Towards a minimum data set to assess soil organic matterquality in agricultural soils

E. G. Gregorich', M.R.Carter2, D. A. Angers3, C. M. Monreall, and B. H' Ellerta

Agriculture and Agri-Food Canada, Research Branch, lQttawa, Ontario, Can-ada KIA 0C6;,Cinirtoielown, prin-ce Edward lsland, Canada ClA 7M8;3ste. Foy, Quebec, Canada GlV 2J3;

and oLethbridge, Atberta, canadaTlJ 481. Received 9 November 1993, acCepted 6 July 1994.

Gregorich, E. G, Carter, M. R., Angers, D. A., Monreal, C. M. and Ellert, B. H. 1994. Towards a minimum data set to

asseis soil organic matter quatity ii agricultural soils. Can. J. Soil Sci. 74 367-385. Soil quality is a composite measure

of both a soil's ability to funct-ion and how welt it functions, relative to a specific use. Soil quality can beassessed using a minimum

data set comprisingioil attributes such as texture, organic matter, pH, bulk density, and rooting depth. Soit organic matter has

particular significa-nce for soil quality as it can influenie many different soil properties including other attributes of the minimum

data set. Asiessment of soil organii matter is a valuable step towards identifying the overall quality of a soil and may be so

informative as to be included in minimum data sets used to evaluate the world's soils.

In this review, soil organic matter is considered to encompass a set of attributes rather than being a single entity. Included

among the attributes and discussed here are total soil organiciarbon and nitrogen, light fraction and macroorganic (particulate)

mattei, mineralizable carbon and nitrogen, microbial biomass, soil carbohydrates and enzymes. These attributes are involved

in various soil processes, such as those related to nutrient storage, biological activity, and soil structure, and can be used to

establish different minimum data sets for the evaluation of soil organic matter quality.

Key words: Biological activity, minimum data set, nutrient storage, soil organic matter, soil quality, soil structure

Gregorich, E. G., Carter, M. R., Angers, D. A., Monreal, C. M. et Ellert, B. H. 1994. Vers l'dtablissement d'un bloc de

doni6es de base pour l'6valuation de-la qualitd de la matiEre organique dans les sols agricoles. Can. J. Soil Sci. 74: 367-385.

La qualit6 du sol est une mesure synth6tique, ir Ia fois de I'aptitudJdu sol i fonctionner et de son efficacitd relative de fonctionne-

-"n1 pour. un usage donn6. Elleieut etie 6valu6e d partii d'un bloc de donn6es minimum embrassant des propri6t6s comme

la texiure, la matiEre organique, ie pH, Ia densit6 apparente et la profondeur de la rhizosphdre. La matiEre organique du sol

rev6t un r6le particulier dans ia qualil6 du sol, en celi qu'elle peut influer sur de nombreuses autres propri6t6s du sol, y compris

d,autres 6l6ments contenus dans le bloc de donndes minimum. L'dvaluation de la matibre organique est une 6tape importante

dans la caract6risation de la qualit6 globale du sol. Elle peut m6me 6tre assez instructive pour 6tre inclue dans les blocs de donndes

minimums utilis6s pour 6valuer toui les sols de Ia planite. Dans la pr6sente mise au point bibliographique, on entend par qualitd

de la matidre o.guniqu" du sol les r6sultats d'un ensemble de propri6t6s, plut6t qu'un concept unique. Parmi les nombreuses

qualit6s de la malibre organique, nous avons retenu ici le carbone et I'azote totaux, la fraction l6gdre et les matidres solides (macro-

organiques), Ie carbonJet I'azote min6ralisables, la biomasse microbienne, les hydrates de carbone du.sol et les enzymes. Ces

pr6pri6t6s interviennent dans divers processus 6daphiques comme le stockage des 6l6ments nutritifs, I'activit6 biologique et la

it^itu.e du sol. Elles peuvent ctre uiilis6es pour l;etablissement de blocs de donn6es minimums pour I'6valuation de la qualit6

de la matidre organique du sol.

Mots cl6s: Activit6 biologique, bloc de donn6es minimum, stockage des 6l6ments nutritifs, matidre organique du sol, qualit6

du sol, structure du sol

soil formation factors (e.g., parent material and topography),

and also to changes related to human use and management

(dynamic soil quality) (Pierce and Larson 1993).

The concept of sustainability implies a passage of time.Over time a soil may be sustained in its ability to function

as a viable component of an ecosystem and/or to produce

crops, it may be degraded, or it may be improved or

aggraded. The success of soil conservation efforts and

management to maintain soil quality depends on an under-

standing ofhow soils respond to agricultural use and practice

over time. To be useful to these practices, methods to

quantify soil quality must assess changes in selected soil

attributes over a prescribed period of time, in order to be

useful in determining best management strategies.

Present approaches to quantify soil quality are concerned

with either characterization of different facets or attributes

Maintaining or enhancing soil quality is a key factor insustaining the soil resources of the world. High quality soilswill not only be better producers of food and fibre for the

world's growing population, but will also play a major rolein stabilizing natural ecosystems and in enhancing air and

water quality.Soil quality can be briefly defined as the degree of htness

ofa soil for a specific use. Broader definitions describe soilquality as the sustained capability of a soil to accept, store,and recycle water, nutrients, and energy (Anderson and

Gregorich 1983). In addition to this, soil quality alsoaddresses the capacity of a soil to retain, disperse and

transform chemical and/or biological materials and thusfunction as an environmental filter or buffer. The quality ofany soil depends in part on the soil's natural or inherentcomposition (inherent soil qualiry), which is a function of

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368 CANADIAN JOURNAL OF SO't SC'ENCE

ofquality (i.e. descriptive approach), or are concerned withthe identifrcation of specific indicators or parameters that willassess the ability or capacity of an attribute to function ina desired manner (i.e. indicative approach). The latterapproach involves the idea of characterizing a soils' health(Doran and Parkin 1994). Quantif,,ing soil quality requiresthat a minimum data set be defined, comprisins measuresof various soil attributes or critical properiies (,.keyindicators", Larson and Pierce 1991). Tochiracterize howsoil quality changes over relatively short time periods, thesecritical properties must be sensitive to chinges in soilmanagement, soil perturbations, and inputs into the soilsystem. Furthermore it is necessary that each critical propertybe easily measured, and the measurements be reproduCiUte.In many cases the critical property will not be measureddirectly but by use of an index (an associative property) orby use of pedotransfer functions that emDloy a relatedprop€rty (Bouma 1989). Most measures of changes in soilquality are comparative and made with reference to a base-line level. The baseline level may be a different treatmentor management, such as the virgin state (e.g., cultivated vs.uncultivated grassland), or it may be a threshold value.

Soil organic matter is a key attribute of soil qualiff (Larsonand Pierce l99l: Doran and parkin lgg4l.ltis the primarysource of, and a temporary sink for, plant nutrients inagroecosystems and is important in maintaining soil tilth,aiding the infiltration of air and water, promotins waterretention, reducing erosion. and controlling the effic"acy andfate of applied pesticides. Assessment of sJil orcanrc marreris therefore a valuable step towards identiffing"the overallquality of a soil and may be so informative as to be includeduniversally in minimum data sets used to evaluate the soilsof the world. Organic matter itself can be characteri zed bymeasuring several different components which are involvedwith various soil processes, often in an irrdependent manner.Thus, the multi-faceted role of soil organii matter must betaken into consideration in the assessment of qualitv. Thiswill require the identification of separate minimum data setsto characterize organic matter for the followine functions:soil structure. nutrient storage. and biological a-ctivity. Thechoice ofattributes for each data set would be based on theirsensitivity to the specific function and the provision ofavailable methodology, including ease of dupiication, andfacility for accuracy and speed.

Recent work in Canada has focused on developing ageneral framework for evaluating soil quality (Acton indPadbury 1993) and changes in soil orginic matter underCanadian climate and soil condirions. This review highlightsCanadian research on soil organic matter. The objectivei ofthis review were (l) to outline key attributes which can beused to evaluate soil organic matter quality and (2) to discussminimum data sets of attributes to quantiff the essential rolesof organic matter in soil. The following properties arediscussed: soil organic carbon (C) and nitrbgen (N), thelight fraction of the soil and macroorganic malter. minera_lizable C and N. microbial biomass, cirbohydrates. and soilenzymes. Soil organic C and N contents provide a measure-ment of a soil's total inventory of organii matter, while theremaining properties reflect forms of labile orsanic matter

that are subject to relatively rapid turnover or are involvedwith biochemical and/or organo-mineral reactions. Theseproperties also have a functional role in soil and thus mayprovide information relating to the magnitude of that functionin nutrient storage, biological activity and soil structure. Foreach property the rationale for its use as an indicator andmethods for its measurement are provided, along with waysthe property can be used to assess changes in soil organicmatter quality.

ORGANIC MATTER ATTRIBUTES

Soil Organic Carbon and NitrogenSoil organic matter comprises a range of humified andbiologically active compounds, including readily decompos-able material, plant litter and roots, and dead and livingorganisms. The chemically well-defined non-humic sub-stances that contribute to the organic C and N contents insoil consist of low molecular weight aliphatic and aromaticacids, carbohydrates, amino acids, and their polymericderivatives such as polypeptides, proteins, polysaccharides,and waxes (Schnitzer 1991). These compounds have arelatively rapid turnover in soil and are used readily assubstrates by soil microorganisms. Humic substances makeup a significant portion of the total organic C and N in soil(Anderson 1979). They consist of complex polymeric organiccompounds with high molecular weight and are intimatelyassociated with soil inorganic constituents. The complexchemical structure of humic substances makes them moreresistant to decomposition than the non-humic materials.

The methods used to estimate organic C and N are wellestablished and have been used extensively in soil organicmatter research. Most humic substances are about 50-58%organic C, which is usually determined by either wet or dryoxidation methods (Tiessen and Moir 1993). In the mostcommonly used wet oxidation method, organic C is oxidizedby potassium dichromate in the presence of sulphuric acidwith external heating (Nelson and Sommers 1982). In dryoxidation (combustion) methods, organic C is converted toCO2 by burning the organic matter in air or 02 in a furnace.The evolved CO2 can be measured by (i) titrating the CO2adsorbed in NaOH with acid, (ii) by thermal conductivity,or (iii) by infrared adsorption meaiurement techniques.

Two methods that have been used for over 150 yr forthe determination of total N are the Kjehldahl method (awet-oxidation procedure) and the Dumas method (a dry-combustion method) (Bremer and Mulvaney 1982' McGilland Figueiredo 1993). Recently near-infrared reflectancespectroscopic techniques have been used to estimate total Cand N in soils (Dalal and Henry 1986; Morra et al. l99l).

Organic C and N contents in soil are a result of a complexbiochemical interaction between substrate additions of C andN in fertilizers and in plant and animal residues, and lossesof C and N through microbial decomposition and minerali-zation and erosion. Changes in inputs, such as fertilizers andresidues (Janzen l987a,b; Campbell et al. 1991a), whichregulate soil microbial activity and mineralization rates,will ultimately be reflected in the total organic C and Ncontent of soil. Moisture and, probably to a greater degree,

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GREGOR'CH ET AL. - ASSESSMENT OF SO't ORGAN//C MATTER IN AGRICIJLTURAL SO'IS 369

temperature are the factors most strongly influencingmineralization rates in soil (Stanford et al. 1973; Stanfordand Epstein I9741' Campbell et al. l98l). The relative impact

of management practices on soil organic C and N levels willchange with soil climate.

Changes in soil quality are usually assessed by comparingthe organic matter parameters between sites subjected to

specific agricultural practices and reference sites (Table 1).

Comparisons of total organic C and N, for example, have

been made between native soil and cultivated soils (Tiessen

et al. 1982). as well as between soils under different crop

rotations, fertilization regimes, and tillage treatments (Carter

and Rennie 1982; Campbell et al. 1986, 199lb). The amount

of organic matter in soil has been compared at differentscales; at the landscape (Voroney et al. 1981; Gregorich and

Anderson 1985), field plot (Campbell et al. 1991b)' and

particle size (Tiessen and Stewart 1983; Gregorich et al.

1989) levels.To accurately assess the effects of land use or management

practices on total organic C and N, the thicknesses and

bulk densities of the soil layers in the freld must be consi-

dered. Because comparisons of changes induced by manage-

ment practices may be hampered by the changes in the

massei of soils under consideration, comparisons are usually

based on mass per unit area. Comparisons of the masses oftotal organic C and N in the A horizon, solum, or on an

equivalent mass basis can be made (Ellert and Bettany 1995).

The assessment of organic C and N as indicators of soilquality should also include consideration of inherent soilproperties and site-specific processes. For example, textureplays an important role in determining the amount of organic

matter that may be stabilized in soil. Soils with relativelyhigh clay contents tend to stabilize and retain more organic

matter than those with low clay contents (Jenkinson 1977;

Ladd et al. 1990). Removal of organic-rich topsoil by erosion

is a process that influences the level of organic matter in soil(Voioney et al. 1981; Gregorich and Anderson 1985). Soilredistribution by tillage and water and/or wind erosion can

have a major impact on the total amount of soil organic C

and N (de Jong and Kachanoski 1988). Therefore estimates

of soil erosion and deposition may be required when assessing

changes in soil organic matter quality, particularly when

comparing land use and management practices that affect the

relative area of soil covered by residues'The amount and rate of change in total organic C and N

may depend more on the initial levels of these elements

than on the treatment or management practlce lmposeo on

the soil. Campbell et al. (199la'b) suggested that one reason

the effects of fertilization and cultural practices on changes

in soil organic C and N in nvo long-teffn crop rotation studies

conducted on Chernozemic Black soils over 30 yr were

different was because the initial levels of organic matter were

different. Thus, soils with relatively high initial levels oforganic matter may be less likely to reflect any-perturbations,

an-d rnuy require prolonged and intense perturbation to show

significant degradation compared to soils with initially lower

organic matter contents.itt" C,N ratio may also provide information on the

capacity of the soil to store and recycle energy and nutrients 'In agriiultural soils the C:N ratio is relatively constant and

is usluatty within a narrow range, from 10 to 12. Jenny (1941)

observed that under similar conditions of moisture, the

C:N ratio of grassland soils decreases as the mean annual

temperature increases, probably as the result of more inten-

sive decomposition of organic matter at higher temperatures'

Agriculturil practices such as cultivation, fertilization and

."iidu" management influence the soil C:N ratio' Several

studies have sfiown that the C:N ratio becomes narrower with

cultivation (Voroney et al. 1981; Campbell and Souster 1982;

Bowman et al. 1990). Liang and MacKenzie (1992) reported

that the C:N ratio increased within 3 yr in soils under

continuous corn receiving high levels of N fertilizer'Rasmussen et al. (1980) found that long-term changes in soil

C:N ratios were proportional to the rate of N loss; C:N ratios

were highest in ioili in which wheat straw was burned and

lowest in soils receiving manure or pea vines' They suggested

that the residue treatments influenced the C:N ratio because

the turnover of C was delayed by a deficiency of available

N for microbial decomPosition'

Light Fraction and Macroorganic Matterfhi tigirt fraction and macroorganic matter are mainly plant

residuis; however, residues derived from animals and

microorganisms may also be present in various stages ofdecomposition. These pools are significant to soil organic

matter turnover in agricultural soils because they serve as

a readily decomposable substrate for soil microorganisms and

as a short-term reservoir ofplant nutrients. A large portion

of the microbial population and enzyme activity in soil is

associated with the light fraction (Kanazawa and Filip 1986)'

and soil respiration rates are correlated with the light fraction

content (Janzen et al. 1992).The density and sieving methods used for the physical

separation of this fraction of organic matter are straight-

foiward, reliable, and reproducible (Gregorich and Ellert

1993). The sieving procedures used to isolate macroorganic

matter are the same as those used in particle-size analysis,

except that pretreatments for removing organic matter,

carbonates, and iron oxides are eliminated' One concern

during the wet-sieving step is the possible disintegration offragilE organic fragments ind subsequent lower recovery ofmacroorganic matter.

The light fraction is isolated from the mineral part of soils

bv suspe-nding the soil in a dense liquid (usually between 1'5

una Z.b g cm-3) and leaving the heavy fraction to settle to

Table 1. Total organic C and N along native grassland and cultivatedtoposequences (from Gregorich and Anderson 1985)

Native CultivatedSlopeposition

Organic C 1Mg ha-t)A horizon A horizon Solum

UpperMidLower

UpperMidLower

6.513.618.5

2-.J

6.29.1

2.58.5

20.5

44o495

3.45.8v.5

'79

r60193

.,4

61

103

Organic N (Mg ha-t)

)695

235

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370 CANADIAN JOURNAL OF SO't SC'ENCE

the bottom while the light fraction floats to the surface. Avariety of heavy liquids have been used in densimetricfractionation procedures, including bromoform (Greenlandand Ford 1964), carbon tetrachloride (Scheffer lgjl\. andtetrabromomethane/benzene (McKeague l97l). Use of inor_ganic media, such as NaI, in density separation techniquesobviates the problems with toxicity, caibon contamination,and coagulation of suspended particles associated with theuse of organic solvents.

Light fraction organic matter contains most of the macro_organic matter, which is that organic matter associated withthe sand-sized fraction (50-2000 pm), but the light fractionis also present in all particle-size fractions (Turchenek andOades 1979). Thus, the light fraction and macroorganicmatter may not be identical because the organic matteradhering to sand grains has a different chemicafcomoositionfrom that not associated with sand grains (Zhang et l. teSS;.Fllert and Gregorich (1994) reporred that the iwo fractionsfrom 20 soils differed in several iespects (Table 2). Gregorichet al. (1995) observed large (differences between the 6l3Cof the light fraction and the macroorganic matter from a soilunder maize (Zea mays L.) for 25 yr.

The light fraction usually represents 0.1-4% of the totalweight of cultivated topsoils but has up to 15 times more Cand l0 times more N than the whole soii (Greenland and Fordl)64; Dalal and Mayer 1986, 1987; Janzen et al. 1gg2).Chemical characterization of the light fraction has indicatedthat it is in an intermediate state of decomposition betweenfresh plant tissue and soil organic matter. ine C,N ratio ofthe light fraction is usually wider than that of the whole soil(Greenland and Ford 1964) and ofthe particle-size fractions,reflecting the dominant influence of plant material on thispool of organic matter. Compared to plant tissue, the lightfraction has a relatively narrow C:N ratio (Molloy etil.1977) and high ash

"ontent (Malone and Swartout 1969;

Spycher et al. 1983), suggesting that it has undergone somedecomposition and/or humifi cation.

The light fraction and macroorganic matter provideinformation on the extent to which plint residues have beenprocessed by the decomposer community in soils. Thesefractions are generally free of mineral particles and there-fore lack the protection from decomposition that suchparticles imparr. Thus the light fraction (Sollins et al. l9g4;Bonde et al. 1992) and macroorganic matter (Christensen1987; Gregorich et al. 1989) have been shown to decomposequickly compared with organic matter in whole soil or

Table 2. Comparison between the mean compositions of macroorganicmatter and Iight fractions in 20 contrasting ioils from Ontario (from

Ellert and Gregorich 1994)

associated with mineral particle fractions, despite having awide C:N ratio.

Macroorganic matter is rapidly depleted when a soil isbrought under cultivation. A Chernozemic soil cultivated for4 yr had a light fraction 40% less than a native equivalent,with a 76% smaller light fraction after 90 yr of cultivation(Tiessen and Stewart 1983). Similarly, it is increased rapidlywhen a degraded soil is put into a continuous forage cropsuch as alfalfa (Angers and Mehuys 1990). The rate of lossof organic C from the light fraction was 2-l I times greaterthan that from the heavy fraction in five Australian soils(Dalal and Mayer 1986). Gregorich et al. (1995) reportedthat more than70% of the C in the light fraction had turnedover whereas only 16% of the C associated with the coarsesilt fraction had turned over since the start of maize croppingin an Ontario soil. Janzen etal. (1992) found that ttre ianeeoflight fraction C in soils from different cropping rotatioiswas twice as great as the range of total organic C content.They also reported that this greater range in light fractioncontents allowed a much greater precision to be achieved inthe separation of treatment effects.

The dominant influence of plant-derived materials in thelight fraction is reflected in its response to inputs ofresidueto the soil; its utility as an indicator of organic matter qualityin agricultural soils is linked to this factor. For exampie, theamount of light fraction is greater under perennial foragesor continuous cropping than under frequent summerfallow(Janzen et al. 1992) and is greater in well-fertilized soils(Shaymukhametov et al. 1984).

The light fraction and macroorganic matter is a validindicator of soil quality in several respects. As a nonhumifiedfraction of organic matter, the size of the light fraction isa balance between residue inputs and persistence, anddecomposition as determined by the soil environment(Gregorich and Janzen 1995). The light fraction and macro-organic matter constitute a relatively large amount of C andN contained in a small mass of soil and may contain a largeportion of the total C in soil. Most of this labile material isunprotected by soil mineral particles and has a short turnovertime, which gives the light fraction a prominent role as aC substrate and source ofnutrients. This pool is responsiveto management practices and may provide an earlier indica-tion of the effects of soil management and cropping systemsthan the total amount of organic matter in soils.

Mineralizable Carbon and NitrogenMore than '75% of soil organic matter exists as compoundsthat are only slowly decomposable; the remainder is presentas readily decomposable or "mineralizable" compounds.This mineralizable fraction contributes to nutrient cyclingand is the interface between autotrophic organisms thatsynthesize complex compounds from inorganic constituentsand heterotrophic organisms that decompose the organiccompounds and allow the inorganic constituents to be usedagain. Thus, the amount of mineralizable organic matter ina soil is an indicator of organic matter quality, because itaffects nutrient dynamics within single growing seasons,organic mafter content in soils under contrasting managementregimes, and C sequestration over extended periods of time.

Variable

MacroorganicMatter Lightfraction

Mean 95Vo Clz Mean 95% Cl

% soil mass in fraction 33.1% soll C in fraction 20.8C enrichmenty O.73Ash concentration (%) 93.6C/N of fraction 19.3

1.31 0.0-2.'77.58 4.2-tl1r.4 8.0- t 542.4 37-4626.4 24-29

z)-+Jr6-25

0.58-0.879r-961'7 -21

295% Confidence Intervals for the means! %C in fraction/%C in whole soil.

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GREGOR'CH ET AL. - ASSESSMENT OF SO't ORGANIC MATTER IN AGRICULTURAL SO'IS 371

Carbon mineralization, determined as the gross flux ofCO2 from the mineralizable fraction, indicates the totalmetabolic activity of the heterotrophic soil organisms. It may

also be used to assess other soil biological parameters,

including the decay of plant residues, the persistence oforganic wastes, the contribution of soil organic matter to

atmospheric CO2, and the impact of pollutants oncatabolism by soil organisms. Nitrogen mineralization, deter-

mined as the net flux of inorganic N from the mineralizablefraction, indicates the balance between gross mineralizationand immobilization by soil organisms. The soil microbes

immobilize or assimilate a portion of the N derived fromdecomposing organic matter, and excess N that is notrequired by the microbes accumulates as inorganic N (Harte

and Kinzig 1993). Thus, N mineralization is often measured

to assess the capacity of soil organic matter to supply

inorganic N, mainly NO3, which is the main form of plant-available N and mobile N that leaches through the soil.Isotopic tracers are required to distinguish between gross

mineralization and immobilization of N because the twoprocesses occur concurrently as soil organic matter isdecomposed.

Several approaches, dictated by soil characteristics and study

objectives, have been used to measure C and N mineralizationin the field and in the laboratory. Lack of a standard method

hampers comparisons of results from different studies. Soilincubations, frequently carried out under controlled environ-

mental conditions (temperature, moisture, and aeration) in the

laboratory can be viewed as rapid and simple "bioassays" ofthe actively cycling fractions of soil organic C and N (Andenon

1982; Campbell et al. 1993).Mineralizable C is usually measured as CO2 accumulated

in an alkali trap that is sealed within the same chamber as

the soil sample, but several other techniques have been used,

including scrubbing of CO2 from air passed over incubating

soils, measurement of CO2 accumulated in headspace airabove incubating soils, and automated methods using

electrolytic detectors or infrared gas analyzers (Sparlingand West 1990; Nordgren 1988; Heinemeyer et al' 1989).

Mineralizable N is usually measured as inorganic N removed

from incubated samples by shaking soils with an extractingsolution, such as 2 M KCl, or by gently leaching soils withsolutions, such as 1 mM CaCl2. Ammonium is the

immediate product of amino acid decomposition, but Nmineralization is measured as ammonium plus nitrateproduction because ammonium often is oxidized to nitrate.The limitations of laboratory incubations for assessing Nmineralization have been reviewed by Harmsen and van

Schreven (1955), Keeney (1980, 1982), and Binkley and Hart(198e).

Simple incubations with single time intervals can be used

to estimate the mineralizable fraction, but fluctuations inCO2 and inorganic N arising from microbial dynamics willnot be apparent. Changes in mineralization rates are made

evident by using incubations with sequential measurements,

typically made by trapping CO2 and leaching or extractinginorganic N at periodic intervals. Such time-series measure-

ments often indicate that the release of CO2 and inorganicN from the mineralizable fraction is curvilinear during the

incubation. Asymptotic models are sometimes fitted to time

series data to estimate the "potentially mineralizable C or

N" which are extrapolated estimates of the size of the

mineralizable fractions (Campbell et al. 1993)'

Mineralizable C or N is usually calculated as the sum ofCO2 or inorganic N released during each interval in the

incribation, tfie quantities being dependent on the incubation

or bioassay conditions used in the determinations' The

duration of,the incubation greatly influences the total amount

of mineralizable C and N detected; other influential factors

include sample pre-fieaftnent (especially drying)' temperature,

moisture. aeration, and measurement interval. In a recent

study (Ellert and Gregorich, unpublished data)' close rela-

tionihips between the cumulative amounts of C mineralized

during22wk and the amounts mineralized during 3 or l0 wk

rugg"it that shorter incubations adequately characterized the

mineralizable fraction of soil C (Fig' l). The relationships

between cumulative mineralization during 22 wk and 3 or

l0 wk, although highly significant (P < 0.0001), were less

close for N (Fig. 2) than C (Fie. 1).

Mineralizable-C and N may be determined simultaneously

in a single incubation, but combined data are rarely reported

(Table 3). Under optimal conditions in the laboratory. rates

of C mineralization typically range fr-om ] to 30 pg I -' d -'in mineral soils and 150-800 pE g- | d -' in organic layers'

Corresoonding rates of N mineralization range from 0'3 to

-2.5 is.s-rt-r in mineral soils and from 3.0 to 15 pg

g-r d-T in organic layers (Table 3). Carter and Rennie(fSAZ) reported greater rates of C and N mineralization in

thin layeis of surface soil under zero tillage compared to

conventional tillage because crop residues were concentrated

at the surface ofzero-tilled soils. Janzen's (1987b) compar-

isons of crop rotations indicated that the proportions of the

total store of C and N in the mineralizable fractions decreased

with increasing frequency of fallow, suggesting that fallowwas detrimental to organic matter quality (Table 3). Inanother study, the amounts of mineralizable C and N were

greater in fertilized than unfertilized soils, but the proportions

of total soil organic C and N in the mineralizable fractions

were similar, because fertilized soils also contained greater

amounts of total C and N (Janzen 1987a).

Mineralizable C and N are usually correlated with total

soil organic C and N. The quality of the soil organic matter

may be distinguished from the quantity by calculating the

proportion of ioil organic C or N found in the mineralizable

t u"iions. Similar proportions of soil C and N in the miner-

alizable fractions of soils under contrasting management

regimes indicate that, regardless of changes in the absolute

quintities of total or mineralizable C and N, the qualiry oft-he soil organic matter has remained unchanged. In a study

of aggregate characteristics (Elliot 1986) macro-aggregates

f > O.: mm) in native grassland and cultivated soils contained

greater amounts of mineralizable C and N than did micro-iggregates, but the proportions of total aggregate C and N

in itrehineralizable fractions were similar or greater in the

micro-aggregates (Iable 3). Macro-aggregates tended to contain

-ote otganii matter and less sand, but from ttre data in Table 3

it is unclear whether the organic matter in macro-aggregates

was more decomposable than that in micro-aggregates'

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10 wk Y = 9.08 + 0.522X ; R2= 0.96

3wk Y =-12.4 +0.167X:R2=0.88

372 CANADIAN JOURNAL OF SO't SC'ENCE

3000

1 000

6000 8000

Cumulative C mineralized during 22 weeks (pg g-1 soit )

Fig' l' Relationship between cumulative C mineralized from forest and cultivated soils at nine sites in ontario during long-term (22wk)incubations and shorter periods (3 or 10 wk) (Ellert and Gregorich, unpublished)

2000

oan

o,o)t_ot<oo3oo(r)(t)'r-

oc)

.N

o.E

C)o(6

E

o

40002000

The C:N ratio of the mineralizable fraction reflects thecomposition of the active fraction of soil organic matter, butdifferences may arise from analytical errors, shortages ofbioavailable C or N, or the predominance of decomposableresidues with a particular C:N ratio. The ratios of C:Nmineralized are probably unrealistically narrow in Janzen'sstudy (1987a), because C and N were estimated in separateincubations under slightly different condirions (Table 3). Thewide ratios of C:N mineralized and the high proportions ofmineralizable C reported by Miller and Johnson (t}Oly

^uyreflect analytical problems. The ratios of C:N mineralizedin forest soils and organic horizons are often relatively widebecause the soils receive inputs of woody residues with wide,C:N ratios (Table 3). The wide ratios of C:N mineralizedin forest soils from Saskatchewan and Ontario were consistentwith the cultivation-induced narrowing of the total soil C:Nratio (Ellert, unpublished).

Mineralizable C may be combined with other soil data,such as microbial biomass C, to calculate additional indicesof soil organic matter quality, such as specific respiratoryactivity (Anderson and Domsch 1993). The rates of minerali_zation observed under optimal conditions in the laboratoryare only potentials that are rarely attained under fieldconditions. In fact, sub-optimal conditions in the field may

favour the build-up of mineralizable fractions that canbe decomposed rapidly under optimal conditions in thelaboratory.

Microbial BiomassThe importance of microfauna to soil quality has long beenrecognized (Waksman and Starkey 1924). Microbial biomassis a critical attribute of soil organic matter quality as itprovides an indication ofa soils' ability or capacity to storeand recycle nutrients and energy. As a measure of organicmatter quality, it also seryes as a sensitive indicator of changeand future trends in organic matter levels and equilibria.Microbial biomass is a key variable of soil organic matter,functioning both as an agent for the transformation andcycling of organic matter and plant nutrients within the soiland as a sink (during immobilization) or source (duringmineralization) of labile nutrients. The microbial componentaccounts for l-3% and 2-6% of soil organic C and N,respectively (Jenkinson 1987). Thus, it serves within the soilas a store of labile organic matter. In addition, microbiallymediated N mineralization can provide over 50% of plantN needs annually, while N flux through the microbialbiomass can be 2-4 times that of plant uptake (paul andVoroney 1980).

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10 wk Y = 3.57 + 0.471X; R2= 0.86

3wk y= 4.63 + 0.115 X;R2= 0.53.

GREGOR'CH ET AL. - ASSESSMENT OF SO't ORGANIC MATTER IN AGRICULTURAL SO'LS 373

50 100 150 200 250 300 350

Cumulative N mineralized duting22weeks (pg g-r soil )

Fig. 2. Relationship between cumulative N mineralized from forest and cultivated soils at nine sites in Ontario during long-term (22 wk)

incubations and shorter periods (3 or 10 wk) (Ellert and Gregorich, unpublished).

Due to its dynamic nature, microbial biomass quicklyresponds to changes in soil management and soil perturba-

tions (Carter 1986) and to soil environment (Insam et al.

1989; Skopp et al. 1990; Duxbury and Nkambule 1994)' The

microbial biomass is also sensitive to various toxicities insoil (Domsch et al. 1983; Jenkinson 1987). The utility ofthe soil microbial biomass measurement is illustrated in itsuse as an independent parameter to validate organic mattermodels (Jenkinson 1990; Paustian et al. 1992). Microbialbiomass is also related to various soil structure indices (Carter

1992; Angers et al. 1993b). Generally, indirect methodsprovide easy and efficient means to measure microbialbiomass in soil (Brookes 1985; Parkinson and Coleman1991). The three main indirect methods are: chloroformfumigation incubation (Jenkinson and Powlson 1976),

substrate-induced respiration (Anderson and Domsch I 978),

and extractable biomass adenosine 5'-triphosphate (ATP)'The advantages of each method have been reviewed byJenkinson (1987) and Sparling and Ross (1993). The fumi-gation technique has the advantage of providing a directmeasurement of the C, N, P, and S contents of the microbialbiomass. Recently this method has been improved byeliminating the incubation procedure and directly extractingbiomass constituents after removal of the fumigant (Jenkinson

75

50

25

00

-7F

125

100

50

25

1

1

oo

b,a)

atjoo3o

(t)

o,'==oo.NEo.E

zq)

o

E()

1987). Analytical procedures for fumigation-direct extrac-

tion have been summarized recently by Voroney et al. (1993)'

Much work has been done to improve the reproducibilityof rnicrobial biomass measurements. Direct extraction solved

many of the problems associated with the fumigation incu-

bation technique. However, the processing of soil samples

can still influence measurements of the labile microbial

biomass (Ross 1992). The main concerns are sieving ofsampled soil, soil water content at sampling versus at

fumigation, and storage of soil samples prior to analysis

(Jenkinson 1987). For soil quality measurements' some

degree of standardization in these areas is required.ihe determination of microbial biomass does not by itself

provide information on microbial activity (Jenkinson 1987).

Some measure of soil microbial biomass turnover, such as

CO2-respired or enzyme activity, is required to assess

miciobiil activity (Brookes 1985; Anderson and Domsch

1986; Anderson and Domsch 1993; Sparling and Ross

1993). Long-term studies of microbial biomass can provide

information on changes in the amount and nutrient content

of biomass over time, which can be associated with differ-

ences in microbial activity and organic matter quality

(Carter 1986; Duxbury and Nkambule 1994). The absolute

amount of biomass at any one time cannot indicate whether

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374 CANADIAN JOURNAL OF SO't SC'EA'CE

Table 3. Mineralizable C and N determined in laboratorv incubations

Mineralization

Amount mg g-r" rate pg I -l Y Min./totalxl,ocation, reference &incubation conditions cSoil

SoilC:N C:N c N c(%) N(%)

lYestern Canada: Chernozems under conventionat (CT) or zero (ZT) tillageC-arter a-nd Rennie (1982) CT Lethbridge, 0_2 cm 9-.g l.1612 wk, 25'C ZT Lethbridge, 0_2 cm l0.l Z.O4

CT Melfon, 0-5 cm 9.8 1.30ZT Melfort, 0-5 cm 12.5 1.68CT Elstow, 0-5 cm 9.6 1.30ZT Elstow, 0-5 cm 10.1 2.16CT Scott, 0-5 cm 10.2 1.22ZT Scott, 0-5 cm 9.6 l.2O

0.540.790.510.6'7

0.790.70o.490.42

0.0920.1840.1280.1900.1020.1660.1000.100

0.1400.2000.1 15

0.150

0.0830.0&0.0560.040

3.94.0AA

^<

+.J6.34.05.3

t.93.90.81.7t.2z-51.21.3

2.12.51.82.1

1.81.5

1.0

3.1o.22.01.4

1.41.01.9

NANANA

5-ZJ.f1.41.6

4.92.32.7

12.6 13.81 t. 1 24.310.2 15.58.8 20.0

12.7 15.513.0 25.712.2 14.5r2.0 t4.3

1.10 2.42.19 4.31.52 0.92.26 t.21-21 1.61.98 3.0l. 19 1.41.l9 t.'7

l.l1 0.81.59 0.90.91 0.8l. 19 0.9

1.19 1.80.91 1.60.80 1.2o.5'7 l. I

14.83 3.30.03 NA1.24 1.70.83 r.7

1.40 | .71. 15 1.01.19 2.0

t4.23 6.'10.69 3.0o.37 2.2

2.15 5.43.40 5.60.80 2.91.35 2.4

2.r4 12.24.46 5.10.59 7.71.27 6.5

Alberta: Dark Brown Chernozems with and without N & p fenilizerJanzen (1987a) W,0_10 cm; check 11.018 wk, 30'C W, 0_10 cm; fertilized lt.lW:continuous wheat FWW, 0_10 cm; check 10.0F![W:fallow-wheat-wheat FWW, 0-10 cm; fertilized 10.6

Alberta: Dark Brown Chemozems und,er contrasting crop rotationsJanzen (1987b) W, 0-15 cm g.g10 wk, 30'C FWW-Forage, 0_15 cm 9.gFW:fallow-wheat F!yW, 0_15 cm g.g

FW, 0-15 cm 9.6

Forest, 0-15 cmPasture, 0-15 cmCultivated, 0-12 cm

Ontario : Podzols (Petawawa Forest)Hendrickson and Robinson (1984) Mixed litter3 wk, 30"C Mineral soil, 0_5 cm

Mineral soil. 5-10 cm

Saskntchewan: Gray htvisolsEllert, unpublished9 wk, 30'C

Ontario: Gleysols (Plainfeld)Ellert, unpublished22 wk, 30"C

Colorado: Contasting soilsMiller and Johnson (1964)2 wk, 30"C, 0.05 bars

Alaska: Organic layers of tundra soilsNadelhoffer et al. (1991)13 wk, l5'C

Brazil: Intosol under sugar caneSalcedo et al. (1985)1l wk,28'C

Forest LFH 20.i 20.80Forest Ae, 0-21 cm 11.7 NA*Recently Cleared,0-15 cm 17.6 l.15Wheat/Fallow, 0-16 cm l2.g 0.80

16.3 4.25l3.8 2.61t2.t 2.32

NA 18.23NA 0.63NA 0.40

Cultivated, <25O pmCultivated, >25O pm

Alpine meadowDryland wheatIrrigated, noncalcareousIrrigated, calcareous

Tussock, Oe+OaSlope shrub/lupine, OeWet sedge, ORiverside, Oe+Oa

0-20 cm; check0-20 cm; 60 kg N/ha

0.934 220.002 NA0.078 150.052 15

9.5 11.210.9 9.98.7 6.9

10.5 6.0

2015

IJ

330.2NA18.3t2.7

27.617.015.1

61 868.0M 30.151 t9.2

16 33.6t6 54.319 15.515 20.7

16.9 36.Itl .4 50.838.3 22.624.5 3l . 1

0.2160.1770.183

0.2990.0140.008

0.0430.0680.0160.027

0.0210.0450.0060.013

Nebraska: Micro- and mtcro-aggregates in native grassrand and curtivated soilsElliott (1986) Grassland, SI_3OO pm g.j 0.6720 d,25"C Grassland, >300 pm 10.0 1.09

Cultivated, 53-300 pm 9.6 0.31Cultivated, )300 am 10.5 0.41

saskatchewan: Micro- and nncro-aggregates in native grassrand and curtivated soirs

!un1a 10 Germida (1988) Grassland, <25ii pm 10.3 0.362 wk, 25"C Grassland, >25O um l0.g 0.51

r1.7 0.2310.2 0.31

NA 4.42NA 1.60NA 2.NNA 1.19

20.9 19.00r9.7 21.0015.3 14.0019. I 4l.00

9.6 0.259-6 0.23

0.058 760.029 560.016 1260.028 42

4.t4 25.0 NA2.04 50.2 NA1. 13 43.l NA2.O2 q.9 NA

3 15.9t14.4142.884.6

0.035 543 208.80.009 2333 230.80.090 156 153.80.350 tr7 4s0.5

0.0310.028

0.38 t.40.10 3.20.99 1.03.85 4.3

0.400.36 r.2

0.10.00.1o.7

1.61.4

8.2 3.38.3 3.0

zCumulative amount of C or N mineralized during incubation for the period indicated under ..Incubation Conditions,'vLinear rate calculated as the cumulative amount of c or N mineralizid divided by the duration of the incubation.xProportion of total soil C and N mineralizable during + wt : t.*-i, Z8jlto-t]tl'oit organic C or N.wNot available.

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GREGOR'CH ET AL. _ ASSESSMENT OF SO't ORGANIC MATTER IN AGRICULTURAL SO'LS 375

soil organic matter quality is increasing or decreasing' Toanswer this dilemma, the microbial biomass can be comparedto a related soil parameter. For example, the ratio ofmicrobial biomass C to total organic C (Anderson andDomsch 1986. 1989: Wu and Brookes 1988; Carter 1991)

or the ratio of CO2-C respired to microbial biomass C(Anderson and Domsch 1986, 1990) provides a measure oforganic matter dynamics. The latter ratio is well suited forthe substrate-induced respiration method, and its applicationfor soil organic matter quality assessment has been addressed

by Visser and Parkinson (1992). Generally, changes in theratio reflect both inputs and outputs of organic matter intothe soil and conversion of organic matter to microbial C.

Studies using the ratio of microbial biomass C to totalorganic C have demonstrated the utility of this index to

monitor organic matter changes in agricultural systems(Carter and Rennie 1982; Anderson and Domsch 1989;Carter 1991; Sparling 1992).ln most cases, the ratio mustbe assessed against a local reference or baseline (e.g.,grassland) in the same soil type (Carter 1991). Differencesin soil clay content, mineralogy, and vegetation can influencethe proportion of microbial biomass C in total organic C(Sparling 1992). Thus, the application of the ratio index ismainly confined within similar soil types and croppingsystems.

Figure 3 illustrates the ratio approach to characterizingchanges in organic matter quality under two soil types' Inthe Charlottetown fine sandy loam (Orthic Humo-FerricPodzol) from eastern Canada, an equilibrium ratio value ofabout 1.2 is associated with 300 p"g g-' microbial biomassC, while in the Kairanga silty clay loam (equivalent toRegosol or Dystric Brunisol) from New Zealand theres[ective values are approximately 1.8 and 620 p'gC g-t.

a

1Ooo

ooa

oo

a) Charlottetown Fine Sandy Loam

2.0

200

Generally, a ratio above or below equilibrium would indicate

that soil C is increasing (aggrading phase) or decreasing

(degrading phase), respectively. However, actual or absolute

incieases insoil C would be dependent on overriding climatic

factors, such as the ratio of precipitation to potential

evapotranspiration (Mele and Carter 1993). Overall, deter-

mination of these critical values provides information on both

microbial biomass dynamics and organic matter equilibria.

CarbohydratesCarbohydrates represent a significant pool in soil organicmatter (5-20% of the total soil organic C). Soil carbohydrates

originate from plants, animals, and microorganisms, theircomposition varying accordingly. Most of the carbohydratefraction is present as a mixture of complex polysaccharides,

which in turn are composed of monosaccharides. Fivemonosaccharides usually represent more than 9O% of the

total hydrolyzable carbohydrates: glucose dominates,

followed by galactose, mannose, arabinose, and xylose.

Galactose and mannose are believed to be produced mainly

by microbes, whereas arabinose and xylose originate mostly

from plants (Cheshire 1977).Carbohydrates contribute to soil quality primarily through

their role in the formation and stabilization of soil structure.

Of all the organic matter fractions in soil, the polysaccha-

rides, because of their chemical structures, are likely to be

the most readily available source of energy for organisms

(Cheshire 1979). Physical protection of these polysaccha-

rides may, however, reduce this availability.Several approaches have been developed for the determi-

nation of soil carbohydrate content, most involving extraction

or hydrolysis followed by quantitative determination of the

hydiolysed saccharides (I,owe 1993). Sulfuric acid is usually

z-3

0.0

1.5

sU)(t(d,nE'-.9-o.go.g 0'5

c(dC')

o0.0

o

o

o

o3. o

o

o

oajO o

b) Kairanga Silty ClaY Loam

900 1100600 0

Microbial biomass C (Pg g-1 soil)

Fig. 3. Comparison of microbial biomass C to organic C in the microbial biomass in cropping systems ( o ) and grassland refe-rence areas

1J; in two ioil types; (a) Charlottetown fine rundy lourn' data from spring cereals under various tillage systems (adapted from Carter

iSgfl; Ol Kairanga silty clay loam: data from various time durations for maize or cereal cropping (adapted from Sparling 1992).

700

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376 CANADIAN JOURNAL OF SO'T SC'EVCE

used for hydrolysis; the determination of saccharide contentcan be done colorimetrically or by using gas or liquidchromatography.

In general, the amount of carbohydrates extractedincreases with the acid concentration a;d temperature ofhydrolysis. Satisfactory total hydrolysis of soil carbohy-drates can be achieved using a simple sequential treatment,starting with concentrated H2SOa, followed by diluteH2SO4 (Oades etal. 1970; Cheshire 1979). The concen_trated acid is used to hydrolyse the recalcitrant cellulose.Water at 20oC extracts less than l% of the total carbo_hydrate content; water at 80oC extracts 5-10%; and acidhydrolysis extracts l0-l0O%, depending on the acid con-centration and temperature of reaction. Extractability alsovaries greatly with soil type.

Simple colorimetric methods, variable in their selectivity(Cheshire 1979), are used to determine the total carbohydratecontent of the hydrolyzates. Some methods are subject tointerferences (Martens and Frankenberger 1990). Thi mostwidely used are the anthrone-sulfuric acid (Brink et al. 1960).thealkaline ferricyanide (Cheshire 1979). and the phenoi-sulfuric acid (Dubois et al. 1956) methods. Gas chroma_tography (Oades et al. t97O Baldock et al. t9g7) andhr^B]r_performance liquid chromatography (Angers et al.1988; Martens and Frankenberger t990) are used if thecarbohydrate composition of soil hydrolyzates is of interest.Measurements of carbohydrates are usually done on finely-ground air-dry soil and are therefore highly reproducible.Most of the methodological uncertainties irise from the widevariety ofextraction and hydrolysis procedures used. Thereis a need for standardizing these procedures ifcarbohydratesare to be used for assessing soil quality.

Soil carbohydrates have been primarily studied in relationto soil aggregation. Several studies have found sood correla_tions.betw_een carbohydrate content and soil mairoaggregatestability (Rennie etal. 1954; Acton er al. 1963 HaynesindSwift 1990; Angers et al. 1993b); however, others have not(e.g., Carter et al. 1993). Other components of the soilorganic matter such as the hydrophobic aliphatic fraction(Capriel et al. 1990) and fungal hyphae (Tisdall and Oades197.9) are probably involved in macroaggregate stability.Baldock et al. (1987) compared the effecti of 15 yr of maiiecropping to 15 yr of bromegrass on the water-stable aggre_gation of a silty loam. They found large differences in soilaggregation induced by the two crops but no difference intotal carbohydrates, and consequently no correlation betweenthe two factors. Their extraction procedure was a completehydrolysis r ^ing concentrated sulfuric acid. They suggestedthat if carbohydrate materials were responsible fbr the rapidchanges in aggregate stability caused by changes in crop-ping practices, then a specific component of cirbohydraiematerial must be involved.

The use of dilute-acid hydrolysis treatmenrs (0.5 M-2.5 MH2SO4) has produced different results from the above.Angers and Mehuys (1989) measured an increase in water-stable aggregation ofa clay soil after only 2 yr ofalfalfa orbarley, compared with continuous maize. This increase inwater-stable aggregation was concurrent with an increase incarbohydrates as measured by hydrolysis with dilute acid

0.5 M). The involvement of carbohydrates in aggregatestabilization was confirmed with a selective periodate treat-ment. Roberson et al. (1991) studied the shortterm (2 yr)influence ofcover crops on soil aggregation and soil organicmatter fractions. They found that cover crops significantlyincreased resistance to aggregate-slaking and the carbohydratecontent of the soil's heavy fraction extracted with dilute acid(2.5 M). They argued that this carbohydrate fraction wasmainly composed of microbial extracellular polysaccharides.Their conclusion was supported by the correlation betweenbiomass C and this carbohydrate fraction (r : 0.73x*;.

Hot-water extractable carbohydrates change rapidly whencropplng systems are modified, and these changes arecorrelated with changes in aggregation (Haynes and Swift1990; Haynes et al. 1991; Angers et al. l993a,b). Thiscarbohydrate fraction may also represent the specificcomponent of carbohydrate material referred to by Baldocket al. (1987). As suggested by Haynes and Swift (1990), thispool probably equates with mucigel, predominantly ofmicrobial origin, and Angers et al. (1993a) found a strongcorrelation (0.78**) between this carbohydrate pool andmicrobial biomass C.

Angers et al. (1993a) found rhar rhe rario of both mild-acid and hot-water soluble carbohydrates to total organic Cwas greater under no-till than under moldboard-plowed soilafter only three cropping seasons (Fig. a), suggesting anenrichment of labile carbohydrates in the organic matterunder reduced tillage. Similar results have been obtainedpreviously by Angers and Mehuys (1989), when comparingthe effects of cropping to alfalfa (Medicago sativaL.),barIey(Hordeum wlgare L.), and maize on dilute-acid hydrolyzablecarbohydrates (Fig. 5). Haynes et al. (1991) also found thathot-water soluble and dilute-acid hydrolyzable carbohydrateschanged more rapidly than total organic C when manage-ment practices were changed from arable to pasture. Theseresults suggest that these labile fractions ofthe carbohydratepool could be sensitive indicators of changes in organic mhtterquality, especially in comparisons of cropping systems. Theinvolvement of labile carbohydrates in the short-term changesin aggregate stabilify should reinforce this suggestion.However, there still remain uncertainties as to whether hot-water soluble or dilute-acid hydrolyzable fractions betterrepresent the labile carbohydrate fraction. More experimentalwork is needed to clarify this point.

Soil EnzymesSoil enzymes are molecular sub-systems of soil organismsthat can be used as indicators ofsoil quality iftheir activitiesare affected by environmental variables and farming prac-tices. Enzymes are biological catalysts that lower the energyrequired to activate biochemical reactions. Soil enzymes areproteins that are synthesized by plants and soil organismsduring metabolism and are found in living organisms (bioticenzymes), or in dead cells of microbial and plant tissues(abiotic enzymes), or complexed with organic and mineralcolloids (Dick 1994). The total enzyme activity of a soildepends on the amount of extra- and intra-cellular enzymes(Skujins 1967). A system of heterogeneous soil enzymesoperating in a cascade manner controls the decomposition

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GREGORICH ET AL. - ASSESSMENT OF SO'L ORGANIC MATTER IN AGRICULTURAL SO'LS 377

hot water dilute acid

0.70

at,

o- 0.65ct

p

-co-o 0.60

(sou)ct. - 0.55

s

of soil organic matter and human-added amendments. Plantresidue components must be depolymerized and transformedbefore becoming the backbone of soil humus. The enzyme

B-glucosidase depolymerizes cellulose into subunits ofglucose that can be used by soil heterotrophs as carbon and

energy sources. Other important glycosidases are a- and

B-galactosidase (Tabatabai 1982). Mineralization of soilorganic-N to NHa+ is accomplished by a series of enzymaticreactions involving proteases, deaminases, amidases, and

ureases. Arylsulfatase and acid and alkaline phosphomo-noesterases control the S and P dynamics in terrestrialecosystems.

Enzyme activities are critical indicators of organic matterquality because enzymes control nutrient release for plantand microbial growth (Skujins 1978; Burns 1978), gas

exchange between soils and the atmosphere (Conrad et al.

1983), and physical soil properties (Martens et al. 1992).

Soil enzyme research has been reviewed by Skujins (1967),Burns (1978), and Klein et al. (1985). Recently Dick (1994)

suggested that soil enzyme activities be used as biochemical/biological indicators of soil quality.

Methods used to measure the activity of soil enzymes aregenerally simple, rapid, accurate, and reproducible (Tabatabai

H.ffis

1o.o og)(n

o

-9.5 qoJ

o-

0)9.0

o@

r::illi'l:

:::i::r.lari::::

MP

TILLAGE TREATMENTMP = MOLD BOARD PLOWED CP = CHISEL PLOWED NT = NO-TILLAGE

NTCPCPMP

Fig. 4. Effects of 4 yr of tillage practices on the proportion of C present as hot-water extractable and dilute-acid hydrolyzable carbohy-drates

ini Kamouraska cliy. The vJrtical bars represent tire Least Significant Differences at the 0.05 level. Adapted from Angers et al. (1993a)'

(Based of 407o C in carbohydrates.)

1982; Peterjohn 1991). The activity of an enzyme is

determined by measuring product formation or substrate

remaining during incubation of soil samples (Tabatabai

1982). Methods have been standardized for substrate

concentration, temperature and time of incubation, buffers,and pH. Most standard methods used in terrestrial ecosystem

studies involve the incubation of soil samples with a sub-

strate at 37'C for a few hours (Table 4) and have been used

successfully in soils differing in parent materials, climaticareas, and agronomic management.

The sensitivity of soil enzymes to environmental and

farming disturbances can be quantified using two approaches:

measuring enzyme-related activities and determining kineticparameters as defined by the Michaelis-Menten model.

In general, soil enzyme activities are directly proportionalto the content ofsoil organic matter (Skujins 1967; Franken-

berger and Dick 1983; Baligar and Wright l99l; Baligar et

al. 1991). Soil enzyme activities are higher in surface than

in subsurface horizons and follow the distribution oforganicC in the soil profile (Baligar and Wright l99l; Baligar et

al. l99l; Frankenberger and Tabatabai 1991). Erosion and

excessive tillage, which decrease soil organic matter content

and the thickness of the A horizon, may therefore induce

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378 CANADIAN JOURNAL OF SO't SC'ENCE

ct,

o(uL

o) z.s-co-o

o7.O

o(5

o;e

6.5

Soil enzyme activities respond to cultivation, additions offertilizer and organic amendments. Adenosine deaminaseactivity has been shown to contribute significantly to Nmineralization and was higher in an Andept under forest thanunder cultivation (Sato et al. 1986). Cultivation of nativegrasslands and forest ecosystems decreased soil organic Cand the activities of dehydrogenase, urease, phosphomono-esterases, and arylsulfatases in a soil climosequence of theCanadian prairies, and the activity of these enzymesdecreased even further in crop rotation systems that includesummerfallow (Dormaar 1983; Gupta and Germida 1986).Dehydrogenase and urease activities were lower in a BrownChernozem fertilized with N and P than in the same soilfertilized only with P (Biederbeck and Campbell 1987). Thecontinuous application of P for 11 yr to soil under continuouswheat and wheat-fallow rotations inhibited the activity ofphosphomonoesterases compared to that measured in plotsfertilized with N and P. Enzyme activities were found toincrease in a Gray Luvisol after additions of NPKS, NS, andmanure (Khan 1970). A significant negative correlationbetween NHa- and L-histidase was found in a BlackChernozem fertilized with NHaNOs at a rate of 70 ke Nha-r iBurton and McGill 1992). Fields cropped to gi"nmanure for 27 yr showed significantly higher activities ofurease, phosphomonoesterases, and dehydrogenase thanthose receiving inorganic fertilizers (Bolton et al. 1985),which is consistent with results reported for a Belgian soil(Verstraete and Voets 1977). In comparison with anunamended soil, additions of poultry manure, sewage sludge,barley straw, or fresh alfalfa hay increased the activity ofkey enzymes of the C, N, P, and S cycles. Addition of plantmaterials significantly increased B-glucosidase activityrelative to that measured with additions of poultry manureand sewage sludge (Martens et al. 1992). Different croppingsystems produced a significant effect on B-glucosidaseactivity within 2 yr, even though there was no measurabledifference in total C content (Dick 1994).

We hypothesize that kinetic parameters used to characteizeenzyme content and maximum reaction velocity (Vmax),enzyme-substrate affinity (Km), and inhibition reactions (Ki)may be used to assess the change caused by environmentalfactors, substrates, or chemical compounds added to soils.

BARE CORNSOIL

CROPPING

BARLEY ALFALFA

TREATMENT

Fig. 5. Effects of 2 yr of different crops on the proportion of Cpresent as dilute-acid hydrolyzable carbohydrates in a Kamouraskaclay. The vertical bar represents the Least Significant Differenceat the 0.05 level. Adapted from Angers and Mehuys (19g9).(Based of 40% C in carbohydrares.

losses in total amount and activity of enzymes by dilutingthe concentration of organic C in cultivated Ap horizons withsoil from the B horizon. Overgrazing and erosion resultedin decreased enzyme activities in semi-arid soils (Sarkisyanand Shur-Bagdasaryan 1967). Temporal fluctuations ofenzyme activities are related mainly to differences in soilmoisture and are almost independent of small variations insoil organic C and N (Ross et al. 1984). The temporal andspatial variabilities of soil enzymes are unknown at thelandscape level in degrading, sustaining, and aggradingsoils and may need to be defined in relation to reference sitesbefore enzyme parameter values are used as indices ofsoil qualiry.

Table 4. Methods to measure the activity of some enzymes involved in the C, N, P and S cycles in terrestrial ecosysrems

Enzyme Substrate Incubationz Reference

Dehydrogenase0-glucosidaseUreaseProteaseHistidaseGlutaminaseNucleosidaseArylsulfatasePhosphomonoesterases

TriphenyltetrazoliumP-nitrophenyl - D-glucosidaseUreaCaseinL-histidineL-glutamineAdenosineP-nitrophenylsulfateP-nitrophenylphosphate

37, 243't, 1

50, I37, 48tt,I30,7237, I3'7, 72pH 6.5r, I lx

Casida et al. (1964)Tabatabai (1982)Tabatabai (1982)Ladd and Butler (1972)Frankenberger and Johanson (1982)Frankenberger and Tabatabai (1991)Sato et al. (1986)Tabatabai and Bremner (1970)Tabatabai and Bremner (1969)

zTemperature ("C) and time (h).YAcid phosphalase.xAlkaline phosphatase.

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GREGOR'CH ET AL. - ASSESSMENT OF SO't ORGANIC MATTER IN AGRICULTURAL SO'IS 379

In Canadian prairie and boreal soils, Km for arylsulfataseand Ki for urease have been shown to be sensitive to culti-vation, management, and additions of substrates (Gupta andGermida 1986; Monreal et al. 1986). Km and Vmax valuesfor arylsulfatase were lower in cultivated than in referencenative Chernozems and Gray Luvisols (Gupta and Germida1986) and were highly correlated with organic C (r' :0.97 and 0.99, respectively, P < 0.01). Critical Km valuesfor arylsulfatase in cultivated degraded Gray Luvisol and

Dark Brown Chernozems ranged between 1.72 and2.67 mM(Fig. 6). The boundaries shown in Fig. 6 are an attempt toseparate arylsulfatase kinetic parameters in degraded soilsfrom those in reference or undisturbed soils. Further develop-ment of indices for soil quality requires research to moreaccurately define the boundaries for kinetic parameters indegrading, sustaining, and aggrading systems.

MINIMUM DATA SETSIn this review the approach taken to assessing soil organicmatter 'quality' is analogical (rence the use of terms "health"and "fitness"). Use of analogy underlines that "quality"relates to the value placed by society upon the functions ofsoil organic matter quality in the environment. It is a wayof examining the whole soil, not just its parts. Data sets are

the tools necessary to provide a picture of soil quality.

Components of the data set will change depending on the

type ofpicture required and who requires the picture. Thus,

the strategy of data sets is dynamic and flexible. They become

the means whereby interest groups (e.g., scientists) or society

can relate to, utilize, or evaluate soil for a specific reason

or purpose. Without data sets, the functional role of soil (incontrast to its analytical parts) remains an enigma.

The development of minimum data sets involves the

selection of a small subset of attributes that will provide

a practical assessment of a specific soil organic matterfunction. Table 5 summarizes the attributes of soil organicmatter quality and the most frequently used methods ofmeasurement. Many of the attributes are interrelated and may

be used to estimate other attributes through the use offunctional relationships called pedotransfer functions (Bouma

1989; Pierce and Larson 1993). Numerous pedotransfer func-

tions have been developed from information in a specific area

or from data on different soil types; a complete list wouldbe extensive and has not been included here. A limitednumber of processes that could be estimated frompedotransfer functions are given in Table 5.

The attributes listed in Table 5 are not considered a strict"minimum data set" since not all of them are required to

30,x(oE

If

oEJofot^

.t

Organic carbon (%)

Fig. 6. Relations between Km, Vmax of arylsulfatase and organic C in Gray Luvisol and Dark Brown Chernozems. Points in the lower

left box are data from sites cultivated for 5, 40 and 69 yr (adapted from Gupta and Germida 1986).

Km = 1.9 + 5.9 log (%C) R'= 0.97

Vmax = 88 + 1382 log (%C) R'? = 0.99

(cultivated for 5y, 40y and 69Y)

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380 CANADIAN JOURNAL OF SO'L SC'ENCE

Table 5. Attributes of soil organic matter quality, methods, soil organic matter quality indicators, and related pedotransfer functions

Soil organic matterattribute Methodologyz

Soil organic matter qualityindicators

Related processes orattributes whichcan be estimated bypedotransfer functions

Organic Cand total N

Light fraction

Mineralizable Cand N

Microbial biomass

Carbohydrates

Enzymes

Wet or dry oxidation

Sieving or densimetricfractionation

Soil incubation

Fumigation extractionor substrate- inducedrespiration

Hot-water extraction

Table 4

Carbon and nitrogen massand balance

Plant residue decompositionorganic matter storage

Total metabolic activity ofsoil organismsNet flux of inorganic Nfrom mineralization andimmobilization process

Carbon and nitrogen inmicroorganismsLabile carbon and nitrogen

Soil structureLabile carbon

K' V.u*, K;

Soil structureNutrient supply

Total organic CSoil respiration rateNutrient supply

Microbial activityNutrient supply

Soil structureNutrient supply

Soil structuralstability

Biochemical activityNutrient supply

zMethodology discussed in text.

characterize changes in soil organic matter quality. Differentsoil processes require different minimum data sets, becauseof the multi-faceted role of soil organic matter. Table 6outlines various minimum data sets for processes involvingsoil structure, nutrient storage, and biological activity. Soilstructural processes, such as the formation and stabilizationof aggregates and macropores, are affected by the totalorganic matter, microbial biomass, and carbohydrates.Nutrient storage in soils can be assessed by evaluating thequantity of organic C and N. In addition, the total amountof, and the proportion of total organic C and N in, microbialbiomass, mineralizable C and N, and the light fraction willalso provide information on soil nutrient storase. Attributessuch as microbial biomass, enzymes, and mi-neralizable Cand N are measures of biological activity in soils.

A holistic approach to assessing soil organic matter qualityis important because the suitability of a soil for sustainingplant growth in agroecosystems is a function, not only ofthe biological activity, but also of the soil chemical andphysical properties. Appropriate use of a minimum data setwill depend on how well the relevance of these indicatorsis interpreted in consideration of the agricultural system ofwhich they are a part. For example, soil pH, which is

Table 6. Some minimum data sets for estimating soil organic matterquality

Processes Minimum data sets

Soil structure

Nutrient storage

Biological activity

Total organic C and NMicrobial biomassCarbohydrates

Total organic C and NMicrobial biomassMineralizable C and NLight fraction and macroorganic matter

Microbial biomassEnzymesMineralizable C and N

substantially lower in soils under blueberries than it is undermaize production, plays an critical role in determining thebiological activity in soil. Bulk density in soils under differenttillage treatments may be substantially different, and accuratecomparisons of biological properties may require that theresults be expressed on a volumetric basis (Doran and Parkin1994). Thus, the interpretation ofthe relevance ofbiologicalindicators in the absence of soil physical and chemicalattributes may be of little value or possibly misleading forassessing soil organic matter quality in agricultural soils.

CONCLUSIONS AND SUMMARYConcerns about the effects of agricultural practices on theenvironment and the effect of the environment on cropproduction have kindled interest in quantifying their impacton soil quality. Use of a minimum data set, comprising anumber of soil biochemical properties sensitive to manage-ment, perturbations, and inputs to the soil, is a criticalfirst step for assessing soil quality. Soil organic matter isconsidered a key attribute of soil quality. In this review,organic matter is characterized to distinguish sub-attributesor biological and biochemical properties that describe thequality of organic matter.

Although much progress has been made in determiningsensitive indicators of soil organic matter quality, more workis required, for example, to determine the variability of eachproperty in the data set. Each properfy may need to becharacterized for its temporal variation during a growingseason and for its spatial variation, both laterally across alandscape and vertically through a soil profile. Theseproperties should also be examined in different agriculturesystems over a wide range of climatic and soil conditionswhere it is known that soil quality, and in particular soilorganic matter quality, has been affected. Many studieshave assessed the response of biological properties to thedegradation of soil quality but the response ofthese properties

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GREGORICH ET AL. _ ASSESSMENT OF SO't ORGANIC MATTER IN AGRICULTURAL SO'IS 381

to the amelioration of soil quality may be different and should

also be characterized.Other key properties could be added to this minimum data

set in the future. Water soluble organic carbon (WSOC)is a very active soil organic component, and flow of C

through it supplies substrate for biomass turnover (McGillet al. 1986). Thus, WSOC is closely linked to the microbialbiomass and in the future could be included in a minimumdata set used to assess biological activity in soil. Soil meso-

and macrofauna fragment and redistribute plant residues

which can enhance decomposition of organic matter and

nutrient cycling (Coleman et al 1989; Linden et al. 1994).

They can also modiff soil structure through the formationofmacropores and aggregates (Lee and Foster l99l). Thus'these faunal indicators could be chosen for minimum data

sets to evaluate nutrient storage and soil structure. Therelative molar distribution of amino-acid N has shownpromise for describing changes in soil organic matter quality(Campbell et al. 1991b). Measurements of DNA, ATP, and

AEC in soil could potentially be reliable indicators ofbiological activify (Tsai and Olson 1992, Ciardi et al' 1993).

However, more research is required to determine how these

properties are affected by agricultural management practices,

to establish a standard set of procedural and pretreatmentprotocols, and to evaluate the quantitative relationships ofthese attributes to soil quality.

ACKNOWLEDGEMENTSThis review is the result of discussions and research by the

authors for the Soil Quality Evaluation Program funded

through the National Soil Conservation Program' We thank

Drs. H.H. Janzen and C.A. Campbell for their thoroughreviews of early versions of the manuscript and Dr. D.F.Acton for his able leadership in the SQEP.

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