Pea (Pisum sativum L.) is a key diversification crop but current varieties are not very competitive against weeds. The objective of this study was to identify, depending on the type of cropping system and weed flora, (1) the key pea parameters that drive crop production, weed control and weed contribution to biodiversity, (2) optimal combinations of peaparameter values and crop-management techniques to maximise these goals. For this, virtual experiments were run, using FLORSYS, a mechanistic simulation model (Colbach et al., 2021, Field Crops Res 261:108006). This individual-based 3D model simulates daily crop-weed seed and plant dynamics over the years, from the cropping system and pedoclimate. Here, this model was parameterized for seven pea varieties (Cameor, China, DCG0449, Enduro, Isard, Kayanne, 886/1), from literature and experiments. The latter focused on potential plant morphology and shading response. Differences between varieties depended on the analysed parameter, e.g., varieties were very similar in terms of leaf biomass ratio (LBR, leaf biomass divided by above-ground plant biomass) whereas specific leaf area (SLA, ratio of total leaf area divided by total leaf biomass) at early stages was lower for the two tested spring varieties (Cameor and Kayanne) than for the five winter varieties (except Isard). Then, ten virtual varieties were created by randomly combining variety-parameter values according to a Latin Hypercube Sampling (LHS) plan, respecting parameter ranges and correlations observed in the actual varieties. A global sensitivity analysis was run, using another LHS plan to combine pea varieties, crop rotations and management techniques in nine contrasting situations (e.g., conventional vs organic, no-till, type of weed flora). Simulated data were analysed with classification and regression trees (CART). We highlighted (1) Parameters that drive potential (weed-free) yield and competitivity against weeds, depending on variety type (spring vs. winter) and cropping system. These are pointers for breeding varieties to regulate weeds by biological interactions; (2) Rules to guide farmers to choose the best pea variety, depending on the production goal and the cropping system; (3) The trade-off between increasing yield potential and minimizing yield losses due to weeds when choosing pea variety and management, especially in winter peas. In short, any parameter values that delayed and/or reduced crop emergence decreased potential yield and increased yield loss due to weeds. Conversely, parameter values that increased crop canopy volume (e.g., large LBR during reproduction stages) and crop growth duration (e.g., delayed flowering onset) had the opposite effect. Shading response was crucial: the more pea varieties increased plant height and leaf biomass per unit biomass when shaded, the better they controlled weeds. These main rules describing pea ideotypes were the same for all performance goals, management strategies and analyses scales. But the key parameters depended on variety type and aims. For instance, parameters driving germination and pre-emergent growth were crucial for111 reducing yield loss in winter pea but not in spring pea or for potential yield. Some variety features only fitted to particular systems, e.g., parameters delaying pea emergence were only beneficial in case of herbicide-spraying and disastrous in unsprayed systems. The more the grown variety differed from the weed-controlling ideotype, the more management rules were needed to compensate. Conversely, if one of the two main weedcontrol levers, herbicide or tillage, was missing from the cropping system, the choice of the pea-variety and/or of other management levers became more important. We are now applying this methodology to identify ideal trait combinations for wheat-peaintercrops. Funding INRAE, ANR PeaMUST (ANR-11-BTBR-0002), EU Horizon 2020 (N 727217 ReMIX), French Ministry of Agriculture and Food (CADAR RAID).