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V. Soil Organic Matter as a Candidate Environmental Indicator

V. Soil Organic Matter as a Candidate Environmental Indicator

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cluding litter, light fraction, microbial biomass, water-soluble organics, and stabilized organic matter (humus)” will be used in this text.

In general, SOM content is positively correlated with positive soil status

(Reeves, 1997; Bauer and Black, 1994). The methodology for SOM measurement

is established and in widespread use (Nelson and Sommers, 1996). With proper

techniques, the measurement of SOM is very precise. In comparison to many soil

measures, SOM content has small spatial and short-term temporal variability.

There is a large body of basic and applied research focused on SOM character and

the relationship between SOM and agroecosystem properties.

The SOM capacity of a soil is dependent on other soil properties such as soil

texture. Therefore, changes in SOM content must be interpreted in the context of

site-specific variables. SOM is not a comprehensive indicator. For example, SOM

content is not an accurate indicator of excess salinity and poor drainage. In addition, many larger scale parameters, such as land conversion and rural economic

prosperity, cannot be quantified with the SOM indicator.



A. THE FUNCTIONS OF SOM IN SOILS

SOM plays a mostly beneficial role in determining the biological, physical,

and chemical qualities of a soil (Stevenson, 1994, 1986). SOM is a nutrient and

energy source for soil organisms and a nutrient source for plants. It improves soil

structure, strengthens soil aggregates, increases water retention, chelates metals,

buffers the soil pH, interacts with xenobiotics, and retains cations and anions in

the soil system.

The positive relationship between SOM content and crop yield has been observed in many agroecosystems (Reeves, 1997). Bauer and Black (1994) correlated an increase of 15.6 kg haϪ1 of wheat grain yield to a 1 Mg haϪ1 increase of

SOM in the northern Great Plains. The influences of increased SOM on soil

physical properties tends to reduce runoff and erodibility. This may be a result of

increased infiltration and percolation rates, water-holding capacity, aggregate

strength, and crusting resistance.

The relationships described previously between SOM and soil properties are not

necessarily causative. For example, SOM is correlated with the clay content of a

soil, making it difficult to separate the effects of SOM and clay on soil properties.



B. SOM AND ATMOSPHERIC CARBON

The carbon component of SOM is a dynamic pool in the global carbon cycle

and forms the largest terrestrial carbon pool (Paul and Clark, 1996). Kern and

Johnson (1993) sought to estimate the potential effect of conservation tillage on



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87



U.S. terrestrial carbon pools. They reviewed 17 studies that compared SOM contents in conventional tillage and no-tillage systems. They estimated that the increase in SOM carbon that would result from the adoption of no-till agriculture on

76% of the planted cropland in the United States would be equivalent to 0.7–1.1%

of the total projected U.S. fossil fuel carbon emissions for the next 30 years. The

authors assumed that the effects of tillage were not dependent on climatic and soil

variables.



C. FACTORS CONTROLLING THE SOM CONTENT OF SOILS

1. The Factors of Soil Formation

The five factors of soil formation—climate, organisms, relief, parent material,

and time—largely determine the SOM content of soils ( Jenny, 1941; Stevenson,

1994). In general, the degree of impact of the factors of soil formation on SOM

decreases in the order climate Ͼ vegetation Ͼ topography ϭ parent material Ͼ

age for loamy soils in the United States. Climate influences SOM content primarily through temperature and precipitation. Temperature is negatively correlated to SOM content, probably due to increased microbial activity at higher temperatures (Stevenson, 1986). In general, precipitation stimulates plant growth,

thereby increasing organic matter inputs into soils (Stevenson, 1994). Soil moisture changes may decrease or increase SOM decomposition rates, depending on

the balance between water and oxygen availability. An example of the effect of

vegetation on SOM is the greater quantity of SOM found in grassland (Mollisols)

soils than in forest (Alfisols) soils. The mechanisms of this effect may include increased biomass production, nitrification inhibition, decreased aeration, and a

more extensive rhizosphere in grasslands (Stevenson, 1986). Topography influences SOM contents through impacts on microclimate, drainage, and erosion.

Anaerobic conditions in poorly drained soils cause SOM accumulation due to

slow decomposition rates and incomplete catabolism. Erosion tends to remove

SOM-enriched soil (Rasmussen and Collins, 1991). The impact of parent material is primarily through its influence on soil texture (Stevenson, 1986). The positive relationship between SOM content and fine soil texture is well established.

Soil age is most important in young soils, in which SOM accumulation rates typically exceed rates of decay.

2. Management

The tillage of natural lands leads to a loss of SOM (Jenny, 1941; Paustian et al.,

1997). This may be a result of erosion, increased oxygen availability, reduced biomass production, and the disruption of aggregates. Erosion removes the surface



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O. H. SMITH ET AL.



layer, which is frequently the soil with the greatest SOM concentration. In addition, erosion preferentially removes fine soil particles, which are SOM enriched.

Rasmussen and Collins (1991) argue that soil erosion is often overlooked in SOM

field experiments because soil erosion rates are rarely measured and field experiments are usually located on fertile, level to gently sloping land.

In general, management practices that are considered beneficial to the health of

the agroecosystem, such as cover crops, conservation tillage, manure and residue

inputs, and erosion prevention practices, also increase SOM contents. Many agronomic practices that increase yields will also increase biomass production, thereby increasing organic matter inputs. However, there are many exceptions to these

generalizations.

The impacts of management practices on SOM are a result of a complex and interacting set of factors. Most of the research concerning the impacts of management practices on SOM contents has been conducted using long-term field plot

studies. Although much has been learned in these studies (Paustian et al., 1997),

additional research is needed to quantify the interactions between and among the

factors of soil formation and management practices. For example, Campbell et al.

(1999) investigated the impacts of texture in an 11-year comparison of no-till and

conventional tillage practices at three sites in the Brown soil zone in semiarid

southwestern Saskatchewan. They observed that the no-till systems contained

more SOM than conventionally tilled systems only in finer-textured soils.



D. ABSOLUTE AND RELATIVE MEASURES OF SOM

SOM content may be quantified in absolute or relative terms. Absolute measures

are simply the amount of SOM in a system. Relative measures estimate the change

in SOM content over time. The use of absolute measures would require the estimation of SOM baseline conditions, most likely based on the factors of soil formation and past management practices. Although this is an area of major research,

the relationship between the factors of soil formation, management practices, and

SOM contents is not sufficiently understood to make widespread, accurate baseline estimates. In addition, the data set that would be required is not available. For

these reasons, relative measures of SOM content should be used. With sufficient

sampling intensity, a time step of 5 years should be appropriate for most applications. The impacts of management on SOM content are generally detectable after

several years but may take decades.

Relative measures need to be understood in the context of soil variability. For

example, a 10% increase in SOM content in a clay soil and in a sandy soil should

be interpreted differently. These interpretations are a function of the particular use

of the SOM measure. For example, for issues of atmospheric carbon, net changes

should be considered. The topic requires further research.



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89



E. THE MEASUREMENT AND EXPRESSION OF SOM QUANTITIES

There are three standard methods to express SOM quantities: gravimetric, volumetric, and equivalent mass. The gravimetric and volumetric methods may not

adequately account for soil depth and density (Hammer et al., 1995). The simplest

way to express the quantity of SOM in soil is as the mass of carbon or nitrogen per

mass of dry soil (mass basis). This method characterizes the average mass SOM

per mass soil in the sampling depth. Expression of SOM on a mass basis cannot

account for the total quantity of soil present (Doran and Parkin, 1994). For example, this method may not accurately estimate SOM losses due to erosion.

In principle, the solution is to express SOM quantities as a mass carbon or nitrogen per unit area of soil—in effect integrating the mass per unit volume over

sufficient depth to include all the organic matter. However, it is generally impractical to sample to a depth that would include all the organic matter in the system.

A more practical solution is to express SOM quantities per unit volume of soil (volumetric basis). This method is most effective when most of the SOM of the site is

contained in the sampling depth, which is often not the case. This method may

overestimate SOM in high bulk density soils. For example, consider a study that

compares a low and a high bulk density soil of otherwise identical composition.

Both soils will have identical gravimetric SOM contents, but the high bulk density soil will have a greater volumetric SOM content. This is particularly problematic when treatments, such as tillage, affect bulk density.

One solution to this problem is to express the quantity of SOM on an equivalent mass basis (Ellert and Bettany, 1995). This approach adds a step to the volumetric method of calculation. Volumetric measurements are corrected by adjusting the sampling depth to give an equivalent mass. This is done by sampling a

smaller depth in heavier soils or a greater depth in lighter soils. In the example of

a low and a high bulk density soil, a portion of the high bulk density soil would be

removed mathematically, restoring the equivalence in SOM content estimation.

Soil density, particularly that of the surface soil, is variable over short periods of

time. The equivalent mass basis is not sensitive to soil density changes.

Surface residues are rarely included in the quantification of SOM (Paustian et

al., 1997). This underestiimates SOM sequestration in high-residue systems such

as conservation tillage and forests. However, in soils with large SOM contents,

residues account for a small percentage of the total SOM storage of the system.

Carbon storage in residue is more important to consider in lower SOM content

soils.

Typically, SOM concentrations are stratified in soils with the greatest concentration near the surface. The degree of stratification is dependent on soil properties and management. For example, tillage homogenizes SOM in the plow layer.

At the other extreme, forest soils contain a surface layer composed primarily of

organic materials. A vertically stratified sampling system should be used in order



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to estimate SOM distribution. The vertical distribution of SOM is important in understanding SOM function and the causes of soil degradation. In general, sampling

depths should be based on soil horizons as opposed to standard depths. However,

standard depths may be used to divide soil horizons (e.g., 0–2 cm, 2–6 cm, and 6

cm to the bottom of the A horizon).



VI. INDICATOR RANKING

Although SOM as a candidate EI was discussed in particular detail, the caveats

for measuring, determining, and using indicator values apply to any indicator variable that is examined. Furthermore, using a combination of indicators presents special challenges in determining the weight and ranking assigned to each indicator.

Development of indicator rankings should include criteria of indicator appropriateness based on biological relevance, repeatability of measure, integration of effects, existing databases, cost-effectiveness, and other criteria. When does a state

variable become an EI? In an interactive group decision-making process that involves multiple and conflicting criteria, many experts, and many potential outcomes, the Delphi method (Erffmeyer et al., 1986) can be useful in gaining consensus. Characteristics of the Delphi technique include anonymity, controlled

feedback, and statistical group response (Khorramshahgol and Moustakis, 1988).

Once the evaluation criteria are weighted (Table III), each of the indicators can be

ranked (Table IV) according to the evaluation criteria by circulating brief, successive questionnaires among experts in the field, yielding a total numeric score for

each indicator. Table V contains the final indicator rankings, lumped by score

ranges into numeric categories, with 1 being the highest rank.

Applying EIs for regulatory or policy purposes necessitates the use of multiple

criteria decision-making tools such as the Delphi process.

Table III

Example Criteria, with Associated Example Weights, That May

Be Used to Rank Any Indicator

Criteria



Average weight (sums to 100)



Biological relevance

Repeatability

Integrated measurement

Cost-effectiveness

Existing database

Infrastructural support

Etc.



35

20

20

10

10

5





91



ENVIRONMENTAL INDICATORS OF AGROECOSYSTEMS

Table IV



Example Criteria for One Indicator and Its Associated Ranking and Weighted Rank Scores

Criteria relating to

crop productivity indicator

Cost-effectiveness

Biological relevance

Repeatability

Integrated measurement

Existing database

Infrastructural support

Etc.

Total for crop productivity



Rank (0–9)



Criteria weights



3

9

3

4

4

2





10

35

20

20

10

5





Indicator

weighted rank

30

315

60

80

40

10



535



VII. CONCLUSIONS AND RECOMMENDATIONS

Concerns regarding issues of ecology and sustainability of agricultural systems

as well as detrimental environmental impacts from agriculture have resulted in the

development of indicators to estimate environmental trends and conditions. To appropriately describe and evaluate the sustainability of an agroecosystem, a wide



Table V

Matrix of Candidate Agroecosystem Indicators Based

on Potential Rankings

Indicator



Rank



Crop productivity

Soil productivity

Nutrient-holding capacity

Erosion

Contaminants

Microbial status

Irrigation water quantity and quality

Beneficial insect abundance and diversity

Agricultural chemical use

Genetic diversity

Status of biomonitor species

Landscape descriptors

Pest-resistance status

Socioeconomic factors

Etc.



1

1

3

2

3

1

2

2

1

2

1

3

1

3





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O. H. SMITH ET AL.



range of biological, physical, chemical, and economic indicators need to be assessed. The following recommendations can be made with regard to the use of EIs:

• Determine which indicators are the most sensitive to change.

• Develop standards for validating bioindicators as EIs.

• Validate the use of landscape metrics with remote-sensing and GIS products for

monitoring landscape change.

• Use a multiscale approach to indicator validation, with studies targeted to local,

landscape, and regional levels.

• Provide funds for programs designed to improve terrestrial assessment methods

and long-term monitoring of soil biological processes, soil organic matter, crop

diversity, movement of pests and pathogens, ecological impacts of propagating

transgenic crops, changes in resistance to pesticides of insects, weeds, and plant

pathogens.

• Conduct field studies to explore linkages between ecological processes and spatial/temporal patterns in agroecosystems.

• Institute policies to preserve crop and wild crop species diversity, which is critical for maintaining a genetic resource base for future plant breeding programs.

• Amend the USDA NASS questionnaire to include bioindicator information and

other data relevant to USEPA EMAP assessments.



ACKNOWLEDGMENTS

We thank John Chorover, Shelby Fleischer, Heather Karsten, and Andy Rogowski for their constructive criticisms and contributions.



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[1998, February 15]



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