Soil biology for resilient, healthy soil

Abstract

Excerpt
What is a resilient, healthy soil? A resilient soil is capable of recovering from or adapting to stress, and the health of the living/biological component of the soil is crucial for soil resiliency. Soil health is tightly coupled with the concept of soil quality (table 1), and the terms are frequently used interchangeably. The living component of soil or soil biota represents a small fraction (<0.05% dry weight), but it is essential to many soil functions and overall soil quality. Some of these key functions or services for production agriculture are (1) nutrient provision and cycling, (2) pest and pathogen protection, (3) production of growth factors, (4) water availability, and (5) formation of stable aggregates to reduce the risks of soil erosion and increase water infiltration (table 2). Soil resources and their inherent biological communities are the foundation for agricultural production systems that sustain the human population.
The rapidly increasing human population is expanding the demand for food, fiber, feed, and fuel, which is stretching the capacity of the soil resource and contributing to soil degradation. Soil degradation decreases a soil's production capacity to directly supply human demands and decreases a soil's functional capacity to perform numerous critical services, which…

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12A JOURNAL OF SOIL AND WATER CONSERVATION
JAN/FEB 2015—VOL. 70, NO. 1
R. Michael Lehman, Veronica Acosta-Martinez, Jeffrey S. Buyer, Cynthia A. Cambardella, Harold P.
Collins, Thomas F. Ducey, Jonathan J. Halvorson, Virginia L. Jin, Jane M.F. Johnson, Robert J. Kremer,
Jonathan G. Lundgren, Daniel K. Manter, Jude E. Maul, Jeffrey L. Smith, and Diane E. Stott
Soil biology for resilient, healthy soil
doi:10.2489/jswc.70.1.12A
R. Michael Lehman is research microbiologist
at the USDA Agricultural Research Service (ARS)
North Central Agricultural Research Laboratory,
Brookings, South Dakota. Veronica Acosta-
Martinez is a research soil scientist at the USDA
ARS Cropping Systems Research Laboratory,
Lubbock, Texas. Jeffrey S. Buyer is a research
chemist at the USDA ARS Sustainable Agricul-
tural Systems Laboratory, Beltsville, Maryland.
Cynthia A. Cambardella is a research soil sci-
entist at the USDA ARS National Laboratory for
Agriculture and the Environment, Ames, Iowa.
Harold P. Collins is a research soil scientist at
the USDA ARS Grassland Soil and Water Re-
search Laboratory, Temple, Texas. Thomas F.
Ducey is a research microbiologist at the USDA
ARS Coastal Plain Soil, Water and Plant Conser-
vation Research Laboratory, Florence, South
Carolina. Jonathan J. Halvorson is a research
soil scientist at the USDA ARS Northern Great
Plains Research Laboratory, Mandan, North Da-
kota. Virginia L. Jin is a research soil scientist at
the USDA ARS Agroecosystem Management Re-
search Unit, Lincoln, Nebraska. Jane M.F. John-
son is a research soil scientist at the USDA ARS
North Central Soil Conservation Research Lab,
Morris, Minnesota. Robert J. Kremer (retired)
is a research microbiologist at the USDA ARS
Cropping Systems and Water Quality Research
Laboratory, Columbia Missouri. Jonathan G.
Lundgren is a research plant physiologist at
the USDA Agricultural Research Service (ARS)
North Central Agricultural Research Laboratory,
Brookings, South Dakota. Daniel K. Manter is
a research soil scientist at the USDA ARS Soil,
Plant, and Nutrient Research Laboratory, Fort
Collins, Colorado. Jude E. Maul is a research
chemist at the USDA ARS Sustainable Agricul-
tural Systems Laboratory, Beltsville, Maryland.
Jeffrey L. Smith (deceased) was a research soil
scientist at the USDA ARS Land Management
and Water Conservation Research Laboratory,
Pullman, Washington. Diane E. Stott is a re-
search soil scientist at the USDA ARS National
Soil Erosion Research Laboratory, West Lafay-
ette, Indiana.
W hat is a resilient, healthy soil?
A resilient soil is capable of
recovering from or adapt-
ing to stress, and the health of the living/
biological component of the soil is cru-
cial for soil resiliency. Soil health is tightly
coupled with the concept of soil quality
(table 1), and the terms are frequently used
interchangeably. The living component of
soil or soil biota represents a small fraction
(<0.05% dry weight), but it is essential to
many soil functions and overall soil qual-
ity. Some of these key functions or services
for production agriculture are (1) nutrient
provision and cycling, (2) pest and patho-
gen protection, (3) production of growth
factors, (4) water availability, and (5) for-
mation of stable aggregates to reduce the
risks of soil erosion and increase water
infiltration (table 2). Soil resources and
their inherent biological communities are
the foundation for agricultural production
systems that sustain the human population.
The rapidly increasing human popula-
tion is expanding the demand for food,
fiber, feed, and fuel, which is stretching the
capacity of the soil resource and contrib-
uting to soil degradation. Soil degradation
decreases a soil’s production capacity
to directly supply human demands and
decreases a soil’s functional capacity to per-
form numerous critical services, which are
valued in trillions of US dollars (Pimental
et al. 1997). The ability to reverse degra-
dation of soil resources and improve soil
services is intimately related to the abil-
ity to promote the biological functioning
or health of the soil. Although this report
primarily considers soil microorganisms,
we fully acknowledge the importance
of higher soil organisms to the mainte-
nance of soil health and provision of soil
services, but leave those phyla to future
discourse. Emerging tools and technolo-
gies have become available to dramatically
advance our understanding of microscopic
soil biota and provide the foundation to
manage soil organisms to enhance primary
productivity, provide multiple ecological
services, rejuvenate soil resilience, and sus-
tain long-term soil resource quality.
RECOGNIZING SOIL MICROBIAL
DIVERSITY AS THE FOUNDATION FOR
SOIL FUNCTION
The soil has long been perceived to harbor
the greatest microbial diversity among all
ecosystems, and advances in analytical and
computational tools have suggested that
approximately one billion bacterial cells,
grouped into 1,000 to 1,000,000 species,
reside in a single gram of soil (Gans et al.
2005; Schloss and Handelsman 2006). The
rate of discovering and characterizing bac-
terial diversity since 1987 is astounding,
growing from a modest 12 phyla to more
than 70 by 2009 (Pace 2009). However,
many of these phyla contain few, if any,
organisms that can be grown and studied in
the laboratory. Within these new phyla are
bacteria that can fix carbon dioxide (CO2)
via multiple pathways not found in plants
(Thauer 2007) and bacteria that gener-
ate energy from sunlight using alternative
light receptors not previously known (Beja
et al. 2000). Given the recency of these
discoveries, it is not surprising that the
contribution of autotrophic soil bacterial
organisms like these to terrestrial carbon
(C) cycle and C sequestration has not been
determined (Trivedi et al. 2013).
FEATURE
Statement Reference
“Soil quality is the capacity of the soil to function.” Karlen et al. (1997)
Soil health is “the continued capacity of soil to function as a vital Doran et al. (1996)
living system, within ecosystem and land-use boundaries, to sustain
biological productivity, maintain the quality of air and water
environments, and promote plant, animal, and human health.”
Assessment of soil quality is usually accomplished through direct Andrews et al. (2004)
measurement of a suite of soil biological, chemical, and physical
properties and processes that have the greatest sensitivity to
changes in soil function.
Table 1
Soil health is often coupled with the concept of soil quality.
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It is now well-established that all life
can be assigned to one of three domains:
Archaea, Bacteria, and Eucarya (Pace 2009)
(figure 1). Eucarya contains fungi and all
visible (and some microscopic) plant and
animal life. Archaea and Bacteria contain
all of the prokaryotes that are commonly
considered “bacteria” that collectively pos-
sess an enormous diversity of physiologies
and environmental tolerances. In a star-
tling example of the rapidly expanding
knowledge of the microbial world, it was
determined in 2006 that members of the
Archaea domain were actually responsible
for most of the nitrification occurring in
some soils, which had for decades been
thought to be performed strictly by a
very limited number of Bacterial gen-
era (Leininger et al. 2006). Members of a
new phylum of bacteria, Acidobacteria,
whose first representative was discovered
in 1991, were virtually unheard of even 15
years ago and are now suspected to be the
numerically dominant organisms in many
soils. However, due to their resistance to
laboratory culturing, there is insufficient
information to establish their functional
roles. An entirely new class of Fungi
(Archaeorhizomycetes) that closely associ-
ate with plants and are ubiquitous in soils
is just now being described, largely based
on a single cultured member (Rosling et
al. 2011). Other recent discoveries, such
as rampant gene exchange within and
between the three domains by multiple
Table 2
Services provided by soil biota and related processes and benefits (Wall et al. 2004; Falkowski et al. 2008; Kowalchuk et al. 2008;
Pritchard 2011).
Soil functions/properties Processes involved Agronomic services Environmental services
Biogeochemical regulation, Carbon, nitrogen, and phosphorus cycles Provide plant nutrients Mitigate atmospheric gases
nutrient retention and delivery Redox reactions Sequester carbon
 Decomposition/humication  Maintain/improvewaterquality
Symbioticandcompensatory Nitrogenxation(bacteria) Provideplantnutrients Maintain/improvewaterquality
associations Nutrient uptake via mycorrhizae (fungi) Enhance water acquisition
Biodegradation/bioremediation of Microbial degradation Reduce pesticide legacy impacts Maintain/improve water quality
wastes, pollutants, and agrochemicals
Pathogen dynamics Host-pathogen interactions Suppress disease Maintain/improve water quality
(regulation and competition)
Soil structure and stability Soil aggregation/porosity Increase aeration Reduce erosion risks
 Buildsoilorganicmatter Reducecompaction Mitigateoodanddrought
  Improvewaterinltration Sequestercarbon
Increase water holding capacity
Weed dynamics Germination and growth Suppress weed germination, Maintain/improve water quality
growth, and persistence
Figure 1
Tree of Life based rRNA gene sequence comparisons (reprinted with permission
from Pace et al. [2009]).
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mechanisms, emphasize the genetic and
functional plasticity of the microscopic
world that exists in soil (Nelson 1999). Gene
exchange has practical implications for
antibiotic resistance (Forsberg et al. 2012)
and also severely complicates attempts to
classify microorganisms, determine their
ecological relationships, and develop useful
models with the predictive power necessary
for management applications.
Advances in analytical and computa-
tional tools have accelerated the rate of
discovery of soil microbial diversity and
enabled renewed efforts to link microbial
community structure to abiotic soil prop-
erties, vegetation, land management, and
climate (figure 2). Because conclusions
often depend on the particular method-
ology selected, the application of multiple
molecular and biochemical assays (table 3)
can be particularly useful. A recent study
exemplifies this multipronged approach
(Maul et al. 2014); these researchers used
quantitative polymerase chain reaction
(qPCR) and terminal restriction fragment
polymorphism (TRFLP) of rRNA genes
and phospholipid fatty acid microbial com-
munity analyses to provide phylum-level
detail of community structure response
to cover crop, mulch, season, and rhizo-
sphere compared to bulk soil. Modern
high throughput DNA sequencing of
soil microorganisms has greatly increased
the ability to characterize the taxonomic
diversity within a particular arable soil
(Acosta-Martinez et al. 2008; Sugiyama
et al. 2010), yet most studies only char-
acterize the dominant taxa (100 to 1,000
species) and provide little insight into the
true genetic diversity and potential pres-
ent in the soil. For example, considering
a typical bacterial genome contains 3,000
to 4,000 genes, the number of microbial
genes present in a single gram of soil may
exceed 1012 genes, or 1,000 terabase pairs
of DNA per gram of soil (Vogel et al.
2009). Assuredly, many great discoveries
and surprises lie ahead.
LINKING SOIL MICROBIAL TAXONOMIC
DIVERSITY TO THEIR FUNCTIONS
Soil microbial structures are frequently
used to infer potential functional changes
within the soil microbial community.
Microbial biomass may contribute signifi-
cantly to observed soil functions because
more organisms carrying out a function
may lead to higher rates of that function.
Although there is an emerging under-
standing of the redundancy that exists
within the soil microbial community gene
pool, it is still unclear if there are (1) a
small number of species that dominate the
transcriptome (collection of all mRNA
transcripts), (2) rare groups that dominate
intermittently based on environmental
conditions, or (3) microbial consortia that
express genes in a coordinated fashion
resulting in observed microbial com-
munity functionality. Linking microbial
composition and biomass (e.g., who and
how many) to analysis of soil microbial
gene expression will be key to unravel-
ing the regulation of soil functions that are
desirable in agroecosystems.
It has often been assumed that changes
in the phylogenetic community structure
lead to changes in soil functionality as a
result of differential niche specializations
that have evolved among phyla. For exam-
ple, certain functions can be associated with
particular genera or species, (e.g., nitrogen
[N] fixation). As more genomic informa-
tion is collected within each phylogenetic
clade, however, it is becoming clear that
functional redundancy is most likely the
norm among widely divergent microbial
groups (Allison and Martiny 2008; Ollivier
et al. 2012). Although individuals within a
species or genera may all contain genes to
carry out a specific function, it is rare that a
specific function is exclusively maintained
within only a single genera or species. This
Figure 2
Selected factors affecting soil functions and the provision of ecosystem services. The arrow represents interactions between fac-
tors and within each factor.
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Method Notes Benetstosoilproduction Advantages Disadvantages
CommunityDNAngerprintingmethods
(Automated)ribosomalintergenic DNAproles/patterns Diversityhasbeenused Highthroughput,costeffec- Subjecttooverestimation
spacer analysis ([A]RISA) generated for each to assess soil health. tive, low technical demand. of species richness.
AmpliedribosomalDNA bacterialcommunity. Microbialcommunity Costeffective,low Mosteffectivetosubtype
restriction analysis (ARDRA) Data is generated as responses to changing technical demand, no individual species due to
polymerase chain soil conditions can also specialized equipment generating multiple
reaction amplicons or serve as a determinant necessary. bands per species.
Length-heterogeneity polymerase fragments separated of soil health. High High throughput, highly Limited database support.
chain reaction (LH-PCR) by size (e.g., T-RFLP) throughput, low cost, reproducible, cost effective.
(Denaturing/temperature) gradient or sequence (e.g., DGGE). and reproducible nature Fragments can be extracted Variability between gels/
gelelectrophoreses([D/T]GGE) Somemethodsallow ofseveralngerprinting andsequenced. experimentsmakesgel
 fordownstream methodsmakethem  togelcomparisonsdifcult.
Randomampliedpolymorphic processing(e.g.,DGGE) suitableforlong-term Rapid,highthroughput, Randomnatureofampli-
DNA(RAPD) formethodssuchas monitoringofsoils.Can costeffective,low cationcouldbeaffected
DNA sequencing. be used for monitoring technical demand. by DNA quality resulting
Generally good for large areas and could be in low reproducibility.
Single-strand conformation comparing community considered as potential Fragments can be extracted Reannealing of DNA strands
polymorphism (SSCP) structure, possibly standardized soil tests. and sequenced. Can identify can increase number
diversity. Require new mutations. of bands. Heteroduplex
specialized software DNA can be formed.
Terminal-restriction fragment for post-run analysis Rapid, high throughput, Fragments cannot be
length polymorphism (T-RFLP) and comparison. cost effective, method sequenced, distinct
can be applied to microbial groups may share
   multiplegenetargets. prole. 
Sequence-based methods
Clone libraries Provides DNA (e.g., Provide insight into the Low degree of specialized Time consuming, lower
pyrosequence) or RNA following soil microbial equipment required. throughput than
(e.g.,metatranscriptomics) characteristics: pyrosequencing methods,
sequence either directly abundances (e.g., cloning biases.
Small subunit (SSU) rDNA/rRNA or based on qPCR); diversity (e.g., Low cost per base pair rDNA generally fails to
pyrosequencing hybridization (e.g., 16S rRNA); and of sequence. SSU rRNA distinguish between
microarray). Can allow potential microbial universally found, and microbes actively growing,
for microbial activity (e.g., contains conserved dead, or in stasis. High
 identicationdownto metatranscriptomics, regionsthatallowfor equipmentcosts.Bias
genus and species levels. qRT-PCR). Information phylogenetic discrimination. with DNA/RNA extraction
Provide excellent can be measured both methods and SSU
 estimatesofmicrobial temporallyand  rDNA/rRNAamplication.
Metagenomics activity, biomass, and spatially, allowing for Provides insight into meta- Costly to achieve high
diversity. If not correlations with bolic pathways of entire coverage rates of microbial
contracted to an outside environmental microbial community. Can community. Does not indicate
party, these techniques conditions. Depending result in comp lete which spec ies are active or
come with considerable on the method, data sequencing of previously in stasis. Data analysis is
 start-upcostsfor generatedcanbevery unidentiedanduncultured complex,computer
 equipmentandreagents, specic(e.g.,qPCR)or microbialspecies.No intensive,andtime
though their high broad in nature. In- reliance on known sequences. consuming.
Metatranscriptomics throughput, big data, depth analysis of soil Provides information on gene In bacteria, rRNA accounts
 naturegenerallytendsto microbialsystemsnot expressionprolesof for95%oftotalRNA,
reduce costs on a per provided by other bacterial community at bacterial rRNA removal
 basepairrate.Fora techniquesandcan timeofsampling, difcultandintroduces 
number of these help identify management indicating potential responses biases. Based on assump-
 methods,bioinformatics practicesthatarebenecial toenvironmentalcues.As tionsthatRNAwillbe
can be a bottleneck. or deleterious to microbial with metagenomics, sequence translated into protein
communities. can be unknown beforehand. and subsequently activity.
Table 3
Techniques for soil microbial ecology analysis (Hill et al. 2000; Hirsch et al. 2010; Rincon-Florez et al. 2013).
Table 3 Continued
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is, in part, due to (1) relatively quick gen-
eration times, which allow for adaptation
to environmental variation; (2) ability for
many microbes to carry out conjugation
and the passage of plasmid-borne genes
and elements among individuals; and (3)
genetic competence, which enables hori-
zontal gene transfer across different genera
and facilitates uptake and genomic inte-
gration of exogenous DNA. As a result,
the distribution of many functional traits
across unrelated taxa creates questions as
to the accuracy of using microbial com-
munity phylogenetic (structural) data to
infer functional changes within a particu-
lar community. But, true in situ functional
measurements of specific soil microbial
activities are quite elusive, as the act of
making a measurement or collecting a
sample alters microbial activities.
Despite these challenges, the relation-
ship between soil microbial community
structure and function and whether they
respond in unison to their local envi-
ronment determines the best approaches
to gauge management effects on the
collective function of soil microbial com-
munities. Ecological hypotheses regarding
the biogeography of soil microorgan-
isms and the potential for endemic soil
microbial populations have been used to
examine community structure-function
relationships and evaluate functional
redundancy. A combination of high
throughput DNA sequencing and enzyme
activity were applied to soil fungal com-
munities in a study that spanned local
and continental scales (Talbot et al. 2014).
These researchers found that some fungi
were endemic (unique) to certain locales,
while overall community function was
similar across all the sites. Wholesale
quantification of soil microbial allele
frequency (gDNA) and transcript abun-
dance (mRNA)—an inventory of genetic
potential and activity generally known as
“metagenomics”—has also been used to
address this same question. In contrast to
the findings of the previous study, these
researchers found strong correspondence
between functional and structural diversity
in soil microbial communities from glob-
ally distributed sites (Fierer et al. 2012). In
a separate metagenomic study of prairie
soil bacterial communities, a single phy-
lum, Verrucomicrobia, was responsible for
much of the biogeographical variation
observed and provided evidence in oppo-
sition to functional redundancy (Fierer et
al. 2013). The extent of microbial species
endemism and functional redundancy are
central to measurement of soil health and
resilience, particularly in relation to biodi-
versity (Griffiths and Philippot 2013).
The ability to sequence environmen-
tal DNA to the depth currently available
has changed the questions that can be
posed when exploring the soil microbial
ecosystem. Soil nuclear metagenomes are
being explored as snapshots of varied envi-
ronments, but there are few examples of
replicated sites with a priori agricultural
Table 3 continued
Method Notes Benetstosoilproduction Advantages Disadvantages
Sequence-based methods (continued)
Microarrays   Canbeusedforanalysisof Nonspecichybridization,
DNA or RNA. A large time consuming array
amount of information construction requires
placed on a single array. expensive equipment, target
genes/organisms
determined a priori.
quantitativepolymerasechain   Rapid,reproducible,cost Primerbias,uorescent
reaction (qPCR) (DNA)/ reverse effective, high sensitivity. probe options limit analysis
transcriptase polymerase chain Primer sets can be of to a few targets per assay,
reaction(qRT-PCR)(RNA)   narroworbroadspecicity, targetsbasedonlyon
each microbial gene can known sequences.
serve as a target for study.
Other methods
Culturing Providebiomarker(e.g., Metabolicallyactive Isolatedmicrobesare Lowthroughput,difcult
PLFA, FISH) or soil microbes (except available for additional or impossible to grow
biochemical data (e.g., PLFA). Can study analysis and characterization. many soil microbes.
Communitylevelphysiological community-level specicorganismal Moderatethroughput; Lowerdiscriminatorypower;
proling physiologicalprole interactions(e.g.,FISH) insightsintoheterotrophic oftenbiastowardfaster
[CLPP]) of select microbial and preference for substrate usage; new, improved growing microbes,
species or whole carbon substrates (e.g., platforms are available particularly Biolog.
Fluorescent in situ communities. Not high CLPP). PLFA have data Multiple probes can be Traditional methodology is
hybridization (FISH) throughput in nature, and foundation for used simultaneously. not quantitative. Some
some require comparative studies Highly sensitive, detect probes may not effectively
sophisticated equipment between ecosystems. single cell in complex penetrate certain cells.
for analysis of environments.
Phospholipid fatty acid (PLFA) samples (e.g., FISH). Biomass, community Coarse resolution; lower
structure. throughput, improved with
microplate format.
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management treatments being tested. This
limits evaluations of the environmental or
agricultural management drivers respon-
sible for functional changes within with the
soil microbial community. In a recent break-
through, microarray approaches were used
to measure functional gene abundances in
replicated field plot soils under high input
(i.e., conventional) versus low input (con-
servation) agricultural management (Xue et
al. 2013). These authors made two notable
findings: (a) the abundance of functional
genes for N transformations (denitrification
and ammonification) was closely linked
with independent measures of soil N pools
and fluxes; and (b) functional gene diversity
was significantly higher in the low input
production system compared to the high
input production system.
USING EMERGING KNOWLEDGE AND
ANALYTICAL TOOLS TO IMPROVE SOIL
HEALTH AND RESILIENCE
Ultimately, soil health and resilience will
rely on maintaining functionally diverse,
robust soil biological communities that
support high levels of critical services, sim-
ply by carrying out their life-sustaining
processes. Experimentally, soil biodiver-
sity has been strongly associated with
key ecosystem functions such as decom-
position and nutrient cycling (Wagg et
al. 2014). Some agricultural management
practices can have negative effects on soil
health, while other practices are more
conducive to soil biological health (figure
3). Much of the data that supports exist-
ing soil health assessments of agricultural
management practices is based on bulk
soil measures like biomass, respiration, or
enzyme activity. In a relatively few cases,
specialized organisms such as the obligate
biotropic arbuscular mycorrhizal fungi
(AMF) have been used to demonstrate
positive effects of conservation agricul-
tural practices like cover cropping on soil
health (Lehman et al. 2012). However,
limited knowledge of AMF ecology, fluid
taxonomic assignments, and inadequate
analysis tools currently restrict application
of this specific approach in numerous field
applications. Similarly, there is inadequate
knowledge concerning the soil microbial
consortia responsible for weed (Kremer
and Li 2003) and pathogen (Mendes et
al. 2013) suppressive soils, or the ecology
of plant growth promoting rhizobacteria
(Zahir and Frankenberger 2004) to take
advantage of these biological services.
The challenge at hand is to use the
emerging basic knowledge of soil micro-
bial diversity and modern analytical
tools in the testing of relevant ecological
hypotheses (e.g., endemism and functional
redundancy) under differing agricul-
tural management practices. Since soil
type, climate, and vegetation, and local
management practices are known to influ-
ence soil microbial communities and vary
regionally, research must be performed at
regionally distributed sites by multidis-
ciplinary teams. The known seasonality
effects on soil microbiological dynamics
must be accounted for with temporally
dense sampling schemes. The outcome of
this science will serve as the basis to answer
the following questions that are central to
promoting soil health and resiliency:
1. What are the most useful measures of
soil health?
2. How is soil health linked to manage-
ment decisions, including the use of
biological amendments?
3. What benefits does soil health have for
the individual producer/rancher?
Answering these questions in a scien-
tifically defensible manner will promote
agricultural practices that take full advan-
tage of the services provided by soil biota
while maintaining or improving soil health
and resilience.
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