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V. Soil Organic Matter as a Candidate Environmental Indicator
O. H. SMITH ET AL.
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
ENVIRONMENTAL INDICATORS OF AGROECOSYSTEMS
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
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.
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
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
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.
ENVIRONMENTAL INDICATORS OF AGROECOSYSTEMS
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
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
O. H. SMITH ET AL.
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.
Example Criteria, with Associated Example Weights, That May
Be Used to Rank Any Indicator
Average weight (sums to 100)
ENVIRONMENTAL INDICATORS OF AGROECOSYSTEMS
Example Criteria for One Indicator and Its Associated Ranking and Weighted Rank Scores
Criteria relating to
crop productivity indicator
Total for crop productivity
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
Matrix of Candidate Agroecosystem Indicators Based
on Potential Rankings
Irrigation water quantity and quality
Beneﬁcial insect abundance and diversity
Agricultural chemical use
Status of biomonitor species
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
• 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.
We thank John Chorover, Shelby Fleischer, Heather Karsten, and Andy Rogowski for their constructive criticisms and contributions.
Aber, J. D., and Melillo, J. M. (1991). “Terrestrial Ecosystems.” Saunders, Philadelphia.
Allen, T. F. H., and Hoekstra, T. W. (1992). “Toward a Uniﬁed Ecology.” Columbia Univ. Press, New
Alstad, D. N., and Andow, D. A. (1995). Managing the evolution of insect resistance to transgenic
plants. Science 268, 1894 –1896.
Altieri, M. A. (1995). “Agroecology: The Science of Sustainable Agriculture,” 2nd ed. Westview, Boulder, CO.
Atwill, E. R. (1996). Assessing the link between rangeland cattle and water-borne Cryptosporidium
parvum infection in humans. Rangelands 18(2), 48 – 51.
Barber, S. A. (1984). “Soil Nutrient Bioavailability.” Wiley, New York.
Bauer, A., and Black, A. L. (1994). Quantiﬁcation of the effect of soil organic matter content on soil
productivity. Soil Sci. Soc. Am. J. 58, 185 –193.
Berlinger, M. J., Dijk, B. L., Dahan, R., and Lebiush-Mordechi, S. (1996). Indicator plants for monitoring pest population growth. Ann. Entomol. Soc. Am. 89(5), 611– 622.
Bertsch, P. M., and Hunter, D. B. (1998). Elucidating fundamental mechanisms in soil and environ-
ENVIRONMENTAL INDICATORS OF AGROECOSYSTEMS
mental chemistry: The role of advanced analytical and spectroscopic methods. In “The Future of
Soil Chemistry” (P. M. Huang, Ed.). SSSA, Madison, WI.
Bohlen, P. J., Parmelee, R. W., McCartney, D. A., and Edwards, C. A. (1997). Earthworm effects on
carbon and nitrogen dynamics of surface litter in corn agroecosystems. Ecol. Appl. 7(4), 1341–
Bromenshenk, J. J. (1988). Regional monitoring of pollutants with honeybees. In “Progress in Environmental Specimen Banking” (S. A. Wise, R. Zeisler, and G. M. Goldstein, Eds.), Special Publ.
No. 740. U.S. Department of Commerce, National Bureau of Standards, Washington, DC.
Brooks, R. P., O’Connell, T. J., Wardrop, D. H., and Jackson, L. E. (1998). Toward a regional index of
biological integrity: The example of forested riparian ecosystems. Environ. Monitoring Assessment 51(1), 131–143.
Bundy, L. G., and Meisinger, J. J. (1994). Nitrogen availability indices. P. 951– 984, In “Methods of
Soil Analysis, Part 2—Microbiological and Biochemical Properties” (R. W. Weaver et al., Eds.),
SSSA Book Series No. 5. SSSA, Madison, WI.
Campbell, C. A., Biederbeck, V. O., McConkey, B. G., Curtin, D., and Zentner, R. P. (1999). Soil quality: Effect of tillage and fallow frequency. Soil organic matter quality as inﬂuenced by tillage and
fallow frequency in a silt loam in Southern Saskatchewan. Soil Biol. Biochem. 31, 1–7.
Clarkson, D. T., and Hanson, J. B. (1980). The mineral nutrition of higher plants. Annu. Rev. Plant Phys.
Colunga-Garcia, M., Gage, S. H., and Landis, D. A. (1997). Response of an assemblage of Coccinellidae (Coleoptera) to a diverse agricultural landscape. Environ. Entomol. 26, 797– 804.
Costanza, R., and Daly, H. (1992). Natural capital and sustainable development. Conservation Biol. 1,
Coulson, R. N., Lovelady, C. N., Flamm, R. O., Spradling, S. L., and Saunders, M. C. (1990). Intelligent geographic information systems for natural resource management. In “Quantitative Methods
in Landscape Ecology: The Analysis and Interpretation of Landscape Heterogeneity.” (M. G.
Turner and R. H. Gardner, Eds.), pp. 153 –172. Springer-Verlag, New York.
Cronan, C. S., and Grigal, D. F. (1995). Use of calcium/aluminum ratios as indicators of stress in forest ecosystems. J. Environ. Quality 24, 209 –226.
Doran, J. W., and Parkin, T. B. (1994). Deﬁning and assessing soil quality. p. 3 –21. In “Deﬁning Soil
Quality for a Sustainable Environment” ( J. W. Doran, D. C. Coleman, D. F. Bezdicek, and B. A.
Stewart, Eds.), SSSA Special Publ. No. 35. SSSA, Madison, WI.
Doran, J. W., Slepers, J. S., and Swanson, N. P. (1981). Chemical and bacteriological quality of pasture runoff. J Soil Water Conservation 36(3), 166 –171.
Duelli, P. (1997). Biodiversity evaluation in agricultural landscapes: An approach at two different
scales. Agric. Ecosystems Environ. 62, 81– 91.
Edwards, C. A., Subler, S., Chen, S. K., and Bogomolov, D. M. (1996). Essential criteria for selecting
bioindicator species, processes, or systems to assess the environmental impact of chemicals on
soil ecosystems. In “Bioindicator Systems for Soil Pollution” (N. M. van Straalenand and D. A.
Krivolutskii, Eds.), pp. 67– 84. Kluwer, Dordrecht.
Ellert, B. H., and Bettany, J. R. (1995). Calculation of organic matter and nutrients stored in soils under contrasting management regimes. Can. J. Soil Sci. 75, 529 – 538.
Elmholt, S. (1996). Microbial activity, fungal abundance, and distribution of penicillium and fusarium
as bioindicators of a temporal development of organically cultivated soils. Biol. Agric. Hort. 13(2),
Erffmeyer, R. C., Erffmeyer, E. S., and Lane, I. M. (1986). The delphi technique: An empirical evaluation of the optimal number of rounds. Groups Org. Stud. 11, 120 –128.
ffrench-Constant, R. H., and Roush, R. T. (1990). Resistance detection and documentation: The relative roles of pesticidal and biochemical assays. In “Pesticide Resistance in Arthropods” (R. T.
Roush and B. E. Tabashnik, Eds.). Chapman & Hall, New York.
O. H. SMITH ET AL.
Foissner, W. (1997). Protozoa as bioindicators in agroecosystems, with emphasis on farming practices,
biocides, and biodiversity. Agric. Ecosystems Environ. 62, 93 –103.
Food and Agriculture Organization (FAO) (1990). Use of high-resolution satellite data and geographic information system for soil erosion mapping, FAO RSC Series No. 56. FAO, Rome.
Food and Agriculture Organization (FAO) (1994). Agro-Ecological land resources assessment for agricultural development planning: A case study of Kenya. Making land use choices for district planning, World Soil Resource Reports 71/9. FAO, Rome.
Forman, R. T. (1995). “Land Mosaics: The Ecology of Landscapes and Regions.” Cambridge Univ.
Press, Cambridge, UK.
Fox, T. (1998). EPA seeks refuge from Bt resistance. Nature Biotechnol. 15(5), 409.
Franchini, P., and Rockett, C. L. (1996). Oribatid mites as “indicator” species for estimating the environmental impact of conventional and conservation tillage practices. Pedobiologia 40(3), 217–
Franklin, J. F., and Forman, R. T. T. (1987). Creating landscape patterns by forest cutting: Ecological
consequences and principles. Landscape Ecol. 1, 5 –18.
Frohn, R. C. (1997). “Remote Sensing for Landscape Ecology: New Metric Indicators for Monitoring,
Modeling, and Assessment of Ecosystems.” CRC Press, Boca Raton, FL.
Gardner, R. H., and O’Neill, R. V. (1990). Pattern, process, and predictability: The use of neutral models for landscape analysis. In “Quantitative Methods in Landscape Ecology: The Analysis and Interpretation of Landscape Heterogeneity” (M. G. Turner and R. H. Gardner, Eds.), pp. 289– 307.
Springer-Verlag, New York.
Garland, J. I., and Mills, A. I. (1991). Classiﬁcations and characterization of heterotrophic microbial
communities on the basis of community level sole-carbon source utilization. Appl. Environ. Microbiol. 57, 2351–2359.
Georghiou, G. P. (1986). “Pesticide Resistance: Strategies and Tactics for Management.” National
Academy Press, Washington, DC.
Gliessman, S. R. (1998). “Agroecology. Ecological Processes in Sustainable Agriculture.” Ann Arbor
Press, Ann Arbor, MI.
Gustafson, E. J., and Gardner, R. H. (1996). The effect of landscape heterogeneity on the probability
of patch colonization. Ecology 77, 94 –107.
Hammer, R. D., Henderson, G. S., Udawatta, R., and Brandt, D. K. (1995). Soil organic carbon in the
Missouri Forest–Prairie Ecotone. In “Carbon Forms and Functions in Forest Soils” (W. W. McFee
and J. W. Kelly, Eds.), pp. 201–231. SSSA, Madison, WI.
Herkert, J. R. (1994). The effects of habitat fragmentation on midwestern grassland bird communities.
Ecol. Appl. 4(3), 461– 471.
Hoy, M. A. (1990). Pesticide resistance in arthropod natural enemies: Variability and selection responses. In “Pesticide Resistance in Arthropods” (R. T. Roush and B. E. Tabashnik, Eds.). Chapman & Hall, New York.
Ives, A. R., Turner, M. G., and Pearson, S. M. (1998). Local explanations of landscape patterns: Can
analytical approaches approximate simulation models of spatial processes? Ecosystems 1, 35 – 51.
Jenny, H. (1941). “Factors of Soil Formation.” McGraw-Hill, New York.
Juska, A., Busch, L., and Tanaka, K. (1997). The Blackleg epidemic in Canadian rapeseed as a “normal agricultural accident.” Ecol. Appl. 7(4), 1350 –1356.
Kern, J. S., and Johnson, M. G. (1993). Conservation tillage impacts on national soil and atmospheric
carbon levels. Soil Sci. Soc. Am. J. 57, 200 –210.
Khorramshahgol, R., and Moustakis, V. S. (1988). Delphic hierarchy process (DHP): A methodology
for priority setting derived from the Delphi method and analytic hierarchy process. Euro. J. Operations Res. 37, 347– 354.
Kromp, B. (1990). Carabid beetles (Coleoptera, Carabidae) as bioindicators in biological and conventional farming in Austrian potato ﬁelds. Biol. fertil. Soils 9(2), 182.
ENVIRONMENTAL INDICATORS OF AGROECOSYSTEMS
Lanyon, L. E. (1995). Does nitrogen cycle? Changes in the spatial dynamics of nitrogen with industrial nitrogen ﬁxation. J. Prod. Agric. 8, 70 –78.
Lebrun, P., and van Straalen, N. M. (1995). Oribatid mites: Prospects for the use in ecotoxicology. Exp.
Appl. Acarol. 19(7), 361– 379.
Letourneau, D. K. (1997). Plant–arthropod interaction in agroecosystems. In “Ecology in Agriculture”
(L. E. Jackson, Ed.), pp. 239 –290. Academic Press, San Diego.
Litvaitis, J. A. (1993). Response of early successional vertebrates to historic changes in land use. Conservation Biol. 7(4), 866 – 872.
Loppi, S. (1996). Lichens as bioindicators of geothermal air pollution in central Italy. Bryologist 99, 41.
Magdoff, F., Lanyon, L., and Liebhardt, B. (1997). Nutrient cycling, transformations, and ﬂows: Implications for a more sustainable agriculture. Adv. Agron. 60, 1–73.
Marino, P. C., and Landis, D. A. (1996). Effect of landscape structure on parasitoid diversity and parasitism in agroecosystems. Ecol. Appl. 6, 276 –284.
Matson, P. A., Parton, W. J., Power, A. G., and Swift, M. J. (1997). Agricultural intensiﬁcation and
ecosystem properties. Science 277, 504 – 509.
Meyer, J. R., Campbell, C. L., Moser, T. J., Hess, G. R., Rawlings, J. O., Peck, S., and Heck, W. W.
(1992). Indicators of the ecological status of agroecosystems. In “Ecological Indicators” (D. H.
McKenzie, D. E. Hyatt, and V. J. McDonald, Eds.), pp. 629 – 658. Elsevier, Amsterdam.
Miller, J. N., Brooks, R. P., and Croonquist, M. J. (1997). Effects of landscape patterns on biotic communities. Landscape Ecol. 12, 137–153.
National Research Council (1972). “Committee on Genetic Vulnerability of Major Crops. Genetic Vulnerability of Major Crops.” National Academy of Science, Washington, DC.
National Research Council (1989). “Alternative Agriculture.” National Academy Press, Washington,
National Research Council (1995, February). “A Review of the Biomonitorning of Environmental Status and Trends (BEST) Program: The Draft Detailed Plan. Committee to Review the Department
of the Interior’s Biomonitoring of Environmental Status and Trends Program.” National Academy Press, Washington, DC.
Nelson, D. W., and Sommers, L. E. (1996). Total carbon, organic carbon, and organic matter. In “Methods of Soil Analysis, Part 3. Chemical Methods” (D. L. Sparks et al., Eds.), SSSA Book Ser. No.
5, pp. 961–1010. ASA/SSSA, Madison, WI.
Nizeyimana, E., and Petersen, G. W. (1997). Remote sensing applications to soil degradation assessments. In “Methods for Assessment of Soil Degradation” (R. Lal, W. H. Blum, C. Valentine, and
B. A. Stewart, Eds.), pp. 393– 403. CRC Press, Washington, DC.
Odum, E. P. (1984). Properties of agroecosystems. In “Agricultural Ecosystems: Unifying Concepts”
(R. Lowrance, B. R. Stinner, and G. J. House, Eds.), pp. 3–12. Wiley–Interscience, New York.
Paoletti, M. G., Favretto, M. R., Stinner, B. R., Purrington, F. F., and Bater, J. E. (1991). Invertebrates
as bioindicators of soil use. Agric. Ecosystems Environ. 34, 341– 362.
Paul, E. A., and Clark, F. E. (1996). “Soil Microbiology and Biochemistry,” 2nd ed. Academic Press,
Paustian, K., Collins, H. P., and Paul, E. A. (1997). Management controls on soil carbon. In “Soil Organic Matter in Temperate Agroecosystems” (E. A. Paul, E. T. Elliot, K. Paustian, and C. V. Cole,
Eds.), pp. 15–49. CRC Press, Boca Raton, FL.
Petersen, G. W., Bell, J. C., McSweeney, K., Nielsen, G. A., and Robert, P. C. (1995). Geographic information systems in agronomy. Adv. Agron. 55, 67–111.
Petersen, G. W., Nizeyimana, E., and Evans, B. M. (1997). Applications of geographic information systems in soil degradation assessments. In “Methods for Assessment of Soil Degradation” (R. Lal,
W. H. Blum, C. Valentine, and B. A. Stewart, Eds.), pp. 377– 391. CRC Press, Washington, DC.
Pieri, C., Dumanski, J., Hamblin, A., and Young, A. (1995). “Land Quality Indicators,” World Bank
Discussion Paper No. 315. World Bank, Washington, DC.
O. H. SMITH ET AL.
Pimentel, D., Wilson, C., McCullum, C., Huang, R., Dwen, P., Flack, F., Tran, Q., Saltman, T., and
Cliff, B. (1997). Economic and environmental beneﬁts of biodiversity. BioScience 47, 747–757.
Pimentel, D. C., and Kounang, N. (1998). Ecology of soil erosion in ecosystems. Ecosystems 1, 416 –
Rapport, D. J., Regier, H. A., and Hutchinson, T. C. (1985). Ecosystem behavior under stress. Am. Nat.
Rasmussen, P. E., and Collins, H. P. (1991). Long-term impacts of tillage, fertilizer, and crop residue
on soil organic matter in temperate semiarid regions. Adv. Agron. 45, 93 –134.
Reeves, D. W. (1997). The role of soil organic matter in maintaining soil quality in continuous cropping systems. Soil Tillage Res. 43, 131–167.
Riera, J. L., Magnuson, J. J., Vande Castle, J. R., and MacKenzie, M. D. (1998). Analysis of large-scale
spatial heterogeneity in vegetation indices among North American landscapes. Ecosystems 1,
Riitters, K. H., O’Neill, R. W., Hunsaker, C. T., Wickham, J. D., Yankee, D. H., Timmons, S. P., Jones,
K. B., and Jackson, B. L. (1995). A factor analysis of landscape pattern and structure metrics.
Landscape Ecol. 10, 23 – 40.
Roush, R. T. (1997). Bt-transgenic crops: Just another pretty insecticide or a chance for a new start in
resistance management? Pesticide Sci. 51, 328 – 334.
Samways, M. J., Caldwell, P. M., and Osborn, R. (1996). Ground-living invertebrate assemblages in
native, planted and invasive vegetation in South Africa. Agric. Ecosystems Environ. 59, 19 – 32.
Schmidt, E. L., and Belser, L. W. (1994). Autotrophic nitrifying bacteria. In “Methods of Soil Analysis, Part 2—Microbiological and Biochemical Properties” (R. W. Weaver et al., Eds.), SSSA Book
Ser. No. 5, pp. 159 –178. SSSA, Madison, WI.
Schumann, G. L. (1991). “Plant Diseases and Their Social Impact.” American Phytopathological Society, St. Paul, MN.
Scow, K. M. (1997). Soil microbial communities and carbon ﬂow in agroecosystems. In “Ecology in
Agriculture” (L. E. Jackson, Ed.), pp. 367– 403. Academic Press, New York.
Shuman, L. M., Rayner, P. L., Day, J. L., and Cordonnier, M. J. (1988). Comparison of four phosphorus
extraction methods on three acid southeastern soils. Commun. Soil Sci. Plant Anal. 19, 579–595.
Sposito, G. (1989). “The Chemistry of Soils.” Oxford Univ. Press, New York.
Stefanidou, M., Koutselinis, A., Pappas, F., and Methenitou, G. (1996). Bee head acetylcholinesterase
as an indicator of exposure to organophosphate and carbamate insecticides. Vet. Hum. Toxicol.
Stevenson, F. J. (1986). “Cycles of Soil: Carbon, Nitrogen, Phosphorous, Sulfur, Micronutrients.” Wiley, New York.
Stevenson, F. J. (1994). “Humus Chemistry: Genesis, Composition, Reactions.” Wiley, New York.
Tabashnik, B. E. (1997). Seeking the root of insect resistance to transgenic plants. Proc. Natl. Acad.
Sci. 94, 3488–3490.
Thomann, R. V. (1994). The signiﬁcance of resource scale in water quality and ecosystem modeling and
decision making. In “Towards a Sustainable Coastal Watershed: The Chesapeake Experiment,”
conference proceedings, June 1–3, pp. 20–27. Chesapeake Research Consortium, Edgewater, MD.
Turner, M. G. (1989). Landscape ecology: The effect of pattern on process. Annu. Rev. Ecol. Systematics 20, 171–197.
U.S. Department of Agriculture (USDA) (1997). Agricultural resources and environmental indicators,
1996–97, Agricultural Handbook No. 712. USDA Economic Research Service, Natural Resources and Environment Division, Washington, DC.
U.S. Environmental Protection Agency (1994). Landscape monitoring and assessment research plan,
EPA No. 620/R-94/009. Ofﬁce of Research and Development, Washington, DC.
U.S. Environmental Protection Agency (1997). EMAP research strategy [Online]. http://www.epa.gov.
[1998, February 15]