Ali Almoussawi

Jonathan Roger Michel

Henri Lenoir

Fabien Spicher

Frederic Dupont

Olivier Chabrerie

Déborah Closset-Kopp

Boris Brasseur

Ahmad Kobaissi

Frédéric Dubois

Guillaume Decocq
email: guillaume.decocq@u-picardie.fr

Almoussawi Ali

Lenoir Jonathan

Spicher Fabien

Chabrerie Olivier

Closset-Kopp Deborah

Brasseur Boris

Decocq Guillaume

Direct seeding associated with a mixture of winter cover crops decreases weed abundance while increasing cash-crop yields

Keywords: agricultural practices, weed community, reduced tillage, permanent plant cover, Camelina sativa

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

A considerable increase in food production has been achieved since WWII by using monocultures of high-yielding crop varieties, high amount of fertilizers and pesticides, and increased consumption of fossil fuel, water and topsoil compared with the situation prevailing before WWII [START_REF] Tilman | The ecological consequences of changes in biodiversity: a search for general principles 101[END_REF]. Agriculture intensification, however, led to unprecedented rates of environmental degradation, including soil, air and water pollution, soil erosion, and biodiversity loss [START_REF] Galloway | Transformation of the nitrogen cycle: recent trends, questions, and potential solutions[END_REF][START_REF] Robinson | Post-war changes in arable farming and biodiversity in Great Britain[END_REF][START_REF] Stoate | Ecological impacts of arable intensification in Europe[END_REF].

In particular, arable plant species (weeds) dramatically declined in many rural landscapes over the last few decades [START_REF] Firbank | Assessing the ecological impacts of bioenergy projects[END_REF][START_REF] Rich | Changes in the vascular plant floras of England and Scotland between 1930-1960 and 1987-1988: the BSBI monitoring scheme[END_REF][START_REF] Storkey | Using functional traits to quantify the value of plant communities to invertebrate ecosystem service providers in arable landscapes[END_REF][START_REF] Sutcliffe | Changes in the arable flora of central southern England since the 1960s[END_REF]. Weeds not only represent an important part of plant biodiversity in otherwise highly managed farmlands, but also have cultural and aesthetic values [START_REF] Swift | Biodiversity and ecosystem services in agricultural landscapes-are we asking the right questions? Agriculture[END_REF]. They also deliver important ecosystem services, by serving as forage for pollinators, food for granivorous rodents, birds and insects, as well as shelter for auxiliary arthropods [START_REF] Isaacs | Maximizing arthropodmediated ecosystem services in agricultural landscapes: the role of native plants[END_REF][START_REF] Marshall | The role of weeds in supporting biological diversity within crop fields[END_REF].

Making agriculture more sustainable and reducing its negative impacts on ecosystem integrity and human health, while maintaining or increasing yields, is thus challenging for the 21st century [START_REF] Fedoroff | Radically rethinking agriculture for the 21st century[END_REF]. This is the rationale behind conservation agriculture, a system of agronomic practices that include tillage reduction, permanent soil cover, and crop rotations [START_REF] Hobbs | Conservation agriculture: what is it and why is it important for future sustainable food production[END_REF][START_REF] Nichols | Weed dynamics and conservation agriculture principles: A review[END_REF][START_REF] Palm | Conservation agriculture and ecosystem services: An overview[END_REF]. However, reduced tillage is hardly adopted by farmers since it is believed to increase weed infestation, which in turn can be responsible for decreased crop yields [START_REF] Belz | Allelopathy in crop/weed interactions-an update[END_REF][START_REF] Einhellig | Interactions involving allelopathy in cropping systems[END_REF]. Empirical evidence for this statement is inconstant and crop-specific [START_REF] Armengot | Long-term feasibility of reduced tillage in organic farming[END_REF][START_REF] Légère | Extreme grain-based cropping systems: When herbicide-free weed management meets conservation tillage in northern climates[END_REF] and it is likely that a threshold in weed abundance must be passed before effective yield declines [START_REF] Armengot | Long-term feasibility of reduced tillage in organic farming[END_REF][START_REF] Sans | Tillage effects on weed communities in an organic winter wheat-sunflower-spelt cropping sequence: Tillage effects on weeds in organic farming[END_REF]. Most weeds are annual species which are adapted to cyclic soil disturbances and cropping [START_REF] Gaba | Response and effect traits of arable weeds in agro-ecosystems: a review of current knowledge[END_REF], and hence are r-strategists [START_REF] Grime | Benefits of plant diversity to ecosystems: immediate, filter and founder effects[END_REF] with a short life cycle, high fecundity and fertility, and dense soil seed banks [START_REF] Bakker | Seed banks and seed dispersal: important topics in restoration ecology[END_REF][START_REF] Grime | Benefits of plant diversity to ecosystems: immediate, filter and founder effects[END_REF]. The seed bank is the main source of weed occurrence in crops [START_REF] Cavers | Seed banks in arable land[END_REF]) and inversion tillage system is considered to reduce both seed bank density and recruitment from the seed bank [START_REF] Nichols | Weed dynamics and conservation agriculture principles: A review[END_REF].

There is thus a balanced trade-off to be found between the preservation of weed diversity and maintenance of crop yields. The use of cover crops (CCs) has been suggested an efficient mean of suppressing weed emergence in reduced till systems [START_REF] Baraibar | Weed suppression in cover crop monocultures and mixtures[END_REF][START_REF] Kunz | Allelopathic effects and weed suppressive ability of cover crops[END_REF][START_REF] Rueda-Ayala | Investigation of biochemical and competitive effects of cover crops on crops and weeds[END_REF][START_REF] Teasdale | Potential long-term benefits of no-tillage and organic cropping systems for grain production and soil improvement[END_REF], through direct competition for space and resources or the release of allelochemicals [START_REF] Björkman | Mustard cover crops for biomass production and weed suppression in the Great Lakes region[END_REF][START_REF] Finney | Drivers of nitrogen dynamics in ecologically based agriculture revealed by long-term, high-frequency field measurements[END_REF]. The suppression of certain competitively dominant weed species might release other weed species from competitive exclusion, thereby increasing weed species diversity while increasing yields of the cash crop [START_REF] Clements | Integrated weed management and weed species diversity[END_REF][START_REF] Radicetti | Impact of managing cover crop residues on the floristic composition and species diversity of the weed community of pepper crop (Capsicum annuum L.)[END_REF], but this hypothesis has not been tested so far.

The common practice is to use winter CC between two cash crops, with residues retained on the ground [START_REF] Mirsky | Timing of cover-crop management effects on weed suppression in no-till planted soybean using a roller-crimper[END_REF][START_REF] Teasdale | Tillage and planting date effects on weed dormancy, emergence, and early growth in organic corn[END_REF]. It has been suggested that a mixture of CC species (e.g. grasses and Leguminosae) is more weed suppressive than a monoculture [START_REF] Baraibar | Weed suppression in cover crop monocultures and mixtures[END_REF][START_REF] Lawson | Influence of seeding ratio, planting date, and termination date on rye-hairy vetch cover crop mixture performance under organic management[END_REF] due to functional complementarity [START_REF] Hooper | Effects of biodiversity on ecosystem functioning: a consensus of current knowledge[END_REF][START_REF] Loreau | Biodiversity and ecosystem functioning: current knowledge and future challenges[END_REF] and thus greater CC biomass [START_REF] Baraibar | Weed suppression in cover crop monocultures and mixtures[END_REF][START_REF] Lawson | Influence of seeding ratio, planting date, and termination date on rye-hairy vetch cover crop mixture performance under organic management[END_REF]. However, empirical support is still limited and CC impacts on weed communities likely depend upon CC types, sowing and mulching dates, and cash crop type [START_REF] Alonso-Ayuso | Risk management for forestry planning under uncertainty in demand and prices[END_REF][START_REF] Buchanan | Can winter cover crops influence weed density and diversity in a reduced tillage vegetable system?[END_REF][START_REF] Campiglia | Weed control strategies and yield response in a pepper crop (Capsicum annuum L.) mulched with hairy vetch (Vicia villosa Roth.) and oat (Avena sativa L.) residues[END_REF]. An overlooked alternative is the insertion of a spring or summer short-cycle cash crop between two main crops, allowing the harvest of three cash crops over two years. Several candidate species can be found in the Brassicaceae family, which can be cultivated as oil seed plants and are also well documented for their allelopathic effects on weed germination [START_REF] Haramoto | Brassica cover cropping: I. Effects on weed and crop establishment[END_REF][START_REF] Petersen | Weed suppression by release of isothiocyanates from turnip-rape mulch[END_REF]. However, the efficacy of this alternative in controlling weeds has been poorly documented so far. Here we aim at contributing to fill this gap of knowledge.

Assessing the impact of cropping systems on weed community diversity is not a trivial task. Available studies usually used an experimental design where cash crops and CCs were controlled in a randomized complete block design [START_REF] Alonso-Ayuso | Risk management for forestry planning under uncertainty in demand and prices[END_REF][START_REF] Baraibar | Weed suppression in cover crop monocultures and mixtures[END_REF] but without controlling for local and proximal weed species pools. Such study designs assume that the distribution of weed species is more or less homogeneous both aboveground (i.e. standing individuals and seed rain) and belowground (i.e. soil seed bank). This is obviously an unrealistic assumption, especially in reduced tillage systems. Seeds exhibit highly clustered spatial patterns, both as seed rain and belowground [START_REF] Dessaint | Spatial pattern analysis of weed seeds in the cultivated soil seed bank[END_REF][START_REF] Plue | Consistent seed bank spatial structure across semi-natural habitats determines plot sampling[END_REF], due to the already patchy distribution of mother plants, itself associated with the spatial heterogeneity of the environment [START_REF] Plue | Consistent seed bank spatial structure across semi-natural habitats determines plot sampling[END_REF]. Consequently, the fundamental assumption of the independence of observations underlying most statistical analyses is likely violated. Spatial autocorrelation at the plot scale must thus be compensated for [START_REF] Fortin | Spatial autocorrelation and sampling design in plant ecology[END_REF] or, alternatively, should be overcome by using an adequate study design. Here, we retained the latter option, by controlling for the seed input (i.e. species composition and number of seeds per species).

The objective of this study is to evaluate the respective and combined effect of soil preparation (reduced tillage vs. direct seeding) and CCs (two different CCs vs. no CC) on weed species recruitment and yields in following sunflower cultures. For this purpose, we implemented a controlled field experiment using a randomized block design, with barleysunflower-wheat as principal cash crop rotation and either a functionally diverse mixtureincluding Leguminosae and Brassicaceaeor a Camelina sativa harvested cash crop as winter CC inserted between barley and sunflower. Here, we assume that direct seeding of winter CC, either throughout the diversity-complementarity hypothesis or via the allopathic effect of C. sativa, is likely to suppress the most dominant and competitive weed species while increasing 112 weed species richness and sunflower yields.

Materials and Methods

Study site and experimental design

We conducted our experiment in an arable field located in North France (Sorrus; latitude: 50.463208°; longitude: 1.748206°; altitude 40 m) (Fig. 1A) (Appendix A). The climate is oceanic, characterized by a mean annual precipitation of 872 mm and a mean annual temperature of 10.8 °C. Monthly precipitation are regularly distributed throughout the year and mean temperature ranges from 4.7 °C (January) to 17.6 °C (August). The experiment was installed on a plateau, where the substrate consists of Quaternary loess with a high proportion of sand (ca. 50%), onto which luvisols have developed. Prior to the experiment, soil pH, C and N content were 6.5, 9.43 g kg -1 and 0.91 g kg -1 on average, respectively. The regional landscape is dominated by croplands, intensively cultivated for cereals, rapeseed and sugar beet. Prior to the experiment, the field was cultivated with direct seeding farming practices since 15 years.

We used a randomized complete block design (Figs. 1B and1C) with three blocks (3 replicates), and two factor variables (see (i) and (ii) listed bellow) with two and four levels of treatments, respectively, hence making 8 different combinations of treatments repeated 3 times (N = 24 plots of 12 m × 8 m, with a buffer zone of 1 m between two plots).

(i) Soil preparation treatments (2 levels corresponding to 2 tillage treatments). We compared "reduced tillage" with a non-inversion method (using a Chisel plough) vs. "no tillage", also refereed as "direct seeding".

(ii) Soil cover rotation treatments (4 levels corresponding to 4 scenarios). The first scenario was Camelina (Camelina sativa) as a short-cycle cash crop followed by sunflower (Helianthus annuus) to test the potential allelopathic effect of Camelina on weed recruitment.

The second scenario was an intercropping with a functionally diverse and complementary mixture of CCs (Raphanus sativus var. longipinnatus: 2 kg ha -1 , Fagopyrum esculentum: 6 kg ha -1 , Trifolium michelianum: 3.5 kg ha -1 , Trifolium pratense: 2.5 kg ha -1 and Trifolium hybridum: 2.5 kg ha -1 ) followed by sunflower to test the diversity-complementarity hypothesis on weed recruitment. The third scenario was nothing during winter followed by sunflower, which is the conventional rotation. Finally, the fourth scenario was nothing all the time, i.e. the control treatment.

Site preparation

Chisel plowing was applied on 21st July 2016, just after the previous barley crop was harvested, across 12 out of the 24 experimental plots. On the same day, the winter CC was sown (i.e. CCmix and Camelina) in half of the studied plots (see Fig. 1C for details on plot location across the experimental site). Nitrogen fertilizer (ammonium nitrate, NH4NO3) was applied at a rate of 80 kg.ha -1 to all the plots through the fertilizer hopper on 8th August 2016. In the first two weeks of April 2017, Camelina seeds were harvested and the cover crop residue (i.e. the whole biomass for CC-mix, but only the vegetative part of the plant for Camelina) was grinded in all CC plots and left decaying on the ground, except in the reduced tillage treatment, where deep ripping and power harrowing were performed. The amount of C and N returned to the soil was measured at 1399.0 and 25.3 kg ha -1 in Camelina plots and 3844.2 and 236.5 kg ha -1 in CCmix, respectively. On the same day, glyphosate (Round-up®) and anti-slug treatments were applied over the 24 plots. No weed species showed resistance against glyphosate in the study plots. On 28th April 2017, sunflower (cash crop) was sown in its corresponding plots and, on the next day, herbicide (Prowl400®: 400 g L -1 pendimethaline) was applied over the 24 plots. On 14th June 2017, another round of nitrogen fertilizer (NH4NO3) was applied at a rate of 70-80 kg ha -1 to all plots. All details concerning the planning of events to ensure site preparation are presented in the timeline in Appendix A.

Seed preparation

We had chosen 40 weed species (Appendix B) to cover a large spectrum of life forms (grass vs. forbs) and plant traits such as life span (annuals vs. perennials), canopy height and dispersal strategy (e.g. gravity-, wind-, bird-, ant-dispersed). Seeds were collected from wild plants growing in regional cultivated lands to ensure a local provenance, except for three species (Cyanus segetum, Coriandrum sativum and Reseda lutea) for which we used commercial seeds.

The total number of seeds available for each of the 40 species was divided into 27 equal portions (24 portions for the experimental site and 3 portions for greenhouse germination tests) (see Appendix B for more details on propagule pressure). Then, we pooled each portion of the 40 species into a single mixture to get one mixture for each of the 27 experimental units. By doing so, we ensured a similar composition and propagule pressure across the experimental sites and for the germination test. On 18th May 2017, the seed mixture was sown in each of 24 subplots

(1 m × 4 m) that were disposed inside each 12 m × 8 m experimental plots to avoid edge effects (Fig. 1B). When present, the resident weeds were removed prior to sowing. Each subplot was protected with a porous net during one month to avoid seed predation by birds and small rodents. The next day, we settled the greenhouse experiment in triplicate (3 mixtures of seeds) using the same seed mixture. Seeds were spread over steam-sterilized compost-filled containers and allowed to germinate under a natural light regime and a temperature regime ranging from 25 / 20 °C day / night. The containers were kept moist by regular watering. Three control containers containing only steam-sterilized compost were distributed among the other containers to detect eventual contamination. No contamination was detected. We monitored seedling emergence to determine seed viability. All seedlings were identified, counted and removed at weekly intervals from May 2017 until June 2018.

Vegetation survey and data collection

On 4th July 2017, we performed an exhaustive vegetation survey across each of the 24 experimental plots to record the species-specific percentage cover of all vascular plant species co-occurring at the plot level. It allowed us to get the complete picture of the local species assemblages before we started to collect data on weed species. From the exhaustive vegetation survey, we ranked each of the studied weed species from the most to the least abundant within a given plot. This initial ranking of weed species was used as a common and independent reference point to subsequently analyze the changes in weed species' relative abundance over the monitoring period: from mid-July to September (see section 2.5.3). Indeed, we monitored each of the 40 studied weed species in each subplot at 3 dates throughout the vegetation season (17th July, 29th August, 26th September). More specifically, we counted the total number of individuals per weed species. For highly abundant species (i.e. Echinochloa crus-galli, Poa annua, Senecio vulgaris and Viola arvensis), we randomly put a 14.5 cm × 23 cm wooden frame on the ground and counted the total number of individuals occurring within that frame before multiplying it by the total number of frames we could arrange to estimate the area covered by the species across the subplot.

On 26th September 2017, following the last vegetation survey, we randomly collected 10 sunflower individuals from an area of 1 m 2 within each of the 24 studied subplots (240 individuals in total). Each of the 240 harvested individuals was measured for plant canopy height prior to harvest. Back from the field, sunflower seeds were separated from their corresponding stem before being weighted and counted separately for each of the 240 individuals.

Data analysis

Based on our full factorial experimental design, we aim at testing the respective pure effects of soil preparation (reduced tillage vs. direct seeding) and soil cover rotation (Camelina / sunflower, CC-mix / sunflower, nothing / sunflower, nothing / nothing) as well as the two-way interaction effect between both variables on: (1) weed species richness in July, August and September (i.e. the total number of weed species out of the 40 studied species that germinated per subplot); (2) weed total and species-specific abundance in July, August and September (i.e. the total number of individuals across all weed species co-occurring in a given subplot as well as the species-specific total number of individuals per subplot); (3) weed species' relative abundance changes between the exhaustive vegetation survey from early July and the three subsequent weed monitoring surveys performed in mid-July, August and September (i.e. the change in the rank value of a given weed species according to its total abundance in the subplot and relatively to the total abundance of the other co-occurring species); and (4) sunflower yield at the end of the experiment (i.e. sunflower height average weight of seeds per stem inside a given subplot).

Species richness

Here, the response variable is species richness per subplot (count data: one richness value per subplot). Hence, we used the "glm" function from the "stats" package to fit generalized linear models (GLMs) with a Poisson distribution. We built a list of candidate models including, as predictor variables, soil preparation or soil cover rotation or both simultaneously (see Appendix C for the full list). In addition to these two variables of main interest for the study, some of the candidate models included "block" (B1, B2, B3) and "date" (17th July, 29th August, 26th September) as covariates to account for potential confounding effects. All possible two-way interaction terms involving soil preparation or soil cover rotation were tested. For each candidate model, we computed the Akaike Information Criterion (AIC) and ranked all models according to their AIC values, with the best model being the one with the lowest AIC value [START_REF] Burnham | Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach 2nd[END_REF]. Once the best candidate model was selected, we extracted the coefficient estimates, standard errors and associated p-values for each of the predictor variables listed in the best model. Finally, we ran an Analysis of Variance (ANOVA) of the best model, using Type-II or Type-III ANOVA depending on whether there was a non-significant or a significant interaction term, respectively. We used the "ANOVA" function from the "car" package in R.

Species' abundance

Here, the response variable is multivariate with the individual species abundance by subplot matrix being the matrix of response variables (zero-inflated distribution: several species abundance values, including many zeros, per subplot). Hence, we used a modelling approach very similar to the GLM approach with a negative binomial distribution but adapted to highdimensional data, such as multivariate abundance data in ecology (cf. the species × subplot matrix of abundance values) [START_REF] Wang | mvabund-an R package for modelbased analysis of multivariate abundance data[END_REF]. Similar to our analyses on species richness, we tested the same list of candidate models (Appendix C) using the same model selection procedure as above but running the "manyglm" function from the "mvabund" package in R [START_REF] Wang | mvabund-an R package for modelbased analysis of multivariate abundance data[END_REF]. Once the best candidate model was selected, we extracted the global statistics across all species as well as species-specific statistics such as the coefficient estimates of all the species individually to study their corresponding behavior according to the predictor variables listed in the best model. We used the "anova.manyglm" function from the "mvabund" package to generate an analysis of deviance table for the best candidate model.

Changes in species' relative abundance

For each monitoring period (late July, August, September), we ranked weed species according to their abundance values in the subplot, from the most to the least abundant, similar to the ranking we performed for the initial exhaustive vegetation survey in the rest of the plot (early July). In case of absence of one or several of the species in any of the subplots and at any dates, we ranked these species after the least abundant species. Then, we computed for each species separately, the differences in their rank value between a given monitoring period and the initial exhaustive vegetation survey (i.e. reference point), leading to three rank difference values per species and per subplot. Using each of these values as the response variable, we ran linear mixed-effects models (LMMs) with a Poisson distribution (similar to count data). We used soil preparation, soil cover rotation and block as the three fixed effect variables in the model and set species as a random variable interacting with both soil preparation and soil cover rotation (random slope terms) in the model. Because the three rank differences per species and per subplot are not independent from each other (same reference point each time), we ran our single candidate model three times. Finally, we extracted, for each species, the estimated mean and 95% confidence interval of the rank difference.

Crop growth and yield

Crop yield was quantified by measuring both sunflower height (height of 10 random sunflower plants per 4 m 2 subplot) and the average weight of seeds per stem (g). Both sunflower height and weight of seeds per stem were treated as response variables in GLMs with a Gaussian distribution. We tested the same list of candidate models (Appendix C) and used the same approach for model selection as we did for analyzing species richness and species absolute abundance.

All statistical analysis were performed using the "car", "ggplot2", "gridExtra", "lme4", "Matrix", "MuMIn", "mvabund", "nlme", "reshape2" and "stats" packages in the R software environment version 3.4.1 (R Core Team 2017).

Results

A total of 40 weed species were monitored in both field and greenhouse over the study period.

Twenty-eight species germinated in the greenhouse while 35 species germinated in the field (see Appendix D for details on species germination success) regardless of both the soil preparation and soil cover rotation. The majority of these species were characterized by autumn germination and early spring flowering. Poa annua and Echinochloa crus-galli were the most abundant species across all treatments studied (see Appendix E for detailed information about species richness and species total abundance). These species (Poa annua and Echinochloa crusgalli) were highly abundant in the field, likely due to their presence and persistence in the soil seed bank.

Species richness

The best candidate model to explain weed species richness includes soil cover rotation, date, and the interaction between block and soil preparation (see M24 in Appendix C for the complete model formula). We found a significant interaction effect between block and soil preparation on weed species richness (Table 1 andTable 2), such that reduced tillage had a negative impact on weed species richness in block 3 but a positive impact in blocks 1 and 2 (Table 1). We found no effect of soil cover rotation on weed species richness.

Species abundance

Similarly to the analyses on species richness, the best candidate model to explain the overall abundance (number of individuals) of weed within the subplots includes soil cover rotation, date, and the interaction between block and soil preparation (see candidate model M24 in Appendix C for the complete model formula). Yet, the interaction term between block and soil preparation was not significant this time. We found that the overall abundance of weed, irrespectively of the species considered, was higher (Table 3): during July than during August; when there was no soil cover rotation (nothing / nothing) than when Camelina was used in intercropping followed by sunflower; under reduced tillage than under direct seeding; and in block 3 than in block 1 (see Appendix F for the effect of block and date on the most abundant weed species). In addition, further analyses at the species level (Table 4) showed that the abundance of some weed species are clearly affected by both soil cover rotation and soil preparation (see Appendix G for more details about all the studied species). For instance, the abundance of both Viola arvensis and Fumaria officinalis increased under reduced tillage (Fig. 2A and Table 4, respectively) while the abundance of the most dominant weed species, i.e.

Echinochloa crus-galli, decreased when using a CC-mix in intercropping followed by sunflower (Table 4).

Changes in species' relative abundance (species rank difference)

Irrespective of soil preparation (reduced tillage vs. direct seeding), using Camelina as a winter CC before sunflower had important effects on the ranking of weed species abundance over time relative to the control and conventional treatments which had no effect (Fig. 3). For instance, some perennial species in particular (Artemisia vulgaris, Plantago lanceolata) were clearly positively impacted by Camelina at all dates, as well as several annual species (e.g. Coriandrum sativum, Centaurea cyanus, Poa annua, Matricaria chamomilla) in July and August. At the same time, Camelina had a negative impact on the relative abundance of the geophyte Cirsium arvense, as well as of several annuals (e.g. Persicaria maculosa, Chenopodium album, Senecio vulgaris, Capsella bursa-pastoris, Atriplex patula). Noteworthy, Cyanus segetum and Coriandrum sativum are patrimonial species (i.e. rare species, usually on the red list of threatened species, with a scientific, symbolic or cultural value) while Cirsium arvense is a noxious weed. Using CC-mix as winter cover before sunflower only had a marginal effect on the ranking of weed species abundance during July favouring the relative abundance of some species like Artemisia vulgaris, Coriandrum sativum, and Centaurea cyanus under direct seeding treatment but decreasing the relative abundance of the same species under reduced tillage treatment (see Fig. 3). The opposite was true for Perisicaria maculosa (see Appendix H for relative abundance of the studied weed species).

Crop yield

The best candidate model to explain sunflower height and the average weight of seeds per stem (Appendix I) includes block, soil cover rotation and soil preparation as predictor variables (see candidate model M13 in Appendix C for the complete model formula). None of the variables were significant in explaining sunflower height but the average weight of seeds per stem was significantly higher in block 3 than in block 1 and when CC-mix were used in intercropping (Table 5).

Discussion

In this study, we test the relative importance of soil preparation and winter CCs in explaining weed community dynamics over a single season (i.e. richness, abundance, and change in relative abundance). We evidence that winter CC suppresses the most dominant weed species (e.g. Echinochloa crus-galli), especially when using a Leguminosae-Brassicaceae mixture as CC. Winter CC does not impact weed species richness, while soil preparation (i.e. reduced tillage vs. direct seeding) has complex effects. Interestingly, the use of winter CC-mix was associated with increased cash-crop yield, in contrast with Camelina. Below, we discuss these main results in details.

4.1.Impact of soil preparation

Soil preparation, not CCs, impacts weed species richness with reduced tillage interacting with experimental block to increase species richness in two of the three blocks, as compared to direct seeding treatment, while the reverse was found in the third block. The effect of soil preparation on weed species richness is still debated. Reduced tillage has been shown to increase the number of weed species [START_REF] Santín-Montanyá | Management of weed emergence and weed seed bank in response to different tillage, cropping systems and selected soil properties[END_REF], but this effect likely depends upon crop rotation [START_REF] Legere | Diversity and assembly of weed communities: contrasting responses across cropping systems[END_REF][START_REF] Stevenson | Manure, tillage, and crop rotation: effects on residual weed interference in spring barley cropping systems[END_REF]. Compared to conventional tillage (i.e. inversion tillage; [START_REF] Clements | Integrated weed management and weed species diversity[END_REF] and direct seeding [START_REF] Kraska | Effect of tillage system and catch crop on weed infestation of spring wheat stands (Triticum aestivum L.)[END_REF][START_REF] Plaza | Intensity of soil disturbance shapes response trait diversity of weed communities: the long-term effects of different tillage systems[END_REF], reduced tillage appeared less detrimental to weed species diversity, by providing safe sites for weed establishment. However, in contrast with our findings, other studies have found no increase in weed species richness in reduced tillage systems when compared to other soil preparation treatments (i.e. direct seeding and conventional tillage; [START_REF] Barroso | Impacts of cropping system and management practices on the assembly of weed communities[END_REF][START_REF] Bilalis | Effect of Three Tillage Systems on Weed Flora in a 3-Year Rotation with Four Crops[END_REF].

The 3-years study of [START_REF] Barroso | Impacts of cropping system and management practices on the assembly of weed communities[END_REF] assessed the effect of two different cropping systems (Medicago sativa and Triticum aestivum) under three different soil preparations (herbicide, tillage and reduced tillage) and showed no significant impact of soil preparation on both weed species diversity and abundance. [START_REF] Mas | Tillage system effects on weed communities in a 4-year crop rotation under Mediterranean dryland conditions[END_REF] showed that during a 4-years study of three different crops (Pisum sativum, Triticum aestivum and Hordeum vulgare), direct seeding system recorded the highest weed diversity as compared with other tillage systems (i.e. conventional tillage). Noteworthy, the previously mentioned studies [START_REF] Barroso | Impacts of cropping system and management practices on the assembly of weed communities[END_REF][START_REF] Mas | Tillage system effects on weed communities in a 4-year crop rotation under Mediterranean dryland conditions[END_REF] are considered long-term studies unlike our study which was a short-term assessment (single season), underpinning the importance of the study duration in explaining the significant effect of soil preparation on weed community assembly. It has been argued however, that there is no clear increase or decrease of weed diversity with the change of tillage practices alone [START_REF] Barroso | Impacts of cropping system and management practices on the assembly of weed communities[END_REF]. This suggests that the interaction between tillage treatment and crop rotation from one side [START_REF] Legere | Diversity and assembly of weed communities: contrasting responses across cropping systems[END_REF][START_REF] Swanton | Weed Succession under Conservation Tillage: A Hierarchical Framework for Research and Management[END_REF], and between tillage treatment with herbicide use from the other side [START_REF] Locke | Herbicide-soil interactions in reduced tillage and plant residue management systems[END_REF][START_REF] Mohler | A model of the effects of tillage on emergence of weed seedlings[END_REF], might be crucial to weed community structure. In our study, the block effect cannot be attributed to differences in interspecific competition among blocks [START_REF] Grime | Benefits of plant diversity to ecosystems: immediate, filter and founder effects[END_REF][START_REF] Huston | A General Hypothesis of Species Diversity[END_REF] since the dominant grass species (Echinochloa crus-galli) as well as the overall abundance of all weed species were the lowest in the block where species richness was lower, irrespective of the soil treatment. Instead, the block effect may be attributed to small-scale differences in unmeasured local environmental conditions, such as soil fertility [START_REF] Stevenson | Weed species diversity in spring barley varies with crop rotation and tillage, but not with nutrient source[END_REF] or other abiotic factors, which can have an overriding effect on soil preparation in explaining weed species richness [START_REF] Pal | Can management intensity be more important than environmental factors? A case study along an extreme elevation gradient from central Italian cereal fields[END_REF].

While reduced tillage increases the relative abundance of some annual species (e.g. Fumaria officinalis and Viola arvensis), direct seeding rather promotes perennial species (e.g.

Artemisia vulgaris

). This is highly consistent with former findings [START_REF] Santín-Montanyá | Weed abundance and soil seedbank responses to tillage systems in continuous maize crops[END_REF][START_REF] Thomas | Influence of Tillage Systems on Weed Abundance in Southwestern Ontario[END_REF][START_REF] Travlos | Weed-Species Abundance and Diversity Indices in Relation to Tillage Systems and Fertilization[END_REF]. For instance, [START_REF] Thomas | A Multistudy Approach to Understanding Weed Population Shifts in Medium-to Long-Term Tillage Systems[END_REF] found that perennial species (e.g. Cirsium arvense and Sonchus arvensis) tend to dominate in direct seeding systems while annual species (e.g. Setaria viridis) are associated with a range of tillage systems: from intensive conventional tillage to direct seeding systems. Similarly, [START_REF] Bilalis | Effect of Three Tillage Systems on Weed Flora in a 3-Year Rotation with Four Crops[END_REF] showed that the abundance of annual species (e.g. Stellaria media) is high in conventional and reduced tillage systems, while perennials (e.g. Malva sp.) increase their abundance under direct seeding system. It has been suggested that the proportion of perennial weeds increases as tillage is gradually reduced from conventional tillage to direct seeding, since most annuals are adapted to cyclic soil disturbances [START_REF] Gaba | Response and effect traits of arable weeds in agro-ecosystems: a review of current knowledge[END_REF], and mostly recruit from soil seed banks [START_REF] Auskalniene | Changes in weed seed bank and flora as affected by soil tillage systems[END_REF][START_REF] Shaukat | Spatial pattern analysis of seeds of an arable soil seed bank and its relationship with above-ground vegetation in an arid region[END_REF]). Another explanation would be that herbicides are more effective against annuals than against perennials [START_REF] Derr | Weed control in container-grown herbaceous perennials[END_REF], and the use of reduced tillage systems (or even conventional tillage) may help in managing herbicide-resistant weeds that may characterize direct seeding systems (i.e. perennials); this is considered one of the emerging challenges especially in cereal cropping systems [START_REF] Thomas | No-tillage and conservation farming practices in grain growing areas of Queensland-a review of 40 years of development[END_REF]. However, our results support the hypothesis of a species-specific response to soil preparation, as previously suggested by several studies [START_REF] Blackshaw | Application method of nitrogen fertilizer affects weed growth and competition with winter wheat[END_REF][START_REF] Derksen | Impact of agronomic practices on weed communities: tillage systems[END_REF][START_REF] Tuesca | A long-term study of weed flora shifts in different tillage systems[END_REF]. The weak difference between reduced tillage and direct seeding in our study can also be explained by the fact that even the direct seeding system experiences cyclic soil disturbances at the time of harvesting, so that the soil disturbance regime may not strongly differ between the two systems [START_REF] Mohler | Weed life history: identifying vulnerabilities[END_REF].

Impact of cover crops

It is noteworthy that the use of Camelina as winter CC increases the relative abundance of several patrimonial species (e.g. Coriandrum sativum) at the expense of certain noxious species (e.g. Cirsium arvense), irrespectively to the type of soil preparation. Camelina impacts the relative abundance of weed species in a way which is independent from the biological type. Some perennials (e.g. Artemisia vulgaris) as well as some annuals (e.g. Matricaria recutita) are favored by Camelina, while some other perennials (e.g. Cirsium arvense) and annuals (e.g.

Atriplex patula) are suppressed (see Fig. 3).

In comparison, the winter CC-mix (Leguminosae-Brassicaceae) has a weak effect on the relative abundance of weed species, which further depends upon soil preparation. For example, Coriandrum sativum is favored in direct seeding plots only, while Persicaria maculosa is favored in reduced tillage plots only. This is consistent with several studies, where CCs were associated with lower weed abundances (i.e. higher soil fertility) [START_REF] Drinkwater | Legume-based cropping systems have reduced carbon and nitrogen losses[END_REF][START_REF] Plaza | Intensity of soil disturbance shapes response trait diversity of weed communities: the long-term effects of different tillage systems[END_REF][START_REF] Teasdale | Contribution of cover crops to weed management in sustainable agricultural systems[END_REF], though this suppressive effect is likely species-specific [START_REF] Creamer | Mechanisms of weed suppression in cover crop-based production systems[END_REF] and soil preparation-dependent [START_REF] Shrestha | Effect of tillage, cover crop and crop rotation on the composition of weed flora in a sandy soil[END_REF]. Winter CCs are thought to exert a physical barrier against upward seedling growth since they have a head start over weeds that allows them to pre-empt space and resources before weeds and thus to outcompete them and ultimately to prevent the soil seed bank from replenishment [START_REF] Lawley | The Mechanism for Weed Suppression by a Forage Radish Cover Crop[END_REF][START_REF] Weber | Weed Control Using Conventional Tillage, Reduced Tillage, No-Tillage, and Cover Crops in Organic Soybean[END_REF]. Even after grinding, the resulting CC mulch still forms a physical barrier against seed germination. Camelina is believed to further impact weed germination via allelopathic effects, especially towards annual species [START_REF] Leather | Weed control using allelopathic crop plants[END_REF][START_REF] Massantini | Evidence for allelopathic control of weeds in lines of soybean[END_REF].

In contrast with Camelina as a winter CC, the Leguminosae-Brassicaceae CC increases both weed diversity and yields of the cash crop (as measured via mean seed mass per stem of sunflower). These positive effects may be attributed to the Leguminosae species which are welldocumented for supplying nitrogen via their N2-fixing symbiotic bacteria [START_REF] Mazzoncini | Long-term effect of tillage, nitrogen fertilization and cover crops on soil organic carbon and total nitrogen content[END_REF][START_REF] Rangel | Leguminosae native nodulating bacteria from a gold mine As-contaminated soil: Multi-resistance to trace elements, and possible role in plant growth and mineral nutrition[END_REF], increasing soil organic matter [START_REF] Raphael | Soil organic matter in crop rotations under no-till[END_REF], reducing soil compaction and erosion [START_REF] Baumhardt | North American Soil Degradation: Processes, Practices, and Mitigating Strategies[END_REF], improving the C:N ratio associated with microbial community [START_REF] Frasier | High quality residues from cover crops favor changes in microbial community and enhance C and N sequestration[END_REF] and breaking up pest and disease cycles [START_REF] Flint | Pests of the garden and small farm: a grower's guide to using less pesticide[END_REF]. This is supported by the higher amount of C and N returning to the soil with CC-mix than with Camelina (3844.2 vs 1399.0 and 236.5 vs 25.3 kg ha -1 for C and N, respectively; see Site preparation section) and consistent with earlier findings which show a CC-induced increase of crop yield when Leguminosae are included [START_REF] Snapp | Evaluating cover crops for benefits, costs and performance within cropping system niches[END_REF][START_REF] Tonitto | Replacing bare fallows with cover crops in fertilizer-intensive cropping systems: A meta-analysis of crop yield and N dynamics[END_REF], while Camelina is associated with lower crop yields [START_REF] Gesch | Double-cropping with winter camelina in the northern Corn Belt to produce fuel and food[END_REF][START_REF] Johnson | Yield tradeoffs and nitrogen between pennycress, camelina, and soybean in relay-and doublecrop systems[END_REF].

Concluding remarks

Our study clearly shows that winter Leguminosous-Brassicaceae CC intercrop combined with direct seeding ensures higher weed diversity and sunflower yields than with reduced tillage or other soil cover types. On the other hand, Camelina as a monospecific winter CC ensures selective weed control towards less noxious weeds but more patrimonial, non-problematic weeds. As a CC, Camelina has the potential to allow reducing herbicide application [START_REF] Crowley | Evaluation of Camelina Sativa as an Alternative Oilseed Crop[END_REF][START_REF] Crowley | Factors affecting the composition and use of camelina[END_REF]. Its weak detrimental effect on sunflower yield is likely compensated by the fact that, as an oil seed plant, Camelina represents a second cash crop whose seeds have a high economic added value [START_REF] Keske | Is it economically feasible for farmers to grow their own fuel? A study of Camelina sativa produced in the western United States as an on-farm biofuel[END_REF].

Both types of rotation thus meet the criteria of a sustainable agriculture. However, further work is needed to make sure that these results hold true on the long-term and to assess whether they can be retrieved for other cash crops or in other soil and climate contexts. Bold values represent significant (p<0.05) effects across all 40 studied weed species.

Coefficient estimates are available at the species level (see Table 4 and Appendix G). Wald value in "mvabund" estimates the covariance matrix of parameter estimates using a sandwichtype estimator that assumes the mean-variance relationship in the data is correctly specified and that there is an unknown but constant correlation across all observations. The intercept represents the average weed species abundance value for block1 in August under direct seeding treatment and under the Camelina / sunflower rotation treatment.

Effect

Figure 1 :

 1 Figure 1: Experimental site (A), where the field was organized in a randomized block design (B) with 3 repetitions for every treatment (C) in studying the effect of two different soil preparations (reduced tillage vs direct seeding) and four different soil cover rotations on weed community.

Figure 2 :

 2 Figure 2: Abundance of weed species according to (A) the difference in soil preparation (reduced tillage vs. direct seeding treatments) and (B) the different soil cover rotation (4 levels).Only the most abundant species are presented.

Figure 3 :

 3 Figure 3: Estimated mean and 95% confidence interval of the rank difference values from the model used (see section 2.5.3 in materials and methods) at 3 different dates (July -August -September) studying the relative abundance change of weed species as function of soil cover rotations (4 levels) and soil preparation (reduced tillage vs direct seeding). CS corresponds to Camelina and sunflower rotation. COS corresponds to CC-mix with sunflower rotation. NN

Table 1 :

 1 Coefficient estimates from the best candidate model (see M24 in Appendix C) linking species richness to soil preparation, soil cover rotation, date and block effect. Bold values represent significant (p<0.05) effects. The intercept represents the average weed species richness value for block1 in July under direct seeding treatment and under the Camelina / sunflower rotation treatment. Estimates need to be interpreted against the intercept value.Hence, the average weed species richness value for block1 in July under reduced tillage treatment and under the Camelina / sunflower rotation treatment is 1.403+0.631 = 2.034.

	Effect	Estimate Std. Error z value Pr(>|z|)
	(Intercept)	1.403	0.194	6.003	<0.001
	block2	-0.048	0.220	-0.22	0.825
	block3	0.579	0.192	3.008	0.002
	reduced tillage	0.631	0.190	3.308	<0.001
	CC-mix / sunflower	0.020	0.142	0.142	0.886
	nothing / nothing	0.060	0.141	0.424	0.671
	nothing / sunflower	-0.218	0.152	-1.43	0.151
	August	-0.237	0.126	-1.87	0.060
	September	0.093	0.130	0.717	0.473
	block2: reduced tillage	-0.030	0.274	-0.11	0.912
	block3: reduced tillage	-0.729	0.253	-2.87	0.004

Table 2 :

 2 Output of Type III ANOVA representing the best candidate model (M24 in Appendix C) studying species richness with the change of soil preparation, soil cover rotation, date and block. Bold values represent significant (p<0.05) effects. The intercept represents the average weed species richness value under direct seeding treatment and the interaction with rotation, block and date variables.

		Sum Sq 1 2 Df	F value	p value
	(Intercept)	471	1	122,5	<0,001
	rotation	21	3	1,79	0,1595
	reduced tillage	55	1	14,33	<0,001
	block	20	2	2,61	0,0814
	date	19	2	2,50	0,0909
	reduced tillage:block	49	2	6,41	0,0029 3

1 Sum of squares or the total variance of the dataset. 2 Degree of freedom

Table 3 :

 3 Overall statistics of the best candidate model selected (see M24 in Appendix C for the model formula) to study the impacts of soil preparation, soil cover rotation, date and block on weed species abundance (outcomes of the "manyglm" function from the "mvabund" package).

Table 4 :

 4 Detailed statistics of the best candidate model selected (see M24 in Appendix C for the model formula) at the species level to study the impacts of soil preparation, soil cover rotation, date and block on weed species abundance individually. Bold values represent significant (p<0.05) effects (A) and their corresponding coefficient (B) across the five affected weed species (see Appendix G for the complete species abundance analysis). The intercept in table B represents the weed species abundance value for block1 in August under direct seeding treatment and the Camelina / sunflower rotation treatment.

	A) P-Table	Echinochloa crus-galli	Fumaria officinalis	Senecio vulgaris	Veronica persica	Viola arvensis
	block	0,078	0,986	0,986	0,974	0,953
	reduced tillage	0,251	0,04	0,988	0,966	0,001
	rotation	0,038	1	0,084	1	1
	date	0,484	0,16	0,001	0,012	0,911
	block:reduced tillage	0,943	0,997	0,974	0,417	0,343
	B) Coefficient Table	Echinochloa crus-galli	Fumaria officinalis	Senecio vulgaris	Veronica persica	Viola arvensis
	(Intercept)	7,63	-13,61	-5,58	-24,95	-0,12
	block2	-0,43	0	-0,33	0	0,13
	block3	-1,48	0	1,09	12,61	2,03
	reduced tillage	-1,02	12,1	-0,94	9,34	2,19
	CC-mix / sunflower	-1,05	-0,41	1,09	2,2	0,44
	nothing / nothing	0,62	-0,41	3,71	0,38	-0,46
	nothing / sunflower	0,03	-0,41	2,48	-0,59	-0,16
	July	-0,75	2,08	7,4	0	-0,87
	September	-0,5	-9,21	1,32	13	-0,67
	block2:reduced tillage	0,64	-1,79	-0,68	1,97	0,92
	block3:reduced tillage	-0,23	-1,1	-0,77	-10,86	-1,37

Table 5 :

 5 Coefficient estimates from the best candidate model (see candidate model M13 inAppendix C) linking crop yield, either measured as sunflower height (A) or as the average weight of sunflower seeds per stem (B), to soil preparation, soil cover rotation (only 3 levels here as the nothing / nothing control treatment could not be considered for crop yield) and block effect. Bold values represent significant (p<0.05) effects. The intercept represents the average crop yield value (height in cm or weight in g) for block1 under direct seeding treatment and the Camelina / sunflower rotation treatment. Estimates need to be interpreted against the intercept value. Hence, the average sunflower seed mass per stem for block3 under the CC-mix / sunflower rotation treatment is 34.7+42.1+21.9 = 98.7 g.(

A) Sunflower height (cm) Estimate Std. Error t value Pr(>|t|) (Intercept)

		146	8.92	16.3	<0.001
	block2	-3.83	8.92	-0.42	0.675
	block3	7.66	8.92	0.85	0.407
	CC-mix /sunflower	-0.66	8.92	-0.07	0.941
	nothing/sunflower	1.99	8.92	0.22	0.826
	reduced tillage	12.6	7.28	1.73	0.107
		(B)

Weight of seeds per stem (g) Estimate Std. Error t value Pr(>|t|) (Intercept)

		34.7	9.69	3.58	<0.001
	block2	9.89	9.69	1.02	0.327
	block3	42.1	9.69	4.34	<0.001
	CC-mix /sunflower	21.9	9.69	2.26	0.042
	nothing/sunflower	5.08	9.69	0.52	0.609
	reduced tillage	9.46	7.91	1.19	0.254

Acknowledgements

AA greatly acknowledge the "Clover ME" for funding his PhD thesis. We would like to thank Emilie Gallet-Moron for her contribution in the mapping and GIS work and her help in organizing Figure 1; Manuella Catterou and Jérôme Lacoux for the soil analyses. This work is part of the NEWSUN project, which was financially supported by the SAS PIVERT, within the framework of the French Institute for the Energy Transition (Institut pour la Transition Energétique ITE P.I.V.E.R.T. selected as an Investment for the Future: "Investissements d'Avenir" by the French Government under the reference ANR-001").

Author contributions

G.D., F.D, J.L., A.A., and D.C.K conceived the research idea as well as the analytical framework; A.A., F.S., B.B., F.D., O.C. and F.D. collected data in the field; A.A., F.S. and J.L. performed all statistical analyses; EGM provided maps and GIS support. A.A. led the paper writing. All co-authors discussed the results, provided feedback, and commented on the initial version of the manuscript.

Author declaration

We wish to draw the attention of the Editor to the following facts that may be considered as potential conflicts of interest and to significant financial contributions to this work. We wish to confirm that there are no known conflicts of interest associated with this publication.

We confirm that the manuscript has been read and approved by all named authors and there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that all of us have approved the order of authors listed in the manuscript.

We confirm that we have given due consideration to the protection of intellectual property associated with this work and that there are no impediments to publication, including the timing of publication, with respect to intellectual property. In so doing we confirm that we have followed the regulations of our institutions concerning intellectual property.

We understand that the Corresponding Author is the sole contact for the Editorial process (including Editorial Manager and direct communications with the office). He/she is responsible for communicating with the other authors about progress, submissions of revisions and final approval of proofs. We confirm that we have provided a current, correct email address which is accessible by the Corresponding Author and which has been configured to accept email from (guillaume.decocq@u-picardie.fr) Approved by all authors. NSN corresponds to soil left in winter and cultivated with sunflower as summer crop without tillage (i.e. direct seeding). NST corresponds to soil left in winter and cultivated with sunflower as summer crop with reduced tillage.