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TECHNICAL REPORTS

Waste Management

Simulating Urban Waste Compost Effects on Carbon and Nitrogen Dynamics Using a Biochemical Index

Environment and Arable Crops Research Unit, Institut National de la Recherche Agronomique, 78850 Thiverval-Grignon, France

^* Corresponding author (Benoit.Gabrielle{at}grignon.inra.fr)

Received for publication March 1, 2004.

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Composting has emerged as a valuable route for the disposal of urban waste, with the prospect of applying composts on arable fields as organic amendments. Proper management of urban waste composts (UWCs) requires a capacity to predict their effects on carbon and nitrogen dynamics in the field, an issue in which simulation models are expected to play a prominent role. However, the parameterization of soil organic amendments within such models generally requires laboratory incubation data. Here, we evaluated the benefit of using a biochemical index based on Van Soest organic matter fractions to parameterize a deterministic model of soil C and N dynamics, NCSOIL, as compared with a standard alternative based on laboratory incubation data. The data included C mineralization and inorganic N dynamics in samples of a silt loam soil (Typic Hapludalf) mixed with various types of UWC and farmyard manure. NCSOIL successfully predicted the various nitrogen mineralization–immobilization patterns observed, but underestimated CO₂ release by 10 to 30% with the less stable amendments. The parameterization based on the biochemical index achieved a prediction error significantly larger than the standard parameterization in only 10% of the tested cases, and provided an acceptable fit to experimental data. The decomposition rates and C to N ratios of compost organic matter varied chiefly according to the type of waste processed. However, 62 to 66% of their variance could be explained by the biochemical index. We thus suggest using the latter to parameterize organic amendments in C and N models as a substitute for time-consuming laboratory incubations.

Abbreviations: BIO, biowaste compost • BSI, biological stability index • CEW, Wende cellulose extract • FYM, cattle farmyard manure • GWS, green waste and sewage sludge compost • HEMI, hemicellulose fraction • LIC, lignin fraction • MD, mean deviation • MSW, municipal solid waste compost • OM, organic matter • OPT, optimum parameterization scenario • RMSE, root mean squared error • SOL, soluble organic molecules • SSE, mean experimental error • UWC, urban waste compost

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
THE MANAGEMENT OF URBAN WASTE has become a major issue worldwide, with steadily growing volumes to be disposed of and increased public awareness of the resulting pressure on the environment. Amid the range of waste treatments currently available, incineration and landfilling are the most frequent, and are commonly combined to meet the needs of local communities. However, both treatment alternatives raise a range of environmental problems, which have recently lead the French government to schedule a ban on most types of landfill disposal. Composting of urban waste has emerged as a valuable alternative because of the high proportion of organic matter in urban waste. The biodegradable fraction (including food scraps, grass clippings, and tree trimmings) is estimated at about 25% (fresh weight) in France, along with an additional 25% made up of paper and cardboard. Composts have long been used in agriculture, and UWC may be applied in arable fields as organic amendment to maintain soil organic matter as well as supply nutrients to crops (Stratton et al., 1995). Proper management of UWC requires a capacity to predict its effects on C and N dynamics in the field.

Similar to other organic amendments, there exists a body of work on the effect of UWC on agricultural system variables encompassing physical effects on soil structure and water balance (Movahedi Naeini and Cook, 2000a; Agassi et al., 1998), N availability to crop (Sánchez et al., 1997; Hadas and Portnoy, 1997), crop yield (Allievi et al., 1992; Movahedi Naeini and Cook, 2000b), and nitrate losses (Gerke et al., 1999; Mamo et al., 1999a). Unfortunately, such investigations are usually associated with a rather crude characterization of the exogenous organic matter (OM) involved, particularly regarding biological and biochemical aspects. Because these properties vary widely according to the range of biodegradable materials and process characteristics (Stratton et al., 1995; Butler et al., 2001), it is difficult to generalize the above results.

Process-oriented modeling offers in principle a unique means of addressing this variability. Deterministic C and N models have the capacity to simulate a wide range of soils and exogenous OM types (Molina and Smith, 1998). However, they require breaking down exogenous OM into a number of discrete pools, and estimating the pools' functional characteristics (i.e., decomposition rate, initial C and N contents). A standard method consists of optimizing these parameters against laboratory incubation data (Barak et al., 1990). This approach was shown to result in good simulations, in particular with UWC-amended soils (Hadas and Portnoy, 1997; Mamo et al., 1999b). However, it is costly and time consuming, which prompted attempts at relating model parameters to the biochemical, chemical, or physical characteristics of OM.

There have been few such attempts for exogenous OM, and they exclusively involved plant residues. Quemada and Cabrera (1995) and Henriksen and Breland (1999) showed that stepwise chemical digestion, such as that proposed by Goering and Van Soest (1970), could be quite successful in partitioning carbon between model compartments. However, it came at the cost of calibrating other parameters, mostly the decay-rate constants for the pools with rapid turnover time (i.e., carbohydrates, cellulose, and hemicellulose). In addition, some of the decay rates were shown to be specific to the type of residues simulated, such as crop species or plant compartment. This hampers extrapolation to UWC, because these are composed of a broad variety of biodegradable materials.

On the other hand, Linères and Djakovitch (1993) proposed a simpler and more generic approach by combining Van Soest–type fractions into an index predicting the proportion of stable organic matter. Their biological stability index (BSI) gave accurate estimates of recalcitrant OM for a wide range of organic materials, including cereal straw, bark chips, saw dust, peat, and UWC. The BSI could, therefore, be expected to help in the parameterization of UWC in C and N models.

Within the framework of a long-term field experiment setup in the Paris vicinity to evaluate the agronomic value and the environmental effects of UWC (Houot et al., 2002), we set out to evaluate a deterministic C and N model, NCSOIL (Molina et al., 1983), for its prediction of C and N dynamics in soils amended with various types of UWC. Two parameterization methods were tested, one based entirely on incubation C and N data and the other based on the BSI. The objectives of this study were to (i) evaluate the capacity of NCSOIL to simulate the effect of UWC application on soil C and N dynamics and (ii) compare the use of incubation data versus BSI to parameterize exogenous UWC organic matter.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Compost and Soil Samples
Three UWCs were studied: (i) a biowaste compost (BIO) resulting from the co-composting of green waste and the source separated organic fraction of municipal wastes, (ii) a co-compost of green waste and sewage sludge (GWS), and (iii) a municipal solid waste compost (MSW). Cattle farmyard manure (FYM) served as a reference organic amendment. Together with the composts, FYM will be referred to as exogenous organic matter, as opposed to the endogenous organic matter present in the soil before the application of the amendments.

Composts were collected from several industrial composting units in 1998 and 2000. The MSW and 1998 BIO composts were made with the siloda process (OTVD French technology) and the other composts were made with the windrow process. Fermentation lasted 22 d to 13 wk, and maturation lasted 4 to 17 wk with the exception of the 1998 MSW compost, which was only left to mature for 5 d due to timing constraints for field application.

Compost and FYM samples were mixed with soil taken from the same field site in both years. The soil is a Typic Hapludalf located at Feucherolles (48°90'N and 1°95'E; 50 km west of Paris, France). Its surface horizon has a silt loam texture, with 19% clay and 6% sand. It has neutral pH (6.9 in water 1:1), negligible CaCO₃ content, and an organic carbon content of 11.0 g C kg^–1 dry soil.

Laboratory Measurements
Soil (for controls) and soil–compost or soil–FYM mixtures were incubated in the laboratory under controlled conditions (temperature set at 28°C, and soil water content at 80% of field capacity). The samples contained a mix of 50 g of air-dried soil and 0.5 to 1 g of compost or FYM (dry matter basis), together with 2.5 mg of N as NH₄NO₃. The application rate corresponded to a field application of about 60 Mg UWC dry matter ha^–1, which is twice the current farming practice but was selected to maximize the effects of UWC application. Samples were incubated in hermetically sealed jars (0.5 L), with four replicates for either CO₂ release or inorganic N measurements. Carbon dioxide release was monitored with traps containing 10 mL of NaOH (1 mol L^–1), which were sampled and replaced on a weekly basis. Upon sampling, the jars were left open for a few minutes to renew the air in the headspace and avoid anaerobic conditions. Carbon dioxide traps were analyzed by colorimetry (Skalar Analytical, Breda, the Netherlands). On the same day, four replicate jars were destructively sampled for analysis of inorganic N (as NH⁺₄ and NO^–₃) in the soil–compost or soil–FYM mixtures. Analysis was also done by colorimetry after extraction with 200 mL of KCl (1 mol L^–1), with a soil to KCl ratio of 1:5 (40 g oven-dry soil to 200 mL KCl). Incubations were done for 156 d in 1998 and 77 d in 2000.

Biochemical Fractionation
The biological stability index (BSI) was measured on all types of exogenous OM, on separate samples before mixing with the soil. It uses two types of fractions, obtained from the Goering and Van Soest (1970) and Wende (Association française de normalisation, 1993) methods. The former yielded three OM fractions: neutral detergent-soluble organic molecules (SOL), hemicellulose (HEMI), and a residual fraction containing lignin and cutins (LIC), while the latter extracted a raw cellulose fraction (CEW).

Before fractionation, compost samples were air-dried, sieved, and ground to pass a 1-mm sieve. For the Van Soest fractionation, samples containing 2 g of calcinated sand and 1 g of exogenous OM were sequentially boiled for 0.5 h in neutral detergent, acid detergent, and sulfuric acid (0.36 mol L^–1) solutions. These steps yielded the neutral detergent fiber (NDF), acid detergent fiber (ADF), and acid detergent lignin (ADL) fractions, respectively. They were used to calculate the SOL, HEMI, and LIC fractions with the following relationships:

[1]

where the fractions are expressed in g g^–1 dry organic matter. The Wende cellulose fraction CEW was obtained after successively boiling for 0.5 h the same initial sand and exogenous OM sample in sulfuric acid (0.13 mol L^–1) and sodium hydroxide (0.23 mol L^–1) solutions.

The BSI was then calculated as (Linères and Djakovitch, 1993):

[2]

where BSI and the OM fractions are expressed as a mass fraction of total OM (g g^–1 dry organic matter). The BSI represents a relatively stable fraction in the compost. For example, a BSI of 0.69 means that 69% of the compost OM is recalcitrant to degradation, the rest (31%) being considered labile. The fractions and BSI data of the UWC and FYM samples used in the incubations are listed in Table 1, together with total organic C and inorganic N contents.

View larger version (15K): [in this window] [in a new window]   Fig. 1. Schematic of C and N flows in the NCSOIL model. The term OM is organic matter.

The first step of both scenarios consisted of fitting a two-compartment, exponential C-decay model against the CO₂ release data of the incubated soil–compost mixtures, after subtracting the contribution from endogenous soil OM (that is, the CO₂ released by the controls). This separation is based on the hypothesis that the application of compost does not modify the mineralization of endogenous OM (Mamo et al., 1999a). Cumulative CO₂–C release (C_release) was thus modeled as:

[3]

where C_release is in mg C kg^–1 dry soil, t is time (d), C_l and C_r are the sizes of the labile and resistant fractions, respectively (mg C kg^–1 dry soil), and k_l and k_r are their respective decomposition rates (d^–1). Because the resistant pool decomposes very slowly, the k_rt term is close to zero and the second term on the right-hand side of Eq. [3] may be approximated as C_rk_rt. Thus, Eq. [3] simplifies to:

[4]

Because the total C content in the exogenous OM was measured (Table 1), C_r may be calculated from C_l as (C_total – C_l), where C_total is the total exogenous carbon (mg C kg^–1 dry soil). There are thus only three unknown parameters in Eq. [4]: C_l, k_l, and k_r. In the OPT parameterization scenario, all three parameters were fitted against the observed CO₂ release data by nonlinear regression. In the BSI scenario, on the other hand, C_l was calculated from BSI as (1 – BSI) x C_total. Second, k_r was set to a typical value of 6.85 10^–5 d^–1, reported in the review of Houot (2002) for compost OM. Thus, only k_l was adjusted by nonlinear regression.

In both scenarios, Eq. [4] was fitted against observed data with the nonlinear least-square function nls of the R statistical package (Bates and Chambers, 1992). The nls function uses ordinary least squares, which is justified in the case of the CO₂ release data because their standard deviations were small and varied little over time (see for instance Fig. 2) . Because they do not take internal recycling by the microbial biomass into account, the decomposition rates k_l and k_r are apparent values and should be corrected before introduction into NCSOIL. They were thus multiplied by a factor of 1.5, corresponding to a microbial recycling efficiency of 33%. This value is in the medium range reported for use in NCSOIL (Molina et al., 1983). Lastly, the C to N ratio of the labile fraction of exogenous OM was optimized with NCSOIL in both scenarios. Parameter optimization was done with a Marquardt steepest-descent algorithm, in which the figure of merit was a weighted sum of squares of residuals (analogous to a ² statistics), combining three output variables: cumulative CO₂ release, soil NO₃–N, and NH₄–N contents (Barak et al., 1990). The figure of merit was calculated as:

[5]

where i is the index of the measured variable used for optimization (CO₂ release, soil nitrate, or soil ammonium content), j is the sampling index (sampling time), O_ij and S_ij are the simulated and observed values, respectively, SD_i is the standard deviation of the observations, and DF is the number of degrees of freedom. The term ² may thus be viewed as a multivariable, weighted least-square optimization criterion.

View larger version (30K): [in this window] [in a new window]   Fig. 2. NCSOIL-simulated (lines) and observed (symbols, ±1 SD) data for C and N mineralization kinetics during the 1998 and 2000 incubations for the farmyard manure (FYM) and municipal solid waste compost (MSW). Two NCSOIL parameterizations scenarios are depicted: the optimum (OPT, solid lines) and biological stability index (BSI, dotted lines) based.

Model Evaluation
The simulations of NCSOIL were compared with observed data using graphics to capture dynamic trends, and statistical indicators gave an idea of the model's mean prediction error. We used three standard criteria (Smith et al., 1996): (i) the correlation between observed and simulated data (r), (ii) the mean deviation (MD), and (iii) the root mean squared error (RMSE). The latter two are defined for simulated variable i as:

[6]

[7]

where S_ij and O_ij are the time series of the simulated and observed data for variable i, and E denotes the expectancy. Mean deviation indicates an overall bias with the predicted variable, while RMSE quantifies the scatter between observed and predicted data, which is readily comparable with the experimental error on the observed data. Following the methodology of Smith et al. (1996), the hypothesis that MD is zero was tested using a two-tailed t test. Root mean squared error values were partitioned into the random error due to the variability in the measured data (SSE) and model lack of fit, and the significance of lack of fit was obtained from a F test on the ratio of lack of fit to SSE. Lastly, the significance of the coefficient of determination r² was assessed using a conventional F test.

	RESULTS AND DISCUSSION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Biological Characteristics of Composts
Table 2 presents the proportions of labile carbon in exogenous OM estimated using the OPT and BSI scenarios. The OPT scenario yielded the best fit to the experimental data, and its estimated fraction of labile C may be used to indicate compost and FYM biodegradability. The fraction varied within an overall range of 1 to 27% of total exogenous C, and revealed a predominant effect of compost type over that of sampling year. Within each compost category, the highest variation across years occurred with the BIO compost, which was more stable in 2000 than in 1998. Its labile fraction represented 10% of total C in 1998, compared with 1% in 2000. This was probably due to a longer maturation period during composting (17 wk in 2000 vs. 9 wk in 1998). It is also evidenced by the differences in the Van Soest fractions across years (Table 1): compared with 1998, the 2000 BIO compost contained less soluble OM (30 vs. 47% in 1998) and more lignin (49 vs. 29%).

A decrease in soil mineral N was observed during the first weeks of the MSW incubations (Fig. 2), revealing a net immobilization by soil microflora. This could be expected because MSW was the least mature compost, and fresher composts are known to induce this kind of response (Bernal et al., 1998). The next least stable amendment, FYM, did produce a net immobilization phase in 2000 but not in 1998 (Fig. 2). This may be due to its much higher ammonium content in 1998, compared with 2000 (Table 1), which may have sufficed to meet the needs of the decomposers. Lastly, the relatively stable BIO compost induced net immobilization in 1998 but not in 2000 (Fig. 3) . The various immobilization–mineralization patterns observed could be neither related to particular BSI values, nor to the overall C to N ratios of composts. Net immobilization indeed occurred over the whole range of C to N ratios measured in the composts. On the other hand, the C to N ratio of the labile fraction was a good indicator because C to N ratios greater than 20 systematically resulted in a net immobilization phase (Table 2).

View larger version (27K): [in this window] [in a new window]   Fig. 3. NCSOIL-simulated (lines) and observed (symbols, ±1 SD) data for C and N mineralization kinetics during the 1998 and 2000 incubations for the green waste and sewage sludge compost (GWS) and biowaste (BIO) composts. Two NCSOIL parameterizations scenarios are depicted: the optimum (OPT, solid lines) and biological stability index (BSI, dotted lines) based.

View larger version (24K): [in this window] [in a new window]   Fig. 4. NCSOIL-simulated (lines) and observed (symbols, ±1 SD) data for C and N mineralization kinetics during the 1998 and 2000 incubations for the control soil samples.

Overall, although the two parameterization methods resulted in contrasted parameter values, it is noteworthy that they yielded the same ranking in terms of mineralizable C fraction, decomposition rates, and potential to induce a temporary net immobilization of inorganic N.

From a statistical point of view, Table 3 compares the goodness of fit achieved by the two parameterization scenarios, based on three indicators: (i) the coefficient of determination r² of the relationship between observed and simulated data, (ii) the MD, and (iii) the RMSE. The correlation between modeled and measured data was always highly significant (p = 0.01), with r² values greater than 0.90, and only one exception involving the simulation of inorganic N for the 2000 MSW compost. Likewise, BSI achieved a less significant correlation than OPT in only one instance, regarding the simulation of CO₂ release with the 2000 farmyard manure. Mean deviations between measured and modeled data were not significantly different from 0 for the simulation of soil inorganic N content (p = 0.01). Regarding CO₂ release, they were significantly different from 0 in seven cases out of eight for BSI and OPT. Lastly, RMSEs provided the most stringent test. They were always significantly greater than mean experimental error for CO₂ release, whether with BSI or OPT, and in half of the situations for soil inorganic N. Compared with inorganic N, the fact that RMSEs were much higher than experimental error for CO₂ release was due to lower standard deviations on the observed data. Also, we had selected in the NCSOIL optimization a parameter that was mostly related to N dynamics (the labile fraction's C to N ratio).

View this table: [in this window] [in a new window]   Table 3. Statistical indicators for the goodness of fit of NCSOIL in the simulation of soil mineral C and N dynamics with the two parameterization scenarios for green waste and sewage sludge (GWS), municipal solid waste (MSW), and biowaste (BIO) composts and cattle farmyard manure (FYM) in 1998 and 2000 (dry matter basis).

Use of the Biological Stability Index to Parameterize NCSOIL
The ultimate goal of this parameterization exercise was to try and derive standard NCSOIL parameter sets for the various amendments directly from their BSI. If we deem the BSI parameterization scenario to give satisfactory simulation results, we still need to estimate the C to N ratio and decomposition rate of the labile fraction a priori. To this end, Fig. 5 shows the relationship between these two parameters, as obtained in the BSI parameterization scenario, and the value of the index itself. Although the points are somewhat scattered over the plotting area, higher BSI values tend to be associated with lower C to N ratios or decay rates, with one exception. The 2000 GWS compost had a high BSI value (0.83) associated with a high decay rate.

View larger version (12K): [in this window] [in a new window]   Fig. 5. Relationship between the biological stability index (BSI) and two parameters of the labile fraction estimated in the BSI parameterization scenario: the C to N ratio (open symbols) and the decay rate (closed symbols). The decay rate of the 2000 green waste and sewage sludge compost (GWS) is singled out as an outlier in the regression analysis. The regression equations read as follows: C to N ratio = –74.26BSI + 58.17 (r = 0.80, N = 8, p < 0.05); decay rate (d–1) = –0.0371BSI + 0.029 (r = 0.78, N = 7, p < 0.05).

	CONCLUSIONS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
This study demonstrated the capability of a deterministic model, NCSOIL, to simulate the effect of various types of organic amendments on soil C and N dynamics at the laboratory scale. However, some shortcomings were noted, with NCSOIL underestimating CO₂ release with the less stable amendments. The model gave good predictions of N dynamics, and showed that a temporary net immobilization of mineral nitrogen occurred if the C to N ratio of the amendments' labile fraction was greater than 20.

Two methods were tested to partition exogenous organic matter into labile and resistant fractions: one based on a direct fitting against incubation data and the other based on a biochemical index (i.e., independent of the incubation data). Although the latter method yielded much larger estimates of the labile fraction in exogenous organic matter, its goodness of fit was similar to that achieved by the former method in 90% of the cases tested. Furthermore, the biochemical index could be used to predict the other parameters of the labile fraction (C to N ratio and decomposition rate), because it accounted for 62 to 66% of their variance. We thus suggest that BSI could be used to parameterize organic amendments within NCSOIL with a much lower cost than the reference procedure based on time-consuming incubations of soil samples.

	ACKNOWLEDGMENTS

 
The authors would like to thank J.-N. Rampon for invaluable help in the analysis of soil and compost samples, E. Personeni for her early contribution to the work, and D. Clergeot and M. Poitrenaud (CREED, Véolia Environnement, France) for technical and financial support. Helpful comments from three anonymous reviewers are also acknowledged. Additional support was provided by INRA through the AGREDE grant program.

	REFERENCES

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 

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