# Seedling emergence vigor, establishment success, and biomass yield stability of cover crop mixtures compared to pure stands

Alexandre Wojciechowski, Célia Seassau, Lionel Alletto, Jay Ram Lamichhane

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Alexandre Wojciechowski, Célia Seassau, Lionel Alletto, Jay Ram Lamichhane. Seedling emergence vigor, establishment success, and biomass yield stability of cover crop mixtures compared to pure stands. Field Crops Research, 2026, 335, pp.110165. 10.1016/j.fcr.2025.110165. hal-05303236

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# Seedling emergence vigor, establishment success, and biomass yield stability of cover crop mixtures compared to pure stands

Alexandre Wojciechowski, Célia Seassau, Lionel Alletto, Jay Ram Lamichhane*

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

Université de Toulouse, INRAE, INPT, EI PURPAN, AGIR, Castanet-Tolosan, France

## ARTICLE INFO

**Keywords:**

Emergence rates

Post-emergence losses

Nitrogen yield

Ecosystem services

## ABSTRACT

**Context:** Cover cropping has the potential to enhance the sustainability of cropping systems across temperate regions. However, poor establishment of cover crops (CCs), often driven by unfavorable weather conditions, remain a major barrier to their adoption. Sowing CC mixtures may mitigate the risks of poor establishment compared to pure stands, thereby lowering spatio-temporal variability in biomass production, which is critical for consistent ecosystem service provision. Yet, it is unclear whether CCs establish more successfully in mixtures than in pure stands, and whether a better establishment results in greater biomass production that enhances ecosystem services.

**Objective:** We aimed to: i) analyze seedling emergence dynamics, final emergence rates, and emergence vigor (i. e., speed of emergence) in CC mixtures vs. pure stands; ii) determine the relationship between seedling establishment success and final biomass production; and iii) evaluate the effect of CC mixtures vs. pure stands on soil cover, weed suppression, nitrogen (N) catch crop, and N green manure services.

**Methods:** A 2-year field experiment (2020–2022) was conducted in Southwestern France, testing 11 pure stands and six two-species mixtures of brassicas, legumes, and grasses. CCs were sown in autumn and grown for eight months, with a fallow treatment as control. Data were analyzed using analysis of variance.

**Results:** Significant intra- ($p < 0.001$) and inter-annual ($p < 0.001$) variability was observed in CC seedling emergence dynamics. Brassica CCs showed the highest emergence vigor (77 ± 21°Cd), and their establishment success was positively correlated with final biomass yield (0.4 ± 0.2–8.6 ± 0.6 t.ha⁻¹; r = 0.61, $p < 0.001$). In contrast, legumes, such as faba bean, showed the lowest emergence vigor (147 ± 52°Cd), and their biomass yield (3.1 ± 0.1–6.8 ± 0.4 t.ha⁻¹) depended mainly on post-establishment climatic conditions. Seedling emergence vigor and establishment success did not differ significantly between CC mixtures and pure stands. Single species CC yielded more variable biomass between years, mixtures produced more stable yields (2.1 ± 0.1–9.9 ± 0.3 t. ha⁻¹). Soil cover was similar between CC pure stands (75.0 ± 0.0–100.0 ± 0.0 %) and mixtures (66.7 ± 5.3–100.0 ± 0.0 %), whereas poor establishment reduced soil cover (25.0 ± 7.9–50 ± 0.0 %). All CCs effectively suppressed weeds (up to 100 % reduction in biomass) and supplied N through scavenging and/or green manuring (up to 203 ± 18 kgN.ha⁻¹) compared to fallow.

**Implications:** In systems dominated by monocultures or short rotations under high-input management and climatic uncertainty, Brassicaceae-Fabaceae mixtures represent a promising option to ensure good establishment, stable biomass production, and delivery of key ecosystem services.

## 1. Introduction

In temperate regions, cover crops (CCs) are typically introduced for temporal diversification of conventional cropping systems (Blanco-Canqui et al., 2015; Daryanto et al., 2018; Schipanski et al., 2014; Van Eerd et al., 2023). CCs in these regions are mainly sown during the fallow period, i.e., between the harvest of one cash crop and

the sowing of the next (Justes and Richard, 2017). The key objectives of cover cropping include reduction of nitrate leaching and groundwater pollution (Delcour et al., 2013), improvement of soil properties and erosion control (Langdale et al., 1991), enhancement of soil organic matter and carbon storage (Joshi et al., 2023), and improvement of nutrient availability (Justes et al., 2012). Following termination, CCs may be incorporated into the soil or left as mulch (Blanco-Canqui et al.,

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: jay-ram.lamichhane@inrae.fr (J.R. Lamichhane).</footnote>
2015; Daryanto et al., 2018; Schipanski et al., 2014; Van Eerd et al., 2023).

In France, the fallow period may range from a few months to over eight months, depending on the cash crop sequence. In Southwestern France, where durum wheat (Triticum durum Desf.)-grain maize (Zea mays L.) rotations dominate, the fallow often extends from August to April, leaving on average only 38 % of soils covered (Agreste, 2020). Consequently, these systems face high risks of soil erosion (Le Bissonnais et al., 2001) and nitrate leaching (Constantin et al., 2015b; Di and Cameron, 2002), leading to the need for substantial mineral N fertilizer inputs (e.g., 169 and 184 kg N·ha⁻¹ applied to wheat and grain maize, respectively; Agreste, 2020).

In temperate climates, CCs most commonly grown belong to the Brassicaceae (viz. Brassicas), Fabaceae (viz. legumes), and Poaceae (viz. grasses) families (Scavo et al., 2022). Each of them provides distinct ecosystem services due to different functional traits associated with different functions in the agroecosystem (Norberg et al., 2001). Brassicas are effective catch crops and can suppress soil-borne pathogens (Michel, 2008; Morris et al., 2020). Grasses strongly scavenge soil mineral N (Langelier et al., 2021; Nouri et al., 2022), and certain species, such as rye, also provide allelopathic weed suppression. Legumes contribute N through biological fixation, offering potential to reduce mineral fertilizer use (Finney et al., 2016; Kaye et al., 2019; White et al., 2017). With the increasing cost of mineral N fertilizers, legume CCs receive increasing attention to reduce N inputs in conventional cropping systems (Tonitto et al., 2006; Wittwer and van der Heijden, 2020). Their relatively low C/N ratios until flowering enable rapid N release (< 25; Dabney et al., 2001), whereas non-legume CCs with higher C/N ratios (> 25) often mobilize N late in their cycle (Finney et al., 2016; White et al., 2017).

Because single CC species typically deliver a narrow range of services (Justes and Richard, 2017), mixtures are increasingly promoted for their functional complementarity, which can enhance ecosystem services and buffer disservices (Finney et al., 2017; Kramberger et al., 2013). Yet, despite their potential and policy incentives in the EU (e.g., EU regulation 641/2014; European Commission, 2019), CC adoption in temperate regions remains limited. Poor establishment, often due to adverse weather, is a key barrier (Feng et al., 2024; Nowak et al., 2022; Van Eerd et al., 2023). Sowing CC mixtures may mitigate this risk compared with pure stands, lowering variability in biomass and ecosystem services. delivery However, CCs in practice are most often grown in pure stands. In South-West France, legumes, grasses, and brassicas CCs planted before grain maize represent 44 %, 22 %, and 9 % of the total acreage, respectively, while only 20 % involves mixtures (Agreste, 2020).

Understanding inter- and intra-specific variability in seedling emergence vigor (i.e., speed of emergence), final emergence rate, and key causes of pre- and post-emergence losses is critical for designing mixtures with appropriate sowing rates and species proportions to ensure robust establishment. Yet, little is known about whether mixtures establish better compared with pure stands, and whether successful establishment translates into higher biomass and greater ecosystem service provision. Evidence is particularly scarce regarding inter- and intra-specific variability in tolerance to abiotic and biotic stress during heterotrophic growth (Lamichhane and Alletto, 2022). While literature reports often highlight poor CC establishment, explicit data on emergence rates, post-emergence density, and pre- and post-emergence mortality causes remain limited (Boselli et al., 2020; Liu et al., 2019; Salmerón et al., 2011), with few detailed studies (e.g., mechanical stresses and damping-off affecting white mustard, Sinapis alba L; Dornainvil et al., 2005).

Establishment and biomass yield of CC mixtures depend on several factors, including species phenology, seed size, sowing depth and method, growth period, and fertilization (Chapagain et al., 2020; Lavergne et al., 2021). Mixtures that include highly competitive species, such as Indian mustard (Brassica juncea L. Czern.) and forage radish (Raphanus sativus L. var. longipinnatus), and weaker competitors, such as

hairy vetch (Vicia villosa Roth), may combine effective weed suppression with N contributions (Halde et al., 2014; Holmes et al., 2017; Lawley et al., 2011; Smith et al., 2020). While mixtures often yield more biomass than the average of their components (i.e., overyielding), they rarely exceed the most productive pure stand (i.e., transgressive over-yielding; Lavergne et al., 2021). Importantly, mixtures tend to provide more stable biomass across years and environments than pure stands (Elhakeem et al., 2021), as their resilience increases if they contain at least one stress-tolerant species (Loreau and Hector, 2001).

To date, no experimental evidence has linked emergence vigor to biomass yield in single vs. mixtures of CC species. Therefore, the objectives of this study were to: i) analyze seedling emergence dynamics and vigor in CC mixtures and pure stands; ii) determine the relationship between seedling establishment success and biomass yield; and iii) evaluate the effect of CC mixtures compared with pure stands on soil cover, weed regulation, N-related services (catch crop and green manure). A fallow treatment was used as a baseline for comparison.

## 2. Materials and methods

### 2.1. Site description

Field trials were established at the INRAE experimental station in Auzeville-Tolosane, Occitanie-Toulouse, France (N 43° 31' 41", E 1° 30' 3") during the 2020–2021 and 2021–2022 cropping seasons. The trials were conducted in different fields in each season. The site has an oceanic climate with both Atlantic and Mediterranean influences. Soil characteristics of the experimental fields are presented in Table 1.

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

Key physico-chemical, weather, and management-related characteristics of the experimental site. Soil physicochemical characteristics were derived from classical soil analysis. Weather data were collected from the nearest meteorological station, while management information (e.g., sowing and termination dates, preceding crops) was recorded during the trial.

<table><thead><tr><th rowspan="2">Site characteristics</th><th>2020-2021</th><th>2021-2022</th></tr><tr><th>Auzeville<br>43°31'37"N<br>1°30'12"E</th><th>Auzeville<br>43°31'56"N<br>1°30'14"E</th></tr></thead><tbody><tr><td>Soil texture (clay:silt:sand in %)</td><td>29:34:37</td><td>32:28:40</td></tr><tr><td>Soil type</td><td>Clay loam</td><td>Clay loam</td></tr><tr><td>pH</td><td>7.0</td><td>8.0</td></tr><tr><td>Soil C/N</td><td>7.3</td><td>8.0</td></tr><tr><td>Soil organic matter (g/kg)</td><td>8.4</td><td>11.4</td></tr><tr><td>P<sub>2</sub>O<sub>5</sub> (g/kg)</td><td>0.06</td><td>0.04</td></tr><tr><td>K (g/kg)</td><td>0.2</td><td>0.2</td></tr><tr><td>Ca (g/kg)</td><td>14.1</td><td>19.3</td></tr><tr><td>Mg (g/kg)</td><td>2.3</td><td>1.8</td></tr><tr><td>Fe (g/kg)</td><td>0.01</td><td>0.01</td></tr><tr><td>Mn (g/kg)</td><td>0.03</td><td>0.03</td></tr><tr><td>Al (g/kg)</td><td>0.12</td><td>0.03</td></tr><tr><td>Soil mineral N at sowing (kg/ha)</td><td>23</td><td>79</td></tr><tr><td>Previous crop</td><td>Soybean</td><td>Winter wheat</td></tr><tr><td>Sowing date</td><td>10th November<br>2020</td><td>14th September<br>2021</td></tr><tr><td>Termination date</td><td>19th April 2021</td><td>28th March 2022</td></tr><tr><td>Plot size (m²)</td><td>73.2</td><td>62.2</td></tr><tr><td>Sampled area (m²)</td><td>0.64</td><td>0.64</td></tr><tr><td>Sum of rainfalls during the growth period (mm)</td><td>298</td><td>379</td></tr><tr><td>Sum of ETPP (mm)</td><td>215</td><td>235</td></tr><tr><td>Duration of CC sowing to establishment (Julian days)</td><td>20–40</td><td>7–10</td></tr><tr><td>Duration of CC growth until termination (Julian days)</td><td>121–141</td><td>186–189</td></tr><tr><td>Global radiation sum (MJ/m²)</td><td>1707</td><td>1708</td></tr><tr><td>Accumulated growing degree days *</td><td>788</td><td>1129</td></tr></tbody></table>

<footnote>* A base temperature of 4°C was used for all cover crops (Elhakeem et al., 2021; Mirsky et al., 2023; Fernandez Pulido et al., 2023)</footnote>
Before CC sowing, the field was planted with soybean (Glycine max L.) and winter wheat (Triticum aestivum L.) in the 2020–2021 and 2021–2022 cropping seasons, respectively. CCs were sown on 10th November 2020 and on 14th September 2021 at a depth of 3 cm using an experimental seeder, while they were terminated on 19th April 2021 and 28th March 2022 using a shredder or roller-crimper, followed by their incorporation into the soil with a disc harrow on the same day. No fertilizers and pesticides were applied to CCs in both years.

## 2.2. Meteorological data

Weather conditions differed markedly between the two experimental years (Fig. 1). Cumulative rainfall during the CC growing season was 298 mm in 2020–2021 and 379 mm in 2021–2022, i.e., 29 % and 17 % lower than the 10-year average (419 mm in 2021 and 460 mm in 2022). Rainfall distribution also varied: the first CC season (November-April) received 21 % less rainfall than the second season (September-March).

In the first 15 days after sowing, cumulative rainfall and average air temperature were 16 mm and 10.4°C in 2020, and 31 mm and 18.8°C in 2021. In the first two months, rainfall reached 146 mm in 2020 but only 78 mm in 2021. December and January were the wettest months in both years, with 129 and 84 mm in 2021 and 80 and 79 mm in 2022, respectively. Cumulated winter rainfall (December to February) was 253 mm in 2020–2021 and 177 mm in 2021–2022.

During spring regrowth, rainfall was lower in March-April 2021 (28 mm) than in March 2022 (64 mm). The highest average daily maximum temperature occurred in April 2021 (16.8°C) and in September 2021 (23.8°C), while the lowest average daily minimum was recorded in January 2021 (1.9°C) and in January 2022 (0.7°C). Freezing temperatures were recorded on 22 days in 2020–2021 and 26 days in 2021–2022 with minima of –5.5°C and –4.8°C, respectively. Frost did not kill any CC species in 2021, but in 200 it caused mortality in common vetch cv.Spido, faba bean, and common vetch-field pea mixture.

Due to differences in sowing date, accumulated thermal time was 1432°Cd in the first season and 1913°Cd in the second (Tb = 4°C). Because no consensus exists on base temperatures for CC vegetative growth, we used a uniform base temperature of 4°C for all but common

vetch and cereal rye, for which published values were applied (1.25 and –4.52°C, respectively; Pessotto et al., 2023). Degree-day sums were calculated from emergence to termination (Elhakeem et al., 2021; Fernandez Pulido et al., 2023; Mirsky et al., 2023).

## 2.3. Experimental design

In both years, we used a randomized complete block design with six blocks. Each block comprised 18 microplots (one per treatment), resulting in 11 pure stands, six two-species mixtures, and one fallow control (Table 2). Each microplot measured 17 m x 3.6 m (Table 1). The annual experimental layout is detailed in Fig. A.1.

The selection of Brassica-Fabaceae mixtures was based on functional trait complementarity, including rooting depth, growth rate, and nitrogen fixation capacity. For example, Indian mustard (Brassica juncea (L.) Czern.) was paired with common vetch (Vicia sativa L.) to combine rapid canopy closure and competitive nutrient uptake (Rouge et al., 2022), while biological nitrogen fixation (Parr et al., 2011). Similarly, winter radish (Raphanus sativus L. var longipinnatus) was combined with faba bean (Vicia faba L.) to exploit vertical root complementarity, as radish develops deep taproots (Kristensen and Thorup-Kristensen, 2004), whereas faba bean roots remain concentrated in upper soil layers (Jensen et al., 2010). These trait-based pairings were designed to test agroecological mechanisms such as resource partitioning, nitrogen capture, and competitive dynamics.

The pure stand treatments included three brassicas [Indian mustard (Brassica juncea (L.) Czern. cv.Etamine), white mustard (Sinapis alba L. cv.Architect), and winter radish (Raphanus sativus L. var longipinnatus cv Structurator)], five legumes [winter faba bean (Vicia faba L. cv. Nanaux and Iréna) and spring faba bean (Vicia faba L. cv.Organdi and Scuro), common vetch (Vicia sativa L. cv.Spido and Veronica), and purple vetch (Vicia bengalensis L. cv.Titane)], and one grass [cereal rye (Secale cereale L. cv.Wastauro)]. Mixture treatments comprised: four Brassicaceae-Fabaceae mixtures [Indian mustard-faba bean (cv. Etamine-Nanaux), one winter radish-faba bean (cv. Structurator-Nanaux), and two Indian mustard-common vetch mixtures (cv. Etamine-Spido and Etamine-Veronica)]; one legume-legume mixture

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Monthly mean air temperature (minimum, average, and maximum), cumulative rainfall, and reference evapotranspiration (Penman, ETPP) from the start (November 2020) to the end (September 2022) of the experiment in Auzeville-Tolosane, France (a). Seedbed minimum, average, and maximum temperatures from cover crop sowing to the end of the emergence phase in 2020 and 2021 (b). Climatic data were extracted from the INRAE CLIMATIK platform (v.2.2.10).</figcaption></figure>
<caption>**Table 2**</caption>

Cover crop species grown in pure stands and mixtures during the two experimental years (2020 and 2021), with their key characteristics. TSW: thousand seeds weight; nd: no data; \*species only included in mixtures, not as pure stands. In all mixtures, each species was sown at 50 % of its pure stand density.

<table><thead><tr><th>Botanical family</th><th>Species (cultivar)</th><th>TSW (g)</th><th>Germination rate (%)</th><th>Plant density (plants. m<sup>-2</sup>)</th><th>Seeding rate (kg. ha<sup>-1</sup>)</th></tr></thead><tbody><tr><td rowspan="10">Fabaceae</td><td>Winter faba bean (Organdi/Diva)</td><td>2020</td><td>2021</td><td>2020</td><td>2021</td><td>2020</td><td>2021</td></tr><tr><td>Winter faba bean (Iréna)</td><td>462</td><td>418</td><td>89</td><td>100</td><td>45</td><td>45</td></tr><tr><td>Spring faba bean (Nanaux)</td><td>542</td><td>514</td><td>91</td><td>100</td><td>45</td><td>45</td></tr><tr><td>Spring faba bean (Scuro)</td><td>212</td><td>210</td><td>100</td><td>100</td><td>45</td><td>45</td></tr><tr><td>Common vetch (Veronica)</td><td>275</td><td>311</td><td>100</td><td>100</td><td>45</td><td>45</td></tr><tr><td>Common vetch (Spido)</td><td>54.2</td><td>53</td><td>100</td><td>80</td><td>80</td><td>80</td></tr><tr><td>Purple vetch (Titane)</td><td>69.4</td><td>67</td><td>100</td><td>100</td><td>80</td><td>80</td></tr><tr><td>Fodder pea* (Arkta)</td><td>45.3</td><td>37</td><td>100</td><td>100</td><td>80</td><td>80</td></tr><tr><td>White mustard (Architect)</td><td>nd</td><td>100</td><td>nd</td><td>100</td><td>nd</td><td>80</td></tr><tr><td>Indian mustard (Etamine)</td><td>8.6</td><td>8.6</td><td>100</td><td>70</td><td>80</td><td>80</td></tr><tr><td rowspan="2">Brassicaceae</td><td>Winter radish (Daïkon CS)</td><td>3</td><td>2.6</td><td>100</td><td>100</td><td>80</td><td>80</td></tr><tr><td>Cereal rye (Wastauro)</td><td>14.4</td><td>14.4</td><td>75</td><td>100</td><td>60</td><td>60</td></tr><tr><td>Poaceae</td><td>Faba bean-Indian mustard (Nanaux-Etamine)</td><td>nd</td><td>16.2</td><td>nd</td><td>100</td><td>nd</td><td>200</td></tr><tr><td>Hydrophyllaceae</td><td>Faba bean-Winter radish (Nanaux-Daïkon CS)</td><td>nd</td><td>2</td><td>nd</td><td>70</td><td>nd</td><td>180</td></tr><tr><td rowspan="4">Brassicaceae-Fabaceae</td><td>Faba bean-Indian mustard (Veronica-Etamine)</td><td>nd</td><td>nd</td><td>nd</td><td>nd</td><td>50 % of each species</td><td>50 % of each species</td></tr><tr><td>Common vetch-Indian mustard (Spido-Etamine)</td><td>nd</td><td>nd</td><td>nd</td><td>nd</td><td>50 % of each species</td><td>50 % of each species</td></tr><tr><td>Common vetch-Indian mustard (Veronica-Spido)</td><td>nd</td><td>nd</td><td>nd</td><td>nd</td><td>50 % of each species</td><td>50 % of each species</td></tr><tr><td>Fodder pea-Common vetch (Arkta-Spido)</td><td>nd</td><td>nd</td><td>nd</td><td>nd</td><td>50 % of each species</td><td>50 % of each species</td></tr><tr><td>Fabaceae-Fabaceae</td><td>Faba bean-Phacelia (Iréna-Maja)</td><td>nd</td><td>nd</td><td>nd</td><td>nd</td><td>50 % of each species</td><td>50 % of each species</td></tr><tr><td>Control</td><td>Bare fallow</td><td>nd</td><td>nd</td><td>nd</td><td>nd</td><td>50 % of each species</td><td>50 % of each species</td></tr></tbody></table>

[common vetch-field pea (Pisum sativum L. cv.Arkta)]; and one legume-non legume mixture [faba bean-phacelia (Phacelia tanacetifolia Benth. cv.Maja)].

Seed material varied slightly across years. In 2021–2022, faba bean cv.Organdi was replaced by cv.Diva (with similar characteristics), and winter radish cv Structurator was commercialized as Daïkon CS. The sowing density in mixtures followed the substitutive design, with each species sown at 50 % of its pure stand density (Table 2). Densities were adjusted based on germination rates, determined by laboratory tests (25 seeds/Petri dish, four replicates, incubated at 25 ± 1°C with 8 mL water). Most species showed 100 % germination, except for faba bean cv.Diva (89 %), faba bean cv.Irena (91 %), and winter radish (75 %) in 2021, and common vetch cv. Veronica (80 %), white mustard (70 %), and phacelia (70 %) in 2022 (Table 2). Seeds were not treated.

## 2.4. Seedbed characterization and soil sampling

Before CC sowing, the seedbed structure was assessed based on the percentage of soil aggregates > 20 mm (Lamichhane et al., 2021). Seedbed aggregate size, mass, roughness, and spatial distribution influence the seed-soil contact and the path of emerging seedlings, thereby affecting emergence success (Dürr and Aubertot, 2000). In both years, the seedbed was coarse, with > 25 % of aggregates exceeding 20 mm. High residue levels from soybean and wheat harvests contributed to this coarse structure (Fig.A.2). Depending on their density, both surface and incorporated residues can act as mechanical obstacles to emergence, with incorporated residues having stronger effects (Dorsainvil et al., 2005).

Soil mineral Nitrogen (SMN) was measured before sowing and after CC termination. Before sowing, five random samples were collected per block at three depths (0–30, 30–60, and 60–90 cm). After CC termination, five random samples were collected per microplot at the same depths. Samples from each depth were pooled at the block level (before sowing) or the microplot level (after termination) and analyzed at the INRAE Occitanie-Toulouse laboratory. SMN (NO3- and NH4 + ) and soil water content (SWC) were determined. SMN was quantified via KCl extraction followed by colorimetric analysis using a continuous flow autoanalyzer (Skylar 51,000, Skalar analytic, Erkelenz, Germany; Standard NF ISO 14256–2).

Immediately after CC sowing, soil temperature and moisture sensors (TEROS12, METER, Inc. USA) were installed in three microplots (one

per block; Fig.A.1). Sensors were placed at three depths (-3cm, -5 cm, and -10 cm; three sensors per depth) to record hourly temperatures and volumetric water content until CC emergence. Hourly rainfall and air temperature data were obtained from an on-site automatic weather station.

Degree-day accumulation for the CC establishment phase (germination to emergence) was calculated using species-specific base temperature for germination (Tribouillois et al., 2016b) and soil temperatures at the sowing depth (Fig. 1b). For faba bean, since base temperature differ by cultivar (0°C and for cv.LAURA seed weight 443 g; 1.2°C and 360 g for cv.SSNS, seed weight 360 g), we applied the average value of 0.6°C to all cultivars. Similarly, a base temperature of 1.1°C was used for fodder pea.

## 2.5. Seedling emergence dynamics and emergence vigor

Seedling emergence was recorded on six 3-m linear transects/microplot (6 replicates/treatment; 3 m²/microplot), corresponding to 36 observations per treatment. A seedling was considered emerged when its first leaves (cotyledons or coleoptile) appeared above the seedbed surface (Fehr et al., 1977). The counting was performed every two days until the number of emerged seedlings stabilized for three consecutive observations.

At each counting date, the number of emerged seedlings was converted into plant density (plants.m⁻²) and emergence rate (%) based on the actual sowing density. Emergence dynamics were modeled for each replicate using a Gompertz function (Raveneau et al., 2011):

$$ \text{Emergence rate}(t)_{\text{Simulated}} = a * \exp\left(-\left(\frac{b}{c}\right) * \exp(-c * t)\right) \quad (1) $$

Where *t* is the thermal time (degree days), *a* is the final emergence rate (%), and *b* and *c* are the curve parameters. The quality of model fitting was evaluated using root mean square error (RMSE; Eq.2) and model efficiency (EF; Eq.3).

$$ \text{RMSE} = \text{mean}\left(\left(\text{Emergence}_{\text{observed}}(t) - \text{Emergence}_{\text{simulated}}(t)\right)^2\right) \quad (2) $$

$$ \text{EF} = 1 - \frac{\sum (\text{Emergence}_{\text{observed}} - \text{Emergence}_{\text{simulated}})^2}{\sum (\text{Emergence}_{\text{observed}} - \text{mean}(\text{Emergence}_{\text{observed}}))^2} \quad (3) $$

The speed of seedling emergence was quantified as the thermal time
required to reach 50 % emergence (TT50 % emergence) based on the fitted Gompertz model.

## 2.6. Causes of non-emergence and post-emergence damage

The causes of non-emergence were identified by adapting a previously developed method (Lamichhane et al., 2020a; Table A.1). In each microplot, three points without seedling emergence were chosen, and soil was excavated to retrieve non-germinated seeds or non-emerged seedlings. Once the seeds or seedling parts were found, their status was annotated, and the possible causes of non-emergence were recorded.

Post-emergence seedling damage was assessed at the end of the emergence phase by adapting another visual diagnostic key (Lamichhane, 2020b; Table A.2). Several abiotic and biotic stresses were specifically considered.

* Non-optimal sowing depth: since the optimal sowing depth for faba beans is 5–8 cm (Siddique and Loss, 1999), shallow sowing depth (< 1 cm) was considered highly susceptible to bird predation.

* Soil clods > 30 mm (longest axis) were considered potential physical barriers to seedling emergence (Dürr and Aubertot, 2000).

* Soil surface crusting: although crust strength was not measured, crust layers > 2 mm were considered sufficient to hinder seedling emergence (Awadhwal and Thierstein, 1985).

* Waterlogging: this was observed when water was observed on the soil surface in association with decayed and rotted seedlings (Striker, 2012).

At the end of the CC growing periods (April 2021 and March 2022), canopy cover was visually estimated and expressed as a percentage of soil surface covered. At the same time, canopy height of both mixtures and pure stands was measured.

## 2.7. Biomass harvest

Before CC termination, aboveground biomass was harvested from two quadrats (0.64 m² each) in the microplot. For brassica species, both above- and below-ground biomass was harvested. In the case of winter radish, plants were separated into three components: aerial biomass (leaves and stems), above-ground root (portion visible above the soil surface), and below-ground root (portion embedded in the soil).

For legumes and grasses, only aboveground biomass was harvested due to the difficulty of retrieving the intact root systems. Their below-ground biomass was estimated using published root-to-shoot ratio coefficients (k_Br; Table A.3). These coefficients represent the proportion of belowground biomass relative to aboveground biomass.

Weeds were harvested from both CC and fallow treatments to quantify weed suppression by CCs (Blanco-Canqui et al., 2015). Weed pressure was to be spatially homogeneous within each experimental block (Mahé et al., 2022). Only weed biomass was measured, instead of the weed density.

In CC mixtures, biomass was separated by species to determine species-specific contributions. For each sample, plant counts were recorded to determine final stand density. Plant height was measured on six representative plants per plot in pure stands, whereas in mixtures, canopy height was measured at six random points per microplot in addition to six representative plants. Foliar area was recorded for the same six representative plants and used to calculate the leaf area index (LAI).

Biomass samples were weighed fresh and then oven-dried at 80 ± 1°C for 48 h to determine dry matter yield. Dried samples of CCs and weeds were ground, and one sub-sample per replicate was analyzed for total carbon (C) and nitrogen (N) concentrations at the INRAE Occitanie-Toulouse laboratory using the Dumas combustion method (Elementar MicroVario Cube, Germany).

Total plant N and C contents (kg.ha⁻¹) were calculated as:

$$N_{\text{content}} = \%N_{\text{plant}} * \text{Biomass}_{\text{dry}} \quad (4)$$

$$C_{\text{content}} = \%C_{\text{plant}} * \text{Biomass}_{\text{dry}} \quad (5)$$

The C/N ratio was calculated by dividing total C content by total N content.

## 2.8. Comparison of cover crop mixtures versus pure stands

To evaluate the effect of Brassicaceae-Fabaceae mixtures compared to pure stands, we calculated the partial land equivalent ratio (LERp) for each species (Garnier et al., 1997; Willey and Osiru, 1972), as follows:

$$LERp = \frac{\text{Yield}_{\text{MC}}}{\text{Yield}_{\text{SC}}} \quad (6)$$

Where YieldMC and YieldSC are the biomass yield of the same species in a mixture and a pure stand, respectively.

The land equivalent ratio (LER) of a mixture was then obtained by summing the LERp values of the two component species as follows:

$$LER = LER_{\text{Brassica}} + LER_{\text{Legume}} \quad (7)$$

We compared LERp to a classical reference ($C_{\text{ref}}$ = 0.5) for a 50 %-50 % mixture and to an adapted reference ($A_{\text{ref}}$) calculated as:

$$A_{\text{ref}} = \frac{\text{Density}_{\text{Mix}}}{\text{Density}_{\text{Single}}} \quad (8)$$

where DensityMix and DensitySingle are the plant densities of a species in a mixture and a pure stand, respectively. We thus obtained a classical LER, based on comparison with $C_{\text{ref}}$, and a real LER, based on comparison with $A_{\text{ref}}$.

An LER > 1 indicates that the mixture outperformed pure stands due to complementarity or facilitation. An LER < 1 suggests reduced performance caused by competition. An LER = 1 suggests no difference between mixtures and pure stands.

To assess whether mixtures outperformed pure stands, we calculated two levels of overyielding (Garnier et al., 1997). i) overyielding: mixture biomass was compared with the mean biomass of the component species grown in pure stands; ii) transgressive overyielding: mixture biomass was compared with the biomass of the most productive component species grown in pure stands (Garnier et al., 1997; Wendling et al., 2017).

## 2.9. Statistical analyses

All statistical analyses were performed with R software (R Core Team, 2023). Pearson's correlation matrices were generated using the "corrplot" package (Wei and Simko, 2021) to assess the correlation between seedling establishment variables and biomass productivity for each CC species and botanical family. Graphical plots were created with the "ggplot2" package (Wickham, 2016).

The significance of fixed effects was determined using analysis of variance (ANOVA), with a significance threshold of $p < 0.05$. When ANOVA results were significant, pairwise comparisons were performed with Tukey's HSD test using the "agricolae" package (de Mendiburu, 2021). Model assumptions of normality and homogeneity in variances were checked graphically (Zuur et al., 2009).

# 3. Results

## 3.1. Seedling emergence dynamics and emergence vigor

Final emergence rates of CC pure stands and mixtures over the two growing seasons are reported in Table 3. Final plant densities were significantly higher in 2021–2022 than in 2020–2021 ($p < 0.001$), both
<caption>**Table 3**</caption>

Final emergence rate (%) of cover crops grown in pure stands and as species mixtures during 2020–2021 and 2021–2022. Values are means ± standard error (n = 6). -: no data collected.

<table><thead><tr><th rowspan="2">Botanical family</th><th rowspan="2">Cover crop</th><th colspan="2">Final emergence rate (%)</th></tr><tr><th>2020–2021</th><th>2021–2022</th></tr></thead><tbody><tr><td rowspan="4">Brassicaceae</td><td>White mustard (Architect)</td><td>28.1 ± 18.0 e</td><td>73.0 ± 15.9 ab</td></tr><tr><td>Indian mustard (Etamine)</td><td>37.7 ± 16.9 cde</td><td>96.1 ± 1.9 ab</td></tr><tr><td>Winter radish (Daikon CS)</td><td>77.5 ± 9.4 ab</td><td>97.9 ± 2.0 a</td></tr><tr><td>Spring faba bean (Nanaux)</td><td>79.6 ± 6.3 ab</td><td>74.6 ± 5.2 ab</td></tr><tr><td rowspan="8">Fabaceae</td><td>Spring faba bean (Scuro)</td><td>83.2 ± 4.2 ab</td><td>78.1 ± 3.0 ab</td></tr><tr><td>Winter faba bean (Diva)</td><td>67.9 ± 7.2 abcd</td><td>88.3 ± 2.7 ab</td></tr><tr><td>Winter faba bean (Irena)</td><td>65.1 ± 5.5 abcde</td><td>82.7 ± 3.5 ab</td></tr><tr><td>Common vetch (Spido)</td><td>90.5 ± 1.5 ab</td><td>75.1 ± 15.7 ab</td></tr><tr><td>Common vetch (Veronica)</td><td>66.9 ± 3.2 abcd</td><td>91.4 ± 2.9 ab</td></tr><tr><td>Purple vetch (Titane)</td><td>84.2 ± 2.5 ab</td><td>90.5 ± 2.4 ab</td></tr><tr><td>Cereal rye (Wastauro)</td><td>-</td><td>75.7 ± 5.7 ab</td></tr><tr><td>Brassicaceae-Fabaceae</td><td>Indian mustard-Spring faba bean</td><td>35.0 ± 4.0 de</td><td>82.7 ± 19.8 ab</td></tr><tr><td></td><td>Indian mustard (Etamine)</td><td>100.0 ± 11.3 a</td><td>87.6 ± 15.8 ab</td></tr><tr><td></td><td>Winter radish-Spring faba bean</td><td>53.2 ± 6.6 bcde</td><td>94.8 ± 5.2 ab</td></tr><tr><td></td><td>Winter radish (Daikon CS)</td><td>100.0 ± 7.0 a</td><td>76.4 ± 4.5 ab</td></tr><tr><td></td><td>Spring faba bean (Nanaux)</td><td></td><td></td></tr><tr><td></td><td>Indian mustard-Common vetch</td><td>73.4 ± 6.1 abc</td><td>78.9 ± 16.0 ab</td></tr><tr><td></td><td>Indian mustard (Etamine)</td><td>88.9 ± 1.6 ab</td><td>93.9 ± 2.5 ab</td></tr><tr><td></td><td>Common vetch (Spido)</td><td></td><td></td></tr><tr><td></td><td>Indian mustard-Common vetch</td><td>68.5 ± 4.0 abcd</td><td>96.8 ± 3.2 a</td></tr><tr><td></td><td>Indian mustard (Etamine)</td><td>72.6 ± 4.8 abcd</td><td>88.5 ± 5.1 ab</td></tr><tr><td></td><td>Common vetch (Veronica)</td><td></td><td></td></tr><tr><td>Fabaceae-Hydrophyllaceae</td><td>Winter faba bean-Phacelia</td><td>-</td><td>80.2 ± 5.6 ab</td></tr><tr><td></td><td>Winter faba bean (Irena)</td><td>-</td><td>72.8 ± 7.4 b</td></tr><tr><td>Fabaceae-Fabaceae</td><td>Common vetch-Field pea</td><td>-</td><td>85.0 ± 4.7 ab</td></tr><tr><td></td><td>Common vetch (Spido)</td><td>-</td><td>97.7 ± 1.2 a</td></tr><tr><td></td><td>Field pea (Arkta)</td><td>-</td><td>&lt; 0.001</td></tr><tr><td>p-value</td><td></td><td>&lt; 0.001</td><td>&lt; 0.001</td></tr></tbody></table>

for CC pure stands and mixtures. Final emergence rates differed significantly between pure stands and species in mixtures in both 2020–2021 (*p* < 0.001) and 2021–2022 (*p* < 0.001).

Brassica CCs, which showed the lowest emergence in 2020–2021 under unfavorable climatic conditions, exhibited markedly improved establishment in 2021–2022, whereas legumes maintained relatively stable emergence across both years. In 2020–2021, the lowest emergence rates were observed in white mustard (28.1 ± 18.0 %) and Indian mustard (37.7 ± 16.9 %) pure stands, as well as Indian mustard in the Indian mustard-Spring faba bean mixture (35.0 ± 4.0 %). By contrast, Indian mustard showed higher emergence in mixtures with common

vetch (73.4 ± 6.1 % with cv.Spido; 68.5 ± 4.0 % with cv.Veronica). Winter radish emerged better in pure stands (77.5 ± 9.4 %) than in the Winter radish-Spring faba bean mixture (53.2 ± 6.6 %). Across both years, legumes in pure stands and mixtures consistently displayed higher emergence rates (65.1 ± 5.5–100 ± 7.0 %) compared with Brassicas. In 2021–2022, emergence rates of all CCs exceeded 50 % and were significantly higher than in 2020–2021, especially for the Brassicas. The lowest emergence rates were observed in the Winter faba bean-Phacelia mixture (72.8 ± 7.4 %).

Parameters of the Gompertz function fitted to CC emergence are presented in Table A.4. Emergence duration was significantly longer in 2020–2021 (20–40 days, depending on species) than in 2021–2022 (7–12 days; *p* < 0.001; Fig.A.3). The thermal time to 50 % emergence was significantly higher in 2021–2022 than in 2020–2021 (*p* < 0.001), with mean values of 153 and 134 °Cd, respectively (Fig. 2 and Table 4). In both years, Brassicas emerged most rapidly (20 days in 2020–2021; 7 days in 2021–2022; Fig.A.3), while Spring and Winter faba beans were the slowest (30 days in 2020–2021; 10 days in 2021–2022).

For both years, Brassicas and cereal rye showed the lowest TT50 % (77 ± 21 and 102 ± 23 °Cd, in 2020–2021 and 2021–2022, respectively; Table 4), whereas legumes had the highest TT50 % (147 ± 52 °Cd; Table 4). Variation in emergence duration among treatments was lower in 2021–2022 (5 days of gap; Fig.A.3) than in 2020–2021 (20 days of gap; Fig.A.3). Among Brassicas, winter radish had a lower TT50 % (54 ± 22 °Cd; Table 4) than white mustard (107 ± 18 °Cd; Table 4) and Indian mustard (70 ± 19 °Cd; Table 4). Among legumes, common vetches had a lower TT50 % (97 ± 24 and 98 ± 15 °Cd; Table 4) than purple vetch (129 ± 27 °Cd; Table 4) and Spring and Winter faba beans (from 179 ± 26–199 ± 26 °Cd; Table 4). No significant intraspecific difference was observed in the TT50 % between common vetches and faba beans (*p* = 0.385).

## 3.2. Causes of non-emergence and post-emergence damage

In 2020–2021, waterlogging was the key cause of non-emergence for white mustard and Indian mustard. For other species, the identification of non-emergence causes was not possible due to widespread waterlogging.

In 2021–2022, shallow sowing depth (0–1 cm) was the main cause of non-emergence for faba bean cultivars Nanaux, Irena, and Diva, accounting for 18 % of non-emerged seedlings on average, and within this group, a large proportion of seeds (60–80 %) were not found, most likely due to bird predation. Only faba bean cv.Scuro showed marked sensitivity to biotic stress, with 20 % of seedlings failing to emerge. Cereal rye was most affected by soil clods > 30 mm, which caused 25 % of seedling losses. For White mustard, combined effects of biotic stress (especially wireworms; Agriotes spp.), and soil surface crusting (> 2 mm thickness) led to 27 % emergence losses. Among vetches, common vetch cv.Spido suffered higher emergence losses (25 %) than the common vetch cv. Veronica (9 %) and purple vetch (10 %).

Post-emergence damage also varied significantly between years (*p* < 0.001). In 2020–2021, waterlogging due to prolonged standing water caused severe seedling mortality, ranging from 58 % to 100 % in white mustard (72 % on average) and Indian mustard (62 % on average) in pure stands. In several replicates, seedlings of Indian mustard (two replicates) and white mustard (three replicates) completely perished soon after emergence due to waterlogging (Fig. 2). Winter radish, faba bean, and common vetch treatments also showed reduced plant density 30 days post-emergence, though to a minor extent. Waterlogging additionally caused post-emergence mortality in mixtures: 65 % in Indian mustard-faba bean and 47 % in winter radish-faba bean. By contrast, mortality was lower in Indian mustard-common vetch mixtures (27 % for cv.Spido and 31 % for cv.Veronica).

Other biotic stresses contributed to post-emergence damage. Slugs caused 5–10 % seedling loss depending on CC species. Flea beetles (Altica spp.) affected brassica CCs in both years, with incidence of 15 %
<figure><img src="image_5.png" /><figcaption>Fig. 2. Emergence dynamics of cover crop pure stands (a) and mixtures (b) during the 2020–2021 and 2021–2022 growing seasons. Thermal time was calculated using a species-specific base temperature. Points indicate individual replication values (n = 6), and error bars represent standard errors of treatment means. CV: cereal rye; WM: white mustard; IM: Indian mustard; WR: winter radish; CV: common vetch; SFB: spring faba bean; WFB: winter faba bean; PH: phacelia; FP: field pea.</figcaption></figure>

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

Thermal time required to reach 50 % of seedling emergence (TT50 % emergence) of cover crops grown in pure stands and as species in mixtures during 2020–2021 and 2021–2022. Values are means ± standard error (n = 6). :- no data collected.

<table><thead><tr><th rowspan="2">Cover crops</th><th colspan="2">TT50 % Emergence<br>°Cd</th></tr><tr><th>2020-2021</th><th>2021-2022</th></tr></thead><tbody><tr><td>Control (Fallow)</td><td>-</td><td>-</td></tr><tr><td>White mustard (Architect)</td><td>121 ± 13 b</td><td>94 ± 20 bc</td></tr><tr><td>Indian mustard (Etamine)</td><td>64 ± 21c</td><td>76 ± 14 bc</td></tr><tr><td>Winter radish (Daikon CS)</td><td>53 ± 23c</td><td>55 ± 21c</td></tr><tr><td>Spring faba bean (Nanaux)</td><td>195 ± 28 a</td><td>163 ± 24 a</td></tr><tr><td>Spring faba bean (Scuro)</td><td>226 ± 28 a</td><td>173 ± 20 a</td></tr><tr><td>Winter faba bean (Irena)</td><td>216 ± 27 a</td><td>170 ± 22 a</td></tr><tr><td>Winter faba bean (Diva)</td><td>224 ± 15 a</td><td>172 ± 28 a</td></tr><tr><td>Common vetch (Spido)</td><td>98 ± 17 b</td><td>96 ± 26 bc</td></tr><tr><td>Common vetch (Veronica)</td><td>95 ± 17 b</td><td>102 ± 12 b</td></tr><tr><td>Purple vetch (Titane)</td><td>127 ± 28 b</td><td>132 ± 27 b</td></tr><tr><td>Cereal rye (Wastauro)</td><td>-</td><td>102 ± 23 b</td></tr><tr><td>Spring faba bean-Indian mustard (Nanaux-Etamine)</td><td></td><td></td></tr><tr><td>Indian mustard</td><td>66 ± 11c</td><td>70 ± 12 bc</td></tr><tr><td>Spring faba bean</td><td>214 ± 28 a</td><td>165 ± 13 a</td></tr><tr><td>Spring faba bean-Winter radish</td><td></td><td></td></tr><tr><td>Winter radish</td><td>53 ± 23c</td><td>61 ± 21 bc</td></tr><tr><td>Spring faba bean</td><td>202 ± 28 a</td><td>169 ± 29 a</td></tr><tr><td>Common vetch-Indian mustard</td><td></td><td></td></tr><tr><td>Indian mustard</td><td>62 ± 12c</td><td>72 ± 23 bc</td></tr><tr><td>Common vetch (Spido)</td><td>97 ± 24 b</td><td>96 ± 19 bc</td></tr><tr><td>Common vetch-Indian mustard</td><td></td><td></td></tr><tr><td>Indian mustard</td><td>70 ± 18c</td><td>77 ± 12 bc</td></tr><tr><td>Common vetch (Veronica)</td><td>101 ± 27 b</td><td>103 ± 29 b</td></tr><tr><td>Winter faba bean-Phacelia (Irena-Maja)</td><td></td><td></td></tr><tr><td>Winter faba bean</td><td>-</td><td>158 ± 15 a</td></tr><tr><td>Phacelia</td><td>-</td><td>94 ± 29 bc</td></tr><tr><td>Common vetch-Field pea (Spido-Arkta)</td><td></td><td></td></tr><tr><td>Common vetch (Spido)</td><td>-</td><td>95 ± 20 bc</td></tr><tr><td>Field pea (Arkta)</td><td>-</td><td>114 ± 23 b</td></tr><tr><td>p-value</td><td>&lt; 0.001</td><td>&lt; 0.001</td></tr></tbody></table>

and 100 % in 2020–2021 and 2021–2022, respectively, although the damage was not severe. In mixtures, flea beetle incidence on Brassicas was lower (60 %) than in pure stands (100 %). Wild hare (*Lepus euro-paeus*) caused 20 % of post-emergence mortality in common vetches.

### 3.3. Soil cover

Results of soil cover by CC pure stands and mixtures are reported in Table 5 and Fig. A.4. CCs were grown for 161 days (1432 °Cd) in 2020–2021 and for 196 days (1913 °Cd) in 2021–2022. Soil cover was significantly lower in 2020–2021 than in 2021–2022 (p < 0.001).

In both years, significant differences in spring soil cover were observed among CC species and treatments (p < 0.001). Brassica CCs provided poor soil cover in 2020–2021 but reached levels comparable to

<caption>**Table 5**</caption>

Final soil cover rate (%) of cover crops grown in pure stands and mixtures during 2020–2021 and 2021–2022. Soil cover in mixtures refers to the total soil cover provided by both species. Values are means ± standard error (n = 6).

<table><thead><tr><th rowspan="2">Cover crop</th><th colspan="2">Soil cover (%)</th></tr><tr><th>2020-2021</th><th>2021-2022</th></tr></thead><tbody><tr><td>White mustard (Architect)</td><td>25.0 ± 7.9 d</td><td>83.3 ± 8.3 a</td></tr><tr><td>Indian mustard (Etamine)</td><td>50.0 ± 0.0c</td><td>87.5 ± 8.5 a</td></tr><tr><td>Winter radish (Daikon CS)</td><td>45.8 ± 4.2 c</td><td>83.3 ± 8.3 a</td></tr><tr><td>Spring faba bean (Nanaux)</td><td>75.0 ± 0.0 b</td><td>62.5 ± 5.6 b</td></tr><tr><td>Spring faba bean (Scuro)</td><td>83.3 ± 5.3 ab</td><td>66.7 ± 5.3 b</td></tr><tr><td>Winter faba bean (Irena)</td><td>75.0 ± 0.0 b</td><td>66.7 ± 5.3 b</td></tr><tr><td>Winter faba bean (Diva)</td><td>75.0 ± 0.0 b</td><td>50.0 ± 0.0 b</td></tr><tr><td>Common vetch (Spido)</td><td>100.0 ± 0.0 a</td><td>100.0 ± 0.0 a</td></tr><tr><td>Common vetch (Veronica)</td><td>95.8 ± 4.2 a</td><td>100.0 ± 0.0 a</td></tr><tr><td>Purple vetch (Titane)</td><td>100.0 ± 0.0 a</td><td>100.0 ± 0.0 a</td></tr><tr><td>Cereal rye (Wastauro)</td><td>-</td><td>87.5 ± 5.6 a</td></tr><tr><td>Spring faba bean cv. Nanaux-Indian mustard</td><td>66.7 ± 5.3 b</td><td>83.3 ± 8.3 a</td></tr><tr><td>Spring faba bean cv. Nanaux-Winter radish</td><td>70.8 ± 4.2 b</td><td>79.2 ± 10 a</td></tr><tr><td>Common vetch cv. Spido-Indian mustard</td><td>87.5 ± 5.6 ab</td><td>95.8 ± 4.2 a</td></tr><tr><td>Common vetch cv. Veronica-Indian mustard</td><td>75.0 ± 0.0 b</td><td>100.0 ± 0.0 a</td></tr><tr><td>Winter faba bean cv. Irena-Phacelia</td><td>-</td><td>87.5 ± 5.6 a</td></tr><tr><td>Common vetch cv. Spido-Field pea</td><td>-</td><td>95.8 ± 4.2 a</td></tr><tr><td>p-value</td><td>&lt; 0.001</td><td>&lt; 0.001</td></tr></tbody></table>
legumes in 2021–2022. In contrast, legumes consistently displayed high soil cover in both years.

In 2020–2021, legumes nearly or fully covered the soil (90 % on average), with common vetch vs. Spido (100 %), Veronica (96 %), and purple vetch (100 %) reaching complete cover. In contrast, Brassicas provided only partial cover (Indian mustard 50 %, winter radish 46 %, and white mustard 25 %). Faba beans offered intermediate coverage (60 %), with no significant cultivar effect.

Cover crop mixtures also showed a significant effect on soil cover ($p < 0.001$). The Common vetch cv.Spido-Indian mustard mixture achieved 87 % cover, higher than the corresponding Common vetch cv. Veronica-Indian mustard mixture (75 %). The Faba bean-Winter radish mixture covered 71 % of the soil, significantly outperforming winter radish pure stands (46 %). The Faba bean-Indian mustard mixture provided low soil cover (58 %), similar to its components in pure stands (faba bean 75 %, Indian mustard 50 %).

In 2021–2022, soil cover ranged from 50 % to 100 %. On average, Brassicas, legumes, and Brassicaceae-Fabaceae mixtures covered 87 %,

75 %, and 94 % of the soil, respectively. Among legumes, faba bean cv. Diva showed significantly lower soil coverage (75 %) compared with other cultivars, while common vetch vs. Spido and Veronica again achieved complete cover (100 %). Mixtures generally provided more stable soil cover (75–100 %) than pure stands, with less variability. Mixtures including common vetch reached the highest cover (100 %), while those with faba beans also improved soil cover (85 %) compared to pure faba beans stands (60–75 %).

### 3.4. Cover crops biomass yield

Data on above- and below-ground biomass of CC pure stands, mixtures, and weeds are shown in Fig. 3 and Table A.5. Overall, CC biomass yield differed significantly between years ($p < 0.001$), with lower production in 2020–2021 than in 2021–2022 ($2.4 \pm 1.2$ and $5.1 \pm 2.6$ t DM.ha⁻¹, respectively). A significant effect of botanical family was also detected ($p = 0.003$). Brassicas and mixtures displayed very low biomass in 2020–2021 but were the most productive groups in

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Aboveground and belowground biomass of weeds and cover crop species in pure stands and mixtures, collected in April 2021 (2020–2021) and March 2022 (2021–2022). Control plots consisted of weeds only (fallow treatment). Pure stands include biomass of a single species, whereas mixtures include biomass of both species. Belowground biomass of Brassicas was sampled and weighed directly in the field, while that of other species was estimated using values from Table 3. No belowground biomass was sampled for weeds. Error bars represent standard errors of treatment means (n = 6). IM: Indian mustard; WR: winter radish; CV: common vetch; SFB: spring faba bean; WFB: winter faba bean; PH: phacelia; FP: field pea.</figcaption></figure>
2021–2022, whereas legume biomass remained relatively stable across years.

Year-to-year differences were significant for Brassicas (1.1 ± 0.4 and 7.5 ± 1.5 t DM.ha⁻¹ in 2020–2021 and 2021–2022, respectively) and for CC mixtures (2.2 ± 0.7 and 7.9 ± 1.6 t DM.ha⁻¹), but not for legumes (3.2 ± 0.8 and 3.7 ± 1.4 t DM.ha⁻¹). Among legumes, only common vetch cv. Veronica consistently yielded significantly higher biomass across both years (2.7 ± 1.0 and 5.8 ± 1.0 t DM.ha⁻¹, respectively) compared with other species.

In 2020–2021, Brassicas yielded the least biomass (0.7 ± 0.6 t DM.ha⁻¹), legumes produced the most (3.2 ± 0.8 t DM.ha⁻¹), and mixtures showed intermediate biomass (2.2 ± 0.7 t DM.ha⁻¹), with legumes contributing most of the biomass. The Indian mustard-common vetch cv. Veronica mixture yielded significantly less than the corresponding cv. Spido mixture (1.8 ± 0.4 vs. 2.9 ± 0.4 t DM.ha⁻¹; *p* < 0.001).

In 2021–2022, Brassicas were the most productive group (7.5 ± 1.5 t DM.ha⁻¹), while legumes yielded the least biomass (3.7 ± 1.4 t DM.ha⁻¹). No significant differences were observed among Brassica pure stands. Cereal rye yielded similarly to legumes (3.4 ± 0.6 t DM.ha⁻¹). Within legumes, common vetch cv. Veronica again outperformed other cultivars (5.8 ± 1.0 t DM.ha⁻¹ vs. 2.6 ± 0.6 t DM.ha⁻¹ for other common vetches, 2.9 ± 1.2 t DM.ha⁻¹ for purple vetch, and 3.8 ± 0.5 and 2.1 ± 1.0 t DM.ha⁻¹ for faba bean cvs. Nanaux and Diva, respectively). Faba bean cv. Scuro also yielded significantly more than cv. Diva (4.4 ± 0.4 vs. 2.1 ± 1.0 t DM.ha⁻¹).

Mixtures yielded biomass levels similar to pure stands. The only exception was the common vetch-fodder pea mixture, which yielded significantly less than Brassicaceae-Fabaceae mixtures (3.1 ± 0.2 t DM.ha⁻¹; *p* < 0.001). In most Brassica-legume mixtures, biomass was dominated by Brassica species, except for the Indian mustard-common vetch cv. Spido mixture, where the legume contributed more than 50 % of the total yield. In the faba bean-phacelia mixture, both species contributed equally to biomass production.

### 3.5. C and N content and C/N ratio of the cover crops

Results on C and N concentration and content of CCs, as well as their C/N ratio, are presented in Table A.5. The N content of each CC species in pure stands and mixtures is described in Fig. A.6.

Plant C concentration was significantly higher in 2020–2021 than in 2021–2022 (44 ± 1 % vs. 41 ± 3 %; *p* < 0.001). Across both years, CC mixtures acquired significantly higher C than in pure stands (1.8 ± 1.2 vs. 1.5 ± 0.9 t.ha⁻¹; *p* = 0.021). A significant year x CC composition interaction was detected (*p* < 0.001): in 2021–2022, mixtures acquired significantly more C and N than pure stands, while no such difference was observed in 2020–2021. Brassicas contributed most to C and N acquisition in 2021–2022, whereas legumes contributed consistently across both years.

In 2020–2021, no significant difference in total C was found between pure stands and mixtures (1.0 ± 0.3 vs. 1.0 ± 0.5 t.ha⁻¹; *p* = 0.271). In contrast, mixtures accumulated significantly higher C than pure stands in 2021–2022 (3.3 ± 0.7 vs. 2.3 ± 1.0 t.ha⁻¹; *p* < 0.001).

Plant N concentration was significantly higher in 2021–2022 than in 2020–2021 (1.9 % vs. 1.8 %; *p* < 0.001). Legumes, both in pure stands and mixtures, consistently had the highest N concentration (3 %; *p* < 0.001) than brassicas (1.7 %) and cereal rye (1.7 %). No significant intra-specific differences were detected in N concentration.

When biomass production was combined with N concentration, N accumulation varied significantly among botanical families across and within years (*p* < 0.001). In 2020–2021, mixtures acquired intermediate levels of N, while in 2021–2022, they acquired the highest amounts. However, no overall difference was observed between pure stands and mixtures in total N acquisition (*p* = 0.556). Intraspecific variability was only significant for common vetch in 2021–2022: cv. Veronica accumulated more N than cv. Spido, owing to its higher biomass production (*p* < 0.001).

No significant differences were detected in the C/N ratio between years (*p* = 0.673), between pure stands and mixtures (*p* = 0.680), or in the year x CC composition interaction (*p* = 0.496).

### 3.6. Biomass yield and nitrogen content of cover crop pure stands versus mixtures

In 2020–2021, only 10 out of 24 mixture replicates had an LER > 1, while in 2021–2022, almost all the mixtures exceeded this threshold (Fig. 4). The relative contribution of legumes and brassicas to LER differed between years: legumes (LERp > 0.5) were the main contributors in 2020–2021, while brassicas dominated in 2021–2022 (Figs. A.7 and A.8).

In 2020–2021, faba bean and common vetch cv. Spido benefited most in mixtures, particularly in the faba bean-winter radish and the common vetch cv. Spido-Indian mustard combinations. In contrast, no consistent advantage was observed in the faba bean-Indian mustard and the common vetch cv. Veronica-Indian mustard mixtures. Within the faba bean-winter radish mixture, both species benefited in two replicates, whereas reduced biomass was observed for both species in two replicates of the common vetch cv. Veronica-Indian mustard and one of the faba bean-Indian mustard mixtures.

In 2021–2022, common vetch benefited only in the common vetch cv. Spido-Indian mustard mixture, while Brassicas were favoured in all other mixtures. Patterns of N acquisition closely mirrored those observed for biomass yield (Fig. 4).

No significant differences were found between classical and real LER values in either year (Fig. A.9). Mixtures displayed significant over-yielding in aboveground biomass, with mean values of 1.31 ± 0.37 in 2020–2021 and 1.44 ± 0.39 in 2021–2022 (Table 6). Similarly, over-yielding in total N content was observed in both years (1.17 ± 0.35 and 1.46 ± 0.60, respectively).

Transgressive overyielding was not observed in 2020–2021 for either biomass (0.79 ± 0.19) or N content (0.70 ± 0.21). However, trans-gressive overyielding occurred in 2021–2022, with values of 1.29 ± 0.44 for biomass and 1.21 ± 0.53 for N content (Table 6).

### 3.7. Weed regulation during cover crop growth and at their termination

The effect of CCs on weed regulation is presented in Table A.5 and Table A.6. Both experimental year and CC treatment significantly influenced weed dynamics. Weed N concentration (*p* < 0.001) and N content (*p* = 0.045) varied across years (Table A.6). The weed flora composition also shifted markedly: dicotyledons dominated in 2020–2021, while grasses, particularly ryegrass (Lolium perenne L.), were dominant in 2021–2022.

Weed biomass was significantly different between CC and fallow treatment (*p* < 0.001), with a strong CC x year interaction (*p* < 0.001). In the fallow treatment, weed biomass ranged from 0.5 to 1.7 t DM.ha⁻¹, while all CC treatments reduced weed biomass to below 0.5 t DM.ha⁻¹. Brassica and legume pure stands were significantly effective (0.16 ± 0.16 and 0.22 ± 0.22 t.ha⁻¹, respectively), as were their mixtures (0.18 ± 0.18 t.ha⁻¹).

Cereal rye showed the most consistent suppression, with very low variability (0.04 ± 0.04 t.ha⁻¹) and weed biomass consistently below 0.1 t DM.ha⁻¹. Interannual differences were observed: weed biomass was lower in 2020–2021 compared to 2021–2022 in both Brassica (0.10 ± 0.10 vs. 0.22 ± 0.18 t.ha⁻¹) and legume pure stands (0.19 ± 0.19 vs. 0.25 ± 0.24 t.ha⁻¹). In contrast, CC mixtures suppressed weeds equally well across both years (*p* = 0.090).

### 3.8. Correlations between cover crop stand population and final productivity

Correlation patterns among CC traits in pure stands and mixtures are presented in Fig. 5. Biomass yield was strongly shaped by soil nutrient
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Biomass yield performance and nitrogen (N) content of four cover crop mixtures compared to their respective pure stands in April 2021 and March 2022. Overyielding and transgressive overyielding are used as indicators of performance. Each point represents an individual replicate (n = 6). The red dashed line represents the threshold value of 1, where values > 1 show an advantage of the mixture over pure stands. CV-IM: common vetch-Indian mustard; SFB-IM: spring faba bean-Indian mustard; SFB-WR: spring faba bean-winter radish. LER: Land Equivalent Ratio.</figcaption></figure>

<caption>**Table 6**</caption>

Mean transgressive overyielding and overyielding values of aboveground biomass and total N content in four cover crop mixtures compared with pure stands in April 2021 and March 2022. Values are means ± standard error (n = 6).

<table><thead><tr><th rowspan="2">Mixture</th><th rowspan="2">Year</th><th colspan="2">Dry aboveground biomass</th><th colspan="2">Total N content</th></tr><tr><th>Transgressive overyielding</th><th>Overyielding</th><th>Transgressive overyielding</th><th>Overyielding</th></tr></thead><tbody><tr><td rowspan="3">Faba bean-Indian mustard</td><td>2021</td><td>0.76 ± 0.17</td><td>1.28 ± 0.37</td><td>0.74 ± 0.19</td><td>1.32 ± 0.45</td></tr><tr><td>2022</td><td>1.36 ± 0.44</td><td>1.55 ± 0.42</td><td>1.17 ± 0.34</td><td>1.43 ± 0.43</td></tr><tr><td>Mean</td><td>1.06 ± 0.44</td><td>1.41 ± 0.40</td><td>0.98 ± 0.35</td><td>1.38 ± 0.42</td></tr><tr><td rowspan="3">Faba bean-Winter radish</td><td>2021</td><td>0.87 ± 0.23</td><td>1.40 ± 0.52</td><td>0.92 ± 0.16</td><td>1.31 ± 0.35</td></tr><tr><td>2022</td><td>1.19 ± 0.35</td><td>1.35 ± 0.32</td><td>1.23 ± 0.31</td><td>1.33 ± 0.30</td></tr><tr><td>Mean</td><td>1.03 ± 0.33</td><td>1.37 ± 0.41</td><td>1.09 ± 0.29</td><td>1.32 ± 0.30</td></tr><tr><td rowspan="3">Common vetch cv.Spido-Indian mustard</td><td>2021</td><td>0.83 ± 0.17</td><td>1.42 ± 0.27</td><td>0.62 ± 0.15</td><td>1.10 ± 0.26</td></tr><tr><td>2022</td><td>1.33 ± 0.61</td><td>1.58 ± 0.56</td><td>1.76 ± 0.60</td><td>2.10 ± 0.79</td></tr><tr><td>Mean</td><td>1.08 ± 0.50</td><td>1.50 ± 0.43</td><td>1.19 ± 0.73</td><td>1.60 ± 0.77</td></tr><tr><td rowspan="3">Common vetch cv.Veronica-Indian mustard</td><td>2021</td><td>0.70 ± 0.19</td><td>1.13 ± 0.31</td><td>0.58 ± 0.18</td><td>0.98 ± 0.32</td></tr><tr><td>2022</td><td>1.29 ± 0.45</td><td>1.30 ± 0.17</td><td>0.69 ± 0.14</td><td>1.00 ± 0.14</td></tr><tr><td>Mean</td><td>1.00 ± 0.45</td><td>1.22 ± 0.25</td><td>0.63 ± 0.16</td><td>0.99 ± 0.23</td></tr><tr><td rowspan="3">All mixtures</td><td>2021</td><td>0.79 ± 0.19</td><td>1.31 ± 0.37</td><td>0.70 ± 0.21</td><td>1.17 ± 0.35</td></tr><tr><td>2022</td><td>1.29 ± 0.44</td><td>1.44 ± 0.39</td><td>1.21 ± 0.53</td><td>1.46 ± 0.60</td></tr><tr><td>Mean</td><td>1.04 ± 0.42</td><td>1.38 ± 0.38</td><td>0.97 ± 0.48</td><td>1.32 ± 0.52</td></tr></tbody></table>
<figure><img src="image_8.png" /><figcaption>Fig. 5. Correlation matrix of cover crop traits in pure stands and mixtures, illustrating pairwise relationships among final emergence rate, plant height, soil cover, aboveground and total dry matter biomass, plant nitrogen (N) and carbon (C) concentrations, nitrogen uptake, weed biomass, weed nitrogen uptake, soil water content (SWC), and soil mineral nitrogen (SMN) dynamics. Correlation coefficients (r-values) are presented in each cell and color-coded to reflect the strength and direction of the relationship, with darker shades indicating stronger positive or negative correlations (values approaching 1 or -1), whereas lighter shades represent weaker correlations (values near 0).</figcaption></figure>

and water dynamics: it was negatively correlated with the difference in SMN between CC sowing and termination (r = -0.61), but positively correlated with the change in soil water content over the same period (r = 0.47). In turn, the SMN difference was negatively correlated with the change in soil water content (r = -0.72).

Species-specific analyses highlighted that biomass yield of white mustard, Indian mustard, winter radish, and cereal rye was correlated with the SMN difference between sowing and termination of CCs (r = 0.93, r = 0.84, r = 0.90, and r = 0.55, respectively). N acquisition in biomass was also correlated with SMN at sowing, particularly for Brassica pure stands (r = 0.78, Fig. A.12), with weaker correlations observed for brassica-legume mixtures (r = 0.64; Figure A.13) and legume pure stands (r = 0.29; Fig. A.14). Brassica CCs in pure stands and mixtures with lower N uptake relative to soil N availability showed the lowest stand densities. Likewise, cereal rye acquired less N than was available at sowing, which resulted in lower final stand densities compared to sowing densities (Fig. A.15). In contrast, most legume CCs accumulated more N in their biomass than was available in the soil at sowing, except for common vetch cv. Veronica, which showed a positive response of N uptake to increasing SMN.

Biomass yield was also positively correlated with stand establishment and canopy traits: final emergence rate (r = 0.43), final canopy height (r = 0.41), and soil cover (r = 0.51; Fig. 5). Across year x CC treatment interactions, biomass yield was strongly related to the final

canopy height (r = 0.46–0.97). For Brassicas, biomass yield was positively correlated with the final plant density, both in linear (r = 0.68, R²=0.38; Fig. A.12) and exponential model (R²=0.67). Among species, common vetch (cv. Spido and cv. Veronica), white mustard, Indian mustard, and winter radish showed the strongest correlations between final emergence rate and biomass (r = 0.42, 0.88, 0.69, 0.75, and 0.59, respectively). In contrast, no clear relationships were observed for cereal rye, purple vetch, and all four faba bean cultivars.

## 4. Discussion

### 4.1. Pre- and post-emergence seedling losses and cover crop stand establishment

Seed germination ability was generally optimal across CC species, with some exceptions. Winter radish in 2020–2021, and Indian mustard and phacelia in 2021–2022, showed less than 80 % germination. Such variability is not uncommon, as CC seed quality is often suboptimal due to the use of non-certified seed lots (Lamichhane and Alletto, 2022) that may not meet the International Seed Testing Association standards. Tribouillois et al. (2018) reported very low emergence rates of phacelia (23 %), linked to heterogeneous seed germination ability or the presence of hard seeds (Uzun and Aydin, 2004). However, phacelia in our study performed relatively well, with 70 % germination and 66 %
emergence in 2021–2022.

Differences in emergence vigor and final emergence rate were strongly related to sowing date and associated climatic conditions (Constantin et al., 2015a; Tribouillois et al., 2018). In 2020–2021, CCs were sown late in autumn under cool, wet conditions, while in 2021–2022 they were sown earlier under warmer, drier weather. Brassicas, which have higher base temperatures for germination (Tribouillois et al., 2016b), were disadvantaged in the first year but performed markedly better in the second year (Fig. A.18a,b). While sowing mixtures of species with contrasting seed sizes can sometimes reduce emergence (Tribouillois et al., 2018), mixtures in our experiments establishment well (81 % of emergence on average) and outperformed pure stands (76 %; Fig. A.19). The strong Year x CC interaction confirmed that Brassicas were more sensitive to unfavorable conditions than legumes, whereas legumes showed greater resilience across years. This highlights that mixtures buffer against climatic variability: when one species is negatively affected, the other can maintain establishment, compensating for pre- and post-emergence losses.

Both abiotic and biotic factors influenced seedling establishment. Waterlogging was the primary cause of non-emergence in 2020–2021, particularly in white and Indian mustard, consistent with previous reports of mustard sensitivity to excess of soil moisture (Dürr and Aubertot, 2000). In 2021–2022, shallow sowing depth (<1 cm) was the main cause of faba bean losses, as exposed seeds were highly vulnerable to bird predation. Soil clods (<30 mm) hindered emergence in cereal rye, while soil-borne pests and soil crusts contributed to losses in Indian mustard and winter radish. Among biotic stresses, slugs and flea beetles caused important seedling damage in Brassicas, and wild hares grazed common vetch heavily. Such damages are in line with other studies reporting Brassica susceptibility to slugs and flea beetles (White et al., 2016; Li et al., 2024). Importantly, mixtures showed greater resilience to these pressures: e.g., Indian mustard-common vetch mixtures suffered less seedling mortality than pure stands or other combinations.

Taken together, these findings underscore the importance of considering environmental conditions, biotic pressures, and management practices in CC establishment. Management strategies such as optimizing sowing date, ensuring proper sowing depth, selecting resilient cultivars, and using mixtures can reduce pre- and post-emergence losses. While high seed costs remain a concern for farmers (Bergtold et al., 2019), our study used sowing densities close to recommended levels to accelerate soil cover and maximize biomass yield (Ruis et al., 2019). This approach allowed final plant densities to approach target densities (Park et al., 2003; Reiss and Drinkwater, 2020). Moreover, higher sowing rates helped offset establishment losses, as previously demonstrated for vetch (Reiss and Drinkwater, 2020).

## 4.2. Relationships between cover crop establishment success and biomass yield

Overall, the successful establishment of CC mixtures resulted in moderate biomass yields in 2021 and high yields in 2022, underscoring their capacity to stabilize biomass production compared to pure stands. This stability highlights a key advantage of sowing mixtures, which can buffer against unfavorable climatic or biotic stresses that disproportionately affect single species.

Our results revealed strong functional group differences. Brassica productivity was closely tied to establishment success, with poor emergence or early mortality (e.g., from waterlogging in 2020–2021) leading directly to reduced biomass. In contrast, legumes demonstrated greater ability to compensate for pre- and post-emergence losses, often maintaining good biomass yields even at lower stand densities. For instance, no yield penalties were observed in faba bean despite pre-emergence losses from shallow sowing or partially unburied seeds. This aligns with findings in peas and faba beans, where delayed development or reduced emergence did not compromise biomass yield (Hughes et al., 2020; Wilczewski et al., 2022).

Nevertheless, cultivar-specific traits modulated performance. In 2021–2022, faba bean cv. Diva produced less biomass than other cultivars at similar plant densities, due to its shorter stature. Diseases and climatic stress also played critical roles: in spring 2021–2022, faba beans suffered severe foliar diseases, while common vetch cv.Spido CC, purple vetch CC, and the common vetch-field pea mixture were frost-killed, leading to early decomposition. Likewise, in 2020–2021, most white mustard and Indian mustard plants wilted in waterlogged, anoxic soils, limiting stand development.

The significant Year x CC interaction for biomass confirmed these dynamics: Brassicas and mixtures were highly penalized in 2020–2021 under unfavorable conditions, but they outperformed legumes in 2021–2022 when establishment was successful. Conversely, legumes maintained stable biomass across years, confirming their role as a more reliable option for securing a minimum level of biomass under fluctuating weather conditions.

## 4.3. Productivity of cover crop mixtures versus pure stands

The 2-year average CC biomass yield (3.5 ± 2.3 t.ha⁻¹) closely matched the average reported in a meta-analysis of 389 studies in temperate regions (3.4 ± 3.0 t.ha⁻¹; Ruis et al., 2019). However, mixture performance relative to pure stands proved highly context-dependent, as shown by the significant Year x CC interaction. Mixtures including Brassicas suffered strong yield losses in 2020–2021 but benefited from the favorable conditions in 2021–2022. In contrast, mixtures including legumes maintained a more stable yield across years.

The high performance of Brassicas in 2021–2022 was closely related to elevated soil mineral N (78.9 ± 7.1 kg N.ha⁻¹), compared with much lower levels in 2020–2021 (23.2 ± 2.8 kg N.ha⁻¹). This response is consistent with findings for non-legumes such as cereal rye and ryegrass, which accumulate substantially more N when soil N availability is high (Mueller and Thorup-Kristensen, 2001; Thorup-Kristensen, 2001). In 2021–2022, Indian mustard and winter radish showed the highest LERp values, but their associated legumes (faba bean and common vetch cv. Veronica) were negatively affected. A similar dominance of Brassicas to the detriment of legumes has been observed in pea-mustard mixtures (Tribouillois et al., 2016a). The exception was the common vetch cv. Spido-Indian mustard mixture, where vetch achieved a high LERp because it survived frost in mixtures (unlike in pure stands), allowing it to remain competitive. These results confirm that frost tolerance varies not only across species but also among cultivars and developmental stages (Besnard et al., 2011; Kalinová and Moudrý, 2003), as shown for hairy vetch (Wilke and Snapp, 2008).

The role of soil N was further highlighted by comparisons across mixtures and pure stands. In our study, high pre-sowing SMN levels without added fertilizers induced effects equivalent to N fertilization, favouring Brassicas in mixtures. When averaged across treatments, legume and non-legume mixtures did not show a non-significant complementarity, and some (e.g., common vetch cv.Spido-Indian mustard and faba bean-Indian mustard) showed strong competition. Only the faba bean-phacelia mixture yielded balanced biomass contributions, corroborating earlier findings with the pea-phacelia mixture (Wendling et al., 2017). Overall, mixtures tended to exploit abiotic resources as efficiently, or more efficiently, than pure stands (LER > 1), consistent with previous reports (Smith et al., 2014; Wortman et al., 2012). Notably, Brassicas in mixtures established more reliably and acquired more N than when sown in pure stands.

Despite the general benefits of CCs in reducing nitrate leaching compared to fallow, weeds moderated this effect in faba bean, particularly when plants were killed by chocolate spot disease (Botrytis fabae Sardina). In these cases, weeds effectively replaced faba bean biomass, reducing its catch crop service compared to grass and Brassica or cereal rye (Fig. A.20a). Even when Brassica pure stands were successfully established in 2021, their productivity was constrained by soil N availability, whereas in 2022 they achieved higher yields due to more
favorable N conditions (Thorup-Kristensen et al., 2003).

These findings stress the importance of adapting mixture design to soil fertility conditions. In Brassicaceae-Fabaceae mixtures, sowing density should be adjusted according to soil N availability. For example, in high-N soils, Brassicas can easily outcompete legumes, suggesting a higher seeding proportion of legumes would help balance competition. Conversely, in low-N soils, a greater Brassica proportion may be advantageous. However, empirical data on the effect of different sowing densities in CC mixtures remain limited (Bybee-Finley et al., 2022; Wendling et al., 2017) and require further investigation.

## 4.4. Biomass stability of cover crop mixtures versus pure stands

Associating legume and Brassica species in a mixture can secure more stable biomass yields in space and time compared to pure stands. Such stability occurs in two ways: lower spatial variability of a given mixture within the same year (Florence et al., 2019), and reduced temporal variability across years (Elhakeem et al., 2021). In our study, biomass yield variability was similar between pure stands and mixtures, but mixtures showed lower temporal variability, highlighting their buffering role against inter-annual environmental fluctuations.

We observed overyielding of mixtures in both 2021 and 2022, while transgressive overyielding was only observed in 2022. This aligns with earlier findings reporting year-to-year variability in the occurrence of transgressive overyielding (Florence and McGuire, 2020; Lavergne et al., 2021). The lack of transgressive overyielding in 2021 was explained by unfavorable conditions for Brassica establishment and growth (Wendling et al., 2019), especially in pure stands. In contrast, favorable conditions in 2022 supported Brassica productivity both in pure stands and mixtures, resulting in transgressive overyielding of Brassica-legume mixtures. This pattern corroborates previous reports that favorable environments can enhance complementarity effects in mixtures (Loreau and Hector, 2001; Wendling et al., 2019).

Evidence from the literature further highlights the context-dependence of mixture performance. For example, in two-species Brassica-legume mixtures, Tribouillois et al. (2016a) observed LER > 1, indicating that both species yielded higher biomass in mixtures than in pure stands, thereby improving nitrate catch crop and green manure services. In contrast, Smith et al. (2014) found no benefits of mixtures (LER > 1) for weed suppression, biomass stability, or subsequent cash crop productivity. These contrasting results likely reflect differences in ecosystem services targeted and the composition of mixtures: two-species mixture favouring complementarity (Tribouillois et al., 2016a) vs. more complex, five-species mixtures with potentially higher interspecific competition (Smith et al., 2014).

## 4.5. Weed regulation during the fallow period

Weed community compositions differed markedly between years, with broadleaves predominating in 2020–2021 and grasses in 2021–2022. Weed biomass in the fallow treatment was lower in 2020–2021 than in 2021–2022, likely due to higher SMN at CC sowing in 2021–2022. Weed suppression also depended on the Year x CC interaction: Brassica CCs performed poorly in 2020–2021 due to weak establishment, suppressed weeds strongly in 2021–2022 when biomass production was higher, reaching suppression levels comparable to legumes and mixtures. This suggests that the weed-suppressive ability of Brassicas is highly contingent upon favorable establishment conditions.

Consistent with previous meta-analyses (Nichols et al., 2020; Osipitan et al., 2018), CCs suppressed weeds both during growth and after termination. Weed suppression operates through different mechanisms that vary across botanical families (Blanco-Canqui et al., 2015; Lavergne et al., 2021). Although legumes are often considered less competitive than grasses (Adeux et al., 2021; Baraibar et al., 2018; Halde et al., 2014; Smith et al., 2020), we did not detect significant family-level effects. Weed regulation was similar between pure stands and mixtures,

confirming earlier findings (Osipitan et al., 2018, 2019).

Brassica pure stands suppressed weeds by competing strongly for nutrients, water, and light as previously reported (Thorup-Kristensen, 2001; Thorup-Kristensen et al., 2003). Their rapid canopy closure shaded the soil surface (Lawley et al., 2012; Brust et al., 2014), while their capacity for fast SMN uptake, especially in topsoil layers, further limited weed growth (Brust et al., 2014; Holmes et al., 2017; Lawley et al., 2012; Smith et al., 2020). Legumes, by contrast, reduced weed growth mainly via soil cover and, in frost-killed stands, through a dead mulch effect that created a physical barrier to weed germination (Osipitan et al., 2019). This effect was particularly evident in common vetch (pure and in mixture with field pea) but weaker in faba bean, which offered less cover. Moreover, severe foliar disease epidemics in 2022 caused early faba bean die-off, accelerating N mineralization and promoting weed growth in spring (Fig. A.20a). Brassica-legume mixtures combined canopy closure and rapid N uptake, reducing differences in weed suppression between pure stands and mixtures.

Weeds themselves provided an unintended service by scavenging SMN, especially grasses in legume CC plots that perished due to diseases and frost. This explains why SMN levels at CC termination were often similar between CC and fallow. Rye was particularly effective in weed suppression, consistent with previous meta-analysis (Nichols et al., 2020), through both strong canopy cover and allelopathic effects from benzoxazinoids (Macías et al., 2019; Tabaglio et al., 2008). This agrees with reports showing cereal rye reduces weed biomass more effectively than barley (Liebert et al., 2017), although another meta-analysis suggests no consistent effect of grass CCs overall (Osipitan et al., 2018).

Overall, CC biomass yield was higher in 2022, which likely enhanced weed suppression relative to 2021. This supports earlier evidence that CC biomass is the primary determinant of weed suppression (Florence et al., 2019), although not the only one (Rouge et al., 2022). Indeed, we did not always observe stronger weed suppression in the most productive CC treatments, highlighting that other factors—such as functional traits, canopy architecture, and residue effects—also mediate weed suppression (Rouge et al., 2022; Smith et al., 2020).

## 4.6. Overall performance of cover crop pure stands versus mixtures

Brassica-legume CC mixtures delivered intermediate to a high level of ecosystem services, including soil coverage, N catch crop, N green manure, and weed regulation, compared to their respective pure stands. Although mixtures did not exhibit a significantly higher LER overall, the tendency toward LER values in 2022 suggests that mixtures can enhance productivity under favorable conditions (Couëdel et al., 2019). This points to the capacity of CC mixtures to secure yield stability and maintain competitive performance relative to pure stands.

Beyond productivity, mixtures showed greater potential for C enrichment than pure stands. This is consistent with their more stable biomass yields across years, which is crucial for meeting biomass thresholds necessary to improve soil organic carbon—estimated at > 2 t. ha⁻¹ (Blanco-Canqui et al., 2022) or > 3 t.ha⁻¹ annually (Joshi et al., 2023). Thus, by buffering year-to-year variability, mixtures increase the likelihood of sustained soil C accumulation and long-term ecosystem service delivery.

# 5. Conclusion and perspectives

To the best of our knowledge, this is the first study to comprehensively assess the performance of CCs in pure stands and mixtures, from seedling establishment through to final biomass productivity. Our results revealed strong interannual variations in CC performance with respect to soil cover, N catch crop, N green manure, biomass production, and weed regulation services. While mixtures frequently exhibited overyielding, transgressive overyielding occurred solely in one of the two experimental years under favorable environmental conditions, especially when high SMN levels followed the previous cash crop
harvest.

Compared to pure stands, CC mixtures demonstrated a more stable overall performance, whereas pure stands showed contrasting outcomes across years, depending on botanical family and site-specific weather conditions. Within legumes, common vetch emerged as a promising alternative to faba bean, providing equivalent to superior ecosystem services and showing greater resilience to emerging foliar diseases that frequently constrain faba bean, especially when planted in pure stands, as is common in South-West France during the fallow period.

Taken together, these findings suggest that two-species mixtures combining species with contrasting functional traits represent a robust option for farmers seeking to secure more stable biomass production and ecosystem services across fluctuating climatic conditions.

Finally, the dataset generated in this study represents a valuable resource for modeling frameworks such as SIMPLE (Dürr al. et al., 2001), CESIM (Lamichhane et al., 2020b), or more generally the soil-crop model STICS (Brisson et al., 2003). In particular, coupling crop emergence models with crop growth models could improve predictions of CC biomass and ecosystem service provisioning across diverse environments and future climate scenarios.

## CRediT authorship contribution statement

**Alexandre Wojciechowski:** Writing – original draft, Methodology, Investigation, Formal analysis, Data curation. **Lionel Alletto:** Writing – review & editing, Validation, Supervision, Project administration, Funding acquisition, Conceptualization. **Célia Seassau:** Writing – review & editing, Visualization, Validation, Supervision, Project administration, Funding acquisition, Conceptualization. **Jay Ram Lamichhane:** Writing – review & editing, Visualization, Validation, Supervision, Project administration, Funding acquisition, Conceptualization.

## Funding

This work was conducted within the NaturellementPopcorn project, which was supported by the French government as part of the France 2030 Investment Plan operated by Bpifrance. The funding source had no direct involvement in this study.

## 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

The authors thank Damien Marchand, Sophie Ducos-Boué, Franck Pages, Béatrice Quinquiry, and Eric Lecloux for technical assistance, technicians from the UE APC 0802 for field operations, Lucie Souques, and Suzanne Lack for their assistance in fieldwork. We offer special thanks to Nataïs for technical advice. We also thank Semences de France, Agri-Obtentions, Barenbrug, Jouffray-Drillaud, RAGT, LIDEA, and Sem-Partners for providing cover crop seeds.

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.fcr.2025.110165.

## Data availability

Data will be made available on request.

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