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DIVISION S-3-SOIL BIOLOGY & BIOCHEMISTRY

Soil Organic Matter Pools and Their Associations with Carbon Mineralization Kinetics

^a Dep. de Suelos, Facultad de Agronomía, Univ. de Buenos Aires, Av. San Martín 4453 (1417), Buenos Aires, Argentina

ralvarez{at}mail.agro.uba.ar

	ABSTRACT

TOP NOTES ABSTRACT INTRODUCTION Materials and methods Results Discussion REFERENCES

 
The labile component of soil organic matter (SOM) plays an important role in short-term nutrient turnover. Our objectives were (i) to establish the relationships between carbon in soil density fractions with carbon mineralization and the microbial biomass under contrasting conditions, (ii) to compare the goodness of fit of different mathematical models to describe carbon mineralization, and (iii) to evaluate the relationships of the SOM pools and the mineralization parameters estimated by the best kinetic model. Twenty-eight soil samples were collected from a fine, illitic, thermic Typic Argiudoll localized in Argentina. These samples differed in the soil management (pasture and agriculture), tillage systems (chisel tillage, plough tillage, and no-tillage), crop rotation, or depths. Microbial biomass was highly correlated with total carbon and carbon in the SOM light density fraction (density < 1.59 g mL^-1) but less strongly correlated to medium (density 1.59–2.0 g mL^-1) and heavy (density > 2.0 g mL^-1) soil fractions. Carbon in the soil light fraction was strongly related to the carbon mineralized at 10 and 160 d of incubation. The exponential and hyperbolic models showed a good description of the mineralization data (r² > 0.982). The application of models which considered two organic matter pools could not describe the mineralization of some samples. The hyperbolic model estimated higher potentially carbon mineralizable pools (C₀) and semidecomposition time periods than the exponential one. The C₀ estimated by the exponential model were similar to the carbon content in the soil light fraction. This soil organic component seemed to be the driving variable of microbial activity and a good predictor of soil potential carbon mineralization.

Abbreviations: SOM, soil organic matter

	INTRODUCTION

TOP NOTES ABSTRACT INTRODUCTION Materials and methods Results Discussion REFERENCES

 
THE LABILE COMPONENT of soil organic matter plays an important role in the short-term nutrient turnover and is responsible for the temporary soil structural stability (Tisdall and Oades, 1982). One possible way to characterize this fraction is through densimetry. SOM can be divided into (i) light fraction, which consists of mineral-free organic matter composed of partly decomposed plant and animal residues, which turn over rapidly and have a specific density considerably lower than that of soil minerals; and (ii) heavy fraction, composed of more processed decomposition products, which turn over more slowly and have a high specific density because of their intimate association to soil minerals (Christensen, 1992; Barrios et al., 1996). Dalal and Mayer (1986) found for Australian clay soils that the rate of loss of carbon from the light fraction (density < 2 g mL^-1) was 2 to 11 times greater than that of the heavy fraction. In contrast, Janzen et al. (1992) observed in Canadian Mollisolls that carbon in the light fraction (density < 1.7 g mL^-1) was higher in treatments which include perennial forage in the rotation and lower in those with summer fallow. In Pampean Mollisolls of Argentina, the implementation of no tillage produced an accumulation of carbon in soil light fraction (density < 1.6 g mL^-1) at the soil surface (0–5 cm layer) in relation to plowed plots (Alvarez et al., 1995, 1998a).

Carbon and nutrient turnover are mediated by the soil microbial biomass, which responds to residues or tillage management (Dalal et al., 1991). Microbial biomass is usually related to the carbon in soil light fraction and to the in vitro carbon mineralization (Bremer et al., 1994; Alvarez et al., 1995, 1998a). Because soil management generally affects these variables more than total organic carbon, many authors have suggested that they could be early indicators of future trends in total SOM (Bremer et al., 1994).

The mathematical description of in vitro carbon and nitrogen mineralization is another interesting approach to characterizing SOM. The exponential model was widely used to describe the carbon and nitrogen mineralization process (Stanford and Smith, 1972; Riffaldi et al., 1996). From this model the potentially mineralizable carbon pool of soils (C₀) may be estimated. C₀ is assumed to be a readily mineralizable carbon component which mineralized at a constant rate (k) proportional to the size of the pool. Another single-component model is the hyperbolic model, which considers that the time to mineralize 50% of C₀ pool (t_1/2) increases as the incubation time gets longer, according to the rise of carbon chemical protection. Alternatives to these one-component models are those which consider two organic matter pools with different stability to microbial attack (Riffaldi et al., 1996). Several authors (e.g., Bonde and Rosswall, 1987) proposed the use of the double-exponential model to improve the agreement with experimental mineralization data. This model assumes that the organic matter pool can be divided into two components, a labile pool (C_L) decomposing exponentially with a constant rate (k_L), and a resistant pool (C_R) also decomposing exponentially at a much lower constant rate (k_R). A simplification of this model is the exponential and linear version, which consider a labile pool decomposing with an exponential kinetics and a resistant pool decomposing linearly, according to the relative shortness of the incubation periods compared with the turnover of the resistant pool (Bonde and Rosswall, 1987). Other authors have found that the exponential plus a constant model could be useful to describe an initial mineralization flush present in some soil samples (Bonde and Lindberg, 1988). This model contains a parameter (C_L) defined as an easy decomposable organic matter which produced an initial mineralization flush during the first stage of the incubation (Riffaldi et al., 1996), and a resistant pool (C_R) decomposing exponentially. This initial mineralization flush was attributed to the drying and rewetting of samples or other type of sample handling (Beauchamp et al., 1986).

The relationships between SOM pools isolated by physical or chemical techniques and the potentially mineralizable organic pool obtained through the mathematical modeling of carbon mineralization have not been widely investigated under different soil managements. Our objectives were (i) to establish the relationships between carbon in soil density fractions with carbon mineralization and the microbial biomass in a Mollisoll under contrasting cropping conditions, (ii) to compare the goodness of fit of different mathematical models to describe the kinetics of carbon mineralization, and (iii) to evaluate the relationships of the SOM pools and the mineralization parameters estimated by the best kinetic model.

	Materials and methods

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Soil and Experimental Sites
The samples were obtained from the INTA Experimental Station located at Pergamino (Argentina, 33°56'S; 60°34'W). The climate is humid and temperate, with an annual rainfall of 1000 mm and a mean temperature of 16.5°C. The soil is a Pergamino series (fine, illitic, thermic, Typic Argiudoll). The main characteristics of the top 20 cm were 27% of clay, 57% of silt, and pH (soil:water 1:2.5) 5.8, with no statistically significant differences of these soil parameters between depths throughout this layer. Twenty-eight soil samples were collected from different field experiments. These samples differed on the basis of the soil management, tillage systems, crop rotation, or depths (Table 1) . The values of each sample are the mean of four plots. The total C content of the 28 samples ranged from 13.8 to 36.9 g C kg^-1 soil.

Soil density fractionation was performed with both carbon tetrachloride (density = 1.59 g mL^-1) and bromoform–ethanol mixture (density = 2.00 g mL^-1). Air-dry soil samples were sieved (<500 µg) and plant residues were forced to pass the sieve. Five grams of soil was weighed into a 50-mL beaker, and after adding 30 mL of the separation liquid, was vigorously agitated 1 min by hand and then centrifuged at for 5 min. The supernatant was filtered through fiberglass under suction. Carbon in whole soil and in the two light density fractions was determined by wet digestion (Amato, 1983).

The SOM light fraction (light fraction C) was defined as the carbon contained in the supernatant of soil:carbon tetrachloride mixture . Soil organic matter heavy fraction (heavy fraction C) was estimated as the difference between total C and the carbon quantified in the supernatant of soil:bromoform:ethanol mixture . Carbon content of the SOM medium fraction (medium fraction C) was calculated as the difference between carbon in fractions with density < 2 g mL^-1 and the light fraction C.

The metabolic ratio was calculated as the ratio between the carbon respired in 10 d of incubation (respired C) from non fumigated controls and the biomass C. In vitro aerobic carbon mineralization was measured during 160 d (mineralized C), at 30°C and 50% of soil water holding capacity. The equivalent of 100 g of dry soil were incubated in a 400-mL flask and the 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 was fitted to different mathematical models (Table 2) . The models were fitted to carbon mineralization data by the non-linear regression with the Statgraphics software package (Manugistics, Inc., Rockville, MD). The relationships between the different variables were evaluated by regression analysis and tested by their F values.

	Results

TOP NOTES ABSTRACT INTRODUCTION Materials and methods Results Discussion REFERENCES

View larger version (26K): [in this window] [in a new window]   Fig. 1 Relationships between the carbon mineralized in 160 d (mineralized C) with carbon in the different organic matter pools. Depths: • 0–5 cm, 5–10 cm, § 10–15 cm, 15–20 cm

View larger version (28K): [in this window] [in a new window]   Fig. 2 Relationships between the fraction of the total carbon mineralized in 160 d and the fraction of the total carbon in the different soil organic matter fractions. Depths: • 0–5 cm, 5–10 cm, § 10–15 cm, 15–20 cm

View larger version (14K): [in this window] [in a new window]   Fig. 3 Correlations between potentially mineralizable carbon pool (C0) and semidecomposition time (t1/2) estimated by the exponential and hyperbolic kinetic models. t1/2 for the exponential model was estimated as: 0.693/exponential k. Depths: • 0–5 cm, 5–10 cm, § 10–15 cm, 15–20 cm

View larger version (15K): [in this window] [in a new window]   Fig. 4 Correlation between C0 of the exponential with the carbon mineralized in 160 d of incubation (mineralized C) and the light fraction C. Depths: • 0–5 cm, 5–10 cm, § 10–15 cm, 15–20 cm

	Discussion

TOP NOTES ABSTRACT INTRODUCTION Materials and methods Results Discussion REFERENCES

The metabolic ratio (respired C/biomass C) showed a positive and potential relationship with the availability of light fraction C per unit of biomass C [respired C/biomass C = 0.09 x (light fraction C /biomass C)^0.53, ; P < 0.001]. Conversely, the relationships of the metabolic ratio with the medium or heavy fractions C per unit of biomass C were not statistically significant. Only a small proportion of the biomass C is in an active state (McGill et al., 1986; van der Werf and Verstraete, 1987). Probably as the availability of labile carbon source increases per unit of biomass C, the proportion of this biomass in an active state increases. Additionally, the presence of more labile substrate could induce changes in soil microbial biomass composition or its physiological state resulting in a higher production of CO₂ C per unit of biomass C (Jans-Hammermeister, 1996). In a previous study from this Pampean soil, a strong relationship was found between the metabolic ratio of the active soil microbial biomass and the availability of light fraction C per unit of the active microbial biomass (Alvarez et al., 1998a).

The light fraction consists principally of plant residues and appreciable amounts of microbial and microfaunal debris, which have a rapid turnover (Spycher et al., 1983). According to these characteristics the light fraction C was closely correlated to the carbon mineralization in 160 d. But the amount of carbon mineralized was higher than the biomass C and the light fraction C. The amount of SOM present in the light fraction is usually affected by land use (e.g., years under cultivation, rotations, tillage systems) (Dalal and Mayer, 1986; Janzen et al., 1992; Alvarez et al., 1995). The higher amounts of light fraction C corresponded to the samples from the upper 5 cm of soil profile (Fig. 2), under pasture or conservation tillage treatments (not shown). When the SOM density fractions were expressed as a proportion of the total C, the mineralized C/total C ratio was highly correlated with the light fraction C/total C, independent of soil management or depth. Otherwise, an increase in the amount of total carbon in the heavy fraction C produced a decrease in the mineralized C/total C ratio. Organic compounds associated with clay particles are chemically recalcitrant and are more physically protected than the light fraction C (Cambardella and Elliot, 1993). In contrast to the association observed between the mineralized C and light fraction C, in this soil, the percentage of total nitrogen mineralized in 84 d was principally related to the percentage of total nitrogen present in the medium fraction (Alvarez et al., 1998b). These results could be a consequence of the higher the C:N ratio of the light SOM, which may cause nitrogen immobilization during the incubation.

Kinetics Parameters and Their Relationships with the Soil Organic Matter Fractions
The exponential and hyperbolic models fit the in vitro mineralization of all studied samples. As observed by other authors, the hyperbolic model estimated higher C₀ and t_1/2 than the hyperbolic one. Otherwise the two-component models could not be adjusted in some samples (Table 4). The duration of the incubation could affect the goodness of fitting of the two component models. As the incubation time increased these models seemed to better describe the pattern of mineralization because the contribution of carbon mineralized from the resistant pool increases. Dou et al. (1996) found that the mean square error of fitting the exponential plus linear model to nitrogen mineralization decreased in some of the studied treatments as the incubation time decreases from 30 to 15 wk. In the latter case, this model gave negative constant of mineralization, and the introduction to the program of the constraint that this pool should be 0; gave potentially mineralizable nitrogen pools and mineralization constants similar to those estimated by the simple model. In some of our samples, where negative C₀ values were obtained, an initial delay phase was present possibly resulting from microbial regrouping or acclimation (Ellert and Bettany, 1988).

We applied the kinetic models to accumulated data of CO₂ C production during 160 d, using integrated equations. Many authors suggested that using accumulated data also accumulate errors while dampening the noise and giving a false sense of security (Ellert and Bettany, 1988; Hess and Smith, 1995). Hess and Smith (1995) fitted different models to their mineralization data, expressed in differential or integral form. The differential form showed a random pattern; meanwhile, the integral form had distinctly non-random residuals, showing the superiority of the differential approach. In our study, we also analyzed the mineralization data with the differential form of the exponential and doubled-exponential models (Colores et al., 1996). We obtained the same performance as analyzing the cumulative CO₂ C production. The exponential model adjusted to all samples and the doubled exponential could not be fit to 10 samples. The differential form of the simple exponential gave lower coefficient of correlations (r² from 0.310–0.986) than those obtained by the integral form. Exponential C₀ estimated by differential equations were highly and linearly correlated with those estimated by the integrated models, but the regression slope was 1.1 (P < 0.05). This discrepancy between the potentially mineralizable carbon pools estimated by the two forms of the same model could be a consequence of large intervals (1.5–4 wk) between CO₂ C determinations, in relation to the duration of the incubation (23 wk). In experiments 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 capable of describing the carbon mineralization patterns in a wide range of soil management practices and depths.

The carbon in soil light fraction was very strongly correlated with the microbial biomass and its activity. This soil carbon pool was also closely related with the mineralized C in long-term incubations. The exponential model described the mineralization pattern of all samples, from a wide range of soil managements and different depth. The C₀ estimated by this model were similar to the carbon content in the soil light fraction. This soil organic component seemed to be the driving variable of microbial activity and a good predictor of soil potential carbon mineralization.

	NOTES

TOP NOTES ABSTRACT INTRODUCTION Materials and methods Results Discussion REFERENCES

 
This research was supported by the UBACYT AG-089 program of the University of Buenos Aires.

Received for publication December 22, 1997.

	REFERENCES

TOP NOTES ABSTRACT INTRODUCTION Materials and methods Results Discussion REFERENCES

 

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