# Modelling herbicide distribution through soil profiles under conventional tillage and non-tillage

Marwa Douibi, Laure Mamy, M. José Carpio, María J Sánchez-Martín, M.

Sonia Rodríguez-Cruz, Jesús M Marín-Benito

► To cite this version:

Marwa Douibi, Laure Mamy, M. José Carpio, María J Sánchez-Martín, M. Sonia Rodríguez-Cruz, et al.. Modelling herbicide distribution through soil profiles under conventional tillage and non-tillage. Science of the Total Environment, 2025, 977, pp.179344. 10.1016/j.scitotenv.2025.179344. hal-05034991

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# Modelling herbicide distribution through soil profiles under conventional tillage and non-tillage

Marwa Douibi<sup>a</sup>, Laure Mamy<sup>b</sup>, M. José Carpio<sup>a</sup>, María J. Sánchez-Martín<sup>a</sup>, M. Sonia Rodríguez-Cruz<sup>a</sup>, Jesús M. Marín-Benito<sup>a,†</sup>

## HIGHLIGHTS

* Herbicides fate in tillied (CT) and non-tilled (NT) soils was simulated with PRZM and MACRO

* An approach is proposed to model mulch effect on soil water and herbicides dynamics

* The performance of MACRO was higher than that of PRZM in both CT and NT soils

* Models' ability to predict soil herbicide concentrations decreased over time

* Models should allow change in the DT<sub>50</sub> as a function of soil covered by the mulch

## GRAPHICAL ABSTRACT

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

## ARTICLE INFO

Editor: Elena Paoletti

**Keywords:**

Mulch
Conservation tillage
Mobility
PRZM
MACRO
Herbicide

## ABSTRACT

Conservation agricultural practices based on mulching and herbicides use are becoming more widespread. However, they cannot be simulated with pesticide fate models used for risk assessment before placing pesticides on the European market. Thus, this work sought to assess and compare the performance of PRZM and MACRO models to replicate the moisture content and vertical distribution of S-metolachlor, foramsulfuron, and thiencarbazone-methyl herbicides in two agricultural soils under conventional tillage (CT) and non-tillage (NT) over a two-year field experiment. A new approach was developed to consider the impact of the mulch on soil moisture and on the amount of herbicides reaching the NT topsoil. This involved removing the presence of the mulch layer while maintaining its impact on potential evapotranspiration and herbicide adsorption and degradation through the meteorological file and the herbicide application inputs, respectively. In addition, the K<sub>f</sub> of S-metolachlor was calibrated for an acceptable agreement between predictions and observations. MACRO outperformed PRZM in simulating soil water content and herbicide mobility, and both models performed better under CT compared to NT. Model performances decreased the second year, especially in NT soils, because herbicide DT<sub>50</sub> were parameterized with values measured the first year, which were less representative of the second year. This new modelling approach is appropriate for simulating moisture and herbicide transport in NT

<footnote><sup>a</sup> Institute of Natural Resources and Agrobiology of Salamanca (IRNASA-CSIC), Cordel de Merinas 40-52, 37008 Salamanca, Spain</footnote>

<footnote><sup>b</sup> Université Paris-Saclay, INRAE, AgroParisTech, UMR ECOSYS, 91120 Palaiseau, France</footnote>

<footnote>* Corresponding author.
E-mail address: jesusm.marin@irnasa.csic.es (J.M. Marín-Benito).</footnote>
soils with MACRO on a bi-annual scale. However, to assess the fate of herbicides in NT, models require a module
allowing change in DT₅₀ according to the soil surface covered annually by the mulch.

# 1. Introduction

Conservation agricultural practices include minimal soil disturbance (non-tillage and direct sowing), the use of cover crops, mulching (leaving plant residues from crops and cover crops on the soil surface), and diversified crop rotations. Such practices are considered environmentally friendly and sustainable for soil preservation (de Moraes Sá et al., 2022; Gil Ribes et al., 2017; Kassam et al., 2019; Ravichandran et al., 2022). They seek to reduce or counteract the detrimental impacts that conventional agriculture has on soil ecosystems including degradation and erosion while minimizing environmental contamination (Elias et al., 2018; Hassan et al., 2022). Non-tillage (NT) is designed to improve the soil structure (Dang et al., 2020), preserve aggregate stability, and increase biological nitrogen fixation (Choudhary et al., 2016). Additionally, mulch residues on the soil surface help to improve the soil's physicochemical properties by increasing its moisture content, reducing evaporation, regulating its surface temperature, and increasing the amount of total organic matter (OM) in the topsoil (Alletto et al., 2010; Balwinder-Singh et al., 2011; de Moraes Sá et al., 2022; Gil Ribes et al., 2017; Hassan et al., 2022).

However, the adoption of conservation agricultural practices increases weed infestation and is very often associated with an increase in herbicide use (Alletto et al., 2010; Hassan et al., 2022). In addition, the fate of herbicides in soils under NT is modified compared to their fate in soils under conventional tillage (CT). Some studies have shown that higher organic carbon (OC) content in NT systems improves herbicide adsorption and reduces leaching (Aslam et al., 2018; Cueff et al., 2021). In other cases, on the contrary, pesticide leaching increases due to higher soil macroporosity under NT than in CT generating preferential flows (Alletto et al., 2010). Therefore, there is a need of additional field studies involving different pedoclimatic conditions to comprehensively understand the real impact of NT practices on the fate of herbicides. In parallel, as conclusive field experiments are both time consuming and costly, numerous mathematical tools have been developed accordingly (Mottes et al., 2014).

Numerical models provide a quick and inexpensive method for estimating pesticide fate (adsorption, degradation, leaching, volatilization, erosion, runoff) in soil profiles (Siimes and Kämäri, 2003). This is the strategy followed to assess the risk of water contamination by pesticides before their approval at the European level (FOCUS, 2000). The FOCUS (Forum for Co-ordination of pesticide fate models and their USe) group recommends using PRZM (Carsel et al., 2005), MACRO (Larsbo and Jarvis, 2003), PEARL (Leistra et al., 2001), and PELMO (Klein, 2000), as these models were demonstrated to simulate acceptably the environmental fate of pesticides under common agricultural practices (Giannouli and Antonopoulos, 2015; Kolupaeva et al., 2022). However, their ability to replicate the behaviour of pesticides in complex agricultural scenarios including conservation practices remains poorly explored, mainly because they were not developed to consider the resulting impact on water and/or pesticide dynamics (Lammoglia et al., 2017a; Mamy et al., 2024; Marín-Benito et al., 2018). Consequently, the environmental fate of pesticides under conservation agricultural practices including mulching is not simulated during their registration process. For scientific purposes, the models' failure to consider the impact of mulching on moisture content and on the fate of herbicides, which are controlled mainly by their interception by the mulch layer, has been addressed through their due parameterisation and/or calibration. Lammoglia et al. (2017a, 2017b) have combined the crop model STICS (Brisson et al., 2009) and the pesticide fate model MACRO to consider the impact of mulching on pesticide losses. They stressed that STICS-MACRO alone is insufficient to simulate the behaviour of the

pesticides assayed, as it is unable to account for the *in situ* modifications that the mulch causes to the adsorption and degradation of pesticides, whereby considering these processes might improve the evaluation of pesticide fate in complex agricultural systems. Marín-Benito et al. (2018) introduced a calibration step in MACRO to account for the impact of mulch residues on water and herbicide dynamics in soils under conservation agricultural practices. They applied a correction factor to the potential evapotranspiration (ETP) data to simulate the mulch layer's effect on the water balance, according to such observable criteria as mulch thickness, soil coverage, and plant residue decomposition. In addition, the authors have replaced the mulch layer by a high organic content soil layer with specific physical, hydrodynamic, and herbicide-reactivity properties to consider the impact of the mulch layer on water and herbicide transport. MACRO simulated satisfactorily field measurements of water percolation and herbicide leaching thanks to the parameterisation applied. The authors nonetheless stressed that this parameterisation is restricted to the scenario assayed and cannot be proposed for a predictive model without first testing the approach in other soil-chemical-crop-mulch combinations.

Besides the code's limitations for considering the impact of conservation agricultural practices on pesticide behaviour, most fate modelling studies are based on pesticide concentrations in leachates collected using suctions cups or lysimeters buried in the soil (Giannouli and Antonopoulos, 2015; Kolupaeva et al., 2022; Lammoglia et al., 2017a; Mamy et al., 2024; Marín-Benito et al., 2018). The main drawback of these modelling exercises is therefore the volume of field data available for simulations, which is highly dependent on precipitations that are sometimes scarce and insufficient for extracting representative conclusions. Modelling studies based on a high volume of data on pesticide distribution in soil profiles (Marín-Benito et al., 2020; Scorza Júnior et al., 2007; Scorza Júnior and Franco, 2014; Villarreal et al., 2020) would allow refining the calibration to fit field observations under conservation agricultural practices more easily and accurately. This would improve prediction of the impact these agricultural practices have on the environmental fate of pesticides and quantification of their quality/pollution of surface and ground water.

Accordingly, we hypothesised an approach can be developed to adapt the regulatory pesticide fate models to reproduce the herbicide distribution through soil profiles under conservation agricultural practices. Therefore, this study sought i) to simulate soil water content and the distribution through the soil profiles (0–50 cm) of three herbicides, S-metolachlor, foramsulfuron, and thiencarbazone-methyl, in experimental plots under CT and NT by using a new approach to parameterize PRZM and MACRO able to consider the impact mulch has on water and herbicide dynamics in NT soils, and ii) to evaluate and compare the performance of both models for their potential use to predict the environmental fate of herbicides under conservation agricultural practices. Modelling was based on moisture and herbicide content measurements monitored in a two-year field experiment involving two soils under CT and NT management cropped with maize (Douibi et al., 2025).

# 2. Materials and methods

## 2.1. Pesticide fate models

PRZM and MACRO were selected to simulate the field water dynamics and distribution of herbicides in soil profiles because they use different approaches to describe the water and solute movement in the soil (Table S1). The primary mechanisms (leaching, adsorption, degradation, plant uptake, canopy interception) that influence pesticides behaviour in soils are included in the code of both models in order to
replicate how they are transported through the soil profile. The PRZM capacity model describes soil hydrology using a “tipping bucket” approach, whereby the downward water flow occurs when field capacity is exceeded. MACRO is a dual permeability and physically based pref- erential flow model that divides the entire soil porosity into two distinct flow domains. The model uses the Richards equation to simulate soil water flow in micropores and gravity to drive flow in macropores. A soil water pressure head close to saturation and the associated water content and hydraulic conductivity separate the two domains.

PRZM estimates the solute transport by convection and numerical dispersion. Meanwhile, MACRO uses the convection-dispersion equa- tion in micropores, while in macropores it is only convective (Table S1). The transfer between these two domains is determined by an approxi- mate, physically based expression that uses an effective aggregate half- width. The adsorption process is estimated in both models based on the linear or Freundlich instantaneous equation, and degradation is calcu- lated by the first-order kinetic model (or bi-phasic equation in PRZM). MACRO simulates both processes in both micropore and macropore regions. A detailed description of the two models can be found in Carsel et al. (2005) and Larsbo and Jarvis (2003). This research used PRZM 3.21 and a development version of the MACRO model, provided by the MACRO developers, which is able to simulate crop rotations (aiming at replacing the official release MACRO 5.2 in the future).

## 2.2. Field experiment

Simulations were based on a two-year (2019–2021) field experiment conducted at the Muñovela experimental farm belonging to the Spanish Institute of Natural Resources and Agrobiology of Salamanca (IRNASA- CSIC, Spain). A detailed description of the experiment is provided in Douibi et al. (2024a). Briefly, a total of eight experimental plots (9 m × 9 m) were randomly assigned to four treatments, which were the result of combining two different soils (S1 and S2), both with a sandy-loam texture but with different surface OC content (0–30 cm), with two tillage systems, conventional tillage-CT (S1 + CT and S2 + CT), and non- tillage-NT (S1 + NT and S2 + NT), each with two replicates (2 soils × 2

soil tillage systems × 2 replicates) (Table 1).

A maize monoculture system was the main crop in all the plots. Winter wheat was planted as a cover crop in NT soils in the first and second experimental years and then chemically neutralized by glypho- sate and left as mulch on the soil surface. Over this time, the soil was kept uncovered in S1 + CT and S2 + CT plots. Direct seeding was per- formed in all cases, and irrigation events were recorded according to Douibi et al. (2024a) (Fig. S1).

All the experimental plots were fitted with a 120 cm (length) × 5.2 cm (i.d.) PVC pipe prior to the first cover crop sown in October 2019 to monitor and assess the temporal evolution of the moisture content in the soil profile every 7–15 days at depths of 20, 40, and 60 cm using a Troxler Sentry 200-AP electric probe (Troxler International Ltd., NC, USA). At the beginning of the field experiment (30 October 2019), the moisture content corresponding to the whole soil profile considered (0–90 cm depth) was measured to initialise the herbicide fate models (Section 2.4.2). Seventy-six measurements were then taken at depths of 20, 40, and 60 cm in each plot from 30 October 2019 to 17 December 2021.

The three herbicides S-metolachlor, foramsulfuron, and thiencarbazone-methyl were sprayed jointly once per year on the pre- emergence of maize in all the plots in the following formulations: Efica 960EC® (S-metolachlor 96 % w/v ADAMA Agriculture Spain, S.A., Madrid) and Monsoon Active® (thiencarbazone-methyl 1 % w/v and foramsulfuron 3.15 % w/v, Bayer Crop Science S.L., Valencia). The first application was on 9 June 2020, at the rates of 0.621, 0.621, and 0.197 kg active substance ha⁻¹, respectively, and the second was on 8 June 2021, at the rates of 0.975, 0.840 and 0.267 kg active substance ha⁻¹, respectively. Herbicide mobility was evaluated every 10 cm at depths from 0 to 50 cm on different sampling dates in both post-application periods (at 1, 13, 27, 41, 62, 76, and 139 days after the first applica- tion, and at 1, 14, 29, 41, 57, 69, 84, and 111 days after the second application). The herbicides were extracted from the soil and their concentrations were analysed by UHPLC-QTOF-MS (Douibi et al., 2024a).

<caption>**Table 1**</caption>

Main physicochemical and hydraulic characteristics of soil (S1, S2) profiles under conventional tillage (CT) and non-tillage (NT).

<table><thead><tr><th rowspan="2">Treatment<br>Parameter / Soil layer (cm)</th><th colspan="4">S1 + CT</th><th colspan="2">S2 + CT</th><th colspan="2">S1 + NT</th><th colspan="2">S2 + NT</th></tr><tr><th>0–10</th><th>11–30</th><th>31–50</th><th>51–90</th><th>0–10</th><th>11–30</th><th>0–10</th><th>11–30</th><th>0–10</th><th>11–30</th></tr></thead><tbody><tr><td>Sand (%)<sup>a</sup></td><td>80.4</td><td>79.7</td><td>77.4</td><td>72.9</td><td>76.7</td><td>78.8</td><td>80.4</td><td>79.7</td><td>76.7</td><td>78.8</td></tr><tr><td>Silt (%)<sup>a</sup></td><td>4.70</td><td>4.90</td><td>6.00</td><td>7.40</td><td>6.80</td><td>5.00</td><td>4.70</td><td>4.90</td><td>6.80</td><td>5.00</td></tr><tr><td>Clay (%)<sup>a</sup></td><td>14.9</td><td>15.4</td><td>16.6</td><td>19.7</td><td>16.5</td><td>16.2</td><td>14.9</td><td>15.4</td><td>16.5</td><td>16.2</td></tr><tr><td>pH<sup>a</sup></td><td>6.81</td><td>6.79</td><td>7.13</td><td>7.36</td><td>7.67</td><td>7.59</td><td>6.81</td><td>6.79</td><td>7.67</td><td>7.59</td></tr><tr><td>Bulk density (g cm<sup>-3</sup>)<sup>a</sup></td><td>1.50</td><td>1.49</td><td>1.54</td><td>1.61</td><td>1.43</td><td>1.40</td><td>1.55</td><td>1.54</td><td>1.48</td><td>1.45</td></tr><tr><td>Organic carbon (%)<sup>a</sup></td><td>0.69</td><td>0.71</td><td>0.51</td><td>0.27</td><td>1.01</td><td>1.24</td><td>0.68</td><td>0.69</td><td>1.01</td><td>1.24</td></tr><tr><td>Dissolved organic carbon (mg g<sup>-1</sup>)<sup>a</sup></td><td>0.11</td><td>0.17</td><td>0.08</td><td>0.08</td><td>0.15</td><td>0.20</td><td>0.13</td><td>0.14</td><td>0.13</td><td>0.18</td></tr><tr><td>Θ<sub>initial</sub> (m<sup>3</sup> m<sup>-3</sup>)<sup>b,c</sup></td><td>0.197</td><td>0.196</td><td>0.161</td><td>0.167/0.202</td><td>0.160</td><td>0.217</td><td>0.177</td><td>0.170</td><td>0.182</td><td>0.190</td></tr><tr><td>Θ<sub>FC</sub> (m<sup>3</sup> m<sup>-3</sup>)<sup>d</sup></td><td>0.219</td><td>0.227</td><td>0.239</td><td>0.259</td><td>0.252</td><td>0.246</td><td>0.219</td><td>0.229</td><td>0.249</td><td>0.244</td></tr><tr><td>Θ<sub>WP</sub> (m<sup>3</sup> m<sup>-3</sup>)<sup>d</sup></td><td>0.069</td><td>0.068</td><td>0.075</td><td>0.094</td><td>0.073</td><td>0.070</td><td>0.069</td><td>0.068</td><td>0.071</td><td>0.071</td></tr><tr><td>Θ<sub>r</sub> (m<sup>3</sup> m<sup>-3</sup>)<sup>e</sup></td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td></tr><tr><td>Θ<sub>S</sub> (m<sup>3</sup> m<sup>-3</sup>)<sup>e</sup></td><td>0.380</td><td>0.380</td><td>0.370</td><td>0.350</td><td>0.410</td><td>0.410</td><td>0.370</td><td>0.370</td><td>0.390</td><td>0.400</td></tr><tr><td>α (cm<sup>-1</sup>)<sup>e</sup></td><td>0.087</td><td>0.086</td><td>0.096</td><td>0.094</td><td>0.080</td><td>0.080</td><td>0.090</td><td>0.090</td><td>0.080</td><td>0.090</td></tr><tr><td>n (−)e</td><td>1.32</td><td>1.30</td><td>1.27</td><td>1.22</td><td>1.29</td><td>1.31</td><td>1.33</td><td>1.32</td><td>1.28</td><td>1.26</td></tr><tr><td>K<sub>sat</sub> (mm h<sup>-1</sup>)<sup>e</sup></td><td>427</td><td>377</td><td>298</td><td>178</td><td>355</td><td>382</td><td>328</td><td>326</td><td>323</td><td>349</td></tr><tr><td>ZN (−)e</td><td>4</td><td>4</td><td>3</td><td>3</td><td>4</td><td>4</td><td>4</td><td>4</td><td>4</td><td>4</td></tr><tr><td>K<sub>b</sub> (mm h<sup>-1</sup>)<sup>e</sup></td><td>7.72</td><td>7.41</td><td>6.55</td><td>5.02</td><td>6.4</td><td>7.01</td><td>7.72</td><td>7.41</td><td>6.4</td><td>7.01</td></tr><tr><td>ASCALE (mm)<sup>e</sup></td><td>2</td><td>2</td><td>50</td><td>50</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td></tr><tr><td>Θ<sub>b</sub> (m<sup>3</sup> m<sup>-3</sup>)<sup>e</sup></td><td>0.32</td><td>0.33</td><td>0.32</td><td>0.31</td><td>0.35</td><td>0.36</td><td>0.31</td><td>0.31</td><td>0.34</td><td>0.34</td></tr><tr><td>CTEN (cm)<sup>f</sup></td><td>10</td><td>10</td><td>10</td><td>10</td><td>10</td><td>10</td><td>10</td><td>10</td><td>10</td><td>10</td></tr></tbody></table>

<footnote><sup>a</sup> Measured values for 0–50 cm depth. The values for 51–90 cm depth were taken from Carpio et al. (2020).</footnote>

<footnote><sup>b</sup> Data used for 0–20, 20–40, 40–60, and 60–80/80–90 cm depth soil layers of S1 + CT, respectively.</footnote>

<footnote><sup>c</sup> Θ<sub>initial</sub> at 40–60, 60–80 and 80–90 cm depth were 0.269/0.269/0.312 m<sup>3</sup> m<sup>-3</sup> for S2 + CT, 0.163/0.159/0.190 m<sup>3</sup> m<sup>-3</sup> for S1 + NT and 0.203/0.193/0.223 for S2 + NT, respectively, same parameterisation as S1 + CT was used for the rest of parameters in the horizons 31–50 and 51–90.</footnote>

<footnote><sup>d</sup> Estimated by Rosetta's pedotransfer functions (Simúnek et al., 2008).</footnote>

<footnote><sup>e</sup> Estimated by the pedotransfer functions MACRO 5.0/5.1 included in MACRO 5.2.</footnote>

<footnote><sup>f</sup> Default value (Larsbo and Jarvis, 2003).</footnote>
## 2.3. Modelling strategy

Simulations were conducted from 30 October 2019 to 31 December 2021 using field measurements, and the modelling strategy involved two steps. The first step corresponded to the calibration of the models against the moisture and herbicide contents measured in the soil profiles under CT and NT from 30 October 2019 to 31 December 2020 (calibration period, first year). It is worth noting that it was only necessary to calibrate the experimental values of the adsorption coefficient ($K_f$) for the herbicide S-metolachlor (Section 2.4.4). The second step involved testing the models, simulating soil moisture content and herbicide transport without any additional calibration from 1 January 2021 to 31 December 2021, and the results were compared to the field observations measured over this second year (testing period).

## 2.4. Model parameterisation

### 2.4.1. Soil physicochemical and hydrological parameterisation

The CT (S1 + CT and S2 + CT) and NT (S1 + NT and S2 + NT) soil profiles at a depth of 90 cm were split into four horizons of varying thicknesses (Table 1). The physicochemical soil properties were parameterised with measured values. Rosetta pedotransfer functions were used to estimate the water content at field capacity ($\theta_{FC}$, pF = 2) and at wilting point ($\theta_{WP}$, pF = 4.2) for each soil horizon, as required for PRZM (Simünek et al., 2008). In the meantime, the pedotransfer functions MACRO 5.0/5.1 of MACRO 5.2 were used to estimate the van Genuchten soil-water parameters ($\theta_r$, $\theta_s$, $\alpha$ and $n$), the saturated hydraulic conductivities ($K_{sat}$), and the soil characteristics of the macropore domain, such as the size distribution index in the macropores (ZN), the boundary hydraulic conductivity ($K_b$), the parameter controlling the exchange of both water and solute between the micropore and macropore flows (ASCALE), and the water content corresponding to the boundary soil water pressure head between micropores and macropores ($\theta_b$). The MACRO default value was assigned to the border soil water pressure head situated between macropores and micropores (CTEN = 10 cm) (Larsbo and Jarvis, 2003) (Table 1).

### 2.4.2. Initial simulation conditions and climatic data

The topsoil temperature (0–10 cm) and moisture content monitored through the soil profiles (0–90 cm) at the beginning of the experiment were used as initial conditions for the simulations. The temperature in deep soil layers was assumed to be 1 °C below that observed in the topsoil (Marín-Benito et al., 2020). For MACRO, a constant hydraulic gradient equal to 1 was considered for the bottom boundary condition.

Precipitation, and maximum, minimum, and average air temperature data were monitored daily by an onsite weather station (Fig. S1). The reference evapotranspiration (ETref), wind speed, and solar radiation data were obtained from Matacan airport meteorological station (23 km away from the Muñovela farm).

### 2.4.3. Parameterisation of the effect of mulch on soil water content

The ETP, which regulates the soil water content, was calculated for CT plots (ETPCT) by applying a crop factor ($k_c$) corresponding to maize to ETref values as recommended by FOCUS (2000). This crop factor was set to one when the soil was bare. The impact of the mulch layer on the ETP in plots under NT was then determined by calculating the daily difference between the experimental soil water content monitored in NT plots and in the corresponding CT plots. The daily difference was then normalised with respect to the difference in the initial water content observed between NT and CT plots to correct its spatial variability. New ETP values for NT plots were obtained by deducting from the daily ETPCT values used as input in the meteorological file of the CT plots simulations the normalised daily difference in soil water content. These new ETP values were then used in the corresponding meteorological file for the simulations in NT plots after applying the $k_c$ corresponding to winter wheat (ETPNT).

### 2.4.4. Herbicide parameterisation and representation of the mulch effect on herbicide fate

The adsorption parameters ($K_f$, $n_f$) of S-metolachlor, foramsulfuron and thiencarbazone-methyl in the CT and NT topsoils, and the effect of temperature on herbicide degradation rates ($Q_{10}$ in PRZM and TRESP in MACRO) were obtained from laboratory experiments (Douibi et al., 2023, 2024b), and their topsoil half-lives ($DT_{50}$) were provided by the field experiment after the first herbicide application (Douibi et al., 2024a) (Table 2). However, a calibration step of the $K_f$ of S-metolachlor was done considering values provided by the literature (Alletto et al., 2013) to improve the models' performance (Section 3.2). FOCUS (2000) recommendations were followed to calculate the variation in $DT_{50}$ with depth, and the $K_f$ values for deeper soil layers were estimated assuming this coefficient to be proportional to soil OC content (Douibi et al., 2023). The hydrodynamic dispersion (DISP for PRZM) and the dispersity (DV for MACRO) coefficients for each soil treatment were taken from Marín-Benito et al. (2020) (Table 2).

PRZM and MACRO were not designed for modelling the fate of herbicides under conservation agricultural practices involving a mulch layer where a fraction of herbicide is intercepted. An approach was therefore developed to simulate the experimental fate of the herbicides in the NT plots. This involved removing the mulch layer while maintaining its impact on the ETP, as previously indicated, and on herbicide adsorption and degradation. In the field, the herbicides were applied once per year; however, two herbicide applications per year were considered in models' parameterisation. The first application corresponded to the measured amount of herbicide that reached the soil surface on the actual date of application. The second application was assumed to occur on the date when the first irrigation or precipitation event was recorded as a result of the wash-off of the herbicide intercepted by the mulch layer, and corresponded to the amount of compound measured in soil surface at this date by Douibi et al. (2024a). This approach assumes there is a difference between the total rate of herbicides applied and the one measured on the soil surface. This difference between the true rate and the sum of the first and second rates used as inputs in the models is due to irreversible adsorption and/or degradation in the mulch layer.

### 2.4.5. Crop parameterisation

Field observations including crop emergence, flowering and harvest dates, root distribution, the maximum areal coverage of the canopy (COVMAX for PRZM), and the maximum crop height were used to parameterise the development of the winter wheat and maize crops. The maximum rooting depth was estimated as per FOCUS (2000) for winter wheat and maize. Leaf area index (LAI) were taken from the Porto scenario (Portugal), as its cropping periods are close to those at our experimental site (FOCUS, 2000) (Table 3).

## 2.5. Model performance

The following four statistical indices were calculated to evaluate the performance of the PRZM and MACRO models in simulating the soil water content ($m^3 m^{-3}$) and the distribution of herbicide concentrations ($\mu g g^{-1}$ dry soil) in the soil profiles at the different sampling times: the modelling efficiency (EF), the Pearson correlation coefficient ($r$), the coefficient of residual mass (CRM), and the relative root mean square error (RRMSE) (Smith et al., 1996):

$$EF = 1 - \left[ \sum_{i=1}^{n} (S_i - O_i)^2 / \sum_{i=1}^{n} (O_i - O_m)^2 \right] \quad (1)$$

$$r = \frac{\sum_{i=1}^{n} (O_i - O_m) \times (S_i - S_m)}{\left[ \sum_{i=1}^{n} (O_i - O_m)^2 \right]^{1/2}} \quad (2)$$
<caption>**Table 2**</caption>

Main herbicide input parameters used in the simulations with PRZM and MACRO. S1: Soil 1, S2: Soil 2, CT: Conventional tillage, NT: Non-tillage.

<table><thead><tr><th rowspan="2">Parameter</th><th rowspan="2">Soil layer<br>(cm)</th><th colspan="4">S-metolachlor</th><th colspan="4">Foramsulfuron</th><th colspan="4">Thiencarbazone-methyl</th></tr><tr><th>S1 + CT</th><th>S1 + NT</th><th>S2 + CT</th><th>S2 + NT</th><th>S1 + CT</th><th>S1 + NT</th><th>S2 + CT</th><th>S2 + NT</th><th>S1 + CT</th><th>S1 + NT</th><th>S2 + CT</th><th>S2 + NT</th></tr></thead><tbody><tr><td><strong>Adsorption</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>K<sub>f</sub> (mL g<sup>-1</sup>)<sup>a</sup></td><td>0–10</td><td>1.34</td><td>0.80*</td><td>1.34</td><td>2.00</td><td>2.00</td><td>0</td><td>0</td><td>0.090</td><td>0.090</td><td>0.010</td><td>0.010</td><td>0.400</td><td>0.400</td></tr><tr><td></td><td>11–30</td><td>1.39</td><td></td><td>1.39</td><td>2.46</td><td>2.46</td><td>0</td><td>0</td><td>0.090</td><td>0.090</td><td>0.008</td><td>0.008</td><td>0.380</td><td>0.395</td></tr><tr><td></td><td>31–50</td><td>0.82*</td><td></td><td>0.83<sup>a</sup></td><td>0.97*</td><td>0.97*</td><td>0</td><td>0</td><td>0.037</td><td>0.037</td><td>0.007</td><td>0.007</td><td>0.160</td><td>0.163</td></tr><tr><td></td><td>51–90</td><td>1.00</td><td></td><td>0.99</td><td>1.00</td><td>1.00</td><td>0</td><td>0</td><td>0.020</td><td>0.020</td><td>0.004</td><td>0.004</td><td>0.080</td><td>0.087</td></tr><tr><td></td><td></td><td>0.60*</td><td></td><td>0.59*</td><td>0.40*</td><td>0.40*</td><td>0</td><td>0</td><td>1.18</td><td>1.18</td><td>1.89</td><td>1.89</td><td>1.11</td><td>1.11</td></tr><tr><td>n<sub>f</sub><sup>b</sup></td><td>0–10</td><td>0.53</td><td></td><td>0.53</td><td>0.53</td><td>0.53</td><td>0</td><td>0</td><td>0.020</td><td>0.020</td><td>1.89</td><td>1.89</td><td>1.11</td><td>1.11</td></tr><tr><td><strong>Degradation</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>DT<sub>50</sub> (days)<sup>c</sup></td><td>0–10</td><td>0.023</td><td></td><td>0.073</td><td>0.033</td><td>0.050</td><td>0.158</td><td>0.112</td><td>0.117</td><td>0.073</td><td>0.060</td><td>0.048</td><td>0.024</td><td>0.018</td></tr><tr><td></td><td>11–30</td><td>0.023</td><td></td><td>0.073</td><td>0.033</td><td>0.050</td><td>0.158</td><td>0.112</td><td>0.117</td><td>0.073</td><td>0.060</td><td>0.048</td><td>0.024</td><td>0.018</td></tr><tr><td></td><td>31–50</td><td>0.012</td><td></td><td>0.036</td><td>0.017</td><td>0.025</td><td>0.079</td><td>0.056</td><td>0.059</td><td>0.036</td><td>0.030</td><td>0.024</td><td>0.012</td><td>0.009</td></tr><tr><td></td><td>51–90</td><td>0.007</td><td></td><td>0.022</td><td>0.010</td><td>0.015</td><td>0.047</td><td>0.034</td><td>0.035</td><td>0.002</td><td>0.018</td><td>0.014</td><td>0.007</td><td>0.005</td></tr><tr><td>Q<sub>10</sub><sup>d</sup></td><td></td><td>3.80</td><td></td><td>3.80</td><td>2.70</td><td>2.70</td><td>2.61</td><td>2.6</td><td>2.34</td><td>2.3</td><td>2.80</td><td>2.82</td><td>2.00</td><td>2.00</td></tr><tr><td>TRESP<br>(K<sup>-1</sup>)<sup>e</sup></td><td></td><td>0.13</td><td></td><td>0.13</td><td>0.10</td><td>0.10</td><td>0.10</td><td>0.10</td><td>0.09</td><td>0.08</td><td>0.10</td><td>0.10</td><td>0.07</td><td>0.07</td></tr><tr><td><strong>Other characteristics</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>DISP (cm<sup>2</sup> day<sup>-1</sup>)<sup>f</sup></td><td></td><td>2.5</td><td></td><td>2.5</td><td>2.5</td><td>2.5</td><td>2.5</td><td>2.5</td><td>2.5</td><td>2.5</td><td>2.5</td><td>2.5</td><td>2.5</td><td>2.5</td></tr><tr><td>DV (cm)<sup>f</sup></td><td></td><td>10</td><td></td><td>10</td><td>12</td><td>12</td><td>10</td><td>10</td><td>12</td><td>12</td><td>10</td><td>10</td><td>12</td><td>12</td></tr></tbody></table>

a Adsorption coefficients from Douibi et al. (2023). The values for deeper soil layers were estimated assuming adsorption coefficients are proportional to organic carbon content. Values in asterisk (*) correspond to calibrated values for PRZM and MACRO taken for topsoil from Alletto et al. (2013).

b Freundlich exponent from Douibi et al. (2023). The n<sub>f</sub> values determined in the top 0–10 cm were used in 11–90 cm depth.

c Herbicide half-lives from Douibi et al. (2024a) after the first application. Variation of the degradation rate k (k (d<sup>-1</sup>) = ln (2) / DT<sub>50</sub>) with depth: k for 0–30 cm, k × 0.5 for 30–60 cm, k × 0.3 for 60–90 cm (FOCUS, 2000)

d Q<sub>10</sub> factor from Douibi et al. (2024b).

e Exponent in the temperature response function estimated from TRESP = (ln Q<sub>10</sub>) / 10.

f DISP: Pesticide hydrodynamic solute dispersion coefficient, DV: Dispersivity from Marín-Benito et al. (2020).

<caption>**Table 3**</caption>

Crop input parameters for maize under conventional tillage (CT) and for winter wheat and maize under non-tillage (NT) over the 2-year experiment.

<table><thead><tr><th>Crop-Year<br>(Tillage)</th><th>Date</th><th>Crop stage</th><th>Root distribution<sup>b</sup></th><th>COVMAX (%)<sup>c</sup></th><th>Crop height (m)</th><th>Root depth (m)</th><th>LAI (m<sup>2</sup>m<sup>-2</sup>)<sup>d</sup></th></tr></thead><tbody><tr><td rowspan="3">Wheat-1<br>(NT)</td><td>30/10/2019</td><td>Sowing</td><td rowspan="3">0.60</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>07/11/2019</td><td>Emergence</td><td>3</td><td>0.01</td><td>0.01</td><td>0.1</td></tr><tr><td>23/05/2020</td><td>Flowering</td><td>95</td><td>0.5</td><td>0.8</td><td>6.5</td></tr><tr><td rowspan="6">Maize-1<br>(CT and NT)</td><td>02/06/2020<sup>a</sup></td><td>Harvest</td><td rowspan="6">0.67</td><td>90</td><td>0.5</td><td>0.8</td><td>2</td></tr><tr><td>08/06/2020</td><td>Sowing</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>15/06/2020</td><td>Emergence CT</td><td>3</td><td>0.1</td><td>0.01</td><td>0.1</td></tr><tr><td>30/06/2020</td><td>Emergence NT</td><td>3</td><td>0.1</td><td>0.01</td><td>0.1</td></tr><tr><td>17/08/2020</td><td>Flowering CT</td><td>70</td><td>2.5</td><td>0.8</td><td>3</td></tr><tr><td>02/09/2020</td><td>Flowering NT</td><td>70</td><td>2.5</td><td>0.8</td><td>3</td></tr><tr><td rowspan="6">Wheat-2<br>(NT)</td><td>20/11/2020</td><td>Harvest</td><td rowspan="6">0.60</td><td>70</td><td>2.5</td><td>0.8</td><td>2</td></tr><tr><td>26/02/2021</td><td>Sowing</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>13/03/2021</td><td>Emergence</td><td>3</td><td>0.01</td><td>0.01</td><td>0.1</td></tr><tr><td>14/03/2021</td><td>Flowering</td><td>10</td><td>0.1</td><td>0.1</td><td>0.3</td></tr><tr><td>15/03/2021<sup>a</sup></td><td>Harvest</td><td>10</td><td>0.1</td><td>0.1</td><td>0.3</td></tr><tr><td>03/06/2021</td><td>Sowing</td><td rowspan="6">0.67</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="5">Maize-2<br>(CT and NT)</td><td>10/06/2021</td><td>Emergence CT</td><td>3</td><td>0.1</td><td>0.01</td><td>0.1</td></tr><tr><td>25/06/2021</td><td>Emergence NT</td><td>3</td><td>0.1</td><td>0.01</td><td>0.1</td></tr><tr><td>17/08/2021</td><td>Flowering CT</td><td>90</td><td>2.5</td><td>0.8</td><td>3</td></tr><tr><td>02/09/2021</td><td>Flowering NT</td><td>90</td><td>2.5</td><td>0.8</td><td>3</td></tr><tr><td>19/11/2021</td><td>Harvest</td><td>90</td><td>2.5</td><td>0.8</td><td>2</td></tr></tbody></table>

a The cover crop (winter wheat) destruction date was considered as harvest date.

b Fraction of root density in the uppermost 25 % of the root depth.

c Maximum areal coverage of the canopy for PRZM.

d Leaf area index from Porto scenario (FOCUS, 2000).
$$CRM = \left( \sum_{i=1}^{n} O_i - \sum_{i=1}^{n} S_i \right) / \sum_{i=1}^{n} O_i \quad (3)$$

$$RRMSE = (100/O_m) \left[ \sum_{i=1}^{n} (S_i - O_i)^2 / n \right]^{1/2} \quad (4)$$

where $S_i$ and $O_i$ are the simulated and observed values, respectively, $O_m$ and $S_m$ are the mean observed and simulated values, respectively, and $n$ is the number of sampling dates. The optimum value of EF and $r$ is +1, and zero for CRM and RRMSE. If CRM > 0 (< 0), it indicates an under (over)estimation of the observed values.

# 3. Results and discussion

## 3.1. Modelling water content in soil profiles

### 3.1.1. Conventional tillage

Under CT management, MACRO simulated similar water flow dynamics than field observations at 0–20 and 40–60 cm for the entire period simulated, however, at 20–40 cm, the model overestimated (CRM < 0, Table 4) the observed soil water content (Fig. S2). MACRO successfully predicted the fluctuation in soil moistures at 0–20 cm in CT over the two maize cropping periods, but the predictions were less satisfactory over the fallow periods (Fig. S2). Overall, when the 0–20 cm soil layer and the entire experimental period were considered, MACRO predictions of soil water content provided satisfactory statistical indices, as shown by the intermediate EF (0.33 to 0.66), high $r$ (0.63 to 0.81) and low RRMSE (< 11.2) obtained. These indices worsened slightly when the whole soil profile was considered (Table 4). Some authors have also

observed the weaker performance of pesticide fate models when simulating the soil water content measured with depth (Antonopoulos et al., 2013).

According to Ritter and Muñoz-Carpena (2013), a model's ability to predict soil moisture depends on the EF values: an EF < 0.65 is considered unsatisfactory, acceptable when 0.65 < EF < 0.80, good when 0.80 < EF < 0.90, and very good when EF ≥ 0.90. Based strictly on the EF values shown in Table 4, MACRO's ability to simulate the soil water content at 0–20 cm is acceptable for S2 + CT and unsatisfactory for S1 + CT, as well as for S1 + CT and S2 + CT when the entire soil profile is considered. Nevertheless, it is important to note that the average values from two replicated plots per treatment at field scale (linked to spatial variability) are used to compare the simulation output in the determination of the goodness-of-fit statistics. In these cases, positive EF, intermediate $r$ (0.42 to 0.63), CRM close to zero, and low RRMSE (< 15.5) (Table 4) together with simulation outputs within the standard deviation of field measurements (Fig. S2) with high spatial variability may suffice to consider the model's fit as satisfactory (Jarvis, 1995; Thevenot and Dousset, 2015; Villarreal et al., 2020).

Compared to MACRO, PRZM did not accurately simulate the water dynamics through the soil profile in CT over the entire period, especially at 20–40 and 40–60 cm (Fig. S2). On the contrary, the fluctuations in soil water content at 0–20 cm during the maize cropping periods coinciding with precipitation or irrigation events were accurately determined by PRZM (Fig. S2). The model predicted a marked decrease at 0–20 cm over the fallow periods from 27 April 2020 to 15 June 2020 in the first year, and from 8 April 2021 to 29 June 2021 in the second year (Fig. S2), when it dropped to the wilting point. This could be because ETP is controlled in the model by the parameter “maximum soil depth for evaporation” (ANETD) in the absence of crops, however it was set at 25

<caption>**Table 4**</caption>

Goodness-of-fit statistics of PRZM and MACRO modelling of the soil water content (θ), and S-metolachlor, foramsulfuron and thiencarbazone-methyl concentrations in soil 1 (S1) and soil 2 (S2) profiles under conventional tillage (CT) or non-tillage (NT) for the whole simulated period.

<table><thead><tr><th rowspan="2"></th><th colspan="3">PRZM</th><th rowspan="2">RRMSE (%)</th><th colspan="4">MACRO</th></tr><tr><th>EF (−)</th><th>r (−)</th><th>CRM (−)</th><th>EF (−)</th><th>r (−)</th><th>CRM (−)</th><th>RRMSE (%)</th></tr></thead><tbody><tr><td>S1 + CT</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>θ<sub>0-20</sub> cm</td><td>-4.28</td><td>0.30</td><td>0.17</td><td>32.3</td><td>0.33</td><td>0.63</td><td>0.00</td><td>11.2</td></tr><tr><td>θ<sub>20-40</sub> cm</td><td>-2.56</td><td>0.10</td><td>-0.09</td><td>37.02</td><td>-0.08</td><td>0.14</td><td>-0.01</td><td>20.4</td></tr><tr><td>θ<sub>40-60</sub> cm</td><td>-4.68</td><td>0.17</td><td>-0.24</td><td>26.9</td><td>-0.23</td><td>0.17</td><td>-0.01</td><td>12.5</td></tr><tr><td>θ<sub>0-60</sub> cm</td><td>-2.54</td><td>0.33</td><td>-0.09</td><td>30.8</td><td>0.11</td><td>0.42</td><td>-0.01</td><td>15.5</td></tr><tr><td>S-metolachlor</td><td>0.72</td><td>0.90</td><td>-0.45</td><td>133</td><td>0.81</td><td>0.96</td><td>0.51</td><td>106</td></tr><tr><td>Foramsulfuron</td><td>0.88</td><td>0.94</td><td>-0.58</td><td>160</td><td>0.82</td><td>0.97</td><td>0.48</td><td>194</td></tr><tr><td>Thiencarbazone-methyl</td><td>0.79</td><td>0.90</td><td>-0.54</td><td>153</td><td>0.87</td><td>0.94</td><td>0.25</td><td>120</td></tr><tr><td>S2 + CT</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>θ<sub>0-20</sub> cm</td><td>-1.92</td><td>0.50</td><td>0.08</td><td>27.6</td><td>0.66</td><td>0.81</td><td>0.00</td><td>9.58</td></tr><tr><td>θ<sub>20-40</sub> cm</td><td>-0.59</td><td>0.28</td><td>-0.01</td><td>26.6</td><td>-2.74</td><td>0.40</td><td>-0.01</td><td>21.3</td></tr><tr><td>θ<sub>40-60</sub> cm</td><td>-3.03</td><td>0.20</td><td>-0.33</td><td>38.7</td><td>0.52</td><td>0.74</td><td>0.01</td><td>13.4</td></tr><tr><td>θ<sub>0-60</sub> cm</td><td>-1.85</td><td>0.20</td><td>-0.07</td><td>33.6</td><td>0.42</td><td>0.64</td><td>0.01</td><td>15.2</td></tr><tr><td>S-metolachlor</td><td>0.69</td><td>0.90</td><td>-0.50</td><td>143</td><td>0.86</td><td>0.95</td><td>0.38</td><td>87.5</td></tr><tr><td>Foramsulfuron</td><td>0.44</td><td>0.80</td><td>-1.73</td><td>363</td><td>0.85</td><td>0.96</td><td>0.37</td><td>188</td></tr><tr><td>Thiencarbazone-methyl</td><td>0.61</td><td>0.86</td><td>-0.78</td><td>162</td><td>0.84</td><td>0.95</td><td>0.25</td><td>106</td></tr><tr><td>S1 + NT</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>θ<sub>0-20</sub> cm</td><td>-3.68</td><td>0.17</td><td>0.11</td><td>33.2</td><td>0.03</td><td>0.47</td><td>0.03</td><td>15.1</td></tr><tr><td>θ<sub>20-40</sub> cm</td><td>-8.04</td><td>0.00</td><td>0.08</td><td>39.6</td><td>0.33</td><td>0.64</td><td>0.04</td><td>10.8</td></tr><tr><td>θ<sub>40-60</sub> cm</td><td>-1.60</td><td>0.00</td><td>-0.15</td><td>21.6</td><td>0.28</td><td>0.54</td><td>0.00</td><td>11.4</td></tr><tr><td>θ<sub>0-60</sub> cm</td><td>-3.57</td><td>0.20</td><td>0.01</td><td>32.1</td><td>0.32</td><td>0.59</td><td>0.03</td><td>12.4</td></tr><tr><td>S-metolachlor</td><td>-0.37</td><td>0.78</td><td>-0.99</td><td>302</td><td>0.41</td><td>0.46</td><td>0.56</td><td>196</td></tr><tr><td>Foramsulfuron</td><td>0.77</td><td>0.88</td><td>-0.41</td><td>149</td><td>0.65</td><td>0.86</td><td>0.54</td><td>179</td></tr><tr><td>Thiencarbazone-methyl</td><td>0.56</td><td>0.62</td><td>0.29</td><td>127</td><td>0.38</td><td>0.72</td><td>0.57</td><td>150</td></tr><tr><td>S2 + NT</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>θ<sub>0-20</sub> cm</td><td>-3.13</td><td>0.68</td><td>0.03</td><td>33.1</td><td>0.33</td><td>0.68</td><td>-0.06</td><td>13.4</td></tr><tr><td>θ<sub>20-40</sub> cm</td><td>-6.01</td><td>0.20</td><td>-0.03</td><td>50.7</td><td>0.05</td><td>0.65</td><td>-0.12</td><td>18.7</td></tr><tr><td>θ<sub>40-60</sub> cm</td><td>-4.41</td><td>0.17</td><td>-0.17</td><td>20.8</td><td>0.05</td><td>0.48</td><td>0.02</td><td>8.73</td></tr><tr><td>θ<sub>0-60</sub> cm</td><td>-4.01</td><td>0.17</td><td>-0.06</td><td>36.0</td><td>0.26</td><td>0.59</td><td>-0.05</td><td>13.8</td></tr><tr><td>S-metolachlor</td><td>0.35</td><td>0.81</td><td>-0.86</td><td>223</td><td>0.58</td><td>0.82</td><td>0.65</td><td>179</td></tr><tr><td>Foramsulfuron</td><td>0.25</td><td>0.80</td><td>-0.37</td><td>350</td><td>0.86</td><td>0.93</td><td>0.24</td><td>153</td></tr><tr><td>Thiencarbazone-methyl</td><td>0.57</td><td>0.76</td><td>-0.37</td><td>168</td><td>0.76</td><td>0.88</td><td>-0.07</td><td>131</td></tr></tbody></table>

EF: Modelling efficiency, r: Pearson correlation coefficient, CRM: Coefficient of residual mass, RRMSE: Relative root mean square error.
cm according to the PRZM user manual which overestimated the loss of water through evaporation. The drop in the water content to the wilting point was also observed at 20–40 cm and 40–60 cm (Fig. S2), although its intensity decreased with depth. In addition, most of the time PRZM recorded the water content at maximum field capacity at these two depths, indicating that the model always considers soil microporosity below 20 cm to be saturated because the two water sinks, evaporation and transpiration, decrease with depth. In fact, there is no evaporation below 25 cm over the fallow periods according to the aforesaid ANETD value, whereas PRZM underestimated the root water uptake over maize cropping periods. Overall, PRZM's ability to simulate the moisture content in CT soil profiles or even in the 0–20 cm soil layer during the whole experiment was poor, which was confirmed by the statistical indices (EF < 0, r ≤ 0.50, -0.09 ≤ CRM ≤ 0.17, 27.6 ≤ RRMSE ≤ 33.6) (Table 4). This limited ability to accurately simulate the soil water dynamics has often been reported (Gottesbüren et al., 2000; Marín-Benito et al., 2020), although in some cases it has not been considered a limitation for properly simulating solute leaching (Marín-Benito et al., 2020).

### 3.1.2. Non-tillage

The mulch layer on the NT soil surfaces during the first experimental year increased the soil water contents in the topsoil (0–20 cm) by 6% and 5.2% in S1 + NT and S2 + NT, respectively, compared to their corresponding CT soils (Figs. S2 and S3). Mulching is an agricultural practice that generally helps to conserve moisture by shielding the soil from solar radiation, while lowering the soil temperature and increasing resistance to water vapor diffusion from the soil to the atmosphere resulting in the decreases of evaporation, and therefore the ETP (Balwinder-Singh et al., 2011; Martins et al., 2013; Odhiambo and Irmak, 2012). Some studies have reported that the mulching practice reduces the ETP by 5% (Odhiambo and Irmak, 2012), 9% (Martins et al., 2013), 14 to 25% (Shumova, 2013), and by 5 to 27% (Khaledian et al., 2012). Nevertheless, the increase in moisture observed during the first experimental year was not observed during the second one. This is probably because the mulch decreased evaporation and led to a corresponding increase in transpiration, and as a result the mulch had no impact on net soil water content (Balwinder-Singh et al., 2011; Lascano et al., 1994; Li et al., 2008; Ochsner et al., 2010). Similar behaviour has been reported by Shumova (2013), who determined the ETP of soils with and without mulch at 28 agrometeorological stations: in some stations, ETP decreased of up to 14%, while in others no changes were recorded between mulched and bare soils.

Simulations of soil water content were first run without considering the effect of the mulch on the ETP (Section 2.4.3). The unsatisfactory fit between simulated and observed moisture content data obtained with both models throughout the profiles of NT (-13.3 ≤ EF ≤ -0.93, 0.17 ≤ r ≤ 0.28, -0.11 ≤ CRM ≤ 0.48, 22.3 ≤ RRMSE ≤ 56.7; data not shown), as well as at 0–20 cm (-13.6 ≤ EF ≤ -0.77, 0 ≤ r ≤ 0.36, -0.02 ≤ CRM ≤ 0.53, 20.6 ≤ RRMSE ≤ 58.6; data not shown) supported the need to optimise the ETP<sub>NT</sub> values in the corresponding meteorological files.

The optimisation of ETP<sub>NT</sub> values (Section 2.4.3) significantly improved the simulation of soil water contents by MACRO through the entire profiles over the first and second winter wheat (Wheat-1, Wheat-2) and maize (Maize-1, Maize-2) cropping seasons in S1 + NT and S2 + NT (Fig. S3). MACRO struggled to simulate the moisture content at 20–40 cm in S2 + NT, with clear overestimation in the Wheat-2 and Maize-2 periods (Fig. S3). Considering the entire simulation period and the whole NT soil profiles, the optimisation of the ETP<sub>NT</sub> significantly enhanced the model's performance: EF = 0.26 to 0.32, r = 0.59, CRM = -0.05 to 0.03, RRMSE = 12.4 to 13.8 (Table 4). This suggests that MACRO is unable to predict the moisture content in S1 + NT and S2 + NT without the optimisation of the ETP<sub>NT</sub> when mulch effect is included in the model (Lammoglia et al., 2017b; Marín-Benito et al., 2018).

In contrast to MACRO, PRZM was unable to accurately simulate the observed water content dynamics through the soil profile in S1 + NT and

S2 + NT (EF < 0, r = 0.17 to 0.68, CRM = -0.06 to 0.11, RRMSE = 32.1 to 36.0), although a better fit was observed after the optimisation of the ETP<sub>NT</sub> values (see above) (Fig. S3, Table 4). At 0–20 cm, PRZM predicted that the soil water content fluctuated between field capacity and wilting point without following the same dynamics as the observed data. At 20–40 cm, PRZM simulated the NT soil moisture at field capacity over Wheat-1 and Wheat-2 periods and at wilting point during the Maize-1 period (Fig. S3). At this depth, the model predicted a marked decrease in S2 + NT moisture up to the wilting point in the first part of the Maize-2 cycle, whereupon it strongly overestimated it through to the end of the simulation. However, the model simulated more accurately the water content at 20–40 cm in S1 + NT over this Maize-2 cycle (Fig. S3). At 40–60 cm, PRZM predicted the soil water content at field capacity over the whole simulation period (Fig. S3) which was already observed in other situations by Marín-Benito et al. (2020). An overestimation or underestimation of the root water uptake by PRZM explains the simulation of moisture content at wilting point or at field capacity, respectively, over the different cropping cycles and at the indicated soil depths. Overall, the weak statistical indices (Table 4) show that PRZM poorly simulated the moisture in NT soil profiles over the entire period, as well as in the soil layer receiving the most direct and strongest mulch effects (0–20 cm). Previous comparative modelling exercises involving capacity models and those based on the Richards equation have also shown that the latter more closely replicate the observed soil water dynamics (Marín-Benito et al., 2020).

## 3.2. Modelling herbicide distribution in soil profiles

### 3.2.1. Conventional tillage

A detailed description of the measurement of herbicide distribution in the soil profiles modelled here can be found in Douibi et al. (2025) and summarized in Supplementary Material (Figs. 1–4, S4–S7).

PRZM and MACRO accurately replicated the observed mobility of S-metolachlor, foramsulfuron, and thiencarbazone-methyl through the S1 + CT and S2 + CT profiles considering the whole simulation period, although MACRO performed better than PRZM (Table 4, Figs. 1–2, S4–S5). Nevertheless, a calibration step was performed on the K<sub>f</sub> of S-metolachlor (Section 2.4.4) to improve the goodness-of-fit statistics of both models because the results obtained using the experimental K<sub>f</sub> values determined under equilibrium conditions (Douibi et al., 2023, Table 2) were unsatisfactory (CRM << 0, data not shown), as previously reported in modelling studies involving S-metolachlor (Mamy et al., 2024). Vereecken et al. (2011) have reported that the use of adsorption parameters obtained under equilibrium conditions might be unable to predict the environmental fate of pesticides because a non-equilibrium adsorption process mainly controls pesticide mobility in soil.

The correspondence between observations and MACRO simulations was good for the three herbicides (0.81 to 0.87), while it generally varied from acceptable to good (EF<sub>S-metolachlor</sub> = 0.69 to 0.72, EF<sub>For-amsulfuron</sub> = 0.44 to 0.88, EF<sub>Thiencarbazone-methyl</sub> = 0.61 to 0.79) using PRZM (Ritter and Muñoz-Carpena, 2013). In addition, high r values (0.94 to 0.97 with MACRO, and 0.80 to 0.94 with PRZM) were obtained for the three herbicides in CT soils (Table 4). According to the CRM values (Table 4), MACRO underestimated (CRM > 0) the observed concentrations of the three herbicides through the CT soil profiles over the entire simulation period, while PRZM overestimated them (CRM < 0). PRZM's weakest performance corresponds to its poor simulations of the soil water content that directly affects herbicide DT<sub>50</sub> (Figs. 1–2, S4–S5). Previous studies have also recorded a better fit for the dynamics of different herbicides with models based on the Richards equation, including MACRO, than with the PRZM capacity model (Marín-Benito et al., 2020).

### 3.2.2. Non-tillage

A detailed description of the measurement of herbicide distribution in the soil profiles under NT modelled here can also be found in Douibi
<figure><img src="image_5.png" /><figcaption>Fig. 1. Observed and simulated distribution of S-metolachlor, foramsulfuron and thiencarbazone-methyl in soil 1 under conventional tillage (S1 + CT) plots at selected sampling times after the first herbicide application (9 June 2020). Error bars represent the standard deviation of observed mean values of plots treated (n = 2).</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>Fig. 2. Observed and simulated distribution of S-metolachlor, foramsulfuron and thiencarbazone-methyl in soil 2 under conventional tillage (S2 + CT) plots at selected sampling times after the first herbicide application (9 June 2020). Error bars represent the standard deviation of observed mean values of plots treated (n = 2).</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Fig. 3. Observed and simulated distribution of S-metolachlor, foramsulfuron and thiencarbazone-methyl in soil 1 under non-tillage (S1 + NT) plots at selected sampling times after the first herbicide application (9 June 2020). Error bars represent the standard deviation of observed mean values of plots treated (n = 2).</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Fig. 4. Observed and simulated distribution of S-metolachlor, foramsulfuron and thiencarbazone-methyl in soil 2 under non-tillage (S2 + NT) plots at selected sampling times after the first herbicide application (9 June 2020). Error bars represent the standard deviation of observed mean values of plots treated (n = 2).</figcaption></figure>
et al. (2025) and summarized in Supplementary Material.

Following the approach implemented to consider the effect of the mulch on the herbicide application rate in both models (Section 2.4.4), PRZM and MACRO accurately simulated the observed mobility of S-metolachlor (following K<sub>f</sub> calibration, see above), foramsulfuron and thiencarbazone-methyl through the S1 + NT and S2 + NT profiles over the entire simulation period, and better in S2 + NT than in S1 + NT (Table 4, Figs. 3–4, S6 – S7). As observed for CT soils, MACRO predicted the vertical distribution of the three herbicides better than PRZM, as it more closely replicated the water dynamics through the soil profiles (Table 4, Figs. 3–4, S3, S6 – S7). The correspondence between observations and PRZM simulations was acceptable for foramsulfuron (EF<sub>Foramsulfuron</sub> = 0.25 to 0.77), but unsatisfactory for S-metolachlor and thiencarbazone-methyl (EF<sub>S-metolachlor</sub> = -0.37 to 0.35, EF<sub>Thiencarbazone-methyl</sub> = 0.56 to 0.57), whilst for MACRO it ranged from unsatisfactory to good for the three herbicides (EF<sub>S-metolachlor</sub> = 0.41 to 0.58, EF<sub>Foramsulfuron</sub> = 0.65 to 0.86, EF<sub>Thiencarbazone-methyl</sub> = 0.38 to 0.76) (Ritter and Muñoz-Carpena, 2013). Nevertheless, the high *r* values and positive EF values, together with the simulated concentrations falling within the standard deviation of the field observations (with broad variations), indicate that the simulations may be considered satisfactory in most cases, especially with MACRO (Table 4, Figs. 3–4, S6 – S7).

According to the CRM values, MACRO generally underestimated (CRM > 0) the observed concentrations of the three herbicides through the NT soil profiles over the entire simulation period, while PRZM overestimated them (CRM < 0), as observed in the CT simulations (Table 4). These discrepancies between observations and simulations could be due to processes that occurred in the soil over the field experiment which might have affected herbicide adsorption, degradation and/or mobility, but which are not considered in PRZM and MACRO. Among these processes, the formation of non-extractable residues or the formation of herbicide-dissolved organic carbon mobile complexes has already been used to explain the behaviour of the three herbicides (Douibi et al., 2023, 2024a, 2025). These limits in the code of pesticide leaching models have previously been reported in modelling studies at field and laboratory scales (Marín-Benito et al., 2020; Thevenot and Dousset, 2015).

Considering all the herbicides and soil profiles, the weakest simulation was observed for the vertical distribution of S-metolachlor in S1 + NT by PRZM (EF = -0.37) when the entire experimental period was assessed. This low EF value was heavily influenced by the very low EF value obtained by the model for replicating S-metolachlor mobility over the testing period (EF = -7.25, data not shown). However, this was not an isolated case, but rather widespread. The EF of both models for simulating the distribution of herbicides in CT and NT soil profiles decreased over the testing period compared to the calibration one. This decrease in the EF was higher for PRZM than for MACRO (from 0–0.93 to -7.25–0.83, and from 0.32–0.95 to -3.00–0.97, respectively (data not shown)) being more prominent for S-metolachlor than for foramsulfuron and thiencarbazone-methyl, and especially in NT soils. The models' loss of efficiency over time has also been observed in other field studies (Marín-Benito et al., 2020). This is consistent with the difference in the herbicide DT<sub>50</sub> measured in the first and second experimental years in NT and CT soils (Douibi et al., 2024a). Indeed, PRZM and MACRO were parameterised using the herbicides DT<sub>50</sub> measured the first year of the field experiment as only one DT<sub>50</sub> value can be inputted in the models independently of the number of years to be simulated. Furthermore, the variation in substantial properties (amount of mulch, OC content, dissolved OC content, temperature, moisture, etc.) in the soil surface receiving the herbicide could modify its behaviour from one year to the next. In this study, more crop residues were left as mulch on the surface of NT soils in the second experimental year than in the first, as previously indicated. This strongly conditioned the high variation in DT<sub>50</sub> observed in NT soils between the two years because the mulch layer controlled herbicide degradation (Douibi et al., 2024a). Therefore, if the DT<sub>50</sub> are lower (higher) in the second year, an over(under)estimation of

the herbicide concentrations through the NT soil profiles could be ex-
pected. Hence, this suggests that the DT<sub>50</sub> values used to parameterise
the two models were not entirely representative of herbicide behaviour
in the second year, mainly in NT soils.

4. Conclusions

The performance of the PRZM and MACRO models for simulating the soil water content and the mobility of the herbicides S-metolachlor, foramsulfuron, and thiencarbazone-methyl was assessed and compared in two soils under CT and NT management. A modelling approach was developed to account for the difference in soil moisture content between NT and CT soils, incorporating the mulch layer's effect on evapotranspiration. To address the mulch's influence on herbicide adsorption and degradation —overlooked by the models— a two-split herbicide application was used instead of a single annual application. In general, MACRO acceptably replicated both soil moisture and herbicide mobility through the soil profiles, whilst PRZM generally underperformed when simulating the herbicide dynamics because of its poor ability to replicate the water dynamics in the assayed scenarios. The models simulate the vertical distribution of herbicides better in CT soils than in NT soils, but this study showed that MACRO might be used to simulate the water and herbicide dynamics in non-tillage scenarios with an acceptable performance over a two-year period. However, for multi-year simulation, a module needs to be introduced into the model for parameterising the DT<sub>50</sub> values according to the yearly amount of mulch covering the soil surface. This module will help to assess the environmental risk of herbicides used in cropping systems based on NT with a varying percentage of surface covered by the mulch in European registration procedure.

CRediT authorship contribution statement

Marwa Douibi: Writing – review & editing, Visualization, Validation, Investigation, Formal analysis, Writing – original draft. Laure Mamy: Writing – review & editing, Supervision, Methodology, Formal analysis. M. José Carpio: Validation, Investigation, Formal analysis. María J. Sánchez-Martín: Writing – review & editing, Resources. M. Sonia Rodríguez-Cruz: Writing – review & editing, Resources. Jesús M. Marín-Benito: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Resources, Project administration, Methodology, Funding acquisition, Formal analysis, Data curation, Conceptualization.

Declaration of competing interest

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

Acknowledgements

This work has been funded by MCIN/AEI /10.13039/5011000110
33/ ERDF A way of making Europe (Project RTI2018-101587-J-I00).
Marwa Douibi thanks the Algerian Ministry of Higher Education and
Scientific Research for her predoctoral scholarship. The authors thank
the Spanish Agency of Meteorology (AEMET) for its support with the
climatic data over the field experiment. The authors are also very
grateful to Drs. Julien Moeys, Mats Larsbo and Nick Jarvis (Swedish
University of Agricultural Sciences) for providing the unofficial release
of MACRO and to J.M. Ordax for his technical assistance.

**Appendix A. Supplementary data**

Supplementary data to this article can be found online at https://doi.org/10.1016/j.scitotenv.2025.179344.
# Data availability

Data will be made available on request.

## References

Alletto, L., Coquet, Y., Benoit, P., Heddadj, D., Barriuso, E., 2010. Tillage management effects on pesticide fate in soils. A review. Agron. Sustain. Dev. 30 (2), 367–400. https://doi.org/10.1051/agro/2009018.

Alletto, L., Benoit, P., Bolognési, B., Couffignal, M., Bergheaud, V., Dumény, V., Longueval, C., Barriuso, E., 2013. Sorption and mineralisation of S-metolachlor in soils from fields cultivated with different conservation tillage systems. Soil Till. Res. 128, 97–103. https://doi.org/10.1016/j.still.2012.11.005.

Antonopoulos, V.Z., Georgiou, P.E., Kolotouros, C.A., 2013. Soil water dynamics in cropped and uncropped fields in northern Greece using a dual-permeability model. Hydrolog. Sci. J. 58 (8), 1748–1759. https://doi.org/10.1080/02626667.2013.816424.

Aslam, S., Iqbal, A., Lafolie, F., Recous, S., Benoit, P., Garnier, P., 2018. Mulch of plant residues at the soil surface impact the leaching and persistence of pesticides: a modelling study from soil columns. J. Contam. Hydrol. 214, 54–64. https://doi.org/10.1016/j.jconhyd.2018.05.008.

Balwinder-Singh, Eberbach, P.L., Humphreys, E., Kukal, S.S., 2011. The effect of rice straw mulch on evapotranspiration, transpiration and soil evaporation of irrigated wheat in Punjab. India. Agric. Water Manag. 98 (12), 1847–1855. https://doi.org/10.1016/j.agwat.2011.07.002.

Brisson, N., Launay, M., Mary, B., Beaudoin, N., 2009. Conceptual Basis, Formalisations and Parameterization of the STICS Crop Model. Quæ, Versailles.

Carpio, M.J., Rodríguez-Cruz, M.S., García-Delgado, C., Sánchez-Martín, M.J., Marín-Benito, J.M., 2020. Mobility monitoring of two herbicides in amended soils: a field study for modeling applications. J. Environ. Manag. 260, 110161. https://doi.org/10.1016/j.jenvman.2020.110161.

Carsel, R., Imhoff, J., Hummel, P., Cheplick, J., Donigian, A., Suarez, L., 2005. PRZM-3, a model for predicting pesticide and nitrogen fate in the crop root and unsaturated soil pores: user's manual for release 3.12.2. US environ. Prot. Agency (EPA) (420 pp).

Choudhary, M., Ghasal, P.C., Kumar, S., Yadav, R.P., Singh, S., Meena, V.S., Bisht, J.K., 2016. Conservation agriculture and climate change: An overview. In: Bisht, J.K., Meena, V.S., Mishra, P.K., Pattanayak, A. (Eds.), Conservation Agriculture: An Approach to Combat Climate Change in Indian Himalaya. Springer, pp. 1–37. https://doi.org/10.1007/978-981-10-2558-7-1.

Cueff, S., Alletto, L., Dumény, V., Benoit, P., Pot, V., 2021. Adsorption and degradation of the herbicide nicosulfuron in a stagnic Luvisol and Vermic Ulmbrisol cultivated under conventional or conservation agriculture. Environ. Sci. Pollut. Res. 28 (13), 15934–15946. https://doi.org/10.1007/s11356-020-11772-2.

Dang, Y.P., Page, K.L., Dalal, R.C., Menzies, N.W., 2020. No-till farming systems for sustainable agriculture: An overview. In: Dang, Y.P., Dalal, R.C., Menzies, N.W. (Eds.), No-Till Farming Systems for Sustainable Agriculture. Springer International Publishing, pp. 3–20. https://doi.org/10.1007/978-3-030-46409-7-1.

de Moraes Sá, J.C., Lal, R., Friedis, C., de Oliveira Ferreira, A., Tivet, F., Inagaki, T.M., Pomgaoncalves, D.R., Canalli, L.B., Burkner dos Santos, J., Romaniw, J., 2022. Can C-budget of natural capital be restored through conservation agriculture in a tropical and subtropical environment? Environ. Pollut. 298, 118817. https://doi.org/10.1016/j.envpol.2022.118817.

Douibi, M., Krishtammagari, A., Sánchez-Martín, M.J., Rodríguez-Cruz, M.S., Marín-Benito, J.M., 2023. Mulching vs. organic soil amendment: effects on adsorption-desorption of herbicides. Sci. Total Environ. 892, 164749. https://doi.org/10.1016/j.scitotenv.2023.164749.

Douibi, M., Carpio, M.J., Rodríguez-Cruz, M.S., Sánchez-Martín, M.J., Marín-Benito, J. M., 2024a. Control of the field herbicide dissipation by cover crop mulch in conservation agriculture. Agronomy 14, 2284. https://doi.org/10.3390/agronomy14102284.

Douibi, M., Rodríguez-Cruz, M.S., Sánchez-Martín, M.J., Marín-Benito, J.M., 2024b. Sustainable agricultural practices influence s-metolachlor, foramsulfuron and thiencarbazone-methyl degradation and their metabolites formation. Sci. Total Environ. 945, 174039. https://doi.org/10.1016/j.scitotenv.2024.174039.

Douibi, M., Carpio, M.J., Rodríguez-Cruz, M.S., Sánchez-Martín, M.J., Marín-Benito, J. M., 2025. Herbicide and metabolite mobility in soil profiles under conventional tillage and non-tillage: a two-year comparative field experiment. Sci. Total Environ. 969, 178969. https://doi.org/10.1016/j.scitotenv.2025.178969.

Elias, D., Wang, L., Jacinthe, P.A., 2018. A meta-analysis of pesticide loss in runoff under conventional tillage and no-till management. Environ. Monit. Assess. 190 (2), 79. https://doi.org/10.1007/s10661-017-6441-1.

FOCUS, 2000. FOCUS groundwater scenarios in the EU review of active substances. Report of the FOCUS groundwater scenarios workgroup. EC document reference Sanco/321/2000 rev.2, 202 pp.

Giannouli, D.D., Antonopoulos, V.Z., 2015. Evaluation of two pesticide leaching models in an irrigated field cropped with corn. J. Environ. Manag. 150, 508–515. https://doi.org/10.1016/j.jenvman.2014.12.044.

Gil Ribes, J.A., Ordóñez Fernández, J., González Sánchez, E.J., Veroz González, O., Gómez Ariza, M., Sánchez Ruiz, F., 2017. Ed. Asociación Española de Conservación Suelos Vivos (AEACSV). IFAPA Centro "Alameda del Obispo". Edificio de Olivicultura. Avda. Menéndez Pidal, s/n. E-14004 Córdoba (Spain). ISBN: 978-84-617-9450-8. (136 pp.).

Gottesbüren, B., Aden, K., Bärlund, I., Brown, C., Dust, M., Görlitz, G., Jarvis, N., Rekolainen, S., Schäfer, H., 2000. Comparison of pesticide leaching models: results

using the Weiherbach data set. Agric. Water Manag. 44 (1), 153–181. https://doi.org/10.1016/S0378-3774(99)00090-6.

Hassan, W., Li, Y., Saba, T., Jabbi, F., Wang, B., Cai, A., Wu, J., 2022. Improved and sustainable agroecosystem, food security and environmental resilience through zero tillage with emphasis on soils of temperate and subtropical climate regions: a review. Int. Soil Water Conserv. Res. 10 (3), 530–545. https://doi.org/10.1016/j.iswcr2021.01.005.

Jarvis, N.J., 1995. Simulation of soil water dynamics and herbicide persistence in a silt loam soil using the MACRO model. Ecol. Model. 81 (1), 97–109. https://doi.org/10.1016/j.pecr0.1995.03.001.

Kassam, A., Friedrich, T., Derpsch, R., 2019. Global spread of conservation agriculture. Int. J. Environ. Stud. 76 (1), 29–51. https://doi.org/10.1080/0207233.2018.1494927.

Khaledian, M.R., Mailhol, J.C., Ruelle, P., Mubarak, I., 2012. Impacts of direct seeding into mulch on the yield, water use efficiency and nitrogen dynamics of corn. Sorghum and Durum Wheat. Irrig. Drain. 61 (3), 398–409. https://doi.org/10.1002/ird.661.

Klein, M., 2000. PELMO (Pesticide Leaching Model) Version 5.00. User manual, 172 p. https://www.google.com/url?sa=t&source=web&rct=j&opj=89978449&url=http://www.software.ime.fraunhofer.de/PELMO_Manual/PELMO_5.00_user-manual.pdf&ved=2ahUKewiMrvg6q9IJAXkFKQEHYeAHpOQFnoECBkQAQ&uSG=AOvVaw1xnakYey2pw2EacSucOzH_S (accessed 25 May 2024).

Kolupaeva, V., Kokoreva, A., Belik, A., Bolotov, A., Glinushkin, A., 2022. Modelling water andPesticide transport in soil with MACRO 5.2: calibration with lysimetric data. Agriculture 12 (4), 505. https://doi.org/10.3390/agriculture12040505.

Lammoglia, S.K., Moeys, J., Barriuso, E., Larsbo, M., Marín-Benito, J.-M., Justes, E., Alletto, L., Ubertosi, M., Nicolardot, B., Munier-Jolain, N., Mamy, L., 2017a. Sequential use of the STICS crop model and of the MACRO pesticide fate model to simulate pesticides leaching in cropping systems. Environ. Sci. Pollut. Res. 24 (8), 6895–6909. https://doi.org/10.1007/s11356-016-6842-7.

Lammoglia, S.K., Makowski, D., Moeys, J., Justes, E., Barriuso, E., Mamy, L., 2017b. Sensitivity analysis of the STICS-MACRO model to identify cropping practices reducing pesticides losses. Sci. Total Environ. 580, 117–129. https://doi.org/10.1016/j.scitotenv.2016.10.010.

Larsbo, M., Jarvis, N., 2003. MACRO 5.0: a model of water flow and solute transport in macroporous soil. Technical description. Rep. Emergo, Swedish University of Agricultural Sciences, Uppsala, Sweden (49 pp.).

Lascano, R.J., Baumhardt, R.L., Hicks, S.K., Heilman, J.L., 1994. Soil and plant water evaporation from strip-tilled cotton: measurement and simulation. J. Agron. 86 (6), 987–994. https://doi.org/10.2134/agronj1994.00021962008600060011x.

Leistra, M., van der Linden, A.M.A., Boesten, J.J.T.I., Tiktak, A., van den Berg, F., 2001. PEARL model for pesticide behaviour and emissions in soil-plant systems: description of the processes. Alterra rep, vol. 13. Wageningen University and Research Centre, Wageningen, the Netherlands (115 pp.).

Li, Q., Chen, Y., Mengyu, L., Zhou, X., Yu, S., Dong, B., 2008. Effects of irrigation and straw mulching on microclimate characteristics and water use efficiency of winter wheat in north Chinas. J. Plant Prod. Sci. 11 (2), 161–170. https://doi.org/10.1626/pps.11.161.

Mamy, L., Marín-Benito, J.M., Alletto, L., Justes, E., Ubertosi, M., Munier-Jolain, N., Nicolardot, B., Bonnet, C., Moeys, J., Larsbo, M., Pot, V., Bedos, C., Benoit, P., Barriuso, E., 2024. Measurement and modelling of water flows and pesticide leaching under low input cropping systems. Sci. Total Environ. 957, 177607. https://doi.org/10.1016/j.scitotenv.2024.177607.

Marín-Benito, J.M., Alletto, L., Barriuso, E., Bedos, C., Benoit, P., Pot, V., Mamy, L., 2018. Pesticide fate modelling in conservation tillage: simulating the effect of mulch and cover crop on S-metolachlor leaching. Sci. Total Environ. 628–629, 1508–1517. https://doi.org/10.1016/j.scitotenv.2018.02.144.

Marín-Benito, J.M., Mamy, L., Carpio, M.J., Sánchez-Martín, M.J., Rodríguez-Cruz, M.S., 2020. Modelling herbicides mobility in amended soils: calibration and test of PRZM and MACRO. Sci. Total Environ. 717, 137019. https://doi.org/10.1016/j.scitotenv.2020.137019.

Martins, J.D., Rodrigues, G.C., Paredes, P., Carlesso, R., Oliveira, Z.B., Knies, A.E., Petry, M.T., Pereira, L.S., 2013. Dual crop coefficients for maize in southern Brazil: model testing for sprinkler and drip irrigation and mulched soil. Biosyst. Eng. 115 (3), 291–310. https://doi.org/10.1016/j.biosystemseng.2013.03.016.

Mottes, C., Lesueur-Jannoyer, M., Le Bail, M., Malézieux, E., 2014. Pesticide transfer models in crop and watershed systems: a review. Agron. Sustain. Dev. 34 (1), 229–250. https://doi.org/10.1007/s13593-013-0176-3.

Ochsner, T.E., Albrecht, K.A., Schumacher, T.W., Baker, J.M., Berkevich, R.J., 2010. Water balance and nitrate leaching under corn in kura clover living mulch. Agron. J. 102 (4), 1169–1178. https://doi.org/10.2134/agronj2009.0523.

Odhiambo, L.O., Irmak, S., 2012. Evaluation of the impact of surface residue cover on single and dual crop coefficient for estimating soybean actual evapotranspiration. Agric. Water Manag. 104, 221–234. https://doi.org/10.1016/j.agwat.2011.12.021.

Ravichandran, M., Samiappan, S.C., Pandiyan, R., Velu, R.K., 2022. Improvement of crop and soil management practices through mulching for enhancement of soil fertility and environmental sustainability: a review. J. Exp. Biol. Agric. Sci. 10 (4), 4. https://doi.org/10.18006/2022.10(4).69712.

Ritter, A., Muñoz-Carpena, R., 2013. Performance evaluation of hydrological models: statistical significance for reducing subjectivity in goodness-of-fit assessments. J. Hydrol. 480, 33–45. https://doi.org/10.1016/j.jhydrol.2012.12.004.

Scorza Júnior, R.P., Franco, A.A., 2014. Enviromental behavior of sulfentrazone and fipronil in a Brazilian clayey latosol: field experiment and simulation. Cienc Agrotec. 38, 415–423. https://doi.org/10.1590/S1413-70542014000500001.
Scorza Júnior, R.P., Jarvis, N.J., Boesten, J.J., Van Der Zee, S.E., Roulier, S., 2007. Testing MACRO (version 5.1) for pesticide leaching in a Dutch clay soil. Pest Manag. Sci. 63 (10), 1011–1025. https://doi.org/10.1002/ps.1434.

Shumova, N., 2013. Evapotranspiration changes in the forest-steppe and steppe zones under soil mulching. J. Hydrol. Hydromech. 61 (2), 140–145. https://doi.org/10.2478/johh-2013-0019.

Siimes, K., Kämäri, J., 2003. A review of available pesticide leaching models: selection of models for simulation of herbicide fate in Finnish sugar beet cultivation. Boreal Environ. Res. 8, 31–51.

Šimůnek, J., van Genuchten, M.Th., Šejna, M., 2008. Development and applications of the HYDRUS and STANMOD software packages and related codes. Vadose Zone J. 7 (2), 587–600. https://doi.org/10.2136/vzj2007.0077.

Smith, J., Smith, P., Addiscott, T., 1996. Quantitative methods to evaluate and compare soil organic matter (SOM) models. In: Powlson, D.S., Smith, P., Smith, J.U. (Eds.),

Evaluation of Soil Organic Matter Models. Springer, pp. 181–199. https://doi.org/10.1007/978-3-642-61094-3_13.

Thevenot, M., Dousset, S., 2015. Compost effect on diuron retention and transport in structured vineyard soils. Pedosphere 25 (1), 25–36. https://doi.org/10.1016/S1002-0160(14)60073-4.

Vereecken, H., Vanderborght, J., Kasteel, R., Spiteller, M., Schäffer, A., Close, M., 2011. Do lab-derived distribution coefficient values of pesticides match distribution coefficient values determined from column and field-scale experiments? A critical analysis of relevant literature. J. Environ. Qual. 40 (3), 879–898. https://doi.org/10.2134/jeq2010.0404.

Villarreal, R., Lozano, L.A., Polich, N.G., Salazar, M.P., Bellora, G.L., Turinetto, M.J., Soracco, C.G., 2020. Influence of soil water holding and transport capacity on glyphosate dynamics in two agricultural soils from pampas region. Geoderma 376, 114566. https://doi.org/10.1016/j.geoderma.2020.114566.