Soil Organic Matter Pools and Their Associations with Carbon Mineralization KineticsR. Alvarez* and C. R. Alvarez

ABSTRACTThe labile component of soil organic matter (SOM) plays an impor-

tant role in short-term nutrient turnover. Our objectives were (i) toestablish the relationships between carbon in soil density fractions withcarbon mineralization and the microbial biomass under contrastingconditions, (ii) to compare the goodness of fit of different mathemati-cal models to describe carbon mineralization, and (iii) to evaluate therelationships of the SOM pools and the mineralization parametersestimated by the best kinetic model. Twenty-eight soil samples werecollected from a fine, illitic, thermic Typic Argiudoll localized inArgentina. These samples differed in the soil management (pastureand agriculture), tillage systems (chisel tillage, plough tillage, andno-tillage), crop rotation, or depths. Microbial biomass was highlycorrelated with total carbon (r2 = 0.777, P < 0.001) and carbon inthe SOM light density fraction (density < 1.59 g mL ') but less stronglycorrelated to medium (density 1.59-2.0 g mL~') and heavy (density> 2.0 g mL"1) soil fractions. Carbon in the soil light fraction wasstrongly related to the carbon mineralized at 10 and 160 d of incuba-tion. The exponential and hyperbolic models showed a good descrip-tion of the mineralization data (r2 > 0.982). The application of modelswhich considered two organic matter pools could not describe themineralization of some samples. The hyperbolic model estimatedhigher potentially carbon mineralizable pools (C(l) and semidecompo-sition time periods than the exponential one. The Co estimated bythe exponential model were similar to the carbon content in the soillight fraction. This soil organic component seemed to be the drivingvariable of microbial activity and a good predictor of soil potentialcarbon mineralization.

THE LABILE COMPONENT of soil organic matter playsan important role in the short-term nutrient turn-

over and is responsible for the temporary soil structuralstability (Tisdall and Oades, 1982). One possible wayto characterize this fraction is through densimetry. SOMcan be divided into (i) light fraction, which consists of

Dep. de Suelos, Facultad de Agronomia, Univ. de Buenos Aires, Av.San Martin 4453 (1417), Buenos Aires, Argentina. This research wassupported by the UBACYT AG-089 program of the University ofBuenos Aires. Received 22 Dec. 1997. *Corresponding author(ralvarez@mail.agro.uba.ar).

Published in Soil Sci. Soc. Am. J. 64:184-189 (2000).

mineral-free organic matter composed of partly decom-posed plant and animal residues, which turn over rapidlyand have a specific density considerably lower than thatof soil minerals; and (ii) heavy fraction, composed ofmore processed decomposition products, which turnover more slowly and have a high specific density be-cause of their intimate association to soil minerals(Christensen, 1992; Barrios et al, 1996). Dalai andMayer (1986) found for Australian clay soils that therate of loss of carbon from the light fraction (density< 2 g mLr1) was 2 to 11 times greater than that of theheavy fraction. In contrast, Janzen et al. (1992) observedin Canadian Mollisolls that carbon in the light fraction(density < 1.7 g mL"1) was higher in treatments whichinclude perennial forage in the rotation and lower inthose with summer fallow. In Pampean Mollisolls ofArgentina, the implementation of no tillage producedan accumulation of carbon in soil light fraction (density< 1.6 g mL"1) at the soil surface (0-5 cm layer) inrelation to plowed plots (Alvarez et al., 1995, 1998a).

Carbon and nutrient turnover are mediated by thesoil microbial biomass, which responds to residues ortillage management (Dalai et al., 1991). Microbial bio-mass is usually related to the carbon in soil light fractionand to the in vitro carbon mineralization (Bremer etal., 1994; Alvarez et al., 1995,1998a). Because soil man-agement generally affects these variables more than to-tal organic carbon, many authors have suggested thatthey could be early indicators of future trends in totalSOM (Bremer et al., 1994).

The mathematical description of in vitro carbon andnitrogen mineralization is another interesting approachto characterizing SOM. The exponential model waswidely used to describe the carbon and nitrogen miner-alization process (Stanford and Smith, 1972; Riffaldi etal., 1996). From this model the potentially mineralizablecarbon pool of soils (C0) may be estimated. Co is assumedto be a readily mineralizable carbon component which

Abbreviation: SOM, soil organic matter.

ALVAREZ & ALVAREZ: SOIL ORGANIC MATTER POOLS AND CARBON MINERALIZATION 185

mineralized at a constant rate (k) proportional to thesize of the pool. Another single-component model isthe hyperbolic model, which considers that the time tomineralize 50% of C0 pool (t\/2) increases as the incuba-tion time gets longer, according to the rise of carbonchemical protection. Alternatives to these one-compo-nent models are those which consider two organic mat-ter pools with different stability to microbial attack (Rif-faldi et al., 1996). Several authors (e.g., Bonde andRoswall, 1987) proposed the use of the double-exponen-tial model to improve the agreement with experimentalmineralization data. This model assumes that the or-ganic matter pool can be divided into two components,a labile pool (CL) decomposing exponentially with aconstant rate (kL), and a resistant pool (CR) also decom-posing exponentially at a much lower constant rate (&R).A simplification of this model is the exponential andlinear version, which consider a labile pool decomposingwith an exponential kinetics and a resistant pool decom-posing linearly, according to the relative shortness ofthe incubation periods compared with the turnover ofthe resistant pool (Bonde and Roswall, 1987). Otherauthors have found that the exponential plus a constantmodel could be useful to describe an initial mineraliza-tion flush present in some soil samples (Bonde andLindberg, 1988). This model contains a parameter (CL)defined as an easy decomposable organic matter whichproduced an initial mineralization flush during the firststage of the incubation (Riffaldi et al., 1996), and aresistant pool (CR) decomposing exponentially. This ini-tial mineralization flush was attributed to the drying andrewetting of samples or other type of sample handling(Beauchamp et al., 1986).

The relationships between SOM pools isolated byphysical or chemical techniques and the potentially min-eralizable organic pool obtained through the mathemat-ical modeling of carbon mineralization have not beenwidely investigated under different soil managements.Our objectives were (i) to establish the relationshipsbetween carbon in soil density fractions with carbonmineralization and the microbial biomass in a Mollisollunder contrasting cropping conditions, (ii) to comparethe goodness of fit of different mathematical models todescribe the kinetics of carbon mineralization, and (iii)to evaluate the relationships of the SOM pools and themineralization parameters estimated by the best ki-netic model.

MATERIALS AND METHODSSoil and Experimental Sites

The samples were obtained from the INTA ExperimentalStation located at Pergamino (Argentina, 33°56'S; 60°34'W).The climate is humid and temperate, with an annual rainfallof 1000 mm and a mean temperature of 16.5°C. The soil is aPergamino series (fine, illitic, thermic, Typic Argiudoll). Themain characteristics of the top 20 cm were 27% of clay, 57%of silt, and pH (soikwater 1:2.5) 5.8, with no statistically signifi-cant differences of these soil parameters between depthsthroughout this layer. Twenty-eight soil samples were col-lected from different field experiments. These samples dif-fered on the basis of the soil management, tillage systems,crop rotation, or depths (Table 1). The values of each sampleare the mean of four plots. The total C content of the28 samples ranged from 13.8 to 36.9 g C kg"1 soil.

Analytical and Statistical MethodsFresh soil samples were homogenized by hand. Soil micro-

bial biomass (biomassC) was determined by the fumigation-incubation method (Jenkinson and Powlson, 1976) as de-scribed by Alvarez et al. (1995). A k factor of 0.45 was usedto convert CO2 C production to biomass C (Oades and Jenkin-son, 1979).

Soil density fractionation was performed with both carbontetrachloride (density = 1.59 g mL"1) and bromoform-ethanolmixture (density = 2.00 g mL"1). Air-dry soil samples weresieved (<500 |o,g) and plant residues were forced to pass thesieve. Five grams of soil was weighed into a 50-mL beaker,and after adding 30 mL of the separation liquid, was vigorouslyagitated 1 min by hand and then centrifuged at 1000 g [relativecentrifugal force (RCF) = 1000] for 5 min. The supernatantwas filtered through fiberglass under suction. Carbon in wholesoil and in the two light density fractions was determined bywet digestion (Amato, 1983).

The SOM light fraction (light fraction C) was defined asthe carbon contained in the supernatant of soiltcarbon tetra-chloride mixture (density = 1.59 g mL"1). Soil organic matterheavy fraction (heavy fraction C) was estimated as the differ-ence between total C and the carbon quantified in the superna-tant of soil:bromoform:ethanol mixture (density = 2.00 gmL"1). Carbon content of the SOM medium fraction (mediumfraction C) was calculated as the difference between carbonin fractions with density < 2 g mL"1 and the light fraction C.

The metabolic ratio was calculated as the ratio between thecarbon respired in 10 d of incubation (respired C) from nonfumigated controls and the biomass C. In vitro aerobic carbonmineralization was measured during 160 d (mineralized C),at 30°C and 50% of soil water holding capacity. The equivalentof 100 g of dry soil were incubated in a 400-mL flask and the

Table 1. Organic carbon content (g kg"1 dry soil) of samples from a fine, illitic, thermic Typic Argiudoll under contrasting soil man-agements.

RotationGrasslandGrasslandWheat-soybean-cornWheat-soybean-cornWheat-soybean-cornWheat— soybean— cornWheat-soybean-corn3 yr corn-soybean3 yr corn-soybean

Years1616161612121244

Tillage system

Plow tillageNo-tillagePlow tillageNo-tillageChisel tillagePlow tillageNo-tillage

Last crop

CornCornWheatWheatWheatSoybeanSoybean

Fertilization(kg N ha "')

9090000

100100

Organic C (g kg~> dry soil)Depth (cm)

0-5

36.9

21.227.919.3

21.514.921.8

5-1019.623.221.719.719.118.519.915.814.6

10-15

16.822.519.919.118.918.016.816.113.8

15-20

21.4

17.117.0

186 SOIL SCI. SOC. AM. J., VOL. 64, JANUARY-FEBRUARY 2000

Table 2. Models used to described carbon mineralization kinetics.Model Equation

Exponential Cmin = C0(l - e~*')(Stanford and Smith, 1972)

Hyperbolic Cmin = C0 tltm t(Jiima et al., 1984)

Cmin = CL(1 - e *LI) + CR(1 - e-*•")Doubled exponential(Bonde and Rosswall, 1987)

Exponential plus linear Cmin = CL(1 - e~*Lt) + C t(Bonde et al., 1988)

Exponential plus a constant Cmin = CL + CR(1 - e~"")(Jones, 1984)__________________ _____________

Cmin = mineralized carbon, d = potentially mineralizable carbon pool,k = mineralization constant, fraction mineralized per time unit, I =time, (|,2 = semidecomposition time, time needed for the decompositionof 50% of C0, CL - carbon labile pool, &L = labile pool mineralizationconstant, CR = carbon resistant pool, kR - resistant pool mineralizationconstant, C = resistant pool mineralization rate, resistant carbon min-eralized per time unit.

CO2 C production was periodically (10, 20, 40, 70, 100, 130,and 160 d of incubation) determined by alkali absorption(Alvarez et al., 1995). The cumulative carbon production wasfitted to different mathematical models (Table 2). The modelswere fitted to carbon mineralization data by the non-linearregression with the Statgraphics software package (Manugis-tics, Inc., Rockville, MD). The relationships between the dif-ferent variables were evaluated by regression analysis andtested by their F values.

RESULTSBiomass C was positively and highly correlated with

total C and light fraction C, but had low relationshipswith medium and heavy fraction C (Table 3). Microbialactivity, evaluated as respired C, was positively relatedto total C and carbon present in the different soil densityfractions, but the light fraction C showed the highestcorrelation with respired C (Table 3). Poor relationshipswere observed between the different SOM densityfractions.

The mineralized C was associated with biomass Cand carbon present in the soil density fractions. Themineralized C presented the highest correlation withthe light fraction C and lowest with the medium fractionC (Fig. 1). The amount of carbon mineralized in 160 dof incubation was around five-fold the biomass C and50% higher than the light fraction C. In contrast, me-dium and heavy fraction C were 1.8 and 14 times higherthan mineralized C. When the biomass C and the SOMdensity fractions were expressed as proportion of totalC, a positive and high relationship was found betweenmineralized C/total C ratio and the biomass C/ total

Table 3. Coefficients of determination (r2) between the SOMpools. All coefficients are significant at P < 0.001.______

Light Medium HeavyBiomass C Respired C fraction C fraction C fraction C

Respired CLight

fraction CMedium

fraction CHeavy

fraction CTotal C

0.875

0.769

0.315

0.5250.777

0.924

0.374

0.4290.751

0.333

0.3750.706

nsns 0.870

r*= 0.857P< 0.001

r*= 0.936P< 0.001

1 2 0 4 8Biomass-C Light fraction-C

V 1

II

r*= 0.349P<0.001

r*= 0.429P<0.001

30Medium fraction-C

0 15Heavy fraction-C

(mg C g'1 soil) (mg c g-i soM)

Fig. 1. Relationships between the carbon mineralized in 160 d (miner-alized C) with carbon in the different organic matter pools. Depths:• 0-5 cm, O 5-10 cm, • 10-15 cm, n 15-20 cm.

C ratio and the light fraction C/total C ratio (Fig. 2).However, the light fraction C/total C showed a highercorrelation with the proportion of total C mineralizedthan did the biomass C/total C. Otherwise, the total Cmineralized was negatively related with the ratio heavyfraction C/total C. No statistically significant relation-ship was found between the medium fraction C/total Cand the proportion of total C mineralized.

0,1

r*= 0.656p< 0.001

r*= 0.830P< 0.001

0,02Biomass-C

Total-C

0,04 0,1Light fraction-C

Total-C

0,2

0,2

s 0,1

r*= 0.035 *P= ns

r*= 0.502PC 0.001

0,2 0,5Heavy fraction-C

Total-C

0 0,1Medium fraction-C

Total-CFig. 2. Relationships between the fraction of the total carbon mineral-

ized in 160 d and the fraction of the total carbon in the differentsoil organic matter fractions. Depths: • 0-5 cm, O 5-10 cm, •10-15 cm, n 15-20 cm.

ALVAREZ & ALVAREZ: SOIL ORGANIC MATTER POOLS AND CARBON MINERALIZATION 187

Table 4. Fit of different models to carbon mineralization data forthe 28 soil samples.________________________

Cases where Minimum Maximum MeanModel model fit r1 r1 r1

ExponentialHyperbolicDoubled exponentialExponential plus linearExponential plus a constant

2828202018

0.9820.9830.9880.9930.984

1 0.9941 0.9951 0.9981 0.9981 0.995

Both the exponential and hyperbolic models providedsatisfactory fits of the cumulative CO2 C productiondata in all samples (Table 4). The double-exponentialmodel gave higher coefficients of correlation than theone-component models, but it could only be fitted tothe mineralization data of 20 soil samples. The exponen-tial plus linear model showed a good fit to the carbonmineralization data, but estimated negative resistantpool mineralization constants (C) for eight samples. Theexponential plus a constant model also described satis-factory the in vitro mineralization, but calculated nega-tive values for the carbon labile pool (Co) in 10 samples.

The potentially mineralizable carbon pool estimatedby the exponential model (exponential C0) and the hy-perbolic model (hyperbolic C0) were highly correlatedbetween each other (Fig. 3). Additionally, their semide-composition times (?) were positive and highly associ-ated too. The hyperbolic model gave Co values that wereabout 55% greater and 1 values 2 to 2.8 times greaterthan those estimated by the exponential model. Whenthese functions were adjusted to mineralization datasets corresponding to shorter incubation time periods(i.e., 130 or 100 d), both models estimated lower poten-tially mineralizable carbon pools (Co), but still gave highr2 values (not shown).

The exponential model was selected because of itssimplicity and the goodness of fit for all samples. Theexponential C0 was closely and linearly related to themineralized C, with a slope of 1.07 (Fig. 4). Thus, similarto the relationships previously noted (Fig. 1 and 2) formineralized C, the exponential Co was highly correlatedwith biomass C (r2 = 0.750, P < 0.001; not shown) andwith light fraction C (Fig. 4). Otherwise, the exponential

S ^y=1.07x1*= 0.919P<0.001

y=1.03xr*= 0.820P<0.001

0 4 8 0 4 8Mineralized-C Light fraction-C(mg C g"1 soil) (mg C g'1 soil)

Fig. 4. Correlation between d of the exponential with the carbonmineralized in 160 d of incubation (mineralized C) and the lightfraction C. Depths: • 0-5 cm, O 5-10 cm, • 10-15 cm, n 15-20 cm.

Co showed low correlation with the heavy fraction C(r2 = 0.410; P < 0.001) and with the medium fractionC (r2 = 0.250, P < 0.001). The mineralization constantof the exponential model (exponential k) presented apositive but weak correlation with the ratio light fractionC/total C (r2 = 0.200, P < 0.05), and a negative associa-tion with the ratio heavy fraction C/total C (r2 = 0.240,P < 0.01).

DISCUSSIONSoil Organic Matter Fractions, Microbial

Activity, and Carbon MineralizationThe biomass C, mineralized C, and light fraction C

have been proposed by many authors to be used asindicators to evaluate the effect of different soil manage-ment practices because changes in these fractions mayprecede future changes in soil organic matter (Janzenet al., 1992; Bremer et al., 1994). In our case, the biomassC varied from 67 to 1270 (Jig C g~' soil (a 19-fold differ-ence), light fraction C from 160 to 6630 jxg C g"1 soil(a 41-fold difference), and respired C from 30 to1060 (jig C g"1 soil (a 35-fold difference), showing moresensitivity to the different soil managements or depthsthan the total C which presented only a 2.7-fold differ-ence between the samples. The biomass C and the mi-

12

o =s 8oS oII

O) 6

y=1.45xr*= 0.982, P<0.001

160

a

80

y= 2.80 xr*= 0.990, P<0.001

Exponential-Co,,-1.

12 1600 80Exponential-t,;2

(mg C g-1 soil) (weeks)Fig. 3. Correlations between potentially mineralizable carbon pool (d) and semidecomposition time (tm) estimated by the exponential and

hyperbolic kinetic models. t,K for the exponential model was estimated as: 0.693/exponential k. Depths: • 0-5 cm, O 5-10 cm, • 10-15 cm,D 15-20 cm.

188 SOIL SCI. SOC. AM. J., VOL. 64, JANUARY-FEBRUARY 2000

crobial activity (respired C) were highly correlated withlight fraction C; so these parameters were probably reg-ulated by this SOM soil density fraction. Janzen et al.(1992) found a similar relationship between the lightfraction C (density < 1.7 g mL"1) and the carbon miner-alized in 14 d of incubation for cold Canadian soils underdifferent rotations, but in that study the biomass C wasnot related with the carbon mineralized during the incu-bation.

The metabolic ratio (respired C/biomass C) showeda positive and potential relationship with the availabilityof light fraction C per unit of biomass C [respiredC/biomass C = 0.09 X (light fraction C /biomass C)053,r2 = 0.430; P < 0.001]. Conversely, the relationships ofthe metabolic ratio with the medium or heavy fractionsC per unit of biomass C were not statistically significant.Only a small proportion of the biomass C is in an activestate (McGill et al., 1986; van der Werf and Verstraete,1987). Probably as the availability of labile carbonsource increases per unit of biomass C, the proportionof tnis biomass in an active state increases. Additionally,the presence of more labile substrate could inducechanges in soil microbial biomass composition or itsphysiological state resulting in a higher production ofCO2 C per unit of biomass C (Jans-Hamermeister, 1996).In a previous study from this Pampean soil, a strongrelationship was found between the metabolic ratio ofthe active soil microbial biomass and the availability oflight fraction C per unit of the active microbial biomass(Alvarez et al., 1998a).

The light fraction consists principally of plant residuesand appreciable amounts of microbial and microfaunaldebris, which have a rapid turnover (Spycher et al.,1983). According to these characteristics the light frac-tion C was closely correlated to the carbon mineraliza-tion in 160 d. But the amount of carbon mineralizedwas higher than the biomass C and the light fraction C.The amount of SOM present in the light fraction isusually affected by land use (e.g., years under cultiva-tion, rotations, tillage systems) (Dalai and Mayer, 1986;Janzen et al., 1992; Alvarez et al., 1995). The higheramounts of light fraction C corresponded to the samplesfrom the upper 5 cm of soil profile (Fig. 2), under pastureor conservation tillage treatments (not shown). Whenthe SOM density fractions were expressed as a propor-tion of the total C, the mineralized C/total C ratio washighly correlated with the light fraction C/total C, inde-pendent of soil management or depth. Otherwise, anincrease in the amount of total carbon in the heavyfraction C produced a decrease in the mineralizedC/total C ratio. Organic compounds associated with clayparticles are chemically recalcitrant and are more physi-cally protected than the light fraction C (Cambardellaand Elliot, 1993). In contrast to the association observedbetween the mineralized C and light fraction C, in thissoil, the percentage of total nitrogen mineralized in 84 dwas principally related to the percentage of total nitro-gen present in the medium fraction (Alvarez et al.,1998b). These results could be a consequence of thehigher the C:N ratio of the light SOM, which may causenitrogen immobilization during the incubation.

Kinetics Parameters and Their Relationshipswith the Soil Organic Matter Fractions

The exponential and hyperbolic models fit the in vitromineralization of all studied samples. As observed byother authors, the hyperbolic model estimated higherC0 and tm than the hyperbolic one. Otherwise the two-component models could not be adjusted in some sam-ples (Table 4). The duration of the incubation couldaffect the goodness of fitting of the two componentmodels. As the incubation time increased these modelsseemed to better describe the pattern of mineralizationbecause the contribution of carbon mineralized fromthe resistant pool increases. Dou et al. (1996) found thatthe mean square error of fitting the exponential pluslinear model to nitrogen mineralization decreased insome of the studied treatments as the incubation timedecreases from 30 to 15 wk. In the latter case, this modelgave negative constant of mineralization, and the intro-duction to the program of the constraint that this poolshould be >0; gave potentially mineralizable nitrogenpools and mineralization constants similar to those esti-mated by the simple model. In some of our samples,where negative C0 values were obtained, an initial delayphase was present possibly resulting from microbial re-grouping or acclimation (Ellert and Bettany, 1988).

We applied the kinetic models to accumulated dataof CO2 C production during 160 d, using integratedequations. Many authors suggested that using accumu-lated data also accumulate errors while dampening thenoise and giving a false sense of security (Ellert andBettany, 1988; Hess and Smith, 1995). Hess and Smith(1995) fitted different models to their mineralizationdata, expressed in differential or integral form. The dif-ferential form showed a random pattern; meanwhile,the integral form had distinctly non-random residuals,showing the superiority of the differential approach. Inour study, we also analyzed the mineralization data withthe differential form of the exponential and doubled-exponential models (Colores et al., 1996). We obtainedthe same performance as analyzing the cumulative CO2C production. The exponential model adjusted to allsamples and the doubled exponential could not be fitto 10 samples. The differential form of the simple expo-nential gave lower coefficient of correlations (r2 from0.310-0.986) than those obtained by the integral form.Exponential C0 estimated by differential equations werehighly and linearly correlated with those estimated bythe integrated models, but the regression slope was1.1 (P < 0.05). This discrepancy between the potentiallymineralizable carbon pools estimated by the two formsof the same model could be a consequence of largeintervals (1.5^ wk) between CO2 C determinations, inrelation to the duration of the incubation (23 wk). Inexperiments where the differential form was applied,the interval between determination was very short(about 1 h), compared with the incubation duration(50 h) (Colores et al., 1996). The exponential model(using the differential or integrated forms) was capableof describing the carbon mineralization patterns in awide range of soil management practices and depths.

ALVAREZ & ALVAREZ: SOIL ORGANIC MATTER POOLS AND CARBON MINERALIZATION 189

The carbon in soil light fraction was very stronglycorrelated with the microbial biomass and its activity.This soil carbon pool was also closely related with themineralized C in long-term incubations. The exponen-tial model described the mineralization pattern of allsamples, from a wide range of soil managements anddifferent depth. The C0 estimated by this model weresimilar to the carbon content in the soil light fraction.This soil organic component seemed to be the drivingvariable of microbial activity and a good predictor ofsoil potential carbon mineralization.