatty acid; SAR, sodium adsorption ratio; VisNIR, visible and near-infrared reflectance spectroscopy. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. © 2020 The Authors. Agronomy Journal published by Wiley Periodicals, Inc. on behalf of American Society of Agronomy is driven by the knowledge that our population is projected to reach more than 9.7 billion people by 2050 (United Nations, 2017), substantially increasing pressure on our soil and other natural resources. Soils play an essential role in provisioning ecosystem services including food, fiber, and fuel, being an integral part of water and nutrient cycles, supporting biodi- versity, mitigating and adapting to climate change, and human spiritual and cultural needs. The global pressures on agricul- tural lands are often anthropogenic in origin, for example, cli- mate change, erosion, or land-use change (FAO, 2015; Smith et al., 2016). Therefore, our contemporary issue is how to best manage our agricultural land under these societal chal- lenges to strengthen long-term environmental sustainability and resilience (IPCC, 2019). The most straight-forward road to achieving sustainability is to care for our soil resource through the promotion and maintenance of soil health. Agronomy Journal. 2020;112:3195–3215. wileyonlinelibrary.com/journal/agj2 3195
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Transcript of Introducing the North American project to evaluate soil health ...
Received: 16 March 2020 Accepted: 18 March 2020 Published online: 6 May 2020
DOI: 10.1002/agj2.20234
F O R U M
Introducing the North American project to evaluate soil healthmeasurements
Charlotte E. Norris G. Mac Bean Shannon B. Cappellazzi Michael CopeKelsey L.H. Greub Daniel Liptzin Elizabeth L. Rieke Paul W. TracyCristine L.S. Morgan C. Wayne Honeycutt
Soil Health Institute, 2803 Slater Road, Suite115, Morrisville, NC 27560, USA
CorrespondenceSoil Health Institute, 2803 Slater Road, Suite115, Morrisville, NC 27560, USA.Email: [email protected]
Funding informationFoundation for Food and Agriculture Research;Samuel Roberts Noble Foundation; GeneralMills
AbstractThe North American Project to Evaluate Soil Health Measurements was initiated with
the objective to identify widely applicable soil health measurements for evaluation
of agricultural management practices intended to improve soil health. More than 20
indicators were chosen for assessment across 120 long-term agricultural research sites
spanning from north-central Canada to southern Mexico. The indicators being evalu-
ated include common standard measures of soil, but also newer techniques of visible
and near-infrared reflectance spectroscopy, a smart phone app, and metagenomics.
The aim of using consistent sampling and analytical protocols across selected sites
was to provide a database of soil health indicator results that can be used to better
understand how land use and management has affected the condition of soil ecosys-
tem provisioning for agricultural biomass production and water resources, as well as
nutrient and C cycling. The objective of this paper is to provide documentation of the
overall design, and methods being employed to identify soil health indicators sensitive
across agricultural management practices, pedologies, and geographies.
1 INTRODUCTION
There is a growing understanding by farmers, agriculturalindustry, food and beverage companies, and policymakers thatsoil management practices need to include goals and mea-sures of long-term environmental sustainability to addresscontemporary pressures (e.g., climate change, water quality)and to satisfy changing consumer awareness. This awareness
Abbreviations: 16S rRNA, 16S ribosomal ribonucleic acid; AWHC,available water holding capacity; CEC, cation exchange capacity; DTPA,diethylenetriaminepentaacetic acid; EC, electrical conductivity; EU,experimental unit; ITS, internal transcribed spacer; Kfs, saturated hydraulicconductivity measured in the field; NAPESHM, North American Project toEvaluate Soil Health Measurements; PLFA, phospholipid fatty acid; SAR,sodium adsorption ratio; VisNIR, visible and near-infrared reflectancespectroscopy.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, providedthe original work is properly cited, the use is non-commercial and no modifications or adaptations are made.© 2020 The Authors. Agronomy Journal published by Wiley Periodicals, Inc. on behalf of American Society of Agronomy
is driven by the knowledge that our population is projected toreach more than 9.7 billion people by 2050 (United Nations,2017), substantially increasing pressure on our soil and othernatural resources. Soils play an essential role in provisioningecosystem services including food, fiber, and fuel, being anintegral part of water and nutrient cycles, supporting biodi-versity, mitigating and adapting to climate change, and humanspiritual and cultural needs. The global pressures on agricul-tural lands are often anthropogenic in origin, for example, cli-mate change, erosion, or land-use change (FAO, 2015; Smithet al., 2016). Therefore, our contemporary issue is how tobest manage our agricultural land under these societal chal-lenges to strengthen long-term environmental sustainabilityand resilience (IPCC, 2019). The most straight-forward roadto achieving sustainability is to care for our soil resourcethrough the promotion and maintenance of soil health.
Agronomy Journal. 2020;112:3195–3215. wileyonlinelibrary.com/journal/agj2 3195
3196 NORRIS ET AL.
But what is soil health? The concept of soil health has beengiven various names over the last century, but our understand-ing of the concept has evolved as well. Contemporary defini-tions developed by soil scientists have stated that soil healthis “the continued capacity of soil to function as a vital livingsystem, within ecosystem and land-use boundaries, to sustainbiological productivity, maintain the quality of air and waterenvironments, and promote plant, animal, and human health”(Doran, Sarrantonio, & Liebig, 1996). Kibblewhite, Ritz, andSwift (2008) provided a definition with a stronger agricul-tural context that included capability to produce food and fiberalong with providing other ecosystem services. For the Inter-national Year of Soils, the Food and Agriculture Organiza-tion of the United Nations officially adopted the World SoilCharter (FAO, 2015). Principle 5 of the Charter states “soilhealth management is sustainable if the supporting, provi-sioning, regulating, and cultural services provided by soil aremaintained or enhanced without significantly impairing eitherthe soil functions that enable those services or biodiversity”.Within society, we recognize all definitions. For clarity suc-cinctness, here we use the definition, used by the U.S. Depart-ment of Agriculture Natural Resource Conservation Service(USDA-NRCS), where soil health “is the continued capacityof a soil to function as a vital living ecosystem that sustainsplants, animals, and humans.”
Promotion and maintenance of soil health presents a multi-faceted challenge. The first part of the challenge is determin-ing what we value, or ask, from a specific soil because soilsprovide many ecosystem services, but not all soils can provideall services equally nor simultaneously. Ecosystem services isone framework that classifies the benefits of soils including:a foundation for infrastructure, a cultural heritage, and habitatfor organisms, in addition to the commonly recognized provi-sion of food, fiber, and fuel (FAO, 2015). In agricultural soils,there have often been trade-offs between food production andother ecosystem services. For example, tillage of soil for cropproduction has been identified with decreased organic mat-ter (Post & Mann, 1990), high fertilizer use on croplands isassociated with higher nutrient concentrations in agriculturalwatersheds (Caraco & Cole, 1999), and grazing can increaseerosion in arid rangelands (Jones, 2000). When we addresssoil health, we specifically refer to agricultural soil health asit relates to the ecosystem services of food production, watersupply and regulation, nutrient cycling, and carbon cycling.We assess soil health through the lens of on-farm soil func-tioning for food production (e.g., providing water, nutrients,and physical support for growth) in addition to its off-farmsoil functions (e.g., retention and purification of water, floodregulation, habitat provisioning, and C storage).
A second part of the soil health challenge must also beaddressed. This is understanding and communicating what thecapacity, or inherent ability, of a soil to support the desiredservice is–in other words, defining what is "healthy" for a
Core Ideas• Identification of measurements for analysis of soil
condition.• Identification of long-term soil health agricultural
management research trials.• Continental-scale soil sampling to account for
intrinsic soil properties and climate.• Approach for linking soil properties and manage-
ment history to ecosystem services.
specific soil and its service. This part is reflected in the Doranet al. (1996) definition of soil health which added the quali-fier of “within ecosystem and land-use boundaries”. Becausesoils develop based on the five soil-forming factors (climate,organisms, relief, parent material, and time), their abiotic andbiotic properties will vary across the landscape. A soil’s indi-vidual functions (e.g., nutrient or water cycling) are relative toinherent properties (e.g., parent material or texture) and loca-tion; for that reason, a soil’s health is best evaluated against areference state (e.g., soil genoforms vs. phenoforms [Rossiter& Bouma, 2018]). For example, a soil developing on a sand-stone parent material will always differ from a soil developingon shale in terms of water and nutrient relations. Understand-ing soil health is therefore contextual, and healthy soils do notrepresent identical capacities to function across a landscape. Ahealthy agricultural soil on the north-central plains of NorthAmerica (e.g., Dark Brown Chernozem in Lethbridge, AB,Canada, also known as a Mollisol) is, therefore, inherentlydifferent to a healthy soil in subtropical southeastern NorthAmerica (e.g., Plinthic Kandiudults in Quincy, FL), or a trop-ical soil in southern North America (e.g., Cambisol in SantaDomingo Yanhuitlán, OA, Mexico, also known as an Incepti-sol). This diversity in inherent soil properties leads to ambigu-ity when communicating about soil health–especially in termsof management practices. Lack of clarity in terminology con-founds our understanding and challenges researchers to deter-mine reliable and robust measures for farmers and policymak-ers that promote soil health for agricultural and environmentalsustainability across the landscape.
This is not a new challenge. Humans have recognizedthe inherent variability of soil and managed the resourceaccordingly for centuries (e.g., as reviewed in Doran et al.,1996). There was a period of intense interest and demand byland managers for improved measures of soil health at the endof the 20th century (Doran, 2002). Researchers responded by,not only looking at individual indicators, but also developingintegrated tools to measure soil health (Karlen, Goeser,Veum, & Yost, 2017). These tools integrated several soilproperty measurements that were both easy to assess andwere perceived to be sensitive to changing management
NORRIS ET AL. 3197
practices. Three tools that currently assess soil healthinclude the Soil Health Management Assessment Framework(Andrews, Karlen, & Cambardella, 2004), Haney Soil Test(Haney, Haney, Hossner, & Arnold, 2010), and Cornell’sComprehensive Assessment of Soil Health (Moebius-Clune,Moebius-Clune, Gugino, Idowu, & Schindelbeck, 2017), andeach relies on a specific suite of measures of soil physical,chemical, and biological properties. These three soil healthtools are valuable for their ease in collection, analysis, inter-pretation, and, therefore, in cultivating an awareness of sus-tainable agricultural management practices. However, whenthe tools have been applied to research sites, they do not con-sistently capture improvements in soil health (Chahal & Eerd,2018; Roper, Osmond, Heitman, Wagger, & Reberg-Horton,2017; van Es & Karlen, 2019). Questions remain regardingapplication, ease-of-use, and scope for these metrics.
With global challenges increasing our need for resilientand long-term sustainable soil, there has been a resurgenceof interest in soil health and recent critical assessments ofour understanding of it (e.g., Bünemann, Bongiorno, Bai,Creamer, & Deyn, 2018; Rinot, Levy, Steinberger, Svoray, &Eshel, 2018). What is missing, and is therefore timely and nec-essary, is a large-scale broad assessment of soil health indica-tors, both old and new, across a wide range of soils, climates,and management systems. This project, the North AmericanProject to Evaluate Soil Health Measurements (NAPESHM),aims to provide this assessment. This is a continental-scaleproject using long-term (>10 yr) agricultural experimentresearch sites to develop relationships between changes in soilcondition as a function of soil properties, climate, and man-agement practices–that is, to identify the sensitivity of widelyapplicable soil measures to changes in soil condition from soilhealth management practices. To achieve the project’s over-all objective, four initial objectives of selecting the relevantmeasures, sampling sites, collection of samples, and acqui-sition of analytical data must be met. Here we outline theapproach taken to meet the initial objectives through (a) iden-tifying measurements of interest in soil health; (b) establish-ing partnerships with long-term agricultural experiment fieldsites in Canada, Mexico, and the United States; and (c) devel-oping a soil sample collection protocol for all measures. Thisproject could only be realized through the vision and coopera-tion of Partnering Scientists from across North America whohave volunteered their long-term research sites to be a part ofthe project, and the financial support provided by numerousfunders.
2 PROJECT DESCRIPTION
2.1 Inception
In 2013, the Farm Foundation (established in 1933) and theSamuel Roberts Noble Foundation (established in 1945) ini-
tiated the Soil Renaissance effort. The Soil Renaissance orga-nized several workshops which brought together a commit-tee of scientists from public and private sectors, farmers, fieldconservationists, and soil test laboratories to review appro-priate indicators and measurement techniques for soil health.The professional judgement of these groups assessed differ-ent measures of soil properties and the corresponding analyt-ical methods considered as sensitive to changes in soil health.Based upon that effort, 28 soil measures were selected for thisproject as indicators of soil health (Table 1). Their assessmentcriteria were for the measurement (a) to be applied regionallyand, when taking soil inherent properties into account, appliedacross the continent; (b) have a clear range of responsesbased on desired agricultural goals; and (c) be responsive tovarying management practices. However, this was the idealand, in the final selection, not all measures chosen met thesecriteria. Some additional measures of soil properties werealso included because they hold promise but required fur-ther research. In addition, three existing soil health evalua-tion programs, namely, Soil Health Management AssessmentFramework (Andrews et al., 2004), the Cornell Comprehen-sive Assessment of Soil Health (Moebius-Clune et al., 2017),and the Haney Soil Test (Haney et al., 2010) were selectedfor evaluation (using the analytical methodologies specifiedwithin each program; Table 2).
2.2 Indicators
2.2.1 Measures of soil physical properties
The soil physical properties measured for this project includesoil particle size analysis, aggregate stability, soil water con-tent at field capacity and permanent wilting point, bulk den-sity, and saturated hydraulic conductivity measured in thefield (Kfs) (Table 1). In this context soil particle size distri-bution is an inherent soil property, not a soil health indica-tor. However, particle size is necessary for referencing manysoil health measures. Agricultural production systems rely oninherent soil physical properties (Letey, 1985), especially asthey relate to the capture and storage of water, the creationof habitat for microorganisms and roots, basic plant nutri-ent supply from clay weathering, and transport of solutes andfine particles. However, conflicting results exist when relat-ing soil management practices to these properties (Blevins,Thomas, Smith, Frye, & Cornelius, 1983; Chaudhary, Singh,Pratap, Pratap, & Sharma, 2005; Hill, 1990; Ismail, Blevins,& Frye, 1994; Tormena, Logsdon, & Cherubin, 2016). There-fore, including these measurements as part of this geograph-ically diverse project is expected to refine knowledge of therelationships among management practices and inherent andmanageable soil physical properties. While most soil physicalproperty indicators were measured using standard methods
3198 NORRIS ET AL.
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NORRIS ET AL. 3199
T A B L E 2 Soil health tools included as part of the North American Project to Evaluate Soil Health Measurements, and the measures theyincorporate
Framework Abbreviation Measures included in frameworkIncluded inthis project
Soil ManagementAssessmentFramework
SMAFa
Nematode maturity index
Metabolic quotient determined from soil respiration and microbialbiomass
Bulk density ✓Total organic C ✓Microbial biomass C
Potentially mineralizable N ✓Soil pH ✓Soil test P ✓Macroaggregate stability ✓Soil depth
Available water holding capacity ✓Electrical conductivity ✓Sodium adsorption ratio ✓
ComprehensiveAssessment ofSoil Health
CASHb
Soil texture - modified method utilizing sieves and decanting ✓
Available water capacity by pressure plate ✓Surface hardness by penetrometer
Subsurface hardness by penetrometer
Aggregate stability by rainfall simulator ✓Organic matter by loss on ignition ✓Soil protein by autoclaved citrate extractable protein index ✓Soil respiration by CO2–C analysis following 4-d incubation of moist
soil✓
Active C by colourimetric changes to K permanganate solution ✓Soil pH by 1:2 soil water suspension ✓Basic extractable P, K, Mg, Fe, Zn and enhanced extractable Al, As,
B, Ba, Ca, Cd, Co, Cr, Cu, Mn, Na, Ni, Pb, S, Sr by modifiedMorgan’s solution (ammonium acetate and acetic acid)
✓
Soil Health Tool HANEYc
CO2–C analysis following 24-h incubation of moist soil ✓Water extractable organic C and N ✓Oxalic, malic, and citric acid (H3A) extractable P, K, Mg, Ca, Na, Zn,
Fe, Mn, Cu, S, and Al✓
Total water and H3A extractable NO3–N, NH4–N, and PO4–P ✓aAndrews et al. (2004).bMoebius-Clune et al. (2017).cHaney et al. (2018).
(e.g., the pipette method as explained in Gee & Bauder, 1986;Table 1), several newer approaches are also evaluated. Forexample, aggregate stability is measured using the standard“wet aggregate stability test” (Kemper & Roseneau, 1986),the Cornell wet aggregate stability test (Moebius-Clune et al.,2017), and a soil aggregate stability smartphone application
(i.e., SLAKES; Fajardo, McBratney, Field, & Minasny, 2016).Because the SLAKES method requires only a smartphone,the method is significantly cheaper and more accessible andtherefore a more viable measurement if it performs similarlyto the other methods. Likewise, the two-ponding head methodused to measure hydraulic conductivity (Reynolds & Elrick,
3200 NORRIS ET AL.
1990) was recently modified by using a multi-pressure headapproach (SATURO, Meter Group Inc.) and was selected foruse in this project.
2.2.2 Measures of soil chemical properties
Crop growth responses to soil management are often indicatedby measures of soil chemical properties, such as pH, electricalconductivity (EC), cation exchange capacity (CEC), and avail-able soil nutrients (Corwin et al., 2003; Ghimire, Machado,& Bista, 2017; Maas & Grattan, 1999; Marschner, 2012).These measures of soil condition are a dynamic combinationof inherent properties and management practices (e.g., CECto clay and organic matter content).Therefore, while accu-rately assessing soil condition through measurement of chem-ical properties can be challenging, it is important becausesoil chemical conditions are known to regulate the abundanceand availability of many of the nutrients necessary for cropgrowth, and therefore overall productivity (Marschner, 2012).
This interdependence is evident with nutrient availabilitybecause soil pH changes how nutrients interact with other con-stituents of the soil; therefore, soil pH constrains soil nutri-ent availability. An index of available soil nutrients was firstobtained for this project by extracting nutrients using theMehlich-3 method (Sikora & Moore, 2014). Then, pH mea-surements were used to trigger additional extractions. If thepH was >7.2, an ammonium acetate extraction was used todetermine concentrations of K, Ca, Mg, and Na ions (Knud-sen, Peterson, & Nelson, 1982). Also, if the pH was >7.2for extraction of Fe, Zn, Cu, and Mn ions, a diethylenetri-aminepentaacetic acid (DTPA) solution was used (Lindsay &Norvell, 1978). While the focus was on nutrients, the Mehlich-3 extracts were also analyzed for Al, As, B, Ba, Cd, Co, Cr,Cu, Fe, Mn, Mo, Ni, Pb, S, Sr, and Zn concentrations (Sikora& Moore, 2014). As both CEC and base saturation are cal-culations based upon extractant concentrations (Sumner &Miller, 1996), they too are pH-dependent measurements, sowhen soil pH was below 7.2, Mehlich-3 results were used(Sikora & Moore, 2014); otherwise base saturation was basedon an ammonium acetate extraction (Knudsen et al., 1982). Achemical indicator that is particularly relevant in warm aridregions, is sodium adsorption ratio (SAR), which is measuredwith inductively coupled plasma spectroscopy following sat-urated paste soil water extraction (Miller, Gavlak, & Horneck,2013). Soils with high SAR values are prone to clay disper-sion (Frenkel, Goertzen, & Rhoades, 1978), reduced infiltra-tion (Suarez, Wood, & Lesch, 2008), and diminished aggre-gate stability (Rahimi, Pazira, & Tajik, 2000).
Soil nutrient analyses of P dynamics are inherently dif-ficult to standardize because of variable fixation affinitiesof phosphate based on soil mineralogy and pH (Olsen &Sommers, 1982), as well as the relationships between plant
uptake and phosphate-solubilizing microorganisms (Sharma,Sayyed, Trivedi, & Gobi, 2013). This study used the Mehlich-3 (Mehlich, 1984), modified Morgan’s (Moebius-Clune et al.,2017), and H3A Haney (Haney et al., 2010) extraction meth-ods for all samples, as well as the Olsen (sodium bicar-bonate; Olsen, Cole, Watanabe, & Dean, 1954) extractionprocedure when pH was >7.2. All extractions were quanti-fied using inductively coupled plasma optical/atomic emis-sion spectroscopy (ICP-OES or ICP-AES) (Olsen & Som-mers, 1982; Sikora & Moore, 2014).
2.2.3 Measures of soil biological processes
The C cycle in soils is an emergent property resulting from theactivity of the biological community. Soil organisms feed onplant litter and root exudates to produce CO2 and their activ-ity also produces partially decomposed material and micro-bial waste products that persist in soils through physical orchemical stabilization (Cotrufo, Wallenstein, Boot, Denef, &Paul, 2013). The quantity of this diverse mixture of organicC compounds varies in native soils as a function of climate,soil texture, and topography (Burke et al., 1989). The mostprecise method to quantify soil C is dry combustion (Nelson& Sommers, 1996). There is broad agreement that cultiva-tion decreases soil C (Post & Mann, 1990). Because of thetight linkage between soil C and other environmental benefitslike increasing water holding capacity and infiltration, thereis a strong interest in identifying the practices that increasesoil organic C and soil health more broadly (Reicosky, 2003).However, there is considerable debate about what agronomicpractices actually increase C in soils that have been intensivelymanaged (e.g., Conant, Easter, Paustian, Swan, & Williams,2007, Luo, Wang, & Sun, 2010).
While the pool of soil C reflects inherent site properties thatvary at time scales of millennia (e.g., topography and soil tex-ture), management and climate effects can vary soil propertiesat scales of years to decades. Hence pools and fluxes of C thatvary at shorter time scales (e.g., labile or active fractions) canalso be used to evaluate soil health and may be more sensitiveto change in climate and management. Short-term soil incuba-tion methodologies (i.e., respiration burst tests assessing theamount of CO2 produced as a result of microbial activity fol-lowing a rewetting event) were adopted in both the Haney SoilTest and the Cornell assessment. These specific approaches,performed under standardized conditions, provide insight intothe availability of C and the activity of soil microbes (Zibilske,1994). In addition, the permanganate oxidizable pool of Cmethod developed by Weil, Islam, Stine, Gruver, and Samson-Liebig (2003) can detect differences in the labile soil C poolas a result of management (Culman et al., 2012).
Total N and C are tightly linked in soils, as indicated bythe tight relationship between total C and N (Hartman &
NORRIS ET AL. 3201
Richardson, 2013). Like total C, dry combustion is commonlyused to estimate the amount of total N in a soil (Nelson &Sommers, 1996). Total N is predominantly organic N, andmicrobial mineralization of this N is crucial for providinginorganic N (NO3
−–N and NH4+–N), the predominant form
of N available for plant uptake (Harmsen & Kolenbrander,1965). Quantifying N mineralization in a soil using an anaer-obic incubation method (Bundy & Meisinger, 1994) can esti-mate the capacity of a soil to supply N to plants.
An indirect approach to evaluating C and nutrient cyclingin soils is quantifying extracellular enzyme activity. Instead ofmeasuring fluxes or pools of C and nutrients directly, enzymeassays quantify the potential for reactions in the soil that areintimately associated with elemental cycling. For this project,the enzymes β-glucosidase, N-acetyl-β-D-glucosaminidase,phosphatase, and aryl sulfatase were selected as representa-tive of the C, N, P, and S cycling, respectively. Standard-ized methods that measure potential enzyme activity usingthe same substrate, pH, temperature, and incubation length areemployed to allow for comparisons across soil types (Acosta-Martinez & Tabatabai, 2011, Deng & Popova, 2011, Klose,Bilen, Tabatabai, & Dick, 2011, Tabatabai, 1994). Changes inenzyme activity have been linked to crop rotations, tillage, andfertilizer management (Acosta-Martinez et al., 2011, Chang,Chung, & Tsai, 2007, McDaniel & Grandy, 2016).
2.2.4 Measures of soil microbiologicalcommunities
Increasing monocultures and agricultural intensification areknown to lower soil microbial diversity and biomass relativeto native systems (Niel, Tiemann, & Grandy, 2014; Tsiafouliet al., 2015); therefore, conventionally managed agriculturalsoils may have reductions in functionality. One measure ofsoil microbial community quantity and composition is thebiomarker technique of phospholipid fatty acids (PLFAs)analysis. The PLFA method provides a measure of totalmicrobial biomass, broad categorization of the bacterialcommunity, and has the advantage of selecting for theactive microbial community (Frostegård, Bååth, & Tunlid,1993). Extraction and analysis of soil PLFAs has provedto be sensitive to identifying differences across a varietyof ecosystem types (Brockett, Prescott, & Grayston, 2012;Hannam, Quideau, & Kishchuk, 2006); however, the methodhas shown to be both sensitive (Arcand, Helgason, & Lemke,2016; Kiani et al., 2017) and insensitive (Helgason, Walley,& Germida, 2010) to long-term agricultural managementpractices. A review by Geisseler and Scow (2014) suggestedfurther research on long-term agricultural studies to inves-tigate effects of fertilizers on soil microbial communities inagricultural settings to address the mixed results. In a recentEuropean-scale analysis of soil microbial communities, the
PLFA technique was successful in differentiating land usesacross bio-geographical regions (Francisco, Stone, Creamer,Sousa, & Morais, 2016). For this project, a miniaturizedversion of the standard Bligh–Dyer extraction procedure(Frostegård et al., 1993; Quideau et al., 2016) was selected toallow for greater throughput and cost optimization to handlethe large sample numbers (Buyer & Sasser, 2012).
Currently, no widely accepted genomic indicators of agri-cultural soil health exist. This is primarily a result of a lack ofreadily available targeted amplicon and shotgun metagenomicsequence data from geographically diverse agricultural soils.However, recently published large-scale studies of environ-mental 16S ribosomal ribonucleic acid (16S rRNA) amplicondata have revealed significant statistical differences amongland management practices in stream biofilm communities(Lear et al., 2013) and among soil environments in soil bacte-rial communities (Hermans et al., 2017). In forest ecosystems,changes in soil health due to compaction and organic mat-ter removal resulted in significantly different soil microbialcommunity structure (Hartmann et al., 2012) and the com-munity’s potential ability to decompose organic matter (Car-denas et al., 2015). Comparisons of management practicesdescribed above, combined with results from studies designedto track microbial community changes following implementa-tion of agricultural management practices (e.g., Rieke, Soupir,Moorman, Yang, & Howe, 2018; Soman, Li, Wander, & Kent,2017), lay the groundwork for applying genomic techniques toaddress broadscale soil health.
Three genomic tools were incorporated in NAPESHM toaddress this gap in knowledge; 16S rRNA amplicon sequenc-ing, internal transcribed spacer (ITS) amplicon sequencing,and shotgun metagenomic sequencing. Soil DNA extraction,primer selection, library preparation, and sequencing ampli-fication followed the Earth Microbiome Project protocols(Marotz et al., 2017; Thompson et al., 2017). Incorporationof targeted 16S rRNA (for archaea and bacteria) and ITS(for fungi) amplicon sequencing provides efficient identifica-tion and characterization of soil community members whilethe shotgun metagenomic sequencing complements microbialcommunity analyses with functional genomic information.
2.2.5 Integrative measures of soil physical,chemical, and biological properties
Proximal sensing techniques, such as visible and near-infrared(VisNIR) diffuse reflectance spectroscopy, provide a rapid,non-destructive method of indirectly measuring many soilproperties simultaneously. Visible and near infrared spec-troscopy primarily measures hydrogen and C bonding asso-ciated with silicate clays, and organic and inorganic C.Many properties associated with healthy soil are related tosilicate clay and organic C interactions in soil. Previous
3202 NORRIS ET AL.
literature has shown that VisNIR can be used to estimate sev-eral physical, chemical, and biological indicators of soil health(Veum, Goyne, Kremer, Miles, & Sudduth, 2014, 2015; Vis-carra Rossel, McGlynn, & McBratney, 2006). Veum et al.(2014) used VisNIR to predict enzyme activity (dehydro-genase and phenol oxidase). This proximal sensing methodis also strongly correlated with organic C, total N, and thebiological Soil Management Assessment Framework score(Veum, Sudduth, Kremer, & Kitchen, 2015). Soil propertiescan also be estimated from VisNIR data collected in situ onsoil surfaces and along soil profiles (Ackerson, Ge, & Mor-gan, 2017; Morgan, Waiser, Brown, & Hallmark, 2009).
2.3 Site selection and locations
Teasing out the influence of inherent soil properties, cli-mate, and management activities on soil condition requiresa large collection of soil samples. To address this issue, theSoil Health Institute invited applications from investigators oflong-term agricultural field experiments across North Amer-ica (Partnering Scientists; Table 3) that were under continu-ous, monitored, replicated (when possible), management for10 yr or more. Sites selected included six different soil ordersof varying inherent properties and land management prac-tices. Seven criteria were used to select sites to include inthe study: (a) physical disturbance (e.g., tillage, erosion, orgrazing); (b) cover crops (e.g., grains, legumes, or combina-tions); (c) crop diversity (e.g., crop rotation or pasture speciesdiversity); (d) nutrient management (e.g., addition of differentamendments); (e) water management; (f) geographical loca-tion and diversity; and (g) being part of national networks.In total, 120 long-term experimental sites from across NorthAmerica were selected for the project (Table 3). Experimentsranged geographically from the northern Breton Plots site inAlberta, Canada (Dyck, Robertson, & Puurveen, 2012) to thesouthern Santo Domingo Yanhuitlán site in Oaxaca, Mexico(Fonteyne, 2017) and from the Pacific to the Atlantic Ocean(Figure 1).
2.4 Sample collection
Plots, referred to as experimental units (EUs), were selectedbased on experimental treatment alignment with project cri-teria, regional relevance, and resource constraints. Further,efforts were made to ensure collection of site level replica-tion when available; however, this was not possible for allsites (e.g., n = 1 for 90-yr-old Breton Plots and 131-yr-oldSanborn Field). At sites where all phases of a crop rotationwere present, the priority was to sample the EUs where thedominant cash crop (e.g., corn [Zea mays L.] or spring wheat[Triticum aestivum L.]) would be harvested that season (i.e.,
F I G U R E 1 Geographical illustration of the 120 sites included aspart of the North American Project to Evaluate Soil HealthMeasurements (NAPESHM).
transitioning out of fallow, green manure, etc.). For sites withcover crops, when possible, EUs selected had the cover cropsterminated, but prior to tillage or other disturbance. Avoid-ance of tillage, fertilization, seeding, or any other plot leveldisturbance directly before sampling was a priority in collec-tion of all EUs and was the driving reason for timely sam-ple collection between spring thaw and summer planting atnorthern sites and during the dormant period between crops atsouthern sites. This target was achieved for all EUs collectedin Canada, for 78% in Mexico, and 96% in USA; all remainingEUs were collected within the same growing season.
In each EU, a sharpshooter spade (38- by 15-cm blade) wasused to create six (four when EUs were smaller than ∼30 m2)15- by 15-cm square holes located in a zigzag pattern acrossthe EU (at least 1 m from the plot edge). The exception tothis pattern occurred at three sites when Partnering Scien-tists identified specific locations to match where previous orongoing studies were being sampled within the field. A soilknife was used to remove one slice of soil from three sidesof each hole (one slice per untouched hole edge). Each slicewas 4-cm wide and 1.5-cm thick to provide a uniform volumethroughout the 15-cm depth sampled. These 18 subsampleswere each placed in a labelled plastic bag for a single com-posite sample (bulk soil) and put in a cooler immediately aftersampling. Care was taken to clean sampling equipment withethanol or isopropyl alcohol between treatments with glovesbeing worn to prevent microbial cross-contamination. Whenthere was variation in the field related to plant rows or beds,half of the samples were taken in row and half between rows.After sampling, the bulk soil sample was thoroughly mixedin a container that was sterilized with ethanol or isopropylalcohol; and approximately 400 g of soil was homogenizedafter passing it through an 8-mm sieve. This subsample wasthen shipped in coolers with ice packs for arrival within 5 d
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F I G U R E 2 Frequency counts of soil order, crop type, and management practice of interest included in the North American Project to EvaluateSoil Health Measurements
to the analytical laboratories for genomic, PLFA, enzyme, andHaney Soil Test analyses. The remaining 2.5 kg of soil samplewas split and shipped to other analytical laboratories to arrivewithin a week of collection.
Near four of the sampling holes in each EU, a 7.6-cm diam.,7.6-cm deep bulk density core was collected by driving ametal or plastic core into the mineral soil surface. Two ofthese cores (plastic) were preserved intact for measuring thesoil-held water at field capacity (–33 kPa), while the remain-ing two (metal) were combined for a dried mass bulk densitymeasurement. Again, when rows or beds were present, halfof the cores were taken in row and half between rows. Onceon each EU, a SATURO device (Meter Group) was used tomeasure Kfs. When rows were present this device was placedwithin the plant row or bed.
3 SUMMARY OF SOILCOLLECTION
The first objective of the NAPESHM project was achievedthrough its amassing a comprehensive agricultural soil collec-tion. The NAPESHM EU soil archive is comprised of 2029soil samples from long-term experimental sites which cap-tured a range of climates (Figure 1), management practices(Figure 2), and inherent soil properties (Figure 3). The siteswere spread across a large geographic area representing spa-tially diverse growing conditions from mean annual temper-ature and precipitation of 5.8 ◦C and 384 mm at the Breton
F I G U R E 3 Particle size analysis results for 1722 of 2029 soilsamples collected 0–15-cm deep as part of the North American Projectto Evaluate Soil Health Measurements
Plots (Dyck et al., 2012) to 17.5 ◦C and 827 mm in SantoDomingo Yanhuitlán (Thornton et al., 2018). As would beexpected from an agricultural project, Mollisols were presentfor more than 45% of the 120 sites. However, another 6 of the
NORRIS ET AL. 3211
12 soil orders in U.S. Taxonomy are represented in the project.Sites with grain crops were the most common, but vegetablecrops and grazing operations were also sampled. More than56% of the sites were in row-crop or drilled grain production(Figure 2). Of greatest interest was the captured diversity ofparticle size distribution among the EUs (Figure 3)–an inher-ent soil property believed to be determinant of the extent thatmanagement impacts soil health.
4 OUTLOOK
This project will report baseline data on the influence of pedo-genesis, location, climate, and management history on soilhealth. In addition, the project will determine the utility andsensitivity of more established and newer soil health indica-tors and soil health evaluation programs for their ability todistinguish differences in soil health. The ultimate goal is todevelop the definitive, comprehensive soil health evaluationprogram for North America, including its individual compo-nent soil health measures, associated protocols, and interpre-tations.
To best assess the results of management practices on ouragricultural soil resource, we must first be able to correctlyinterpret how land management practices are affecting soilhealth. The evaluation of 28 indicators across 120 experimen-tal sites in North America is expected to provide the datafor decision-making that supports effective soil health man-agement practices. As mentioned above, the first objective ofthe project to build a soil archive was achieved through col-lection of 1906 of the total 2029 EUs within 6 mo of form-ing the project team. The remaining samples were collectedwithin 10 mo. These latter collections will be compared toadjacent sites collected earlier in the year. If any of this sub-set is determined to be significantly different in soil conditionto the main collection, the data will be used for validation ofresults from the main data set for collections at different timesof the year. Second, laboratory analyses for all measurements(except genomics) of all samples will be complete within ayear of starting the field campaign. Data analysis will followwith presentations and publications of the results commenc-ing within 2 yr of initiating the research team.
Through this analysis, we plan to integrate data becausewe expect insight to come from combining soil measures inways that address various aspects of soil health rather thanof segregating soil properties into discrete units related tophysics, chemistry, and biology. Instead of relying on individ-ual properties, soil health indicators can be aggregated in waysthat relate to soil functions. For example, the abiotic prop-erty of texture impacts the inherent ability of a soil to storewater. However, biotic properties, such as total organic matter,have been known to increase available water holding capacity(AWHC) by as much as 50% for each 1% increase in organic
matter (Hudson, 1994). Additionally, aggregate stability andthe distribution of aggregate sizes influence AWHC and areinfluenced by bacterial exudates that glue particles together,fungal hyphae and roots that push and hold them, chemicalbonding patterns of various nutrients, and whether or not thesoil has been physically disturbed. By focusing on the mea-surements, or indicators, that describe each function we canstart to answer specific questions posed by various stakehold-ers, such as how does a cover crop, compared to other soilhealth promoting practices, affect the ability of soil to storeand deliver water to the next crop?
Beyond the overall goal of the project, more specific pathsof discovery will also be pursued using the data. For exam-ple, in soil hydrology, we will have paired measures (treatmentand control) of Kfs, AWHC, and bulk density to develop pedo-transfer functions necessary for hydrology models that quan-tify off-farm ecosystem services of water quality and quantity.In addition, we will seek to identify subsets of microbial gen-era that are abundant, easily detected, and related to functionalgenes and soil health measures. If identified, these genera mayhelp link microbial communities to soil functions.
ACKNOWLEDGEMENTSThe NAPESHM project is part of a broader effort titled,“Assessing and Expanding Soil Health for Production, Eco-nomic, and Environmental Benefits”. The project is funded bythe Foundation for Food and Agricultural Research (grant ID523926), General Mills, and the Samuel Roberts Noble Foun-dation. The project is a partnership among the Soil HealthInstitute, the Soil Health Partnership, and The Nature Con-servancy. Profound gratitude is extended to each member ofour Partnering Scientists team identified in Table 3. Partner-ing Scientists provided site access, sampling support (labor),and site history information. Many of these scientists continueto provide support in analyses, interpretations, and in otherways. Thank You!
CONFLICT OF INTERESTThe authors declare no conflict of interest.
ORCIDCharlotte E. Norrishttps://orcid.org/0000-0002-6372-9902G. Mac Bean https://orcid.org/0000-0002-2206-3171Shannon B. Cappellazzihttps://orcid.org/0000-0001-7249-9494Michael Cope https://orcid.org/0000-0001-9398-2936Kelsey L.H. Greubhttps://orcid.org/0000-0003-0450-9077Daniel Liptzin https://orcid.org/0000-0002-8243-267XElizabeth L. Rieke https://orcid.org/0000-0003-2287-3884Cristine L.S. Morganhttps://orcid.org/0000-0001-9836-0669
3212 NORRIS ET AL.
R E F E R E N C E SAckerson, J. P., Ge, Y., & Morgan, C. L. S. (2017). Penetrometer-
mounted VisNIR spectroscopy: Application of EPO-PLS to in situVisNIR spectra. Geoderma, 286, 131–138.
Acosta-Martinez, V., Lascano, R., Calderon, F., Booker, J. D., Zobeck, T.M., & Upchurch, D. R. (2011). Dryland cropping systems influencethe microbial biomass and EAs in a semiarid sandy soil. Biology andFertility of Soils, 47, 655–667.
Acosta-Martinez, V., & Tabatabai, M. A. (2011). Phosphorus cycleenzymes. In R. P. Dick (Ed.), Methods of soil enzymology (pp. 161–183). Madison, WI: SSSA. https://doi.org/10.2136/sssabookser9.c8.
Andrews, S. S., Karlen, D. K., & Cambardella, C. A. (2004). The soilmanagement assessment framework: A quantitative soil quality eval-uation method. Soil Science Society of America Journal, 68, 1945–1962.
Arcand, M. M., Helgason, B. L., & Lemke, R. L. (2016). Microbial cropresidue decomposition dynamics in organic and conventionally man-aged soils. Applied Soil Ecology, 107, 347–359. https://doi.org/10.1016/j.apsoil.2016.07.001.
Blake, G. R., & Hartge, K. H. (1986). Bulk density. In A. Klute (Ed.),Methods of soil analysis: Part 1. Physical and mineralogical methods(2nd ed., pp. 363–382). Madison, WI: ASA and SSSA.
Blevins, R. L., Thomas, G. W., Smith, M. S., Frye, W. W., & Cornelius, P.L. (1983). Changes in soil properties after 10 years continuous non-tilled and conventionally tilled corn. Soil and Tillage Research, 3,135–146.
Brockett, B., Prescott, C. E., & Grayston, S. J. (2012). Soil mois-ture is the major factor influencing microbial community structureand enzyme activities across seven biogeoclimatic zones in westernCanada. Soil Biology and Biochemistry, 44, 9–20. https://doi.org/10.1016/j.soilbio.2011.09.003.
Bundy, L. G., & Meisinger, J. J. (1994). Nitrogen availability indices.In R. W. Weaver, S. Angle, P. Bottomley, D. Bezdicek, S. Smith,A. Tabatabai, & A. Wollum (Eds.), Methods of soil analysis. Part2. Microbiological and biochemical properties (pp. 951–984). Madi-son, WI: SSSA.
Bünemann, E. K., Bongiorno, G., Bai, Z., Creamer, R. E., Deyn, G., deGoede, R., … Brussaard, L. (2018). Soil quality–A critical review.Soil Biology and Biochemistry, 120, 105–125. https://doi.org/10.1016/j.soilbio.2018.01.030.
Burke, I. C., Yonker, C. M., Parton, W. J., Cole, C. V., Schimmel, D. S.,& Flach, K. (1989). Texture, climate, and cultivation effects on soilorganic matter content in U.S. grassland soils. Soil Science Societyof America Journal, 53, 800–805. https://doi.org/10.2136/sssaj1989.03615995005300030029x.
Buyer, J. S., & Sasser, M. (2012). High throughput phospholipid fattyacid analysis of soils. Applied Soil Ecology, 61, 127–130. https://doi.org/10.1016/j.apsoil.2012.06.005.
Caraco, N. F., & Cole, J. J. (1999). Human impact on nitrate export: Ananalysis using major world rivers. Ambio, 28, 167–170.
Cardenas, E., Kranabetter, J., Hope, G., Maas, K. R., Hallam, S., &Mohn, W. W. (2015). Forest harvesting reduces the soil metagenomicpotential for biomass decomposition. ISME Journal, 9, 2465–2476.https://doi.org/10.1038/ismej.2015-57.
Chahal, I., & Eerd, V. L. (2018). Evaluation of commercial soil healthtests using a medium-term cover crop experiment in a humid, tem-perate climate. Plant Soil, 427, 351–367. https://doi.org/10.1007/s11104-018-3653-2.
Chang, H., Chung, R., & Tsai, Y. (2007). Effect of different applica-tion rates of organic fertilizer on soil enzyme activity and micro-bial population. Soil Science and Plant Nutrition, 53, 132–140. https://doi.org/10.1111/j.1747-0765.2007.00122x.
Chaudhary, H. K., Singh, S., Pratap, A., Pratap, A., & Sharma, S. (2005).Efficient haploid induction in wheat by using pollen of Imperatacylindrica. Plant Breeding, 132, 155–158.
Conant, R. T., Easter, M., Paustian, K., Swan, A., & Williams, S. (2007).Impact of periodic tillage on soil C stocks: A synthesis. Soil andTillage Research, 95, 1–10. https://doi.org/10.1016/j.still.2006.12.006
Corwin, D. L., & Lesch, S. M. (2003). Application of soil electrical con-ductivity of precision agriculture. Agronomy Journal, 95, 455–471.https://doi.org/10.2134/agronj2003.4550.
Cotrufo, M. F., Wallenstein, M. D., Boot, C. M., Denef, K., & Paul,E. (2013). The microbial efficiency-matrix stabilization (MEMS)framework integrates plant litter decomposition with soil organicmatter stabilization: Do labile plant inputs form stable soil organicmatter? Global Change Biology, 19, 988–995.
Culman, S. W., Snapp, S. S., Freeman, M. A., Schipanski, M. E., Benis-ton, J., Lai, R., … Wander, M. M. (2012). Permanganate oxidizablecarbon reflects a processed soil fraction that is sensitive to manage-ment. Soil Science Society of America Journal, 76, 494–504. https://doi.org/10.2136/sssj2011.0286.
Deng, S., & Popova, I. (2011). Carbohydrate hydrolases. In R. P. Dick(Ed.), Methods of soil enzymology (pp. 185–209). Madison, WI:SSSA.
Doran, J. W. (2002). Soil health and global sustainability: Translatingscience into practice. Agriculture, Ecosystems & Environment, 88,119–127. https://doi.org/10.1016/S0167-8809(01)00246-8.
Doran, J. W., Sarrantonio, M., & Liebig, M. A. (1996). Soil health andsustainability. Advances in Agronomy, 56, 1–54. https://doi.org/10.1016/s0065-2113(08)60178-9.
Dyck, M., Robertson, J., & Puurveen, D. (2012). The University ofAlberta Breton Plots. Prairie Soils & Crops Journal, 5, 96–115.
Fajardo, M., McBratney, A. B., Field, D. J., & Minasny, B.(2016). Soil slaking assessment using image recognition. Soil andTillage Research, 163, 119–129. https://doi.org/10.1016/j_still.2016.05.018.
Fonteyne, S., & Verhulst, N. (2017). Red de Plataformas de InvestigaciónMasAgro. Resultados. PV2016 y OI 2016-17. Mexico City: CIM-MYT.
Food and Agriculture Organization (FAO). (2015). Status of theworld’s soil resources: Main report. FAO, Rome. Retrieved fromhttp://www.fao.org/documents/card/en/c/c6814873-efc3-41db-b7d3-2081a10ede50/
Francisco, R., Stone, D., Creamer, R. E., Sousa, J., & Morais, P. (2016).European scale analysis of phospholipid fatty acid composition ofsoils to establish operating ranges. Applied Soil Ecology, 97, 49–60.https://doi.org/10.1016/j.apsoil.2015.09.001.
Frenkel, H., Goertzen, J. O., & Rhoades, J. D. (1978). Effects of claytype and content, exchangeable sodium percentage and electrolyteconcentration on clay dispersion and hydraulic conductivity. Soil Sci-ence Society of America Journal, 42, 32–39.
Frostegård, A., Bååth, E., & Tunlid, A. (1993). Shifts in the structure ofsoil microbial communities in limed forests as revealed by phospho-lipid fatty acid analysis. Soil Biology and Biochemistry, 25, 723–730.https://doi.org/10.1016/0038-0717(93)90113-p.
NORRIS ET AL. 3213
Gee, G. W., & Bauder, J. W. (1986). Particle-size analysis. In A. Klute(Ed.), Methods of soil analysis. Part 1 (2nd ed., pp. 383–411). Madi-son, WI: ASA and SSSA.
Geisseler, D., & Scow, K. M. (2014). Long-term effects of mineral fer-tilizers on soil microorganisms–A review. Soil Biology and Biochem-istry, 75, 54–63. https://doi.org/10.1016/j.soilbio.2014.03.023.
Ghimire, R., Machado, S., & Bista, P. (2017). Soil pH, soil organicmatter, and crop yields in winter wheat-summer fallow systems.Agronomy Journal, 109, 1–12. https://doi.org/10.2134/agronj2016.08.0462.
Haney, R., Haney, E., Hossner, L., & Arnold, J. (2010). Modificationsto the new soil extractant H3A-1: A multinutrient extractant. Com-munications in Soil Science and Plant Analysis, 41(12), 1513–1523.https://doi.org/10.1080/00103624.2010.482173.
Hannam, K. D., Quideau, S. A., & Kishchuk, B. E. (2006). Forest floormicrobial communities in relation to stand composition and timberharvesting in northern Alberta. Soil Biology and Biochemistry, 38,2565–2575. https://doi.org/10.1016/j.soilbio.2006.03.015.
Harmsen, G. W., & Kolenbrander, G. J. (1965). Soil inorganic nitrogen.In W. V. Bartholomew & F. E. Clark (Eds.), Soil nitrogen (pp. 43–92).Madison, WI: ASA.
Hartman, W. H., & Richardson, C. J. (2013). Differential nutrient limi-tation of soil microbial biomass and metabolic quotients (qCO2): Isthere a biological stoichiometry of soil microbes? PLoS ONE, 8(3),e57127. https://doi.org/10.1371/journal.pone.0057127
Hartmann, M., Howes, C. G., VanInsberghe, D., Yu, H., Bachar, D.,Christen, R., … Mohn, W. W. (2012). Significant and persistentimpact of timber harvesting on soil microbial communities in north-ern coniferous forests. The ISME Journal, 6, 2199–2218.
Helgason, B. L., Walley, F. L., & Germida, J. J. (2010). Long-term no-tillmanagement affects microbial biomass but not community composi-tion in Canadian prairie agroecosystems. Soil Biology and Biochem-istry, 42, 2192–2202.
Hermans, S. M., Buckley, H. L., Case, B. S., Curran-Cournane, F.,Taylor, M., & Lear, G. (2017). Bacteria as emerging indicators ofsoil condition. Applied and Environmental Microbiology, 83, 1–13.https://doi.org/10.1128/AEM.02826-16.
Hill, R. L. (1990). Long-term conventional and no-tillageeffects on selected properties. Soil Science Society of Amer-ica Journal, 54, 161–166. https://doi.org/10.2136/sssaj1990.03615995005400010025x.
Hudson, B. (1994). Soil organic matter and available water capacity.Journal of Soil and Water Conservation, 49, 189–194.
Intergovernmental Panel on Climate Change (IPCC). (2019). Climatechange and land. In P. Zhai, R. Slade, S. Connors, R. van Diemen, M.Ferrat, E. Haughey, … J. Malley (Eds.), An IPCC special report onclimate change, desertification, land degradation, sustainable landmanagement, food security, and greenhouse gas fluxes in terrestrialecosystems. Geneva, Switzerland: IPCC.
Ismail, I., Blevins, R. L., & Frye, W. W. (1994). Long-term no-tillageeffects on soil properties and continuous corn yields. Soil ScienceSociety of America Journal, 58, 193–198.
Jones, A. (2000). Effects of cattle grazing on North America arid ecosys-tems: A quantitative review. Western North American Naturalist, 60,155–164.
Karlen, D. L., Goeser, N. J., Veum, K. S., & Yost, M. A. (2017). On-farm soil health evaluations: Challenges and opportunities. Journal ofSoil and Water Conservation, 72, 26A–31A. https://doi.org/10.2489/jswc.72.2.26a.
Kemper, W. D., & Rosenau, R. C. (1986). Aggregate stability and sizedistribution. In A. Klute (Ed.), Methods of soil analysis: Part 1. Phys-ical and mineralogical methods (2nd ed., pp. 425–442). Madison,WI: ASA and SSSA.
Kiani, M., Hernandez-Ramirez, G., Quideau, S., Smith, E., Janzen, H.,Larney, F. J., & Puurveen, D. (2017). Quantifying sensitive soilquality indicators across contrasting long-term land managementsystems: Crop rotations and nutrient regimes. Agriculture, Ecosys-tems, and Environment, 248, 123–135. https://doi.org/10.1016/j.agee.2017.07.018.
Kibblewhite, M., Ritz, K., & Swift, M. C. (2008). Soil health in agri-cultural systems. Philosophical Transactions of the Royal SocietyB: Biological Sciences, 363, 685–701. https://doi.org/10.1098/rstb.2007.2178.
Klose, S., Bilen, S., Tabatabai, M. A., & Dick, W. A. (2011). Sulfur cycleenzymes. In R. P. Dick (Ed.), Methods of soil enzymology (pp.125–151). Madison, WI: SSSA.
Klute, A. (1986). Water retention: Laboratory methods. In A. Klute (Ed.),Methods of soil analysis: Part 1. Physical and mineralogical methods(2nd ed., pp. 635–662). Madison, WI: ASA and SSSA.
Knudsen, D., Peterson, G. A., & Nelson, D. W. (1982). Lithium, sodium,and potassium. In A. L. Page (Ed.), Methods of soil analysis. Part2. Chemical and microbiological properties (2nd ed., pp. 228–238).Madison, WI: ASA and SSSA.
Lear, G., Washington, V., Neale, M., Case, B., Buckley, H., & Lewis,G. (2013). The biogeography of stream bacteria. Global Ecol-ogy and Biogeography, 22, 544–554. https://doi.org/10.1111/geb.12046.
Letey, J. (1985). Relationship between soil physical properties and cropproduction. In B. A. Stewart (Ed.), Advances in soil science (pp. 277–294). New York: Springer. Retrieved from https://doi.org/10.1007/978-1-4612-5046-3_8.
Lindsay, W. L., & Norvell, W. A. (1978). Development of a DTPA soiltest for zinc, iron, manganese, and copper. Soil Science Society ofAmerica Journal, 42, 421–428.
Luo, Z., Wang, E., & Sun, O. J. (2010). Soil carbon change and itsresponses to agricultural practices in Australian agro-ecosystems: Areview and synthesis. Geoderma, 155, 211–223. https://doi.org/10.1016/j.geoderma.2009.12.012
Maas, E. V., & Grattan, S. R. (1999). Crop yields as affected by salinity.In R. W. Skaggs & J. van Schilfgaarde (Eds.), Agricultural drainage(pp. 55–108). Madison, WI: ASA, CSSA, and SSSA.
Marotz, C., Amir, A., Humphrey, G., Gaffney, J., Gogul, G., & Knight,R. (2017). DNA extraction for streamlined metagenomics of diverseenvironmental samples. Biotechnology, 62, 290–293. https://doi.org/10.2144/000114559.
Marschner, H. (2012). Marshner’s mineral nutrition of higher plants (3rded.). London: Academic Press.
McDaniel, M. D., & Grandy, A. S. (2016). Soil microbial biomass andfunction are altered by 12 years of crop rotation. SOIL, 2, 583–2016.
Mehlich, A. (1984). Mehlich 3 soil test extractant: A modifi-cation of Mehlich 2 extractant. Communications in Soil Sci-ence and Plant Analysis, 15, 1409–1416. https://doi.org/10.1080/00103628409367568.
Miller, R. O., Gavlak, R., & Horneck, D. (2013). Saturated paste extractfor calcium, magnesium, sodium and SAR. Soil, plant and watermethods for the western region( 4th ed., pp. 21–22). Western Coor-dinating Committee on Nutrient Management. Ft. Collins, CO: Col-orado State University.
3214 NORRIS ET AL.
Moebius-Clune, B., Moebius-Clune, D., Gugino, B., Idowu, O., Schin-delbeck, R., Ristow, A. J., … Abawi, G. S. (2017). Comprehensiveassessment of soil health, the Cornell framework (3rd ed.) Ithaca,NY: Cornell University.
Morgan, C. L. S., Waiser, T., Brown, D. J., & Hallmark, C. T. (2009).Simulated in situ characterization of soil organic and inorganic car-bon with visible near-infrared diffuse reflectance spectroscopy. Geo-derma, 151, 249–256.
Nelson, D. W., & Sommers, L. E. (1996). Total carbon, organic carbon,and organic matter. In D. L. Sparks (Ed.), Methods of soil analysis:Part 3. Chemical methods (2nd ed., pp. 961–1010). Madison, WI:ASA.
Niel, M., Tiemann, L., & Grandy, A. (2014). Does agricultural cropdiversity enhance soil microbial biomass and organic matter dynam-ics? A meta-analysis. Ecological Applications, 24, 560–570. https://doi.org/10.1890/13-0616.1.
Olsen, S. R., Cole, C. V., Watanabe, F. S., & Dean, L. A. (1954). Esti-mation of available phosphorus in soils by extraction with sodiumbicarbonate. Circular 939. Washington, DC: USDA.
Olsen, S. R., & Sommers, L. E. (1982). Phosphorus. In A. L. Page, R. H.Miller, & D. R. Keeney (Eds.), Method of soil analysis. Part 2. Chem-ical and microbiological properties (2nd ed., pp. 403–427). Madison,WI: ASA.
Post, W. M., & Mann, L. K. (1990). Changes in soil organic carbon andnitrogen as a result of cultivation. In A. F. Bouwman (Ed.), Soilsand the greenhouse effect (pp. 401–406). New York: John Wiley andSons.
Quideau, S. A., McIntosh, A. C., Norris, C. E., Lloret, E., Swallow, M.J., & Hannam, K. (2016). Extraction and analysis of microbial phos-pholipid fatty acids in soils. Journal of Visualized Experiments, 114,e54360. https://doi.org/10.3791/54360.
Rahimi, H., Pazira, E., & Tajik, F. (2000). Effect of soil organic mat-ter, electrical conductivity, and sodium adsorption ratio on tensilestrength of aggregates. Soil and Tillage Research, 34, 343–360.
Reicosky, D. C. 2003. Tillage-induced CO2 emissions and carbonsequestration: Effect of secondary tillage and compaction. In L.Garcia-Torres, J. Benites, A. Martinez-Vilela, & A. Holgado-Cabrera(Eds.), Conservation agriculture (pp. 291–300). Dordrecht, theNetherlands: Kluwer Academic Publishing.
Reynolds, W. D., & Elrick, D. E. (1990). Ponded infiltration froma single ring: I. Analysis of steady flow. Soil Science Society ofAmerica Journal, 54, 1233–1241. https://doi.org/10.2136/sssaj1990.03615995005400050006x.
Rieke, E. L., Soupir, M. L., Moorman, T. B., Yang, F., & Howe, A.C. (2018). Temporal dynamics of bacterial communities in soil andleachate water after swine manure application. Frontiers in Microbi-ology, 9, 3197. https://doi.org/10.3389/fmicb.2018.03197.
Rinot, O., Levy, G. J., Steinberger, Y., Svoray, T., & Eshel, G. (2018).Soil health assessment: A critical review of current methodologiesand a proposed new approach. Science of the Total Environment, 648,1484–1492. https://doi.org/10.1016/j.scitotenv.2018.08.259.
Roper, W. R., Osmond, D. L., Heitman, J. L., Wagger, M. G., & Reberg-Horton, C. S. (2017). Soil health indicators do not differentiateamong agronomic management systems in North Carolina soils. SoilScience Society of America Journal, 81, 828. https://doi.org/10.2136/sssaj2016.12.0400.
Rossiter, D., & Bouma, J. (2018). A new look at soil phenoforms–Definition, identification, mapping. Geoderma, 314, 113–121.
Sharma, S. B., Sayyed, R. Z., Trivedi, M. H., & Gobi, T. A. (2013).Phosphate solubilizing microbes: Sustainable approach for manag-ing phosphorus deficiency in agricultural soils. Springerplus, 2, 587.https://doi.org/10.1186/2193-1801-2-587.
Sikora, F. S., & Moore, K. (2014). Soil test methods from the southeasternUnited States. Southern Cooperative Series Bulletin 419. Clemson,SC: Clemson University.
Smith, P., House, J. I., Bustamante, M., Sobocká, J., Harper, R., Pan,G., … Pugh, T. A. (2016). Global change pressures on soils fromland use and management. Global Change Biology, 22, 1008–1028.https://doi.org/10.1111/gcb.13068.
Soman, C., Li, D., Wander, M.M., & Kent, A. D. (2017). Long-termfertilizer and crop-rotation treatments differentially affect soil bac-terial community structure. Plant Soil, 413, 145–159https://doi.org/10.1007/s11104-016-3083-y
Suarez, D. L., Wood, J. D., & Lesch, S. M. (2008). Infiltration intocropped soils: Effect of rain and sodium adsorption ratio-impactedirrigation water. Journal of Environmental Quality, 37, S169–S179.
Sumner, M. E., & Miller, W. P. (1996). Cation exchange capacity andexchange coefficients. In D. L. Sparks (Ed.), Methods of soil analysis:Part 3. Chemical methods (pp. 1201–1229). Madison, WI: SSSA.
Tabatabai, M. A. (1994). Soil enzymes. In R. W. Weaver, S. Angle, P.Bottomley, D. Bezdicek, S. Smith, A. Tabatabai, & A. Wollum (Eds.),Methods of soil analysis: Part 2. Microbiological and biochemicalproperties (pp. 775–833). Madison, WI: SSSA.
Thomas, G. W. (1996). Soil pH and soil acidity. In D. L. Sparks (Ed.),Methods of soil analysis: Part 3. Chemical methods (pp. 474–490).Madison, WI: SSSA.
Thompson, L. R., Sanders, J. G., McDonald, D., Amir, A., Jansson, J.K., Gilbert, J. A., & Knight, R. & The Earth Microbiome ProjectConsortium.(2017) A communal catalogue reveals Earth’s multiscalemicrobial diversity. Nature (London), 551, 457–463.
Thornton, P. E., Thornton, M. M., Mayer, B. W., Wei, Y., Devarakonda,R., Vose, R. S., & Cook, R. B. (2018). Daymet: Daily surface weatherdata on a 1-km grid for North America, Version 3. Oak Ridge, TN:Oak Ridge National Library Distributed Active Archive Center. https://doi.org/10.3334/ORNLDAAC/1328
Tormena, C. A., Logsdon, D. L. K.S., & Cherubin, M. R. (2016). Visualsoil structure effects of tillage and corn stover harvest in Iowa. SoilScience Society of America Journal, 80, 720–726. https://doi.org/10.2136/sssaj2015.12.0425.
Tsiafouli, M. A., Thébault, E., Sgardelis, S. P., Ruiter, P. C., Putten, W.H., Birkhofer, W. H., … Hedlund, K. (2015). Intensive agriculturereduces soil biodiversity across Europe. Global Change Biology, 21,973–985. https://doi.org/10.1111/gcb.12752.
United Nations. (2017). World population prospects: The 2017 revi-sions. New York: United Nations Department of Economic and SocialAffairs.
van Es, H. M., & Karlen, D. L. (2019). Reanalysis validates soil healthindicator sensitivity and correlation with long-term crop yields. SoilScience Society of America Journal, 89, 366–378.
Veum, K. S., Goyne, K. W., Kremer, R. J., Miles, R. J., & Sudduth, K.A. (2014). Biological indicators of soil quality and soil organic mat-ter characteristics in an agricultural management continuum. Biogeo-chemistry, 117, 81–99.
Veum, K. S., Sudduth, K. E., Kremer, R. J., & Kitchen, N. R. (2015).Estimating a soil quality index with VNIR reflectance spectroscopy.Soil Science Society of America Journal, 79, 637–649.
NORRIS ET AL. 3215
Viscarra Rossel, R. A., McGlynn, R. N., & McBratney, A. B. (2006).Determining the composition of mineral-organic mixes using UV-vis-NIR diffuse reflectance spectroscopy. Geoderma, 137, 70–82.https://doi.org/10.1016/j.geoderma.2006.07.004.
Weil, R., Islam, K. R., Stine, M. A., Gruver, J. B., & Samson-Liebig,S. E. (2003). Estimating active carbon for soil quality assessment:A simple method for laboratory and field use. American Journal ofAlternative Agriculture, 18, 3–17.
Zibilske, L. (1994). Carbon mineralization. In R. W. Weaver, S. Angle, P.Bottomley, D. Bezdicek, S. Smith, A. Tabatabai, & A. Wollum (Eds.),
Methods of soil analysis: Part 2. Microbiological and biochemicalproperties (pp. 835–863). Madison, WI: SSSA.
How to cite this article: Norris CE, Bean GM,Cappellazzi SB, et al. Introducing the North Americanproject to evaluate soil health measurements.Agronomy Journal. 2020;112:3195–3215.https://doi.org/10.1002/agj2.20234
