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SOIL BIOLOGY & BIOCHEMISTRY

Modeling Soil Carbon and Nitrogen Dynamics in No-till and Conventional Tillage Using PASTIS Model

^a INRA, Unité d'agronomie de Laon-Reims-Mons, 2 Esplanade Roland Garros, BP 224, 51686 Reims Cedex 2, France
^b INRA, Unité d'Agronomie de Laon-Reims-Mons, rue Fernand Christ, 02007 Laon Cedex, France
^c IRAD, Unité de Recherche Risque Environnemental, avenue Agropolis, 34398 Montpellier Cedex 5, France
^d INRA, Unité d'Agronomie de Laon-Reims-Mons, rue Fernand Christ, 02007 Laon Cedex, France
^e INRA, Unité de Science du Sol d'Orléans, 2163 Domaine de Limère, BP 20619, 45166 Olivet, France
^f INRA, Unité d'agronomie de Laon-Reims-Mons, 2 Esplanade Roland Garros, BP 224, 51686 Reims Cedex 2, France

^* Corresponding author (bernard.nicolardot{at}reims.inra.fr).

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
The performance of the PASTIS model was evaluated to simulate soil C and N fluxes under real field conditions with conventional moldboard plowing (CT) and no-tillage (NT) systems differentiated for 33 yr for a loamy soil in northern France. Afterward, the influences on the C and N fluxes by soil temperature, soil water content, and quantity and localization of soil organic matter (SOM) and crop residues in the soil profile were determined. The model PASTIS was able to provide good simulations for the dynamics of soil water content and temperature, CO₂ emissions, residue decomposition, and N mineralization. Simulation showed that the presence of the mulch layer in NT reduced cumulative total water evaporation and increased water drainage at the bottom of the 25-cm depth. Furthermore, simulation showed that the larger cumulative total CO₂ flux in NT resulted from larger CO₂ emissions as a product of crop residue decomposition and not as product of SOM decomposition. The larger amount of accumulated residues of previous crops in NT more than compensated for the slower residue decomposition rate of surface compared with incorporated residues. It was the water content of the surface crop residues that largely controlled the magnitude of this difference in decomposition rate of the crop residues between CT and NT. This means that, besides the amount of crop residues in both tillage systems, the distribution of rainfall and potential evaporation have a large influence on the differences in C and N fluxes between the two tillage systems

Abbreviations: CT, conventional moldboard plowing • NT, no-tillage • SOM, soil organic matter • TDR, time domain reflectometry

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Compared with moldboard plowing (CT) systems, the introduction of NT systems modifies the location of crop residues and the distribution of SOM in the soil profile. In NT systems, crop residues are located in a mulch layer on top of the soil surface, while in CT systems, these residues are incorporated into the soil. In addition, the NT system induces changes in physical soil properties such as bulk density, the gas diffusion coefficient, and hydraulic conductivity. All these modifications, in turn, interact together on the water, temperature, and C and N dynamics in the soil.

Understanding and quantifying these complex interactions remains a challenging issue. Modeling allows extraction of the effect of each individual change. Many models exist where the decomposition of SOM and incorporated residues are coupled with water and heat fluxes, but only a few models simulate the impact of a mulch layer of crop residues. A mulch module exists in the Expert-N model (Berkenkamp et al., 2002), the APSIM model (Thorburn et al., 2001) and in the PASTIS model (Findeling et al., 2006). The decomposition of surface and incorporated residues in the Expert-N model is mainly differentiated by N limitation and the degree of contact with the soil. Coppens et al. (2006), however, found that the difference in decomposition between surface and incorporated residues is largely determined by the water content of these residues. The mulch modules in APSIM (Probert et al., 1998; Thorburn et al., 2001) and PASTIS (Findeling et al., 2006) take into account the effect of a surface mulch on the water dynamics of the whole mulch–soil system. Furthermore, the water and temperature dynamics and C and N transformations interact with each other in those models. Probert et al. (1998) simulated water and N dynamics in conventional and no-tillage systems with the APSIM model, but they evaluated the model only with long-term differences in water and NO₃ content between the two systems. Coppens et al. (2004) used the PASTIS model to study the short-term effects of crop residue location on the water and C and N dynamics in soil under controlled laboratory conditions. Garnier et al. (2003) evaluated the PASTIS model for field conditions with incorporated straw in a CT system. The PASTIS model has not yet been evaluated for field conditions in NT systems, however.

The aim of our study was twofold: (i) to model the C and N fluxes under real field conditions with CT and NT differentiated for 33 yr, and (ii) to determine, by means of simulations, the influence of the main factors explaining the differences in CO₂ emissions between the two tillage systems. These factors include soil temperature, soil water content, and the amount and localization of humified and fresh organic matter (crop residues). The influence of the location of crop residues as the effect of long-term changes in both biological and physical properties was taken into account.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Experimental Setup
The study site is located at Boigneville in northern France (48°33' N, 2°33' E). Two tillage systems, NT and CT, have been differentiated since 1970. Before that, the two systems were yearly moldboard plowed to 30-cm depth. In this experiment, we studied two plots: the CT plot (Plot 224), which was yearly moldboard plowed to 20-cm depth, and the NT plot (Plot 223), which was only minimally disturbed along the sowing line by the sowing machine (<5 cm). Both have been cropped with a maize (Zea mays L.)–wheat (Triticum aestivum L.) rotation and crop residues have been returned to the soil every year. In the CT plot, crop residues remained on the soil surface until incorporation by tillage, while in the NT plot, crop residues always remained on the soil surface. The modeling period started on 19 Nov. 2003 (corresponding to plowing for CT) and ended at maize sowing on 13 Apr. 2004. During the entire period, the soil remained uncropped. The wheat crop had been harvested at the end of July 2003.

The soil is a Haplic Luvisol. The characteristics of the upper 0- to 25-cm soil layer of the two plots are presented in Table 1. Carbon dioxide was measured using automatic chambers (0.49 m², 0.225 m high) connected to an infrared analyzer (Oorts, 2006). Four chambers for each tillage treatment were pressed 0.10 m into the soil. These gas chambers were placed on one line perpendicular to the direction of the field operations and covered the whole width of the combine harvester (4.5 m) to take into account the horizontal heterogeneity of both soil structure and the distribution of crop residues induced by tillage and harvest operations. The chambers closed four times a day for 5 min at 0400, 1000, 1600, and 2200 h. Carbon dioxide emissions were measured during these closure periods and the daily CO₂ emissions were calculated each day as the average of these four measurements. Outside these closure periods, the chambers were left open to expose the soil inside the chambers to realistic weather conditions. Volumetric soil water content was measured using time domain reflectory (TDR) probes (Model CS616, Campbell Scientific, Shepshed, UK) connected to a data logger (Model CR10X, Campbell Scientific) and installed at 2.5-, 12.5-, and 25-cm soil depths. For each soil layer, these probes were calibrated with manual measurements of gravimetric water content and bulk density in the 0- to 5-, 5- to 20-, and 20- to 25-cm soil layers. Rainfall, potential evaporation, and air temperature were recorded by an automatic weather station at the experiment site. Soil temperature was recorded hourly with thermocouples at 0-, 2.5-, 12.5-, and 25-cm soil depths.

Model Description
The transfer of water, heat, and solute and the transformations of C and N in the soil of the CT and NT plots were modeled with the one-dimensional mechanistic model PASTIS (Prediction of Agricultural Solute Transformations In Soils; Lafolie, 1991), which includes two submodels: (i) a submodel that simulates the transport of water, solutes, and heat using classical equations, i.e., Richard's equation for water flow, the convection–dispersion equation for solute transport, and the convection–diffusion equation for heat flow (this submodel does not consider vapor phase transport, hysteresis of hydraulic properties, or preferential flow); and (ii) a submodel CANTIS (Carbon and Nitrogen Transformations In Soil; Garnier et al., 2001) which simulates the decomposition of organic matter, mineralization, immobilization, nitrification, and humification. Soil organic matter is divided into three nonliving organic pools: fresh, soluble, and humified organic matter and two living pools. The microbial population is split into an autochthonous biomass that decomposes humified organic matter and a zymogenous biomass that decomposes fresh and soluble organic matter. Crop residues are added to the fresh and soluble organic matter pool. The fresh organic matter pool consists of rapidly decomposable material, hemicellulose, cellulose, and lignin. Decomposition of fresh, soluble, and humified organic matter is assumed to follow first-order kinetics.

Garnier et al. (2003) evaluated the PASTIS model on field data collected during a 1-yr period from a CT system with incorporated wheat straw. Findeling et al. (2006) added a mulch module to the PASTIS model to simulate the water and C and N dynamics in the presence of a mulch of surface residues. This module takes into account the rain interception by the surface mulch and simulates changes in evaporation, thermal exchanges, and C and N transformations. The extended PASTIS model was tested with data obtained under controlled laboratory conditions.

Description of the Soil Profiles
The zero depth of the soil profiles was set at the soil–mulch interface. The NT plot was modeled down to 25 cm, which was below the plowing depth in CT. To take into account soil profiles with similar masses of dry soil in the two tillage systems, the CT soil profile was extended by 1 cm (0–26 cm). Below, the soil profiles in CT and NT are considered to be 0- to 25-cm soil profiles to simplify the discussion. The modeling was limited to a 0- to 25-cm soil profile for two reasons: (i) no crop residues were found below that depth, and (ii) total organic C and N concentrations were much lower below than above 25 cm.

The simulated NT soil profile consisted of two soil layers. The 0- to 5-cm soil layer had a loose structure, low bulk density, and high C and N concentrations. The 5- to 25-cm soil layer had a high bulk density, massive structure, and low C and N concentrations (Table 1). Crop residues in the NT plot were situated as a mulch (2 cm thick) on the soil surface.

The simulated CT profile was divided into a 0- to 20-cm recent plow layer and a denser 20- to 25-cm soil layer. The actual 0- to 20-cm plow layer had a very heterogeneous structure with dense and compacted clods, loose soil, voids, and lumps of incorporated straw residues. Since PASTIS is a one-dimensional model, the heterogeneity in the plow layer had to be simplified. First, an average bulk density was taken for the whole recent plow layer (0–20 cm). Second, two vertical soil strips were defined based on field estimations of the proportion of the volume occupied by groups of crop residues across the whole soil area (Oorts, 2006): a soil strip containing all the incorporated crop residues (10% of the soil volume) and a soil strip without residues (90% of the soil surface volume). In the strip with crop residues, residues were homogeneously spread throughout the 5- to 20-cm layer because only a negligible amount of crop residues was observed in the 0- to 5-cm soil layer, as also observed in a study by Staricka et al. (1991). The two CT soil strips were simulated separately and then summed, taking their relative proportion of the soil volume into account.

Rainfall intensity and potential evapotranspiration were the surface boundary conditions for simulating water transport. At the bottom of the soil profile, a water pressure head was imposed. It was calculated using the measured volumetric water content and the water retention curve. The temperatures measured at depths of 0 and 25 cm were set as boundary conditions at the top and bottom, respectively.

Transport Parameters
The water flow parameters used in PASTIS are shown in Table 2. The water retention curve for each soil layer was determined in each tillage plot by measuring the soil water content of undisturbed soil samples under a succession of imposed water pressure heads: –0.1, –1, –10, –50, –100, and –1585 kPa. The suction table and low- and high-pressure chamber methods were used for the pressure ranges 0 to 10, 10 to 300, and 300 to 1600 kPa, respectively (Kabat and Beekma, 1994). The equation of van Genuchten (1980) was fitted to the experimental data:

where S_e(h) is the effective saturation, _r and _s are the residual and saturated volumetric water contents, respectively (m³ m^–3), is the scaling factor of water pressure head (kPa^–1), h is the water pressure head (kPa), and n is a dimensionless empirical shape parameter.

where K_s is the saturated soil hydraulic conductivity (m s^–1), m is a dimensionless empirical shape parameter, and l is the tortuosity parameter set to 0.5. The parameters K_s and m were optimized with the measured soil water content and the hydraulic conductivity data obtained by the WIND method.

Heat flow and solute parameters were taken from Garnier et al. (2001), who worked on a very similar loamy soil in Mons-en-Chaussée in northern France. They estimated the soil thermal conductivity coefficients by fitting the model to field data of temperatures at different depths. Their soil dispersivity of 1 cm, obtained by fitting the model to data from field leaching experiments with Cl^–, was used in our work.

Biological Parameters
The biological parameters of CANTIS were obtained from incubation experiments or taken from Garnier et al. (2003). Incubations of soil without added fresh organic matter were performed on 12.5-mm sieved fresh soil samples taken in the field gas chambers in the 0- to 5-, 5- to 20-, and 20- to 25-cm soil layers at the end of the measurement period. During these incubations, potential C and N mineralization of the soil samples was measured under controlled conditions (15°C and water pressure head of –63 kPa) for 168 d using the method described by Oorts et al. (2006b). The parameters used to describe the decomposition of humified organic matter, i.e., the decomposition rate of the autochthonous biomass k_A, the decomposition rate of the humified organic matter k_H, and the humification coefficient h_A, were estimated by fitting the CANTIS submodel to the C and N mineralization data. It is not possible to enter different biological parameters for different soil layers in the PASTIS model. For the NT treatment, however, the soil from the 0- to 5-cm layer had a higher mineralization rate than the soil from the 5- to 20- and 20- to 25-cm soil layers. In addition, the 0- to 20-cm soil layer of the CT treatment also had a higher mineralization rate than the 20- to 25-cm layer. To minimize this problem, the k_A, k_H, and h_A parameters were optimized with the mineralization data of the upper soil layer (Table 3). We then reduced the initial amounts of humified organic matter in the 5- to 20- and 20- to 25-cm layers of NT and the 20- to 25-cm layer of CT so that the calculated mineralization kinetics using the fixed biological parameters were similar to the measured mineralization kinetics.

Parameters of Mulch Module
The parameters specific for the mulch module were either measured, taken from the literature, or calibrated (Table 4). The covering proportion of the mulch was set to 1, based on field observations. The maximal and minimal volumetric water content of the mulch elements and the mulch density were taken from Garnier et al. (2004), who measured the hydraulic properties of wheat straw at Boigneville. The following parameters were calibrated: the mulch propensity to reduce soil evaporation (), the mulch propensity to water recharge (_m), the maximum depth for available N for mulch decomposition (z_m,zyb), and the proportion of the mulch dry mass in contact with the soil. The calibration of these parameters was necessary for three reasons: (i) no references were found in the literature for them, (ii) they were not easily measurable, or (iii) they changed with the mulch type. The parameters and _m were calibrated with the soil water content of the 0- to 5-cm soil layer. The mulch propensity to water recharge () determines the amount of water that is intercepted by the mulch and consequently the amount of water that is directly transferred to the soil. The parameter z_m,zyb was calibrated within a 0- to 5-cm range by fitting the simulated to the observed NO₃ content of the 0- to 5-cm soil layer. A value of 1 cm gave the best results. The initial proportion of the mulch dry mass in contact with the soil was calibrated by fitting the simulated to the measured CO₂ emissions and resulted in a value of 100% contact of the mulch with the soil. This value is in accordance with the field observations of a thin mulch layer of surface residues with no standing stubble at the beginning of the simulation period. Those residues had remained at least 3.5 mo on the soil surface, which had reinforced the contact with the soil.

where m_i and s_i are the measured and simulated results, and is the average of the n measured results.

	RESULTS AND DISCUSSION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Soil Temperature and Water Content
The temperature dynamics at 2.5- and 12.5-cm soil depth (not represented) were very well simulated with a model efficiency >0.97 (Table 7), with hourly temperatures at the 2.5-cm soil depth fluctuating between –4 and 19°C. Regarding the soil water dynamics, two periods could be distinguished (Fig. 1 ): an infiltration period from 19 Nov. 2003 until 28 Jan. 2004 and an evaporation period from 29 Jan. 2004 until 13 Apr. 2004.

View larger version (37K): [in this window] [in a new window]   Fig. 1. Measured and simulated water contents of the 0- to 5- and 5- to 20-cm soil layers of the conventional tillage and no-tillage plots. Symbols are measured data, lines are simulated data.

The mean differences between measured and simulated values were acceptable for all soil layers except the 0- to 5-cm soil layer of CT (Table 7). Model efficiencies were rather low, but this was mainly due to the small variations in measured water contents. The large overestimation of the water content in the 0- to 5-cm soil layer in CT implied that the calculated values for the reduction function of moisture on SOM and crop residue decomposition were not reliable for this soil layer; however, only a small proportion of the total decomposition in CT occurred in the 0- to 5-cm layer since crop residues were mainly located in the 5- to 20-cm soil layer.

The differences in soil water content between CT and NT (Fig. 1) were not only due to the presence of the mulch layer (e.g., the effect on water evaporation as mentioned below), but also resulted from differences in the water retention curves after 33 yr of tillage differentiation (Table 2). The 0- to 5-cm layer of NT presented both larger volumetric water contents at low water pressure heads and smaller volumetric water contents at high water pressure heads than the other soil layers of NT and CT. This difference in the water retention curve could be due to the larger organic matter content (Gupta et al., 1977) and the lower bulk density (Reicosky et al., 1981) in the 0- to 5-cm layer of NT. As a consequence, the 0- to 5-cm soil layer of NT had larger gravimetric and volumetric soil water contents than the other soil layers, although simulated water pressure heads were similar in all soil layers of the CT and NT profile. No large differences were found between the water retention curves of the CT treatment and the 5- to 25-cm soil layer of the NT treatment, which is in accordance with the literature on this subject (Wu et al., 1992; Benjamin, 1993; Hubbard et al., 1994; Lal, 1999; Blanco-Canqui et al., 2004; Fuentes et al., 2004). The unsaturated hydraulic conductivity as a function of the water pressure head in the equivalent plow layer follows the order NT (0–5 cm) > CT (0–20 cm) > NT (5–20 cm). There is no consensus in the literature on the effect of tillage on the unsaturated hydraulic conductivity as a function of the water pressure head: some researchers have reported larger values for NT (Dao, 1993; Fuentes et al., 2004), while others have reported similar values for CT and NT (Datiri and Lowery, 1991; Wu et al., 1992; Benjamin, 1993).

Water Content of Mulch
The simulated volumetric water content of the mulch remained close to saturation during the infiltration period because evaporation was not large enough to evaporate mulch water between two rainfalls. In contrast, large fluctuations were simulated during the evaporation period (Fig. 2 ).

View larger version (28K): [in this window] [in a new window]   Fig. 2. Simulated water contents of the surface mulch of the no-tillage plot.

View larger version (22K): [in this window] [in a new window]   Fig. 3. Simulated cumulative total evaporation at the top and water drainage at the bottom of the 0- to 25-cm soil profile of the conventional tillage (CT) and no-tillage (NT) plots.

View larger version (19K): [in this window] [in a new window]   Fig. 4. Measured and simulated amounts of C in crop residues of the conventional tillage (CT) and no-tillage (NT) plots. Bars indicate standard errors of the measured values.

The evolution of the NO₃–N content in the 0- to 5- and 0- to 25-cm soil layers was first simulated using a standard procedure with a one-dimensional simulation model, i.e., assuming that the crop residues were homogeneously distributed across the horizontal plane. This procedure provided a good simulation for NT, but not for the CT plot (Fig. 5 ). The model underestimated the amounts of soil mineral N in the CT plot, particularly in the 5- to 25-cm layer. The model was thought to overestimate the amount of immobilized N, probably because it overestimated the availability of mineral N. Indeed, the one-dimensional model assumes that all mineral N present at a given depth is available for residue decomposition at this depth. This hypothesis is likely to be false, mainly because crop residues incorporated by moldboard plowing are not evenly distributed throughout the soil volume, but situated in residue clusters in zones with high porosity between two adjacent furrows, as also observed by Staricka et al. (1991). Gaillard et al. (1999) determined that straw incorporation increases the microbial activity only in a zone of 0.4 cm around the crop residues. This suggests that only the soil mineral N near the residues was available for the microbial biomass that decomposed the residues. The mineral N situated farther from the residues was not available for residue decomposition. The impact of this heterogeneity in residue location was tested with our one-dimensional model by assuming that the modeled soil volume in CT was composed of two vertical soil strips: a first strip free of crop residues (90% of the soil volume) and a second strip containing all the crop residues (10% of the soil volume). These figures were consistent with the observations made in the field. The simulations were run separately for each strip and the outputs were summed according to the respective soil volume of each strip. The model thus parameterized was able to reproduce the observed soil NO₃–N content well (Fig. 6 ). The mineral N had disappeared completely in the strip with residues due to N immobilization, whereas mineral N accumulated in the strip without residues due to net mineralization. The residue decomposition rate was markedly reduced due to this N limitation. The model assumed no mineral N exchange between the two strips so that the NO₃–N present in the strip without residues was not available for residue decomposition in the strip with residues.

View larger version (20K): [in this window] [in a new window]   Fig. 5. Measured and simulated NO3–N contents in the 0- to 5- and 0- to 25-cm soil layers in the conventional tillage plots under the assumption that crop residues in the PASTIS model are homogeneously distributed throughout the entire soil volume. Bars indicate standard errors of the measured values.

View larger version (28K): [in this window] [in a new window]   Fig. 6. Measured and simulated NO3–N contents in the 0- to 5- and 0- to 25-cm soil layers in the conventional tillage (CT) and no-tillage plots, under the assumption that crop residues in CT are located in 10% of the entire soil volume. Bars indicate standard errors of the measured values.

The calculated amount of cumulative NO₃–N leached below the 25-cm soil depth was 22 kg N ha^–1 for CT and 45 kg N ha^–1 for NT during the entire study period. The larger N leaching in NT was mainly the result of the larger initial NO₃–N content and the larger amount of drainage. For both tillage systems, N leaching during the evaporation period was negligible. There is no consensus about the effect of tillage on N leaching in the literature (Elliott and Coleman, 1988; Germon et al., 1994; Shipitalo et al., 2000).

Carbon Dioxide Emissions
The simulations of CO₂ production were also initiated with the standard procedure, assuming an even distribution of crop residues (one soil volume). They lead to the same conclusions as for mineral N: the simulations were quite satisfactory for NT, but they overestimated the cumulative CO₂ production in the CT plot (not shown). The simulations conducted with two soil volumes (two vertical strips) again resulted in very good simulations in the CT plot (Fig. 7 ). It is noteworthy that the cumulative CO₂ production was greater in NT than in CT plot.

View larger version (24K): [in this window] [in a new window]   Fig. 7. Measured and simulated cumulative CO2 emission in the conventional tillage (CT) and no-tillage (NT) plots, under the assumption that crop residues in CT are located in 10% of the entire soil volume.

View larger version (40K): [in this window] [in a new window]   Fig. 8. Measured and simulated daily CO2 emissions in the conventional tillage (CT) and no-tillage plots, under the assumption that crop residues in CT are located in 10% of the entire soil volume. Bars indicate standard errors of the measured values.

View larger version (25K): [in this window] [in a new window]   Fig. 9. Simulated cumulative CO2 emissions as a product of decomposition of soil organic matter (SOM) and crop residues in the conventional tillage (CT) and no-tillage (NT) plots, under the assumption that crop residues in CT are located in 10% of the entire soil volume.

View larger version (35K): [in this window] [in a new window]   Fig. 10. Simulated daily CO2 emissions as a product of decomposition of soil organic matter (SOM) and crop residues in the conventional tillage (CT) and no-tillage (NT) plots, under the assumption that crop residues in CT are located in 10% of the entire soil volume.

	CONCLUSIONS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
The model was able to provide good simulations of the evolution of soil water content, soil temperature, CO₂ emissions, crop residue decomposition, and soil mineral N during a 5-mo period in the two tillage systems. The measurement and simulation of the soil water content of CT turned out to be difficult, due to the large spatial and temporal variations of both soil structures and properties directly after moldboard plowing. The simulation of the NT treatment was easier to carry out because of the more homogeneous soil structure.

The mulch layer exerted a large influence on the water dynamics: evaporation was reduced while water drainage increased. Cumulative and daily CO₂ emissions produced during SOM decomposition were similar in both tillage systems, which was a result of similar CO₂ emission potentials and similar regimes of soil temperature and water pressure head. The observed difference in CO₂ emissions between the two tillage systems (larger in NT) was due to crop residue decomposition. Despite a slower decomposition of crop residues in NT, the larger amount of crop residues gave rise to a larger amount of C decay in NT. The larger amount of crop residues in NT was due to the presence of weathered residues from previous crops resulting from the slower residue decomposition in NT. No large temperature differences were observed between the surface residues in NT and the incorporated residues in CT. The water content of the mulch had the largest influence on the difference in decomposition rate of crop residues between CT and NT. Between rainfall events in periods with larger amounts of evaporation, the rate of residue decomposition in NT was reduced compared with CT due to the low water content of the surface residues.

For long-time differentiated CT and NT fields, the simulation results showed that the large amount of accumulated organic matter on the soil surface in NT can counterbalance the slower decomposition rate and result in larger CO₂ emissions in NT than CT under specific climatic conditions. Finally, as the climatic variables had a large influence on the simulated CO₂ fluxes between CT and NT, it would be interesting to run the model with data from fields under other climatic conditions.

	ACKNOWLEDGMENTS

 
Katrien Oorts was supported by a grant from ADEME and ARVALIS-Institut du Végétal. We would like to thank G. Alavoine, E. Gréhan, C. Herre, M.J. Herre, F. Millon, S. Millon, P. Regnier, and P. Thiébeau for their valuable laboratory and field assistance and ARVALIS-Institut du Végétal, especially J. Labreuche and D. Couture, for the conductance of the long-term field experiment at Boigneville.

Received for publication May 31, 2006.

	REFERENCES

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 

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