Weed management requires a better understanding of the dynamics of the weed seedbank, which is a primary source of weeds in a field. Seeds reaching the ground after seed rain replenish the seedbank and therefore contribute to future weed infestations. Our investigation is based on the hypothesis that a permanent vegetation cover, such as a grassland, can prevent weed seeds from reaching the ground. Therefore, we developed an innovative experimental device to simulate in controlled conditions the seed rain of 12 weed species (Capsella bursa-pastoris, Conyza canadensis, Myosotis arvensis, Papaver rhoeas, Poa annua, Polygonum aviculare, Ranunculus sp., Rumex obtusifolius, Sonchus asper, Stellaria media, Taraxacum officinale and Veronica persicaria). We quantified the interception of weed seeds by a grass cover. Grass cover height, seed size and seed appendage (e.g. pappus, wing or awn) increased seed interception, in contrast to seed weight and shape index. From these results, we established a linear model to predict weed seed interception by a grass cover as a function of their seed trait values. The relationship between the predicted interception and weed community dynamics observed in grasslands was negative for some species, indicating that other processes may be involved depending on weed species. The weed seed interception model will be incorporated into an existing model of weed population dynamics to simulate the impact of grassland insertion into arable crop rotations

241. It is widely agreed that competition regulates plant populations and shapes 25 communities. Many studies have suggested that crop and grassland competition 26 can be used for cost-effective sustainable weed control. However, effective weed 27 management requires a precise knowledge of the effects of agronomic practices 28 and there is a lack of quantitative indicators to compare and predict the success of 29 weed biocontrol by competition. 30 2. We studied weed abundance dynamics over a 12-year period in crop-grassland 31 rotations (rotation treatments consisted of maize, wheat and barley crops, 32 alternating with temporary grassland maintained for three or six years in the 33 rotation and fertilised with two different levels of nitrogen). In addition to 34 classical statistical analysis of the different aforementioned rotation treatments, 35 we also modelled weed abundance as a function of the crop and grassland 36 competition, expressed here by biomasses harvested in the preceding years. 37 3. We show that weed abundance decreases over the years in grassland and 38 subsequent crops only if the grassland receives sufficient nitrogen fertiliser. Our 39 model had a much greater explanatory power than the rotation treatments. This 40 model estimates a critical biomass level above which weeds are suppressed in 41 subsequent years, and below which they tend to thrive. This critical biomass level 42 was 24.3 and 4.7 tonnes ha -1 of dry matter for crops and grassland, respectively, 43 highlighting the greater competitiveness of grasslands than of crops. Several clear 44 differences between weed functional groups emerged. 45 4. Synthesis and applications -This new modelling approach directly links the 46 interannual dynamics of weed populations to current and previous biomass 47 production levels. This approach facilitates the development of environment-48 friendly weed management strategies and paves the way for comparisons of the 49 competitiveness against weeds of crops and grassland under various pedoclimatic 50 conditions and agronomic practices.51 52 Keywords: weed management, weed ecology, weed biocontrol, weed modelling, weed 53 population dynamics 54 55 Introduction 56Most food and feed production systems worldwide make use of synthetic 57 herbicides for weed management. In this context, herbicide use has resulted in serious 58 environmental and ecological problems (Boutin et al., 2014). Highly effective 59 environment-friendly alternatives to chemical weed control, such as the use of crop and 60 grassland competition with weeds, could potentially reconcile agricultural production and 61 environment quality and play a key role in ensure global food security in the future (Petit 62 et al., 2018; Gaba et al., 2018). 63Many previous studies have shown how the manipulation of agronomic practices 64 (e.g. seed rate, crop cultivar and row spacing and direction) to improve the 65 competitiveness of the crop can help to control weeds (Sardana et al., 2017). Other studies 66 have suggeste...

1. It is widely agreed that competition regulates plant populations and shapes communities. Many studies have suggested that crop and grassland competition can be used for cost-effective sustainable weed control. However, effective weed management requires a precise knowledge of the effects of agronomic practices and there is a lack of quantitative indicators to compare and predict the success of weed biocontrol by competition.2. We studied weed abundance dynamics over a 12-year period in crop-grassland rotations (rotation treatments consisted of maize, wheat and barley crops, alternating with temporary grassland maintained for three or six years in the rotation and fertilised with two different levels of nitrogen). In addition to classical statistical analysis of the different aforementioned rotation treatments, we also modelled weed abundance as a function of the crop and grassland competition, expressed here by biomasses harvested in the preceding years.3. We show that weed abundance decreases over the years in grassland and subsequent crops only if the grassland receives sufficient nitrogen fertiliser. Our model had a much greater explanatory power than the rotation treatments. This model estimates a critical biomass level above which weeds are suppressed in subsequent years, and below which they tend to thrive. This critical biomass level was 24.3 and 4.7 tonnes ha -1 of dry matter for crops and grassland, respectively, highlighting the greater competitiveness of grasslands than of crops. Several clear differences between weed functional groups emerged. 4. Synthesis and applications -This new modelling approach directly links the interannual dynamics of weed populations to current and previous biomass production levels. This approach facilitates the development of environment-