# Modelling decomposition of crop residue mulches and the associated N2O emissions in a no-till system in southern Brazil

Bruno Chaves, Joël Léonard, Fabien Ferchaud, Raquel Schmatz, Sylvie Recous, Sandro José Giacomini

► To cite this version:

Bruno Chaves, Joël Léonard, Fabien Ferchaud, Raquel Schmatz, Sylvie Recous, et al. Modelling decomposition of crop residue mulches and the associated N2O emissions in a no-till system in southern Brazil. Soil and Tillage Research, 2024, 240, pp. 106065. 10.1016/j.still.2024.106065. hal-04500734

HAL Id: hal-04500734

https://hal.inrae.fr/hal-04500734v1

Submitted on 12 Feb 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
1 Modelling decomposition of crop residue mulches and the associated N₂O emissions in a no-till system

2 in southern Brazil

3

4 Bruno Chaves<sup>a,b</sup>, Joël Léonard<sup>c**</sup>, Fabien Ferchaud<sup>cd</sup>, Raquel Schmatz<sup>a</sup>, Sylvie Recous<sup>b</sup>, Sandro José

5 Giacomini<sup>a*</sup>

6

7 <sup>a</sup>Department of Soils, Federal University of Santa Maria, 97105-900 Santa Maria, RS, Brazil

8 <sup>b</sup>Université de Reims Champagne-Ardenne, INRAE, FARE, UMR A 614, Reims, 51100, France

9 <sup>c</sup>BioEcoAgro Joint Research Unit, INRAE, Université de Liège, Université de Lille, Université de Picardie

10 Jules Verne, 02000, Barenton-Bugny, France

11 <sup>d</sup>UMR Eco&Sols, Univ Montpellier, CIRAD, INRAE, IRD, InstitutAgro Montpellier, Montpellier, France

12

13 corresponding author:

14 * sjgiacomini@ufsm.br, Federal University of Santa Maria, 97105-900 Santa Maria, RS, Brazil

15 ** joel.leonard@inrae.fr, INRAE, 180 rue Pierre-Gilles de Gennes, 02000 Barenton-Bugny, France

16
## 17 Abstract

In no-tillage systems, crop residues are left on the soil surface and form a mulch, which can result in the stimulation of N₂O emissions through various processes. As a consequence, the accurate description of mulch decomposition and the associated N₂O emissions by soil-crop models is essential to help manage the potential impacts of mulches on the environmental performances of these agroecosystems. Here, we combined the use of a soil-crop model (STICS) and published experimental data to develop a new model representation of mulches (with various masses and qualities) and their decomposition, the carbon (C) and nitrogen (N) dynamics, and effects on N₂O emissions. We used a published dataset from southern Brazil combining two residues with distinct chemical characteristics (vetch, *Vicia sativa* L., and wheat *Triticum aestivum* L.) and four rates of mulch addition (0, 3, 6 and 9 Mg dry matter (DM) ha⁻¹) decomposing over one year. The STICS model with its default parameterization overestimated the remaining mulch masses, particularly at high DM inputs, and underestimated the N₂O emissions. The evolution of the STICS soil and decomposition modules led to two major results: i) we modified the mulch module parameterization, by representing that the whole mulch is accessible to decomposers, whatever its initial mass and thickness; ii) a new potential denitrification function was proposed, which use simulated CO₂ fluxes associated from both soil humus and residue decomposition as proxy for C readily available to denitrifiers. With these new representation and parameterization, the model then accurately reproduced the very large range of magnitudes and the temporal variability of C and N fluxes observed for the two residues and the four mulch masses. These results are promising and the conceptual formalisms generic enough to be potentially developed in other soil-crop models. The next step is now to extend and generalize them to other conditions.

## 38 Keywords: carbon dynamics, N₂O emission, residue decomposition, STICS model, Vetch, Wheat, CO₂ flux

## 40 1. Introduction

Conservation agriculture has been largely adopted worldwide and can provide many agroecosystem services, e.g., increasing carbon (C) storage, soil moisture retention and nutrient cycling (Das et al. 2022; Munodawafa and Zhou, 2008). It often involves a wide diversity of species used in crop rotation (including cash crops and
45

46

47

48

49

50

51

52

53

cover crops), which leads to a wide range of masses and qualities (chemical composition) of crop residues left on the soil surface and may form a mulch (Sithole et al., 2016). The presence of mulch can result in the stimulation of N₂O emissions due, for example, to higher substrate availability near the soil surface and the maintenance of a higher soil moisture, which are factors favoring denitrification in the soil (Peyrard et al., 2016; Fracetto et al., 2017; Pinheiro et al., 2019; Schmatz et al., 2020). However, the accurate prediction of the effect of mulch on N₂O emissions remains problematic: there are multiple control variables and interacting processes that are influenced by the thickness and nature of the mulch. Simulation models are useful tools in this context (Hénault et al., 2005; Plazza-Bonilla et al., 2017; Wang et al., 2021), as they allow us to predict the dynamics of the control variables of N₂O emissions and the different interacting processes that affect N₂O emissions.

54

55

56

57

58

59

60

61

62

63

64

65

66

67

N₂O emissions from soil result mainly from nitrification and denitrification processes (Butterbach-Bahl et al., 2013; Mei et al., 2018), which are strongly affected by substrate availability (NH₄ and NO₃ contents, soluble C), environmental conditions (soil temperature and moisture) and soil characteristics (pH, soil texture) (Charles et al., 2017). Surface residues can affect N₂O emissions directly during their decomposition by increasing soil N and labile C availability to soil microbes (Chen et al., 2013) and indirectly by affecting soil moisture and temperature (Coppens et al., 2007). Denitrification in particular, which occurs under anoxic conditions (Li et al., 2016), is known to be strongly dependent on readily available C fueling denitrifiers for short periods ('hot moments') (Surey et al., 2020). Simplified models often use a constant denitrification potential considered as a soil property that is loosely related to the organic C content (Heinen, 2006). However, a positive correlation between soil CO₂ and N₂O fluxes was observed in several studies, mainly after residue addition (Chen et al., 2013; Baggs et al., 2003; Schmatz et al., 2020), which suggests that when residues decompose, the denitrification potential may be temporarily increased by an increase in C availability, which in turn also depends on residue quantity and quality and leads to more N₂O emissions (Schmatz et al., 2020; Lashermes et al., 2022).

68

69

70

71

72

Several models explicitly simulate the processes involved in mulch decomposition (e.g., STICS, Brisson et al., 2003; PASTIS, Findeling et al., 2007; APSIM, Thorburn et al., 2001). These models simulate mulch decomposition occurring in a layer in contact with the soil, while a layer of overlying residues is not decomposed but feeds the decomposing layer. This approach was developed based on studies showing a decrease in the decomposition rate (k) with increasing mulch thickness, less contact between the soil and residue and limited
73

74

75

76

77

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

100

access to soil resources by microbial biomass (Steiner et al., 1999; Stott et al., 1990). It allows to take into account some heterogeneity in decomposition. Recent studies have, however, proposed a different approach of mulch decomposition, where mulch decomposition is proportional to the initial mass of residue, at least over a large range of mulch thicknesses (Acosta et al., 2014; Dietrich et al., 2017; Dietrich et al., 2019; Halde and Entz, 2016; Williams et al., 2018). This means that the decomposition rate constant *k* is not dependent on mulch thickness, and there is no need to consider a nondecomposable layer. STICS is a soil-crop dynamic model that is intended to be generic, robust and operational and allows the simulation of mulch decomposition dynamics and N₂O emissions. It was shown to be able to simulate crop production and N and water dynamics in a large range of situations (Coucheney et al., 2015; Sierra et al., 2010). However, STICS has rarely been tested specifically for its ability to simulate mulch decomposition in fields (Beaudoin et al., 2008) and its ability to predict N₂O emissions in no-tilled systems (Plaza-Bonilla et al., 2018).

Therefore, the aim of this study was to test then improve existing model representation of mulch of various masses and thicknesses, and of mulch-soil contact, the consequences of initial mulch mass on the mulch C and N dynamics, and the role of decomposing residues on N₂O emissions. For this, we used a published Brazilian dataset combining two residues (vetch and wheat) with distinct chemical characteristics and four rates of mulch addition (0, 3, 6 and 9 Mg DM ha⁻¹) decomposing over one year on soil without growing plants. In the present study, i) we explored the need to consider two distinct mulch layers (decomposable or not) to simulate the decomposition of the mulches of various initial masses, thicknesses and chemical characteristics, and ii) we improved the capacity of the STICS model to simulate the N₂O emissions observed during mulch decomposition, introducing the use of the simulated CO₂ flux as a proxy of C availability to dynamically estimate the denitrification potential.

## 2. Materials and methods

### 2.1. STICS model description

STICS is a daily time-step soil-crop dynamic model that simulates crop growth and the C, N and water cycles (Beaudoin et al., 2023; Brisson et al., 2003). It can be used without a growing plant, which makes it suitable for
bare soil experiments where we only return residue as well as for real agronomic situations for which it has been
thoroughly evaluated (Coucheney et al, 2015). The main formalisms of interest here are those describing residue
decomposition and N₂O emissions.

104

2.1.1 Residue decomposition

106

In STICS, residue decomposition dynamics is simulated using a simple model where the rate of residue decomposition (kg C ha⁻¹ day⁻¹) follows first-order kinetics with respect to the amount of decomposable C. The nature of the residue (through its C:N ratio), soil temperature, soil water content and available N in the vicinity of the residue also influence the decomposition rate. The decomposition model, which was described by Nicolardot et al. (2001), describes three organic compartments: crop residues (R), decomposer microbial biomass (B) and humified organic matter (H). The C and N fluxes between these compartments are governed by seven parameters: two rate parameters (k and λ), two partitioning coefficients (Y and h) and three C:N ratios (crop residue (CNres), microbial biomass (CNbio) and the newly formed humified organic matter (CNhum) (Fig. 1). Except for the C:N ratio of residues, which is an input, the default values of these parameters are obtained from either hyperbolic relationships with CNres (decomposition rate constant of the residues (k), biomass C:N ratio (CNbio) and humification rate (h)) or fixed: decay rate of microbial biomass (λ=0.0076 day⁻¹) and assimilation yield of residue-C by the microbial biomass (Y=0.62 g g⁻¹).

119

2.1.2 Decomposition in the presence of mulch

121

The model simulates mulch decomposition according to a double compartment model in a similar way to APSIM-residue (Thorburn et al., 2001) or to the PASTIS model (Findeling et al., 2007): there is a compartment in contact with the soil that is decomposing, and a compartment above the previous one, which is not decomposing but which feeds the decomposing layer as this one disappears due to decomposition (Fig. 1). The size of the mulch decomposing layer (*qmulchdec*) is defined in Mg DM ha⁻¹, and its default value is 1 Mg DM ha⁻¹. During the simulation, both compartment sizes are dynamic because the lower layer mass decreases with decomposition and the upper mulch layer loses mass as it feeds the lower layer. If the mulch mass is lower than
the parameter *qmulchdec*, only a single mulch layer is considered. Thus changing the value of *qmulchdec* allows to explore different hypothesis regarding the heterogeneity of decomposition. The decomposition parameters used for mulch residues are different from those used for the decomposition of residues mixed into the soil, as it is assumed that reducing the contact with the soil limits the rate of decomposition because of limited access to soil resources. Furthermore, the presence of mulch results in modifications of soil temperature and humidity at the soil surface through rainfall interception, reduction of soil evaporation and change in surface albedo, which are taken into account by STICS and influence the different soil processes (humus mineralization, N₂O emissions, etc.) and mulch decomposition (Beaudoin et al., 2023). In STICS, these effects depend on the proportion of the soil surface covered by the mulch, which in turn depends on the mulch mass.

138

139 2.1.2 Nitrous oxide emissions

140

The STICS model simulates nitrification, denitrification and the associated N₂O emissions (Beaudoin et al., 2023). Nitrification is assumed to occur in the biologically active soil layer (*profhum*). It is proportional to the NH₄⁺ content in the soil (with a default value of the proportion constant *fnx* of 0.8 day⁻¹) and is modulated by environmental conditions (soil temperature, soil water content and soil pH). The effect of temperature is linear until an optimum rate of 30 °C (*tnitopt*) and then decreases above 35 °C (*tnitopt2*). The soil water-filled pore space (WFPS) has a double effect on the nitrification process: nitrification increases with increasing WFPS until field capacity, and then it decreases because of increasing anoxia. The effect of pH on nitrification is linear between a pH where nitrification is zero (*pHminnit* = 4.0) and a pH (*pHmaxnit* = 7.2) where nitrification is maximal. The proportion of nitrified N emitted as N₂O depends on WFPS, used as a proxy for anoxia: that proportion remains close to the minimum value of 0.16% (*rationit*) over most of the WFPS range but strongly increases above 60% WFPS.

Denitrification is estimated from a soil-dependent potential rate (*vpotdenit*) over the depth *profdenit* (default value of 20 cm) and is modulated by the availability of nitrate (NO₃⁻) and environmental conditions (temperature and soil WFPS). The denitrification rate increases with nitrate concentration (*fNO3*) according to a Michaelis–Menten function. The temperature effect (*fT*) is represented by a Gaussian function, with an optimum at 47 °C. Denitrification is an anaerobic process, and it is thus considered to begin only above a WFPS
threshold (Hénault et al., 2005). The default value of this threshold (*wfpsc*) is 0.62, and the denitrification rate increases exponentially with the WFPS above this threshold. The soil pH has a strong effect on the reduction of N₂O to N₂: the reduction increases linearly with pH between *pHminden*, where it is minimal, and *pHmaxden*, where it is maximal, with default values of 5.6 and 9.2, respectively.

161

## 2.2. Field dataset

163

The experimental dataset used in this work was obtained and published by Schmatz et al. (2020). We chose this dataset because the experiment was conducted under field conditions (i.e., close to real agricultural conditions) and allowed us to cover a large range of mulch thickness for two contrasting residue types. In addition, it was conducted without a growing plant, which simplifies both the analysis of results and the modeling, as transpiration and N uptake are not present. The field experiment was carried out at the Federal University of Santa Maria (29°42′54″ S, 53°42′23″ W), state of Rio Grande do Sul, Brazil. The site has an elevation of 90 m, a mean annual rainfall of 1660 mm and a mean annual air temperature of 19.3 °C. The climate is classified as humid subtropical (type Cfa2 in Köppen's classification). The soil is classified as a Typic Paleudalf (Soil Survey Staff, 2010) with 110 g kg⁻¹ clay, 260 g kg⁻¹ silt, 630 g kg⁻¹ sand and a pH<sub>H₂O</sub> of 4.8 in the 0-10 cm layer. The area was managed under no-till for six years before the experiment, with a rotation of winter (oats (*Avena sativa*)) or ryegrass (*Lolium multiflorum*)) and summer crops (black bean (*Phaseolus vulgaris*) or soybean (*Glycine max*)) prior to the study. A detailed description of the study can be found in Schmatz et al., 2020.

Decomposition of two crop residues having a contrasting chemical quality, vetch (*Vicia sativa* L., C:N = 12) and wheat (*Triticum aestivum* L., C:N = 83), and added to the soil surface with no plant growth was followed during a complete year (November 2016 to November 2017). During the experiment, total rainfall was 2337 mm and mean air temperature was 20 °C (Fig. S1). Initial mulch moisture conditions were rather high as a 58 mm rain event occurred one day after addition of the mulches. The treatments were composed of a control treatment (no residue addition) and the two residues added to the soil surface (vetch and wheat) with three different mulch masses (3, 6 and 9 Mg DM applied ha⁻¹), resulting in six treatments plus one control (bare soil). Before the start of the study, vetch shoots were cut at flowering time, and wheat residues were collected at crop maturity. Vetch residues were cut into 20 cm pieces and wheat residues into 5-20 cm pieces before application

157

158

159

160

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184
to the soil, resulting in no differences in the initial thickness of both residues, on average 7 cm (9 Mg DM), 5 cm (6 Mg DM) and 3 cm thickness (3 Mg DM). The residues were placed in litter boxes, delineated by open wooden frames, to standardize the mulch application and prevent any runoff. The dynamics of the decomposition of the mulch was assessed by collecting the remaining residues ten times during the one-year experimental period and analyzing their total C and N contents.

The soil water content (SWC) was measured using FDR (frequency domain reflectometry) sensors placed at two soil depths, 2.5 cm (for the 0-5 cm layer) and 7.5 cm (for the 5-10 cm layer). Soil temperature (°C) was measured at the same time and at the same depths using T-type sensors. The soil inorganic N content ($NH_4^+$ and $NO_3^-$) was measured on the mulch sampling dates for the 0 to 10 cm soil layer. $N_2O$ and $CO_2$ fluxes were measured on 62 dates using insulated, fan-mixed, nonflow-through, nonsteady-state chambers (Rochette and Bertrand, 2008) from 26 November 2016 to 21 November 2017. The kinetics of mulch decomposition and gas emissions have been discussed in detail in Schmatz et al. (2020).

## 2.3. Setting up the standard simulations with STICS

The main inputs required by the STICS model are climate variables, soil characteristics and crop management descriptions. The daily climate data (radiation, minimum and maximum temperature, rainfall, potential evapotranspiration, wind velocity and humidity) were obtained from an automatic meteorological station located 1.6 km from the experimental site. The main soil characteristics (bulk density, initial soil organic nitrogen, soil C:N ratio, soil water content at field capacity and permanent wilting point) were measured from samples collected in 2016 (Schmatz et al., 2020, Table 1).

A preliminary minimal calibration of the model (STICS version 9.1) was performed on the control treatment (bare soil, no residue addition) using the following output variables: soil water content, soil mineral N, $CO_2$ emissions and $N_2O$ emissions. This calibration was intended to obtain reasonable performance levels for the simulation of the main control variables of $N_2O$ emissions before adding mulch. We slightly changed the soil water content at field capacity as well as the mineralization rate to improve the simulation of the soil water content and soil mineral nitrogen dynamics (soil temperature was already well simulated) (Table 2). The denitrification potential was also adjusted to 0.1 kg ha$^{-1}$ day$^{-1}$ to obtain a correct order of magnitude for $N_2O$
emissions. This optimization was done independently for each parameter using a simple simplex optimization algorithm. Then, the effect of the different mulch treatments (mulch of 3, 6 and 9 Mg DM ha⁻¹ and C:N ratio of 12 vs. 83) was simulated without changes to that preliminary calibration and using the default parameterization of the STICS mulch decomposition submodule (qmulchdec = 1 Mg DM ha⁻¹).

## 2.4. Hypothesis tested for further improvement in model simulations

Recent studies have shown that the average relative mulch decomposition rate does not necessarily depend on the initial residue mass added to the soil surface, at least over the large range of residue masses studied (Dietrich et al., 2019; Halde and Entz, 2016). This is in contradiction with the idea, as described in STICS, that only a small part of the mulch in contact with the soil is actually decomposing, while the remaining part only feeds the decomposing mulch as this mulch progressively disappears. We tested different values of the parameter qmulchdec (3, 6, 9 and >9 Mg DM ha⁻¹) to evaluate whether we obtained an improvement in the simulation of mulch decomposition dynamics by rejecting the idea that decomposition is limited to a small layer in contact with the soil.

Regarding N₂O emissions, we hypothesized that because available C for denitrifiers strongly determines the denitrification potential, active decomposition, which produces at least temporarily labile C, should stimulate denitrification and N₂O emissions. The denitrification potential should thus not be considered a soil constant but should depend on C availability. There is no labile C pool in STICS, but we assumed that CO₂ emissions from decomposition could be considered a good proxy of labile C availability. Because the quality of the mineralized substrate also influences how much labile C will be released for a given respiration rate, we separately considered CO₂ resulting from humus mineralization and CO₂ resulting from residue decomposition, and for the latter, we also considered the C:N ratio of the residue with the hypothesis that residues with a low C:N ratio release more labile C for a given CO₂ emission rate. We thus proposed representing the denitrification potential in STICS as the following function:

$$Dp = a \times CO2_{soil} + b \times CO2_{residue} \times N:C_{residue} \quad (1)$$
where Dp represents the denitrification potential (kg ha⁻¹cm⁻¹day⁻¹); CO₂soil is the simulated CO₂ flux from soil humus mineralization (kg ha⁻¹day⁻¹) and CO₂residue is the simulated CO₂ flux associated with residue decomposition (kg ha⁻¹day⁻¹), both being available as STICS outputs; N:C_residue represents the N:C ratio of the decomposing residue; and a and b are constant parameter values to be estimated (by minimizing the sum of squared errors between the observed and simulated values) that are independent of the treatments. Improvements in simulated values were then evaluated.

247

## 2.5. Evaluation of model performance and improvement

249

Beyond presentation of the dynamics of both observed and simulated values for all treatments, the evaluation of the model performance against the measured data and of model improvement associated with the hypothesis tested was assessed using three statistical criteria that cover different complementary aspects of model performance (Smith et al., 1997): the root mean square error (RMSE), the relative error (E%) and the correlation coefficient (r). These criteria are calculated as follows:

255

$$RMSE = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (Si - Oi)^2} \qquad (2)$$

256

$$E(\%) = \frac{100}{n} \sum_{i=1}^{n} \frac{(Oi-Si)}{Oi} \qquad (3)$$

257

$$r = \frac{\sum_{i=1}^{n} (Oi - \bar{O})(Si - \bar{S})}{\sqrt{[\sum_{i=1}^{n} (Oi - \bar{O})^2][\sum_{i=1}^{n} (Si - \bar{S})^2]}} \qquad (4)$$

258

259

where n represents the number of observations, Si and Oi are the simulated and observed values, respectively; ¯O is the mean value of the observed data; and S is the mean of the simulated values. RMSE provides an absolute measure of the total deviation (dispersion + bias) between the simulated and measured data in the data unit. The mean relative difference (E%) indicates the bias between simulations and measurements, expressed as a proportion of the measurement. The correlation coefficient (r) was used to assess how well simulated values followed the same pattern as observed values.

266
## 3. Results

### 3.1. STICS model calibration on the bare soil

The preliminary minimal calibration of the STICS model was performed using the control treatment and trying to best fit the following observed variables: SWC, temperature, soil mineral N and CO₂ and N₂O fluxes. Statistical criteria for model performance after calibration on the control treatment are given in Table 3. Regarding simulated soil water content dynamics, we initially observed very strong and unrealistic drying of the surface soil during evaporation phases, while the plateau water content in humid phases was slightly overestimated. Changing the estimation of the residual soil water content according to the function of the clay content proposed in Dexter et al. (2012) and decreasing the SWC at field capacity from 0.18 g g⁻¹ to 0.16 g g⁻¹ solved these issues. The correlation coefficients reached for the measured vs. predicted values were 0.72 and 0.55 for the 0-5 and 5-10 cm layers, respectively. However, despite the good overall representation of the soil water content dynamics, the maximal soil water content in the 0-5 cm layer following rainfall events remained underestimated, as the model cannot simulate SWC above field capacity (Fig. 2a). STICS simulated the soil temperature well for the 0-5 cm (r = 0.83; RMSE = 2.52 and E(%) = 7.2) and 5-10 cm (r = 0.76; RMSE = 2.61 and E(%) = 6.85) layers. However, under very hot conditions, the model tended to underestimate soil temperature, a pattern observed for both soil layers (0-5 and 5-10 cm) (Fig. 2c, d).

The order of magnitude of NH₄⁺-N variations, from 2 to 14 kg N ha⁻¹ over the evaluated period, was well captured by the model (Fig. 2e), although with a tendency to underestimate (E(%) = 28.0). Soil NH₄⁺-N dynamics in the 0-10 cm layer was not well simulated even after model calibration, resulting in a low correlation between the measured and predicted values (r = -0.31). This is not surprising, as we are on bare soil with no N input and a limited range of variation, with measurement uncertainty contributing to these variations. Soil NO₃⁻-N was fairly well simulated throughout the year, as shown by a significant correlation coefficient between measured vs. predicted values (r = 0.66) (Table 3). However, the model failed to predict the high NO₃⁻-N content observed in the 0-10 cm soil layer at the beginning of the study (Fig. 2f), and on average, the model overestimated NO₃⁻-N contents by 10% (Table 3).
294

295

296

297

298

299

300

301

302

303

304

305

306

307

308

309

310

311

312

313

314

315

316

317

318

319

320

The observed and simulated daily CO₂ emissions agreed well for most of the experimental period (Fig 2h) after calibration, during which the potential mineralization rate of soil organic matter (gmin1) was increased from 0.0007 to 0.0020 to better simulate both NH₄⁺-N levels and CO₂ fluxes. Daily measured and simulated N₂O emissions are shown in Fig. 2g after calibration, where the denitrification potential was strongly reduced (vpodenit changed from 2.0 to 0.1 kg ha⁻¹ day⁻¹) to fit the observed background emissions of the bare soil, as no peak emissions were observed.

## 3.2. Mulch decomposition simulations

When using the soil parameters from the previous minimal calibration and the default mulch decomposition parameterization of STICS, the mulch remaining C was systematically overestimated (Fig. 3), which indicates an underestimation of the decomposition rate. This pattern was especially pronounced for wheat residues but less marked for vetch residues. The mulch N dynamics followed the same pattern as that of mulch C (Fig. 4).

To obtain decomposition rates and mulch decomposition dynamics consistent with the observed values, the maximum amount of decomposing mulch *qmulchdec* had to be increased. The best performance was obtained when all mulch was considered to decompose, without distinguishing a top layer not in contact with the soil (*qmulchdec* ≥ 9 Mg DM ha⁻¹). With this change, STICS satisfactorily reproduced the C dynamics for all treatments (wheat and vetch at 3, 6 and 9 Mg ha⁻¹) (Fig. 3, Table 4). However, the mulch N dynamics were significantly improved only for vetch residues, with high values of the correlation coefficient (0.89, 0.91 and 0.93 for 3, 6 and 9 Mg DM ha⁻¹ added, respectively) (Table 4). For wheat residues, the model simulated strong N immobilization by the microbial biomass in the decomposing mulch (Fig. 4b, d, f, Table 4).

After the change in the maximum amount of decomposing mulch and the rate of residue decomposition, the order of magnitude and pattern of CO₂ fluxes from all treatments were well captured, with fluxes increasing with increasing amounts of residue (Supplementary Fig. S3). The correlation between measured and predicted CO₂ fluxes was high for all treatments, except for Vetch_6 and Vetch_9 (Table 4).

The SWC was well simulated by STICS after the addition of different residue amounts (Supplementary Table S1), except immediately after rainfall events when the soil water content exceeded the soil field capacity.
321

This was particularly marked for wheat residues and was more pronounced as the residue mass increased. Soil temperature variations were satisfactorily reproduced for all treatments by STICS (Supplementary Table S1).

322

323

The soil NH<sub>4</sub><sup>+</sup>-N content was overestimated for vetch residues and underestimated for wheat residues (Supplementary Fig. S2 and Table 4), and for bare soil, the dynamics were not well reproduced (Fig. 2e). Soil NO<sub>3</sub><sup>-</sup>-N contents were, on the contrary, well reproduced by the model, although the correlation between simulated and observed values was only high for vetch residues (Table 4). STICS accurately predicted the increase in the NO<sub>3</sub><sup>-</sup>-N content after residue addition (Supplementary Fig. S3).

324

325

326

327

328

329

### 3.3. N<sub>2</sub>O emissions simulation with mulch

330

331

A first run of simulations was performed, including the changes to improve mulch decomposition, while keeping the constant denitrification potential calibrated for the bare soil ($vpodenit = 0.1 \text{ kg ha}^{-1} \text{ day}^{-1}$). The model was then totally unable to reproduce the strong stimulation of N<sub>2</sub>O emissions induced by the decomposing mulch as well as the strong differences observed between the different mulch amounts and nature (Fig. 5).

332

333

334

335

Using equation (1) to dynamically predict the denitrification potential is a simple although indirect way to include the effect of the increase in labile C on denitrification, with CO<sub>2</sub> emissions being used as a proxy for this available C. The parameters *a* and *b* of equation (1) were fitted using observed N<sub>2</sub>O data, considering that they do not depend on the treatment, and took the following values: *a* = 25×10<sup>-5</sup> cm<sup>-1</sup> and *b* = 0.075 cm<sup>-1</sup>. When running the model, which includes equation (1), to dynamically estimate the denitrification potential, the model performance increased drastically (Fig. 5). The wide range of the order of magnitude of emissions was well reproduced: the model was able to describe maximum emissions ranging from ~0.015 kg ha<sup>-1</sup> day<sup>-1</sup> for the control, 0.06-0.10 kg ha<sup>-1</sup> day<sup>-1</sup> for wheat and 0.3-0.8 kg ha<sup>-1</sup> day<sup>-1</sup> for vetch. Cumulative fluxes were also well reproduced for all treatments, except vetch in the 9 Mg DM ha<sup>-1</sup> treatment (Supplementary Material Table 2). Finally, the dynamics of emissions were also well reproduced, both in terms of the overall evolution of fluxes with higher values at the beginning of the experiment, which then decreased with time and in terms of the timing of peaks associated with rainfall (Fig. 5).

336

337

338

339

340

341

342

343

344

345

346

347

348

## 4. Discussion
349

350

351

352

353

354

355

356

357

358

359

360

361

362

363

364

365

366

367

368

369

370

371

372

373

374

375

376

## 4.1. Performance of the STICS model on bare soil

The correct simulation of mulch decomposition and the associated N₂O emissions strongly depend on the accuracy of the model to predict the main variables that control these processes (soil mineral N, SWC, and temperature). In the bare soil treatment without residue addition, STICS simulated soil temperature well, without needing modification of model parameters, in accordance with other studies that evaluated this variable (Jégo et al., 2014; Plaza-Bonilla et al., 2017). SWC was also fairly well predicted, despite the necessity to slightly reduce the soil water content at field capacity. Field capacity is, however, not straightforward to measure, and values obtained in the laboratory often need small adjustments in the field. The change in how the residual soil water content was estimated from the clay content was useful to better represent the drying of the soil, but it should be better considered a general improvement to STICS, which will also improve performances at other sites, rather than a site-specific calibration. The mineralization rate has to be significantly modified to improve both mineral nitrogen dynamics and CO₂ fluxes. This is an indication that the description of the influence of control variables on mineralization (temperature, SWC, pH, for example) still has limits, which sometimes implies a site-specific calibration. The fact that the soil is not tilled and presents low pH may contribute to this, as STICS has been less evaluated under such conditions. Low pH values can drastically reduce the mineralization of soil organic matter by limiting microbial activity (Kemmitt et al., 2006), which may explain the need to increase this parameter. However, after the mineralization potential was changed, both CO₂ fluxes and nitrate dynamics were well simulated. The NH₄-N content dynamics remained less well simulated, although the order of magnitude of variations was correct. However, variations occur only over a limited range, as there is no fertilization or residue input, and the observed fluctuations could originate partly from measurement uncertainty (Coucheney et al., 2015). N₂O flux is the only variable that strongly benefited from a site-specific calibration of the denitrification potential. This is not surprising, as the denitrification potential can vary over orders of magnitude, and the default value in STICS corresponds to an average value in northern France.

## 4.2. Simulation of C and N dynamics of vetch and wheat mulches of increasing thickness
377

378

379

380

381

382

383

384

385

386

387

388

389

390

391

392

393

394

395

396

397

398

399

400

401

402

403

404

### 4.2.1 Mulch-soil contact limiting decomposition

The main difference between the decomposition of residues left on the soil surface and that of residues incorporated into the soil is the slower decomposition of crop residues left on the soil surface, which is mainly attributed to reduced mineral N availability to decomposers due to limited soil-residue contact (Chaves et al., 2021; Coppens et al., 2007). STICS combines two ways of managing the effect of limited N availability associated with limited soil-residue contact. First, there are two sets of default decomposition parameters, one for incorporated residues, one for residues left on the soil surface, and the latter results in a slower decomposition rate. Then, as in the PASTIS model (Findeling et al., 2007) or APSIM model (Thorburn et al., 2001), mulch decomposition is described using two distinct layers: one decomposing layer in direct contact with the soil and a second layer on top of the previous layer that is not decomposing but feeds the decomposing layer as it decomposes. Additional limitations to decomposition may result from soil temperature, soil moisture and limited N availability induced by the lack of contact. Using default STICS parameters, including the maximum mass of decomposing mulch (1 Mg DM ha⁻¹), the remaining mulch and associated C and N contents were strongly overestimated for both residues (vetch and wheat) regardless of mulch thickness (3, 6, 9 Mg DM ha⁻¹). Such a result was expected because the present dataset is one of the recent studies that have observed that the initial mulch mass has no effect on the average decomposition rate of the mulch. A mass loss proportional to the initial mass, was also observed with sugarcane mulch in the range of 4 to 12 Mg DM ha⁻¹ (Dietrich et al., 2017), hairy vetch, pea, oilseed radish, sunflower, flax and barley in the range 5 to 10 Mg DM ha⁻¹ (Halde and Entz, 2016) and rye residues in the range 0 to 15 Mg DM ha⁻¹ (Williams et al., 2018).

To improve the simulation of mulch decomposition dynamics, we hypothesized that considering all mulch to be in contact with the soil and subject to decomposition might be a convenient representation of the fact that decomposition is proportional to the initial residue mass, at least in the range of residue mass (3, 6 and 9 Mg DM ha⁻¹) considered in the present dataset. With this hypothesis, the whole residue mass is considered to be in contact with the soil, which removes one physical limitation for microbial decomposers to access soil N resources (it remains that decomposition parameters are different for residues left on the soil surface). Testing different values of the maximal amount of decomposing mulch allowed us to check that the best prediction of
405

406

407

408

409

410

411

412

mulch decomposition was always obtained when the total mulch mass was considered in contact with the soil and thus decomposing. Simulation of the whole mulch decomposing in a homogeneous manner does not actually reflect the real process, as there is a gradient in the decomposition rate within the mulch mass, as described by Dietrich et al. (2019). These authors experimentally observed with sugarcane residues added at rates of 4, 8 and 12 Mg ha⁻¹ that the whole mulch was affected by degradation from the start, although more or less quickly depending on the distance to the soil and the moisture gradient, and only the average decomposition rate was proportional to the mulch mass. It is finally quite satisfying to observe that we did not have to change the default values of the decomposition parameters for surface residues.

413

414

415

416

417

418

419

420

421

Mulch N dynamics during decomposition were better predicted for vetch residues than for wheat residues, even after previously mentioned model adjustments. For vetch residues, the modification of the *qmulchdec* parameter value did not result in large changes in simulated mulch N dynamics, with good agreement between the predicted and observed values before and after the change. The high N content of vetch residues (low C:N ratio) probably reduces the importance of the soil-residue contact effect on residue decomposition, as the main source of N for microbial decomposers can be the residue itself, thus reducing the N requirement from soil, as observed by Coppens et al. (2007) and Chaves et al. (2021). That behavior was not observed for wheat mulches: we hypothesize that the low total N of wheat residues induced a strong immobilization of soil N as the total mass of the mulch was considered in contact with the soil and was not well simulated by the model.

422

423

## 4.2.2 Soluble C and N leaching from the mulch

424

425

426

427

428

429

430

431

432

The rapid leaching of soluble C and N fractions from mulches by rain has been observed in different studies (Schreiber, 1999; Thiébeau et al., 2021) and can play an important role in mulch C and N dynamics, contributing to the observed decrease in the C and N contents of mulches. This is especially true for immature residues having high soluble C and N contents and in regions of high precipitation, such as southern Brazil, where our dataset was obtained. In addition to removing C and N from mulch, leaching under high rainfall intensities can increase labile C and mineral N contents in the topsoil layer, which could lead to increased losses of nitrate by leaching. Furthermore, labile C and mineral N are substrates for N₂O emissions, which can then be stimulated (Lashermes et al., 2022). STICS does not simulate losses of soluble compounds from surface-applied residues
under rainfall: these soluble C and N fractions remain in the residues, which could explain the lack of fit between observed and simulated C and N in the mulch in the first days of decomposition (a 58 mm rain occurred just one day after addition of the mulches). This was particularly visible for vetch C and N and is consistent with the simultaneous underestimation of the mineral N content in soil under vetch mulches. Introducing the leaching of soluble C and N from mulch to soil with rainfall would be a useful addition to models such as STICS, even in a simple way. Numerous data on crop residue chemical composition are available (e.g., Jensen et al., 2005; Thiébeau et al., 2021), which could contribute to parameterizing the size of the residue soluble fraction susceptible to leaching under rain, for example, as a function of the C:N ratio of the residue.

441

### 4.3. Taking into account the effect of increased C availability on denitrification during decomposition

443

As many models, STICS considers N₂O emissions resulting from two main processes: autotrophic nitrification and heterotrophic denitrification. Denitrification, which is often the main source of N₂O emissions from soil (Li et al., 2016), occurs under anoxic conditions and is regulated by numerous factors, including substrate availability (C, NO₃), soil moisture, soil aeration and soil temperature (Butterbach-Ball et al., 2013). Soil pH also plays a critical role in regulating the reduction of N₂O into N₂.

While most of these controlling factors are directly or indirectly taken into account in models, C availability and its influence are not explicitly considered (Brilli et al., 2017). Similar to many simple denitrification models (Heinen, 2006), STICS uses the potential of denitrification, which has to be measured (Henault et al., 2005). That denitrification potential is actually a semipotential, as C availability is not imposed, contrary to what is done for nitrate, for which a high content that is not limiting is used. That denitrification potential is considered a soil property, which is constant and does not change when, for example, large amounts of labile C are released during decomposition. Our results indicated that such an approach is acceptable for bare soil where C availability varies only slightly, provided this denitrification potential is measured or calibrated. However, when the model calibrated on the bare soil treatment was used to simulate the mulch treatments, N₂O fluxes were strongly underestimated in all treatments, and differences induced by mulch thickness or nature were not captured. Indeed, as often reported (Li et al., 2016; Miller et al., 2008), higher N₂O emissions are generally observed after residue addition, especially with low C:N ratio residues (mostly residues from
immature crops, such as cover crops), which release a large amount of labile C (and N), which drives soil denitrifier activity (Lashermes et al., 2022; Abalos et al., 2022).

We hypothesized that the CO₂ flux associated with decomposition could be used as a proxy for easily available C for denitrifiers. This hypothesis was supported, for example, by the work of Surey et al. (2020), who found very similar relationships between water-extractable organic carbon and cumulative denitrification and cumulative CO₂ emissions and denitrification. This result is also supported by the often observed correlation between CO₂ emissions and N₂O emissions, as shown, for example, by Xu et al. (2008). Such a correlation generally occurs at early stages of residue decomposition (Lashermes et al., 2022), when the soluble fraction is highly available. In addition to the rate of CO₂ emitted, which relates to the rate of decomposition and thus of release of C compounds, the soluble C content also depends on the overall residue chemical composition and is closely related to its C:N ratio or N content (Chaves et al., 2021), with residues from plants harvested in the vegetative phase or at immature stages having both a low C:N ratio and a high soluble fraction compared to crop residues from mature or senescent crops (Abalos et al., 2022). This was confirmed in this study, with vetch residues having a lower C:N ratio than wheat residue and higher N₂O emissions.

Introducing a dynamic estimation of the denitrification potential in STICS that depends on both the CO₂ flux associated with decomposition and the N:C ratio of the decomposing residue drastically improved the simulation performance for N₂O emissions. This allowed us to capture the huge difference in the order of magnitude of emissions between the bare soil and the different amounts of mulch, as well as the difference in emissions between the vetch and wheat mulches of the same initial thickness (with the N:C ratio of the residue indirectly reflecting the high soluble N content released with vetch). Finally, the dynamics of emissions, both in terms of the declining trend of emissions and timing of peaks, were also significantly improved. Model performance was only slightly lower for the treatment with vetch residues added at 9 Mg DM ha⁻¹. Schmatz et al. (2020) discussed the decrease in N₂O emitted with 9 Mg DM ha⁻¹ compared to 6 Mg DM ha⁻¹, which was attributed to a possible increase in the N₂O to N₂ reduction rate with high soluble C and N availability.

## 5. Conclusion
The modelling of mulch decomposition and N₂O emissions against data involving two crop residues (vetch and wheat) with different mulch masses (0, 3, 6 and 9 Mg DM ha⁻¹), inducing different thicknesses, using the STICS model, allowed us to propose significant improvements in the formalism and parameterization of the decomposition and N₂O modules to better reflect C and N dynamics associated with crop residue recycling in no-tilled cropping systems. First, our results showed that regardless of the range of mass and thickness, the mulch C dynamics was better described when the whole mulch mass was considered decomposing from the start, regardless of the initial mass (here in the range of agricultural practice, from 0 to 9 Mg DM ha⁻¹). These results call into question the present representation of mulch (a decomposing layer fed by a nondecomposing layer) in several models, including STICS, or the size of the decomposing layer, which is much greater than the default value in STICS. This work also demonstrated the importance of simulating mulch C and N transport under rainfall, particularly for agricultural and climatic contexts with no tilled systems and high precipitation regimes, to be able to take into account the fast removal of soluble C and N from mulch to soil after heavy rain.

Last, switching from a constant and soil-dependent denitrification potential to a dynamic potential taking into account the stimulating impact of residue addition via the CO₂ emissions resulting from decomposition and the N:C ratio of residues led to a much better prediction of the pattern and wide range of intensities of N₂O fluxes. These results are very promising for accurately simulating N₂O emissions associated with residue decomposition.

Soil crop models and the possibility they offer to simulate mulch decomposition and the associated N₂O emission are a critical asset to improve crop residue management and reduce soil N losses and greenhouse gases emissions from agricultural ecosystems. The STICS model appears able to produce good simulation performance in the studied context, with a wide range of mulch quantity and quality, which makes it useful to help guiding agricultural practices in southern Brazilian pedoclimatic conditions. However, the role of soil-mulch contact and mulch thickness on decomposition and the dynamic estimation of denitrification potential from CO₂ emissions used as a proxy of C availability need further evaluation in a wider diversity of pedoclimatic and agronomic scenarios to verify if the results obtained here can be generalized and the parameterization improved. Taken together, the confrontation of specific experimental data with the related formalisms of the STICS model makes it possible to provide generic avenues of improvement for soil-plant models simulating the decomposition of residue mulches.
516

517 **Declaration of Competing Interest**

518 The authors declare that they have no known competing financial interests or personal relationships that could
519 have appeared to influence the work reported in this paper.

520

521 Acknowledgements

522 This work was supported by the Brazilian government through the Coordenação de Aperfeiçoamento de Pessoal
de Nível Superior – Brasil (CAPES) – Finance Code 001. The bilateral Brazilian and French collaboration was
523 funded under Program CAPES-PRINT – Programa Institucional de Internacionalização, Process Number
524 88887.373791/2019-00, and by INRAE (The French National Research Institute for Agriculture, Food and
525 Environment) to SR, JL and FF and URCA (Université de Reims Champagne Ardenne) during Chaves leave at
526 UMR FARE in Reims, France.
527

528

529

530 References

531

532 Abalos, D., Rittl, T.F., Recous, S., Thiébeau, P., Topp, C.F.E., Groenigen, K.J. van, Butterbach-Bahl,
533 K., Thorman, R.E., Smith, K.E., Ahuja, I., Olesen, J.E., Bleken, M.A., Rees, R.M., Hansen, S., 2022.
534 Predicting field N2O emissions from crop residues based on their biochemical composition: a meta-
535 analytical approach. Sci. Total Environ. 812, 152532. https://doi.org/10.1016/j.scitotenv.2021.152532

536

537 Acosta, J.A.D.A., Amado, T.J.C., Silva, L.S.D., Santi, A., Weber, M.A., 2014. Decomposição da
538 fitomassa de plantas de cobertura e liberação de nitrogênio em função da quantidade de resíduos
539 aportada ao solo sob sistema plantio direto. Ciência Rural 44, 801-809. https://doi.org/10.1590/S0103-84782014005000002
540

541

542 Baggs, E.M., Stevenson, M., Pihlatie, M., Regar, A., Cook, H., Cadisch, G., 2003. Nitrous oxide
543 emissions following application of residues and fertiliser under zero and conventional tillage. Plant
544 Soil 254, 361–370. https://doi.org/10.1023/A:1025593121839

545

546 Beaudoin, N., Launay, M., Sauboua, E., Ponsardin, G., Mary, B., 2008. Evaluation of the soil crop
547 model STICS over 8 years against the “on farm” database of Bruyères catchment. Eur. J. Agron. 29,
46–57. https://doi.org/10.1016/j.eja.2008.03.001

549

Beaudoin N., Lecharpentier P., Ripoche-Wachter D., Strullu L., Mary B., Léonard J., Launay M., Justes É., eds. 2023. STICS soil-crop model. Conceptual framework, equations and uses, Versailles, éditions Quæ, 516 p. http://doi.org/10.35690/978-2-7592-3679-4

551

552

553

Brilli, L., Bechini, L., Bindi, M., Carozzi, M., Cavalli, D., Conant, R., Dorich, C.D., Doro, L., Ehrhardt, F., Farina, R., Ferrise, R., Fitton, N., Francaviglia, R., Grace, P., Iocola, I., Klumpp, K., Léonard, J., Martin, R., Massad, R.S., Recous, S., Seddaiu, G., Sharp, J., Smith, P., Smith, W.N., Soussana, J.F., Bellocchi, G., 2017. Review and analysis of strengths and weaknesses of agro-ecosystem models for simulating C and N fluxes. Sci. Total Environ. 598, 445-470. https://doi.org/10.1016/j.scitotenv.2017.03.208

555

556

557

558

559

560

Brisson, N., Gary, C., Justes, E., Roche, R., Mary, B., Ripoche, D., Zimmer, D., Sierra, J., Bertuzzi, P., Burger, P., Bussière, F., Cabidoche, Y., Cellier, P., Debaeke, P., Gaudillère, J.P., Hénault, C., Maraux, F., Seguin, B., Sinoquet, H., 2003. An overview of the crop model stics. Eur. J. Agron. 18, 309–332. https://doi.org/10.1016/S1161-0301(02)00110-7

562

563

564

565

Butterbach-Bahl, K., Baggs, E.M., Dannenmann, M., Kiese, R., Zechmeister-Boltenstern, S., 2013. Nitrous oxide emissions from soils: how well do we understand the processes and their controls? Philos. Trans. R. Soc. 368, 20130122. https://doi.org/10.1098/rstb.2013.0122

567

568

569

Charles, A., Rochette, P., Whalen, J.K., Angers, D.A., Chantigny, M.H., Bertrand, N., 2017. Global nitrous oxide emission factors from agricultural soils after addition of organic amendments: A meta-analysis. Agric. Ecosyst. Environ. 236, 88–98. https://doi.org/10.1016/j.agee.2016.11.021

570

571

572

573

Chaves, B., Redin, M., Giacomini, S. J., Schmatz, R., Léonard, J., Ferchaud, F., Recous, S., 2021. Combination of residue quality, residue placement and soil mineral N content drives C and N dynamics by modifying the N access to microbial decomposers. Soil Biol. Biochem. 163, 108434. https://doi.org/10.1016/j.soilbio.2021.108434

574

575

576

577

578

Chen, H., Li, X., Hu, F., Shi, W., 2013. Soil nitrous oxide emissions following crop residue addition: A meta-analysis. Glob. Chang. Biol. 19, 2956–2964. https://doi.org/10.1111/gcb.12274

579

580

581

Coppens, F., Garnier, P., Findeling, A., Merckx, R., Recous, S., 2007. Decomposition of mulched versus incorporated crop residues: Modelling with PASTIS clarifies interactions between residue

582

583
584 quality and location. Soil Biol. Biochem. 39, 2339-2350. https://doi.org/10.1016/j.soilbio.2007.04.005

585 Coucheney, E., Buis, S., Launay, M., Constantin, J., Mary, B., Cortázar-Atauri, I.G. de, Ripoche, D., 588 Beaudoin, N., Ruget, F., Andrianarisoa, K.S., Le Bas, C., Justes, E., Léonard, J., 2015. Accuracy, 589 robustness and behavior of the STICS soil e crop model for plant, water and nitrogen outputs: 590 Evaluation over a wide range of agro-environmental conditions in France. Environ. Model. Softw. 64, 591 177-190. https://doi.org/10.1016/j.envsoft.2014.11.024

592 Das, S., Chatterjee, S., Rajbanshi, J., 2022. Responses of soil organic carbon to conservation practices 593 including climate-smart agriculture in tropical and subtropical regions: A meta-analysis. Sci. Total 594 Environ. 805, 150428. https://doi.org/10.1016/j.scitotenv.2021.150428

595 Dexter, A.R., Czyz, E.A., Richard, G., 2012. Equilibrium, non-equilibrium and residual water: 596 Consequences for soil water retention. Geoderma 177-178, 63-71. 597
598 https://doi.org/10.1016/j.geoderma.2012.01.029

599 Dietrich, G., Recous, S., Pinheiro, P.L., Weiler, D.A., Schu, A.L., Rambo, M.R.L., Giacomini, S.J., 600 2019. Gradient of decomposition in sugarcane mulches of various thicknesses. Soil Tillage Res. 192, 601 66-75. https://doi.org/10.1016/j.still.2019.04.022

603 Dietrich, G., Sauvadet, M., Recous, S., Redin, M., Pfeifer, I.C., Garlet, C.M., Bazzo, H.L.S., 604 Giacomini, S.J., 2017. Sugarcane mulch C and N dynamics during decomposition under different rates 605 of trash removal. Agric. Ecosyst. Environ. 243, 123-131. https://doi.org/10.1016/j.agee.2017.04.013

607 Findeling, A., Garnier, P., Coppens, F., Lafolie, F., Recous, S., 2007. Modelling water, carbon and 608 nitrogen dynamics in soil covered with decomposing mulch. Eur. J. Soil Sci. 58, 196-206. 609
610 https://doi.org/10.1111/j.1365-2389.2006.00826.x

611 Fracetto, F.J.C., Fracetto, G.G.M., Bertini, S.C.B., Cerri, C.C., Feigl, B.J., Siqueira Neto, M., 2017. 612 Effect of agricultural management on N2O emissions in the Brazilian sugarcane yield. Soil Biol. 613 Biochem. 109, 205-213. https://doi.org/10.1016/j.soilbio.2017.02.004

615 Halde, C., Entz, M.H., 2016. Plant species and mulch application rate affected decomposition of cover 616 crop mulches used in organic rotational no-till systems. Can. J. Plant Sci. 96, 59-71. 617
618 https://doi.org/10.1139/cjps-2015-0095

619
620

Heinen, M., 2006. Simplified denitrification models: Overview and properties. Geoderma 133, 444–463. https://doi.org/10.1016/j.geoderma.2005.06.010

621

622

Hénault, C., Bizouard, F., Laville, B., Gabeielle, B., Nicoullaud, B., Germon, J.C., Cellier, P., 2005.

623

624

Predicting in situ soil N2O emission using NOE algorithm and soil database. Global Biogeochem. Cycles 11, 115–127. https://doi.org/10.1111/j.1365-2486.2004.00879.x

625

626

Jégo, G., Chantigny, M., Pattey, E., Bélanger, G., Rochette, P., Vanasse, A., Goyer, C., 2014. Improved snow-cover model for multi-annual simulations with the STICS crop model under cold, humid continental climates. Agric. For. Meteorol. 195–196, 38–51.

627

628

629

https://doi.org/10.1016/j.agrformet.2014.05.002

630

631

Jensen, L.S., Salo, T., Palmason, F., Breland, T.A., Henriksen, T.M., Stenberg, B., Pedersen, A., Lundstro, C., Esala, M., 2005. Influence of biochemical quality on C and N mineralisation from a broad variety of plant materials in soil. Plant Soil 273, 307–326. https://doi.org/10.1007/s11104-004-8128-y

632

633

634

635

636

Kemmitt, S.J., Wright, D., Goulding, K.W.T., Jones, D.L., 2006. pH regulation of carbon and nitrogen dynamics in two agricultural soils. Soil Biol. Biochem. 38, 898–911.

637

638

https://doi.org/10.1016/j.soilbio.2005.08.006

639

640

Lashermes, G., Recous, S., Alavoine, G., Janz, B., Butterbach-Bahl, K., Ernfors, M., Laville, P., 2022. N2O emissions from decomposing crop residues are strongly linked to their initial soluble fraction and early C mineralization. Sci. Total Environ. 806, 150883.

641

642

643

644

https://doi.org/10.1016/j.scitotenv.2021.150883

645

Li, X., Sørensen, P., Olesen, J.E., Petersen, S.O., 2016. Evidence for denitrification as main source of N2O emission from residue-amended soil. Soil Biol. Biochem. 92, 153–160.

646

647

648

https://doi.org/10.1016/j.soilbio.2015.10.008

649

Mei, K., Wang, Z., Huang, H., Zhang, C., Shang, X., Dahlgren, R.A., Zhang, M., Xia, F., 2018.

650

651

Stimulation of N2O emission by conservation tillage management in agricultural lands: A meta-analysis. Soil Tillage Res. 182, 86–93. https://doi.org/10.1016/j.still.2018.05.006

652

653

Miller, M.N., Zebarth, B.J., Dandie, C.E., Burton, D.L., Goyer, C., Trevors, J.T., 2008. Crop residue

654
655 influence on denitrification, N2O emissions and denitrifier community abundance in soil. Soil Biol.
656 Biochem. 40, 2553-2562. https://doi.org/10.1016/j.soilbio.2008.06.024

657 Munodawafa, A., Zhou, N., 2008. Improving water utilization in maize production through
658 conservation tillage systems in semi-arid Zimbabwe. Phys. Chem. Earth 33, 757-761.
659 https://doi.org/10.1016/j.pce.2008.06.027

660 Nicolardot, B., Recous, S., Mary, B., 2001. Simulation of C and N mineralisation during crop residue
661 decomposition: A simple dynamic model based on the C:N ratio of the residues. Plant Soil 228, 83-
662 103. https://doi.org/10.1023/A:1004813801728

663 Peyrard, C., Mary, B., Perrin, P., Véricel, G., Gréhan, E., Justes, E., Léonard, J., 2016. N2O emissions
664 of low input cropping systems as affected by legume and cover crops use. Agric. Ecosyst. Environ.
665 224, 145-156. https://doi.org/10.1016/j.agee.2016.03.028

666 Pinheiro, P.L., Recous, S., Dietrich, G., Weiler, D.A., Schu, A.L., Bazzo, H.L.S., Giacomini, S.J.,
667 2019. N2O emission increases with mulch mass in a fertilized sugarcane cropping system. Biol. Fertil.
668 Soils 55, 511-523. https://doi.org/10.1007/s00374-019-01366-7

673 Plaza-Bonilla, D., Álvaro-Fuentes, J., Bareche, J., Pareja-Sánchez, E., Justes, É., Cantero-Martínez,
674 C., 2018. No-tillage reduces long-term yield-scaled soil nitrous oxide emissions in rainfed
675 Mediterranean agroecosystems: A field and modelling approach. Agric. Ecosyst. Environ. 262, 36-47.
676 https://doi.org/10.1016/j.agee.2018.04.007

678 Plaza-Bonilla, D., Léonard, J., Peyrard, C., Mary, B., Justes, É., 2017. Precipitation gradient and crop
679 management affect N2O emissions: Simulation of mitigation strategies in rainfed Mediterranean
680 conditions. Agric. Ecosyst. Environ. 238, 89-103. https://doi.org/10.1016/j.agee.2016.06.003

682 Rochette, P., Bertrand, N., 2008. Soil-surface gas emissions. In: Carter, M.R. (Ed.), Soil
683 Sampling and Methods of Analysis. CRC Press, Boca Raton, FL, pp. 851-861.

685 Schmatz, R., Recous, S., Weiler, D.A., Pilecco, G.E., Schu, A.L., Giovelli, R.L., Giacomini, S.J., 2020.
686 How the mass and quality of wheat and vetch mulches affect drivers of soil N2O emissions. Geoderma
687 372, 114395. https://doi.org/10.1016/j.geoderma.2020.114395

689
690

Schreiber, J.D., 1999. Nutrient Leaching from Corn Residues under Simulated Rainfall. J. Environ.

691

Qual. 28, 1864–1870. https://doi.org/10.2134/jeq1999.00472425002800060024x

692

693

Sierra, J., Brisson, N., Ripoche, D., Déqué, M., 2010. Modelling the impact of thermal adaptation of
soil microorganisms and crop system on the dynamics of organic matter in a tropical soil under a
climate change scenario. Ecol. Modell. 221, 2850–2858.

695

696

https://doi.org/10.1016/j.ecolmodel.2010.08.031

697

698

Sithole, N.J., Magwaza, L.S., Mafongoya, P.L., 2016. Conservation agriculture and its impact on soil
quality and maize yield: A South African perspective. Soil Tillage Res. 162, 55–67.

699

700

https://doi.org/10.1016/j.still.2016.04.014

701

702

Smith, P., Smith, J.U., Powlson, D.S., Mcgill, W.B., Arah, J.R.M., Chertov, O.G., Coleman, K.,
Franko, U., Frolking, S., Jenkinson, D.S., Jensen, L.S., Kelly, R.H., Klein-Gunnewiek, H., Komarov,
A.S., Li, C., Molina, J.A.E.J., Mueller, T., Parton, W.J., Thornley, J.H.M., Whitmore, A.P., 1997. A
comparison of the performance of nine soil organic matter models using datasets from seven long-term
experiments. Geoderma 81, 153–225. https://doi.org/10.1016/S0016-7061(97)00087-6

703

704

705

706

707

708

Soil Survey Staff, 2010. Keys to soil taxonomy. United States Department of Agriculture-Natural
Resources Conservation Service, Washington, DC.

709

710

711

Steiner, J.L., Schomberg, H.H., Unger, P.W., Cresap, J., 1999. Crop Residue Decomposition in No-
Tillage Small-Grain Fields. Soil Sci. Soc. Am. J. 63, 1817–1824.

712

713

https://doi.org/10.2136/sssaj1999.6361817x

714

715

Stott, D.E., Stroo, H.F., Elliott, L.F., Papendick, R.I., Unger, P.W., 1990. Wheat Residue Loss from
Fields under No-till Management. Soil Sci. Soc. Am. J. 54, 92–98.

716

717

https://doi.org/10.2136/sssaj1990.03615995005400010014x

718

719

Surey, R., Schimpf, C.M., Sauheitl, L., Mueller, C.W., Rummel, P.S., Dittert, K., Kaiser, K., Jürgen,
B., Mikutta, R., 2020. Potential denitrification stimulated by water-soluble organic carbon from plant
residues during initial decomposition. Soil Biol. Biochem. 147, 107841.

720

721

722

https://doi.org/10.1016/j.soilbio.2020.107841

723

724

Thiébeau, P., Girardin, C., Recous, S., 2021. Water interception and release of soluble carbon by
mulches of plant residues under contrasting rain intensities. Soil Tillage Res. 208, 104882.

725
726 https://doi.org/10.1016/j.still.2020.104882

727

728 Thorburn, P.J., Probert, M.E., Robertson, F.A., 2001. Modelling decomposition of sugar cane surface

729 residues with APSIM-Residue. F. Crop. Res. 70, 223–232. https://doi.org/10.1016/S0378-

730 4290(01)00141-1

731

732 Wang, Z., Thapa, R., Timlin, D., Li, S., Sun, W., Beegum, S., Fleisher, D., Mirsky, S., Cabrera, M.,

733 Sauer, T., Reddy, V.R., Horton, R., Tully, K. 2021. Simulations of water and thermal dynamics for

734 soil surfaces with residue mulch and surface runoff. Water Resources Research, 57, e2021WR030431.

735 https://doi.org/10.1029/2021WR030431

736

737 Williams, A., Wells, M.S., Dickey, D.A., Hu, S., Maul, J., Raskin, D.T., Reberg-Horton, S.C., Mirsky,

738 S.B., 2018. Establishing the relationship of soil nitrogen immobilization to cereal rye residues in a

739 mulched system. Plant Soil 426, 95–107. https://doi.org/10.1007/s11104-018-3566-0

740

741 Xu, X., Tian, H., Hui, D., 2008. Convergence in the relationship of CO2 and N2O exchanges between

742 soil and atmosphere within terrestrial ecosystems. Glob. Chang. Biol. 14, 1651–1660.

743 https://doi.org/10.1111/j.1365-2486.2008.01595.x

744
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Conceptual representation of mulch decomposition processes with the two compartment patterns and the C and N fluxes during residue decomposition (dotted lines: N fluxes; continuous lines: C fluxes).</figcaption></figure>

745
746
747
748
749
750

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

751

752

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

753

754

755

756

757

758

<caption>**Figure 2.** Control treatment (bare soil without mulches) measured and simulated soil water content (a, b) and soil temperature (c, d) at the 0-5 and 5-10 cm soil layer, soil mineral nitrogen (NH₄ and NO₃) at the 0-10 cm soil layer (e, f), and N-N₂O (g) and C-CO₂ emissions (h) after model calibration. Error bars for measured values are ± standard error.</caption>
759

<figure><img src="image_11.png" /></figure>

760

<figure><img src="image_12.png" /></figure>

761

<figure><img src="image_13.png" /></figure>

762

**Figure 3.** Measured and estimated mulch C from vetch (a, c, e) and wheat residues (b, d, f), with 3 Mg DM ha⁻¹ (a, b), 6 Mg DM ha⁻¹ (c, d), and 9 Mg DM ha⁻¹ (e, f). Lines represent the values simulated with default STICS mulch parameters (dashed line) and improved parameters (solid line). Error bars for measured values are ± standard error.

763

764

765

766

767
768

<figure><img src="image_14.png" /></figure>

769

<figure><img src="image_15.png" /></figure>

770

<figure><img src="image_16.png" /></figure>

771

772

773

774

775

776

**Figure 4.** Measured and estimated mulch N from vetch (a, c, e) and wheat (b, d, f) residues, with 3 Mg DM ha⁻¹ (a, b), 6 Mg DM ha⁻¹ (c, d), and 9 Mg DM ha⁻¹ (e, f). Lines represent the values simulated with default STICS mulch parameters (dashed line) and improved parameters (solid line). Error bars for measured values are ± standard error.
777

<figure><img src="image_17.png" /></figure>

778

<figure><img src="image_18.png" /></figure>

779

<figure><img src="image_19.png" /></figure>

780

781

782

783

784

785

**Figure 5.** Measured and estimated nitrous oxide emissions from vetch (a, c, e) and wheat (b, d, f) residues, with 3 Mg DM ha⁻¹ (a, b), 6 Mg DM ha⁻¹ (c, d), and 9 Mg DM ha⁻¹ (e, f). Lines represent the values simulated with STICS improved mulch parameters (dashed line) and improved N₂O fluxes (solid line). Error bars for measured values are ± standard error.
<table><caption>**Table 1.** Soil characteristics used in the STICS model.</caption><thead><tr><th></th><th>Depth (cm)</th><th>Values</th></tr></thead><tbody><tr><td>Soil pH</td><td>0-10</td><td>4.8</td></tr><tr><td>Clay content (g kg<sup>-1</sup>)</td><td>0-10</td><td>110.0</td></tr><tr><td>Organic N (g kg<sup>-1</sup>)</td><td>0-10</td><td>0.9</td></tr><tr><td>Organic C (g kg<sup>-1</sup>)</td><td>0-10</td><td>9.6</td></tr><tr><td>Soil C:N ratio</td><td>0-10</td><td>10.7</td></tr><tr><td>Bulk density (g cm<sup>-3</sup>)</td><td>0-5</td><td>1.48</td></tr><tr><td></td><td>5-10</td><td>1.71</td></tr><tr><td></td><td>10-20</td><td>1.74</td></tr><tr><td></td><td>20-65</td><td>1.67</td></tr><tr><td></td><td>65-100</td><td>1.51</td></tr><tr><td>Water content at field capacity (g g<sup>-1</sup>)</td><td>0-5</td><td>18.0</td></tr><tr><td></td><td>5-10</td><td>14.0</td></tr><tr><td></td><td>10-35</td><td>13.4</td></tr><tr><td></td><td>35-65</td><td>13.1</td></tr><tr><td></td><td>65-100</td><td>22.1</td></tr><tr><td>Water content at permanent wilting point (g g<sup>-1</sup>)</td><td>0-5</td><td>10.3</td></tr><tr><td></td><td>5-10</td><td>11.5</td></tr><tr><td></td><td>10-35</td><td>6.0</td></tr><tr><td></td><td>35-65</td><td>5.3</td></tr><tr><td></td><td>65-100</td><td>11.9</td></tr></tbody></table>
<table><caption>**Table 2.** Model parameters adjusted during the calibration of the bare soil treatment.</caption><thead><tr><th>Variable</th><th>Model parameter</th><th>Initial calibration</th><th>New calibration</th></tr></thead><tbody><tr><td>Mineralization rate (day<sup>-1</sup>)</td><td>GMIN1</td><td>0.0007</td><td>0.0015</td></tr><tr><td rowspan="2">Soil water content at field capacity (g g<sup>-1</sup>)</td><td>hccf (0-5 cm)</td><td>17.5</td><td>18.0</td></tr><tr><td>hccf (5-10 cm)</td><td>13.6</td><td>14.0</td></tr><tr><td>Potential nitrification rate (mg N kg<sup>-1</sup> day<sup>-1</sup>)</td><td>vnitpot</td><td>0.8</td><td>0.3</td></tr><tr><td>Denitrification potential (kg N ha<sup>-1</sup> day<sup>-1</sup>)</td><td>vpodenit</td><td>2.0</td><td>0.1</td></tr></tbody></table>
791
792

<table><caption>**Table 3.** Performance of the STICS model for the control treatment after the first calibration step.</caption><thead><tr><th>Variable</th><th>Soil depth (cm)</th><th>r</th><th>RMSE</th><th>E(%)</th></tr></thead><tbody><tr><td rowspan="2">Soil water content (g g<sup>-1</sup>)</td><td>0-5</td><td>0.72</td><td>2.01</td><td>-5.17</td></tr><tr><td>5-10</td><td>0.55</td><td>1.25</td><td>-3.35</td></tr><tr><td rowspan="2">Soil temperature (°C)</td><td>0-5</td><td>0.83</td><td>2.52</td><td>7.20</td></tr><tr><td>5-10</td><td>0.76</td><td>2.61</td><td>6.85</td></tr><tr><td>Soil nitrate (kg N ha<sup>-1</sup>)</td><td>0-10</td><td>0.66</td><td>9.48</td><td>-10.02</td></tr><tr><td>Soil ammonium (kg N ha<sup>-1</sup>)</td><td>0-10</td><td>-0.31</td><td>3.69</td><td>28.02</td></tr><tr><td>C-CO<sub>2</sub> (kg ha<sup>-1</sup> day<sup>-1</sup>)</td><td>-</td><td>0.39</td><td>6.05</td><td>13.95</td></tr><tr><td>N-N<sub>2</sub>O (kg ha<sup>-1</sup> day<sup>-1</sup>)</td><td>-</td><td>0.31</td><td>0.01</td><td>-6.27</td></tr></tbody></table>

793
794

r: Correlation Coefficient; RMSE: Root mean square error of model; and E(%): relative error.
<table><caption>Table 4. Statistical criteria after improvement of mulch decomposition and using the new function to determine the denitrification potential.</caption><thead><tr><th>Variable</th><th>Statistical criteria</th><th>Vetch 3</th><th>Vetch 6</th><th>Vetch 9</th><th>Wheat 3</th><th>Wheat 6</th><th>Wheat 9</th></tr></thead><tbody><tr><td rowspan="3">Soil NO<sub>3</sub><sup>-</sup>, 0-10 cm (kg N ha<sup>-1</sup>)</td><td>r</td><td>0.82</td><td>0.73</td><td>0.82</td><td>0.38</td><td>0.30</td><td>0.37</td></tr><tr><td>RMSE</td><td>8.55</td><td>13.66</td><td>21.80</td><td>7.43</td><td>6.67</td><td>6.65</td></tr><tr><td>E(%)</td><td>-24.18</td><td>-30.19</td><td>-70.42</td><td>-35.65</td><td>-25.58</td><td>-27.51</td></tr><tr><td rowspan="3">Soil NH<sub>4</sub><sup>+</sup>, 0-10 cm (kg N ha<sup>-1</sup>)</td><td>r</td><td>-0.02</td><td>0.30</td><td>0.53</td><td>-0.06</td><td>-0.31</td><td>-0.12</td></tr><tr><td>RMSE</td><td>4.85</td><td>7.48</td><td>9.81</td><td>4.17</td><td>3.93</td><td>4.44</td></tr><tr><td>E(%)</td><td>-3.15</td><td>-24.88</td><td>-32.55</td><td>31.39</td><td>32.04</td><td>37.58</td></tr><tr><td rowspan="3">C mulch (kg ha<sup>-1</sup>)</td><td>r</td><td>0.99</td><td>0.99</td><td>0.99</td><td>1.00</td><td>0.99</td><td>0.97</td></tr><tr><td>RMSE</td><td>100.76</td><td>141.99</td><td>230.33</td><td>57.07</td><td>170.25</td><td>385.36</td></tr><tr><td>E(%)</td><td>-22.79</td><td>-10.69</td><td>-6.73</td><td>1.75</td><td>1.82</td><td>-4.40</td></tr><tr><td rowspan="3">N mulch (kg ha<sup>-1</sup>)</td><td>r</td><td>0.89</td><td>0.91</td><td>0.93</td><td>0.25</td><td>0.48</td><td>0.45</td></tr><tr><td>RMSE</td><td>22.41</td><td>39.47</td><td>50.01</td><td>7.27</td><td>12.94</td><td>19.74</td></tr><tr><td>E(%)</td><td>-86.62</td><td>-68.67</td><td>-52.20</td><td>-58.38</td><td>-57.96</td><td>-60.53</td></tr><tr><td rowspan="3">C-CO<sub>2</sub> (kg ha<sup>-1</sup>day<sup>-1</sup>)</td><td>r</td><td>0.33</td><td>0.25</td><td>0.22</td><td>0.46</td><td>0.46</td><td>0.44</td></tr><tr><td>RMSE</td><td>15.41</td><td>28.44</td><td>43.15</td><td>8.93</td><td>16.76</td><td>14.92</td></tr><tr><td>E(%)</td><td>3.78</td><td>-1.90</td><td>0.65</td><td>-3.41</td><td>-25.11</td><td>-29.90</td></tr><tr><td rowspan="3">N-N<sub>2</sub>O (kg ha<sup>-1</sup>day<sup>-1</sup>)</td><td>r</td><td>0.56</td><td>0.78</td><td>0.74</td><td>0.62</td><td>0.67</td><td>0.62</td></tr><tr><td>RMSE</td><td>0.06</td><td>0.07</td><td>0.10</td><td>0.01</td><td>0.02</td><td>0.02</td></tr><tr><td>E(%)</td><td>-11.38</td><td>-8.26</td><td>-45.43</td><td>-12.58</td><td>-15.05</td><td>-51.51</td></tr></tbody></table>

r: Correlation Coefficient; RMSE: Root mean square error of model; and E (%): relative error.

798

799
800

<table><caption>**Table S1.** Soil moisture and temperature statistical criteria at the evaluation step.</caption><thead><tr><th>Variable</th><th>Soil depth (cm)</th><th>Treatment</th><th>r</th><th>RMSE</th><th>E(%)</th></tr></thead><tbody><tr><td rowspan="6">Soil water content (g g<sup>-1</sup>)</td><td rowspan="6">0-5</td><td>Vetch 3</td><td>0.84</td><td>1.76</td><td>-1.93</td></tr><tr><td>Vetch 6</td><td>0.75</td><td>2.10</td><td>3.46</td></tr><tr><td>Vetch 9</td><td>0.73</td><td>2.06</td><td>2.60</td></tr><tr><td>Wheat 3</td><td>0.82</td><td>2.55</td><td>-14.89</td></tr><tr><td>Wheat 6</td><td>0.61</td><td>2.81</td><td>-5.02</td></tr><tr><td>Wheat 9</td><td>0.75</td><td>1.71</td><td>3.86</td></tr><tr><td rowspan="6">Soil temperature (°C)</td><td rowspan="6">0-5</td><td>Vetch 3</td><td>0.81</td><td>1.09</td><td>3.99</td></tr><tr><td>Vetch 6</td><td>0.80</td><td>1.11</td><td>4.68</td></tr><tr><td>Vetch 9</td><td>0.77</td><td>1.57</td><td>9.46</td></tr><tr><td>Wheat 3</td><td>0.65</td><td>1.26</td><td>1.13</td></tr><tr><td>Wheat 6</td><td>0.69</td><td>1.07</td><td>0.56</td></tr><tr><td>Wheat 9</td><td>0.68</td><td>1.05</td><td>0.40</td></tr><tr><td rowspan="6">Soil temperature (°C)</td><td rowspan="6">5-10</td><td>Vetch 3</td><td>0.78</td><td>2.10</td><td>5.45</td></tr><tr><td>Vetch 6</td><td>0.73</td><td>1.89</td><td>4.38</td></tr><tr><td>Vetch 9</td><td>0.71</td><td>1.89</td><td>4.41</td></tr><tr><td>Wheat 3</td><td>0.76</td><td>2.02</td><td>5.70</td></tr><tr><td>Wheat 6</td><td>0.74</td><td>1.59</td><td>2.86</td></tr><tr><td>Wheat 9</td><td>0.77</td><td>1.48</td><td>3.50</td></tr><tr><td rowspan="6">Soil temperature (°C)</td><td rowspan="6">5-10</td><td>Vetch 3</td><td>0.71</td><td>2.15</td><td>6.12</td></tr><tr><td>Vetch 6</td><td>0.67</td><td>1.84</td><td>4.41</td></tr><tr><td>Vetch 9</td><td>0.66</td><td>1.85</td><td>4.51</td></tr><tr><td>Wheat 3</td><td>0.69</td><td>1.88</td><td>4.72</td></tr><tr><td>Wheat 6</td><td>0.69</td><td>1.50</td><td>3.11</td></tr><tr><td>Wheat 9</td><td>0.71</td><td>1.57</td><td>3.30</td></tr></tbody></table>

801

r: Correlation Coefficient; RMSE: Root mean square error of model; and E(%): relative error.

802
803

804

805

<table><caption>**Table S2.** C-CO₂ and N-N₂O accumulated simulated and observed emissions for the two residues (vetch and wheat) and three mulch masses (3, 6 and 9 Mg DM ha⁻¹) plus the control treatment.</caption><thead><tr><th>Variable</th><th>Treatment</th><th>Observed</th><th>Simulated</th></tr></thead><tbody><tr><td rowspan="8">C-CO₂ accumulated (kg C ha⁻¹)</td><td>Control</td><td>2474.0</td><td>2081.3</td></tr><tr><td>Vetch 3</td><td>3067.5</td><td>2882.9</td></tr><tr><td>Vetch 6</td><td>3754.6</td><td>3750.0</td></tr><tr><td>Vetch 9</td><td>4840.0</td><td>4598.7</td></tr><tr><td>Wheat 3</td><td>3131.6</td><td>3172.5</td></tr><tr><td>Wheat 6</td><td>3326.9</td><td>4252.9</td></tr><tr><td>Wheat 9</td><td>3702.0</td><td>5425.5</td></tr><tr><td rowspan="8">N-N₂O accumulated (kg N ha⁻¹)</td><td>Control</td><td>0.59</td><td>0.87</td></tr><tr><td>Vetch 3</td><td>2.86</td><td>3.12</td></tr><tr><td>Vetch 6</td><td>5.21</td><td>5.60</td></tr><tr><td>Vetch 9</td><td>4.11</td><td>8.14</td></tr><tr><td>Wheat 3</td><td>1.46</td><td>1.50</td></tr><tr><td>Wheat 6</td><td>2.17</td><td>2.28</td></tr><tr><td>Wheat 9</td><td>2.09</td><td>3.01</td></tr></tbody></table>

806

807
<figure><img src="image_20.png" /><figcaption>Fig. S1 Rainfall and mean daily air temperature over the 360 days of the experiment.</figcaption></figure>

808

809
810

<figure><img src="image_21.png" /></figure>

811

<figure><img src="image_22.png" /></figure>

812

<figure><img src="image_23.png" /></figure>

813

814

815

**Fig. S2** Measured and estimated NH<sub>4</sub><sup>+</sup>- N with improved parameters for vetch (a, c, e) and wheat (b, d, f) residues, with 3 Mg DM ha<sup>-1</sup> (a, b), 6 Mg DM ha<sup>-1</sup> (c, d), and 9 Mg DM ha<sup>-1</sup> (e, f). Error bars for measured values are ± standard error.

816
817

<figure><img src="image_24.png" /></figure>

818

<figure><img src="image_25.png" /></figure>

819

<figure><img src="image_26.png" /></figure>

820

821

822

823

824

Fig. S3 Measured and estimated NO₃⁻-N with improved parameters for vetch (a, c, e) and wheat (b, d, f) residues, with 3 Mg DM ha⁻¹ (a, b), 6 Mg DM ha⁻¹ (c, d), and 9 Mg DM ha⁻¹ (e, f). Error bars for measured values are ± standard error.
825

<figure><img src="image_27.png" /></figure>

826

<figure><img src="image_28.png" /></figure>

827

<figure><img src="image_29.png" /></figure>

828

829

830

831

832

833

**Fig. S4** Measured and estimated C-CO₂ with improved parameters for vetch (a, c, e) and wheat (b, d, f) residues, with 3 Mg DM ha⁻¹ (a, b), 6 Mg DM ha⁻¹ (c, d), and 9 Mg DM ha⁻¹ (e, f). Error bars for measured values are ± standard error.