# Tracking ideal varieties for agroecological weed management in organic wheat. A simulation study

Pierre Lebreton, Delphine Moreau, Rémi Perronne, Nathalie Colbach

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Pierre Lebreton, Delphine Moreau, Rémi Perronne, Nathalie Colbach. Tracking ideal varieties for agroecological weed management in organic wheat. A simulation study. European Journal of Agronomy, 2025, 164, 10.1016/j.eja.2024.127501. hal-04932955

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# Tracking ideal varieties for agroecological weed management in organic wheat. A simulation study

Pierre Lebreton<sup>a</sup>, Delphine Moreau<sup>a</sup>, Rémi Perronne<sup>b</sup>, Nathalie Colbach<sup>a,∗</sup>

## ARTICLE INFO

**Keywords:**

Wheat (*Triticum aestivum* L.)

weed damage

Trait

Yield loss

Yield gap

Ideotype

Trade-off

## ABSTRACT

Even though weeds are recognized as one of the main causes of yield losses in organic crop production, breeding for varietal competitiveness is still rare. The objective of the present study was to run simulations with the mechanistic crop-weed model *FLORSYS* to identify (1) variety-intrinsic parameters that make organic wheat (*Triticum aestivum* L.) more competitive against weeds in organic conditions, and (2) wheat ideotypes, i.e., optimal combinations of wheat-parameter values to maximise yield, weed tolerance and weed suppression. *FLORSYS* is based on an individual-based 3D canopy model, simulating daily crop-weed seed and plant dynamics over the years, depending on the cropping system and pedoclimate. The model includes three winter wheat varieties, and 20 virtual varieties were added 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 wheat varieties, crop rotations and management techniques in 2800 contrasting organic cropping systems (e.g., undersowing with clover, no-till, type of weed flora) and 400 conventional systems. Average yield potential per variety was highly correlated to yield in the presence of weeds (R² = 0.79) but only slightly to weed tolerance, i.e., the opposite of yield loss due to weeds (R² = 0.10). Classification and regression trees (CART) identified parameters (notably, low pre-emergent seedling loss, fast post-emergence growth rate, late flowering) and their combinations that drive potential yield and competitiveness against weeds. If nitrogen and water were non-limiting after crop emergence, ideotypes that combine late flowering, late grain maturity and winter hardiness with early highly covering and homogeneous plant establishment, possibly to the detriment of root development, were the most relevant. If crop establishment was too late and/or unsatisfactory, a homogeneously tall canopy whose heavy plants presented a high harvest index was essential. No/low-till systems that rely primarily on mechanical weeding or herbicides needed early-emerging varieties with small/thick leaves and small plants per unit biomass to limit crop damage by weeding tools and improve herbicide penetration into the canopy, respectively. These results are suggestions for breeders and farmers to breed and choose wheat varieties for agroecological cropping systems aiming to better regulate weeds by biological interactions. Further research is needed to link model parameters to variables that could be measured routinely during breeding schemes.

## 1. Introduction

Weeds are considered to be the most harmful pest for crop production, mainly because of competition for resources (light, water, nutrients) (Oerke, 2006). This competition is more important in organic production where weeds must be managed without synthetic herbicides (Lundkvist, 2009), although significant damage can also occur in conventional farming, especially when weeds become resistant to herbicides (Peterson et al., 2018). The decrease in use and efficacy of

herbicides can be compensated by combinations of partially efficient and mostly preventive management techniques (Liebman and Gallandt, 1997; Debaeke et al., 2024), among which crop varieties that are more competitive against weeds (Lutman et al., 2013; Andrew et al., 2015; van der Meulen and Chauhan, 2017; Kucek et al., 2021; Lever et al., 2022).

Wheat is a suitable candidate for breeding competitive varieties due to its worldwide importance. However, breeding for competitiveness against weeds is still rare (Worthington and Reberg-Horton, 2013;

<footnote><sup>a</sup> Agroécologie, INRAE, Institut Agro, Univ. Bourgogne, Univ. Bourgogne Franche-Comté, Dijon F-21000, France</footnote>

<footnote><sup>b</sup> IGEPP, INRAE, Institut Agro, Univ Rennes, Le Rheu 35653, France</footnote>

<footnote>* Corresponding author.
E-mail address: nathalie.colbach@inrae.fr (N. Colbach).</footnote>
Hendriks et al., 2022), even if this criterion is taken into account by some wheat breeders targeting organic agriculture (Löschenberger et al., 2008; Rolland et al., 2017). For decades, crop breeding indeed focused on increasing yield for conventional high-input farming conditions including synthetic herbicides, thus disregarding crop-weed competition frequently occurring in organic farming (Donald, 1968). Now, beyond organic farming production, the demand for sustainable agriculture is growing and regulatory changes will lead to a stricter use of synthetic inputs (Jacquet et al., 2022). These elements require a complete overhaul of the ideotypes guiding the selection of future varieties (Lever et al., 2022). Ideotypes are the combinations of morphological and/or physiological traits, or their genetic bases, optimizing crop performance for a particular biophysical environment, crop management and end-use (Martre et al., 2015; other definitions can be found in Gauffreteau, 2018). Consequently, identifying wheat ideotypes that are competitive against weeds is essential to breed and choose the varieties needed for weed management that relies little or not at all on herbicides, as in integrated and organic crop production (Osman et al., 2016; Rolland et al., 2017; Kucek et al., 2021).

A weed-competitive wheat ideotype would need both a high weed suppression and a high tolerance to weed competition (Lemerle et al., 2001). Weed suppression corresponds to a reduction in weed biomass and/or weed seed production by the crop (Mason et al., 2007; Worthington and Reberg-Horton, 2013; Zerner et al., 2016), and crop tolerance to weeds (hence "weed tolerance") means less yield loss in the presence of weeds (Goldberg et al., 1990; Lemerle et al., 2006; Zerner et al., 2016). Previous studies identified many wheat traits linked to crop competition ability (Debaeke et al., 2024). These traits include high post-emergence vigour (i.e., high relative growth rate and leaf area, speed and rate of plant emergence and biomass accumulation), tall young plants to increase shading ability (but not too-tall at anthesis to avoid lodging), and early heading and maturity (notably to avoid water stress during summer but also weed reproduction) (Andrew et al., 2015; Lever et al., 2022; Debaeke et al., 2024; Hendriks et al., 2024). Similar requirements were reported for pea genotypes (Colbach et al., 2022). The latter and other simulation-based studies also demonstrated the critical role of crop shading response to light competition from weeds (Colbach et al., 2019).

Most studies characterizing the competitiveness of varieties focused on a small number of traits (Lever et al., 2022). Moreover, the optimal trait values needed for organic systems, usually characterized by a more abundant and richer weed flora (Benaragama et al., 2016; Tyser et al., 2021), are unknown. Currently, screening wheat genotypes and/or identifying traits essential for competition ability are usually based on experiments in fields with spontaneous and often heterogeneous weed flora. This approach is time-consuming and also characterized by methodological drawbacks when it comes to assessing weed impacts (Lever et al., 2022). Indeed, it is notoriously difficult to properly estimate crop yield loss caused by weeds because estimates depend on the local spontaneous weed flora, and because it is nearly impossible to ensure a continuous weed-free monitoring to measure potential yield (Colbach et al., 2020c). As a result, the values of the traits related to competition have been shown to greatly vary from year to year, depending on weather conditions or management effects (see references in Andrew et al., 2015; Lever et al., 2022).

Models and simulation-based experiments are an alternative to explore how variety traits and farming practices impact weed control under different environmental scenarios, climatic conditions, and weed flora (Martre et al., 2015). In particular, mechanistic (process-based) models facilitate the identification of limiting factors and relevant species/variety parameters. In contrast to classically measured traits, parameters are species- or variety-dependent constants which, in contrast to most traits, do not vary with environmental conditions. Many studies now rely on simulations to evaluate crop genotypes or to identify ideotypes, e.g., for climate change tolerance (Tao et al., 2017), fruit quality (Memmah et al., 2014), or weed management (Bastiaans et al.,

1997; Colbach et al., 2022). For instance, Colbach et al. (2022) (1) developed a simulation plan and analyses methods based on Latin Hypercube Sampling plans and Data Mining, which allowed them to (2) simulate thousands of contrasting cropping systems including seven real and ten virtual pea varieties, and to (3) identify the most influential pea traits and the best combinations thereof for maximising pea yield potential, pea tolerance to weeds and weed suppression.

The objective of the present study was to identify variety parameters and their combinations (i.e., ideotypes) that make wheat competitive against annual weeds in organic arable cropping systems, with the help of a mechanistic crop-weed model. To achieve this, we adapted for the first time a novel methodology developed in a previous simulation study on pea (Colbach et al., 2022) to winter wheat in the present study. To reproduce the impact of cropping systems and pedoclimate on crop-weed canopy as realistically as possible, simulations were run with the FLORSYS model (Colbach et al., 2021). To our knowledge, FLORSYS is to date the only multispecies multiannual 3D individual-based model that accounts for plant-plant competition for resources between arable crops and weeds, including more than 30 crop species (with different varieties) and more than 30 annual weed species and the effect of most cropping techniques. These model features are essential to represent heterogeneous canopies such as crops infested by multispecies weed flora (Renton, 2013; Gaudio et al., 2019). The investigated variety parameters covered growth and development from seed to plant to seed production, including seed and plant responses to temperature, water and light. The latter is the main resource for which crops and weeds compete in temperate arable cropping systems (Colbach et al., 2023). In order to extent the diversity in tested wheat varieties, virtual varieties (based on parameters previously measured on existing wheat varieties) were added to FLORSYS. Simulations were run across a large range of contrasting arable organic cropping systems (i.e., rotations, with their varieties and cropping operations) and optimal parameter combinations (ideotypes) were identified, depending on cropping-system types (with vs without tillage, with undersown crops, etc.) and selection goals (yield potential, weed tolerance or weed suppression). This allowed determining whether ideotypes and influential variety parameters varied with cropping systems, and which were the most relevant criteria for identifying competitive varieties and the most influential management techniques.

## 2. Material and methods

### 2.1. The virtual field FLORSYS

FLORSYS is a virtual field on which cropping systems can be experimented with a large range of virtual measurements of crop, weed and environmental state variables (see further details and references in Colbach et al., 2021).

#### 2.1.1. Weed and crop life cycle

The input variables of FLORSYS consist of (1) a description of the simulated field (daily weather, latitude and soil characteristics); (2) all the crops (including intercropping and cover crops) and management operations in the field, with dates, tools and options; and (3) the initial weed seed bank, which is usually estimated from regional flora assessments (Colbach et al., 2016). Fig. 1.A illustrates for instance the placement of crop seeds in the field, depending on the sowing pattern.

These input variables influence the annual life cycle of annual weeds and crops, with a daily time-step. Pre-emergence stages (alive, non-dormant vs. dormant, germinated) and pre-emergent growth depend on soil structure, temperature and water potential (Fig. 1.A–C). The crop-weed canopy is represented in 3D, with each individual (crop and weed) plant schematized as a cylinder (above ground) and on top of a spilled cone (below ground) (Fig. 1.D). Post-emergence processes (e.g., photosynthesis, respiration, growth, stress response) are driven by light availability as well as air temperature (Fig. 1.D–G). At plant maturity,
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Simplified flow chart of state variables from sown seeds to plants to seed production for crops in FLORSYS (Colbach et al., 2021), with key variety parameters, management input variables and environmental variables (weather, soil state variables). Step A only runs at sowing, B daily per seed from sowing to germination or death, C daily per seed from germination to emergence or death, D and G daily per plant from emergence to death or full maturity (running from 1 to 11), F daily per plant from onset of grain filling to death or full maturity (from 4 to 8). Step G.10 is also used for calculating plant width, leaf biomass, total leaf area and vertical leaf area distribution. In unshaded plants after emergence, leaf area is calculated directly from temperature in step E, and daily biomass is calculated from leaf area (instead of photosynthesis) by inversing step G.10; all other steps in D and G remain unchanged (Nathalie Colbach 2024).</figcaption></figure>

weed seeds are added to the soil seed bank; crop seeds are harvested to determine crop yield. Water stress is only considered for pre-emergent processes (Fig. 1.B–C), and plant-plant competition is limited to the main resource for which crops and weeds compete in arable temperate cropping systems, i.e., light (Colbach et al., 2023). The root system solely serves as a carbon sink for the plant; though it takes up water (not shown in Fig. 1), this only affects soil moisture (and thus pre-emergent processes of seeds).

### 2.1.2. Parameterized species and varieties

To describe the functioning of a species or a variety, a total of 212 parameters (i.e., species or variety-dependent constant which do not vary with environmental conditions) are needed to simulate the processes leading from seed to plant to seed production. Many of these are needed to translate the effect of environmental conditions, either directly (e.g., base water potential determines germination rate as a function of soil water potential in Fig. 1.B, temperature thresholds determine drive photosynthesis as a function of air temperature in Fig. 1.D) or indirectly. For instance, the height biomass ratio in Fig. 1.G translate the comprehensive effect of daily air temperature and radiation (via biomass production) as well as of past and current shading (by reducing biomass production and triggering etiolation) on plant height. Some of these were aggregated for the present analyses (e.g., the potential specific leaf area parameters corresponding to 11 stages were

translated into potential leaf area after emergence, during vegetative growth and during reproduction), resulting in a final total of 112 parameters.

The model is currently parameterized for 32 frequent and contrasting annual weed species and 32 crop species (section A.4 of the supplementary material online). Currently, three varieties variety of bread wheat are parametrized in FLORSYS: Caphorn, Cézanne and Orvantis (Table 1, section B.1 online). To summarize, Caphorn has the lightest grains, the smallest plants and the thickest smallest leaves but compensates with a large leaf area at emergence and a high harvest index. Cézanne has heavier seeds, taller and wider plants, flowers earlier (which favours biomass allocation to above-ground plant parts to the detriments of the root system) and produces thinner larger leaves when shaded, but is hindered by a small leaf area at emergence. Orvantis also has tall and wide plants, it allocates the most biomass to leaves and produces the thinnest largest leaves. When shaded, this variety reacts the most by allocating biomass to leaves and to plant height, but Orvantis is disadvantaged by a low harvest index and light seeds. For many parameters, the same values were used for the three varieties because no variety-specific data were available (e.g., base temperature for germination, parameters for sensitivity to soil structure, extinction coefficient, temperatures for photosynthesis).
<table><caption>**Table 1**
Key characteristics of the three wheat varieties currently parameterized in FLORSYS.</caption><thead><tr><th>FLORSYS parameter</th><th>Used to calculate (Figure 1)</th><th>Caphorn</th><th>Cézanne</th><th>Orvantis</th></tr></thead><tbody><tr><td>Grain mass (dry, mg)</td><td>Seed number (step F.8)</td><td>42</td><td>45</td><td>42</td></tr><tr><td>Leaf area at emergence (cm²)</td><td>Leaf area over time after emergence (step E)</td><td>0.37</td><td>0.15</td><td>0.26</td></tr><tr><td>Flowering precocity</td><td>Plant stage (step D.3)</td><td></td><td>100 °C days earlier</td><td></td></tr><tr><td>Harvest index (g/g)</td><td>Seed biomass allocation rate (step F.4)</td><td>0.50</td><td>0.51</td><td>0.47</td></tr><tr><td>Maximum plant height/width (cm)</td><td>Plant height (step G.10)</td><td>99/77</td><td>108/84</td><td>103/80</td></tr><tr><td colspan="5">Morphology during reproduction (unstressed conditions) <sup>●</sup></td></tr><tr><td>Specific leaf area (cm²/g)</td><td>Leaf area (step G.10)</td><td>82.7</td><td>83.7</td><td>94.5</td></tr><tr><td>Leaf biomass ratio (g/g)</td><td>Leaf biomass allocation rate (step G.10)</td><td>0.11</td><td>0.09</td><td>0.13</td></tr><tr><td>Height biomass ratio (cm/g)</td><td>Plant height (step G.10)</td><td>34.8</td><td>50.7</td><td>33.5</td></tr><tr><td colspan="5">Shading response during reproduction (variation if 50% shading) <sup>●</sup></td></tr><tr><td>Specific leaf area</td><td>Leaf area (step G.10)</td><td>+10%</td><td>+54%</td><td>+7%</td></tr><tr><td>Leaf biomass ratio</td><td>Leaf biomass allocation rate (step G.10)</td><td>+9%</td><td>+2%</td><td>+114%</td></tr><tr><td>Height biomass ratio</td><td>Plant height (step G.10)</td><td>+12%</td><td>+6%</td><td>+21%</td></tr></tbody></table>

● Averaged from flowering onset to full maturity.

### 2.1.3. Effect of management techniques

Life-cycle processes depend on the dates, options and tools of management techniques (tillage, sowing, herbicides, mechanical weeding, mowing, harvesting), in interaction with weather and soil conditions on the day the operations are carried out (section A.6 online). For instance, weed plant survival probabilities are calculated deterministically depending on management operations, biophysical environment as well as weed morphology and phenological stage; the actual survival of each plant is determined stochastically by comparing this probability to a random probability.

### 2.1.4. Indicators of crop production, crop competitiveness and weed services

FLORSYS simulates crop yield as well as indicators assessing weed impacts on crop production and biodiversity (Mézière et al., 2015; Colbach et al., 2020a) (see section A.7 online). Here, potential crop yield from weed-free simulations and actual yield from weed-infested simulations were considered. Then, two indicators of the competitive ability of the varieties were analyzed: (1) weed tolerance, i.e., crop yield loss, as the relative crop yield difference of simulations with and without weeds, and (2) weed suppression, i.e., field infestation, as the weed biomass averaged from crop emergence to crop harvest. Finally, three indicators of weed services were included, i.e., species richness and evenness of weed flora (as indicators of wild plant biodiversity) and weed-based resources for feeding domestic bees. The indicators are calculated for each cultural campaign (from harvest to harvest), and can then be aggregated over a rotation or a multiannual simulation.

### 2.1.5. Domain of validity

FLORSYS was evaluated with independent field data on weed short and long-term dynamics at French national scale, over a large range of existing arable cropping systems (details in section A.8 online). Crop yields, daily weed species densities and, particularly, densities averaged over the years were generally well predicted and ranked as long as a

corrective function was added to keep weeds from flowering during winter at more southern latitudes (Colbach et al., 2016; Pointurier et al., 2021). In particular, the model is able to discriminate not only species but also varieties of a given species (see example of wheat in section A.8.2 online). Specific experiments had been set up in the past focusing on particular FLORSYS submodels (see summary in section A.8.1 online) showing for instance that the 3D individual-based canopy representation with the species parameters for driving plant morphology used here usually correctly predicts light availability in the canopy.

Higher crop yield losses than those reported in previous field studies mostly resulted from the simulation plan. This does not adapt practices to simulated weed floras and interannual weather variability (as farmers or trial managers would do) in order to discriminate the effect of crop species and management practices on weeds from the effect of weeds on the choice of crops and practices (Colbach and Cordeau, 2018). In previous simulation studies, we further checked the consistency of simulated yield loss regarding weed and crop species and parameters, concluding that the species / variety rankings and features identified as relevant were consistent with literature (Colbach et al., 2019, 2022).

## 2.2. Virtual experiments

### 2.2.1. Create virtual wheat varieties

First, Pearson correlations coefficients were calculated among the 230 parameters describing the three existing wheat varieties (Section 2.1.2), using corr() of Hmisc package R (R Core Team, 2016). As the number of existing wheat varieties was very low, intra-species parameter correlations might be badly estimated. So, inter-species parameter correlations were also calculated, using the 64 species currently included in FLORSYS. Correlations tended to be stronger among wheat varieties (median absolute value of Pearson correlation coefficients = 0.90, section B.3 online) than among species (0.31). For each 2-parameter combination, the maximum correlation coefficient value (in absolute values) of intra and interspecies correlations was used for
constructing the virtual varieties.

Then, the range of variation (i.e., the minimum and maximum values) of each quantitative parameter were determined based on the three existing wheat varieties. This range of variation was extended to [0.9 × min, 1.1 × max] for parameters whose values varied among the three existing wheat varieties (e.g., parameters of Table 1), and to [0.7 × min, 1.3 × max] for the remaining parameters whose value was constant for all three varieties (e.g., base temperature for germination), to increase the range of parameter values covered by the virtual varieties.

Finally, twenty virtual wheat varieties were created by randomly drawing parameter values inside the extended ranges of parameter variation, following a Latin Hypercube Sampling (LHS) plan and using the Pearson-correlation matrix as constraints. This was done using the randomLHS() and LHScorcorr() functions of the lhs package of R. The only exception was the timing of flowering onset (*FloweringTT*, °Cdays since emergence), which varied inside the same range of variation for all varieties and had to be consistent with sowing and harvesting dates (see next Section 2.2.2). As a result each variety (i.e., defined by all parameters other than *FloweringTT*) included several "sub-varieties" depending on their *FloweringTT* value.

### 2.2.2. Simulation plan

The simulation plan aimed to evaluate the impact of wheat parameters and management techniques on the indicators of crop production and weed (dis)services of Section 2.1.4 in contrasting cropping systems with a global sensitivity analysis (i.e., varying factors simultaneously to analyse the sensitivity to factors and their interactions, Saltelli et al., 2000). Seven organic situations were chosen (section C.2 online): reference (annual ploughing and tillage, organic fertilizer, mechanical weeding, weed flora typical of Burgundy and consisting of 32 annual species), no plough, no till and no mechanical weeding (same as reference but either without mouldboard ploughing, without any tillage, or without mechanical weeding, respectively), undersown wheat (same as reference and all wheat crops undersown with clover), harmful or beneficial weed flora pool (same as reference but simulations started with either six weed species harmful for crop production or seven dicot

species promoting trophic resources for domestic bees). The eighth situation was conventional, and replaced mechanical weeding by herbicides and organic by mineral nitrogen fertilizer. Nitrogen was considered to be non-limiting in all eight situations.

For each situation, 400 cropping systems were built by randomly choosing crops and management techniques based on a Latin Hypercube Sampling (LHS) plan and respecting the constraints of the situation (e.g., there could be no tillage except residue shredding in no till). The rotation was always five years long, with two winter wheat crops, winter barley, pea and faba bean, with the crop order chosen randomly for each cropping system. For wheat, varieties were chosen among a pool of 23 varieties: the three existing varieties of Table 1, and the twenty virtual ones of Section 2.2.1. Dates and options of the different techniques (e.g., depth, speed and tool for tillage) were randomly chosen for each crop while respecting agronomic consistency (e.g., winter wheat could not be sown in spring, sowing and harvesting date of wheat varieties allowed full maturation, no tillage after crop sowing). The analysis of the Pearson correlations among simulation factors showed that our simulation plan was adequate to avoid confusing effects among management techniques (section C.2.1 online).

The 8 situations × 400 cropping systems were simulated over 15 consecutive years to assess long-term effects and repeated ten times with different weather series consisting of randomly chosen records from the INRAE Dijon weather station (INRAE Climatic database). Note that operation dates and options did not changed among weather repetitions. In other words, the simulated crop management was not adapted to either weather or simulated weed flora. Each situation × system × repetition was run twice, once starting with weeds and once without, to assess potential crop yield. Crop yield loss due to weeds was then calculated as (yield from weed-free simulation – yield from weedy simulation)/(yield from weed-free simulation). This simulation plan succeeded in producing differences in indicators of wheat performance and weed services across situations (see details in section C.2.2 online).

### 2.2.3. Statistics

To identify the key wheat parameters and crop management techniques that influence the indicators, classification and regression

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Example of regression and classification tree (CART) analysing actual weed-infested yield (response variable) in organic tilled wheat as a function of wheat parameters (predictors). The highlighted branches show the three best ideotypes which are also shown in full detail in Table 4.A, with iY1 in green, iY2 in purple and iY3 in blue. Boxes show predictors and thresholds for splitting branches for the three highlighted branches (with ... indicating that the list is incomplete). Leaves (at the bottom of the tree) and nodes show indicator values normed to [0 =lowest yield,1 =highest yield of simulated data set]; leaves are coloured from red (lowest yield) to green (highest yield). Percentages below nodes show the proportion of individuals that fall into that node. The root node at the top of the tree has 100 % since the dataset has not been split yet (Nathalie Colbach 2023).</figcaption></figure>
trees (CART) (Breiman et al., 1984) were run on annual data (years with wheat), using the R software version 4.1.3 (R Core Team, 2022). Factors consisted of wheat parameters, wheat and other crop management techniques, type of cropping system (organic or conventional), weed flora pool (all 32 species, 6 species harmful for crop production, or 7 species highly beneficial for domestic bees though still harmful for crop production). CARTs predict a continuous response variable, here the indicators of crop production and weed (dis)services, from a set of discrete or continuous predictors, here wheat variety parameters and crop-management techniques. The dataset is recursively divided into two subsets along a threshold value of the predictor to maximize the difference between the subsets (see example in Fig. 2). The branches are combinations of predictor values that lead to the predictions contained in the leaf nodes. The trees were computed with the R package mvpart (De'ath et al., 2014). The optimal tree size was identified internally using a 10-fold cross-validation procedure to avoid overfitting. In what follows, R² refers to the cross-validated R² of the resulting tree.

Predictors (including surrogate variables that would divide the dataset exactly like the primary dividing variables) were ranked according to their relative variable importance predictor (VIP, rescaled here by dividing by the total amount of variability explained by the tree, see Section D.4 of the supplementary material). CARTs were constructed by allowing up to 100 "perfect" substitutes (i.e., with raw agreement = 1) per division. In addition, the probability that the analysed response variable increases as the value of the predictor increases was estimated by summing the relative number of individuals who split the branch to a higher predictor value, over all nodes in the tree including the predictor as a primary or surrogate split variable. The partial R² of a predictor was defined as its relative VIP multiplied by the total R² of the tree. To evaluate the contribution of the predictor classes (situation, wheat parameters, wheat management techniques, other crop techniques), the partial R² was calculated for each class by summing the partial R² of all predictors belonging to the class.

To determine the best wheat ideotypes depending on the production or management target, new CARTs were run for the response variables, now only using wheat-variety parameters as predictor variables and only data from tilled organic cropping systems. In each CART, the two best ideotypes corresponded to the two best branches, i.e., those leading to the leaves with the best indicator value (i.e., highest yields and biodiversity, lowest yield loss and field infestation). The branches consisted of combinations of rules defining wheat-parameter values, corresponding to six series of ideotypes for maximising wheat yield potential (i.e., yield in weed-free simulations, iP ideotypes), actual wheat yield (from weed-inclusive simulations, iY ideotypes), weed tolerance (i.e., low yield loss due to weeds, iT ideotpyes), and weed suppression (i.e., low field infestation, iS ideotypes). CARTs for trophic resources for domestic bees, weed species richness and species evenness gave no results because wheat variety parameters were not influential enough.

To determine the best wheat ideotypes depending on the cropping-system type, CARTs were run on three further subsets of the simulated data set, using systems (1) whose wheat was always under-sown with clover (iC ideotypes), (2) without tillage but with mechanical weeding (iN ideotypes), (3) with herbicides and mineral fertilizer (iH ideotypes). In all cases, CARTs used actual weed-infested yield as response variable and only wheat-variety parameters as predictor variables.

## 3. Results

### 3.1. Key role of wheat variety and wheat techniques to control yield loss due to weeds

The wheat yield potential (from weed-free simulations) depended the most on the wheat-variety parameters (partial R² = 0.57 from classification and regression trees in Table 2), with only a slight effect of crop management techniques (R² = 0.03 and 0.02 for wheat and other-crop techniques, respectively). When weeds were added to the simulations, the wheat-variety effect on wheat yield was more than halved whereas the effect of management roughly doubled. Wheat yield also depended on the weed species pool introduced into the simulation. The total variability explained by these four types of variables (total R² of trees) was approximately 20 % lower than for potential yield, indicating that neglected simulations factors (i.e., weather series, years since simulation onset), stochastic effects, as well as interactions between variables, were more influential than for actual (weed-infested) yield.

Weed tolerance (yield loss due to weeds) and, particularly, weed suppression (field infestation) depended very little on wheat variety. For these indicators, the weed species pool was the most influential, and explained variability was even lower than for actual yield, meaning that weather repetition and stochastic effects were the most influential.

Weed-related biodiversity (weed species richness and evenness, weed-based bee food offer) depended even less on wheat variety and more on the weed species pool. Weed species evenness was not correlated to wheat variety parameters at all. Even the influence of management techniques and weed species pool was close to nil.

### 3.2. The best varieties for crop production and weed management

The actual yield per variety averaged over all organic cropping systems, years and weather repetitions (in weed-infested simulations) was highly correlated to the potential yield (from weed-free situations) (R² = 0.79, Fig. 3.A), i.e., the varieties were ranked more or less the same in terms of yield in the presence and absence of weeds. But, in the presence of weeds, the actual wheat yield was approximately reduced by more than half. Conversely, the correlation between yield loss due to weeds and potential yield was low (R²=0.10) (Fig. 3.B). In other words, a good yield potential did not mean a high tolerance to competition with weeds.

<caption>**Table 2**</caption>

Variability in indicators of wheat yield, wheat competitiveness and weed services explained by simulation factors. Partial R² from classification and regression trees explained by variable types, considering only years with wheat. Cells with partial R² were coloured from white (0) through yellow to green (1).

<table><thead><tr><th rowspan="2">Type of variables</th><th rowspan="2">Potential (weed-free)</th><th rowspan="2">Actual (weed-infested)</th><th rowspan="2">Weed tolerance (yield loss<sup>●</sup>)</th><th rowspan="2">Weed suppression (field infestation<sup>●</sup>)</th><th colspan="2">Plant biodiversity</th><th rowspan="2">Bee food offer by weeds</th></tr><tr><th>Species richness</th><th>Species evenness</th></tr></thead><tbody><tr><td>Wheat parameters</td><td>0.57</td><td>0.26</td><td>0.07</td><td>0.02</td><td>0.01</td><td>&lt;0.01</td><td>&lt;0.01</td></tr><tr><td>Wheat techniques</td><td>0.03</td><td>0.05</td><td>0.03</td><td>0.05</td><td>0.02</td><td>&lt;0.01</td><td>0.04</td></tr><tr><td>Other-crop techniques</td><td>0.02</td><td>0.07</td><td>0.02</td><td>0.05</td><td>0.06</td><td>0.01</td><td>0.05</td></tr><tr><td>Weed species pool</td><td>&lt;0.01</td><td>0.06</td><td>0.17</td><td>0.17</td><td>0.23</td><td>0.02</td><td>0.20</td></tr><tr><td>Total R<sup>2</sup></td><td>0.61</td><td>0.43</td><td>0.29</td><td>0.28</td><td>0.31</td><td>0.03</td><td>0.29</td></tr></tbody></table>

<footnote>● wheat yield loss due to weeds</footnote>

<footnote>● field infestation by weed biomass</footnote>
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Wheat variety ranking in terms of potential (weed-free) and actual (weed-infested) wheat yields (A) as well as wheat yield loss due to weeds (B) in organic systems with unlimiting nitrogen. Ls-means after analyses of variance (see section C.3 in supplementary material online) of yields and yield losses from years with wheat from 7 situations × 400 cropping systems × 10 weather repetitions simulated with FLORSys over 15 years. Broken lines show linear regressions fitted to data points, black continuous line is $y = x$. Note that flowering precocity varies with cropping system for a given variety (Pierre Lebreton 2023).</figcaption></figure>

Note that the high yield losses here partially result from the simulation which started with high weed seed banks and did not adapt crop management to simulated weed infestation or weather conditions (see section 2.2.2).

The three existing varieties (Caphorn, Cézanne, Orvantis) currently parameterized in FLORSys presented similar performances corresponding approximately to the average of all varieties in terms of yield loss, potential and actual yields (respectively 64%, 5.0 t/ha, and 1.9 t/ha, on average across organic cropping systems, weather repetitions and years, Fig. 3). The variety with the best potential yield among the varieties tested here was the virtual variety Virtual1 (11.6 t/ha). This variety also presented the highest actual yield (6.4 t/ha) and one of the lowest yield losses (46%) among the varieties tested (Fig. 3). Virtual1 thus outperformed all varieties (including the existing ones) in terms of crop

production, together with an excellent weed tolerance. It was also first-rate in terms of weed suppression, with an average field infestation of 1.63 t/ha, vs 2.30 t/ha for the worst variety (Virtual5) and 2.02 t/ha on average for the three existing varieties (details in section D.3 in supplementary material online).

The best variety in terms of weed tolerance and weed suppression was Virtual17, with the lowest yield loss (44%) and field infestation (1.65 t/ha) on average. Though its potential (5.9 t/ha) and actual yields (3.4 t/ha) were much lower than those of Virtual1, Virtual17 still outperformed the three existing varieties.

### 3.3. *The ideotypes depend on the production or management target*

Based on the results of the classification and regression trees, optimal
<caption>**Table 3**</caption>

Performance of the two best wheat ideotypes per production or weed-management target in organic tilled wheat identified with classification and regression trees, together with the wheat varieties answering to the rules determining each ideotype. The rules for the best ideotypes are given in Table 4. For each target, cells were coloured from green (best yield, lowest yield loss or field infestation) through yellow to red (vice-versa).

<table><thead><tr><th rowspan="3"></th><th colspan="8">Target</th></tr><tr><th colspan="2">Yield potential (weed-free yield)</th><th colspan="2">Actual yield (weed-infested yield)</th><th colspan="2">Weed tolerance (yield-loss control)</th><th colspan="2">Weed suppression (field-infestation control)</th></tr><tr><th>iP1</th><th>iP2</th><th>iY1</th><th>iY2</th><th>iT1</th><th>iT2</th><th>iS1</th><th>iS2</th></tr></thead><tbody><tr><td colspan="9">A. Average ideotype performance (In bold, the indicator values corresponding to the target)</td></tr><tr><td>Potential wheat yield</td><td colspan="2">Yield from weed-free simulations</td><td>t/ha</td><td>16.2</td><td>18.2</td><td>15.7</td><td>14.9</td><td>9.4</td><td>6.1</td><td>7.4</td><td>2.6</td></tr><tr><td>Actual wheat yield</td><td colspan="2">Yield from weeds-inclusive simulations</td><td>t/ha</td><td>6.3</td><td>5.2</td><td>11.5</td><td>6.9</td><td>5.9</td><td>2.9</td><td>3.5</td><td>0.6</td></tr><tr><td>Wheat yield loss</td><td colspan="2">Relatively to weed-free yield</td><td>%</td><td>62</td><td>77</td><td>28</td><td>57</td><td>40</td><td>53</td><td>55</td><td>75</td></tr><tr><td>Field infestation</td><td colspan="2">Field infestation by weeds</td><td>t/ha</td><td>13.1</td><td>12.6</td><td>1.3</td><td>2.1</td><td>1.7</td><td>2.1</td><td>1.9</td><td>2.0</td></tr><tr><td colspan="9">B. Varieties (Cap = Caphorn, Céz = Cézanne, Orv = Orvantis) answering to the rules determining each ideotype</td></tr><tr><td>Virtual varieties<sup>®</sup></td><td colspan="2">Corresponding to the ideotype rules</td><td></td><td>1 11 14</td><td>10</td><td>1</td><td>3 11</td><td>1 14</td><td>7 13 15</td><td>1 13 15 17</td><td>12 &amp; 15 others</td></tr><tr><td>Existing varieties</td><td colspan="2"></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>All</td></tr></tbody></table>

®in bold, the three best varieties in terms of yields, weed tolerances and/or suppression, averaged over all organic cropping systems and flowering precilities, Fig. 3

combinations of wheat parameter values with crop management techniques were identified, depending on the target in terms of crop production or weed management (e.g., high yield potential or high weed tolerance) in tilled organic wheat (Table 3).

### 3.3.1. Actual yield in weed-infested fields is the best criterion to select varieties

The classification and regression trees (CARTs) using wheat yield from weed-free simulations as response variable indeed produced ideotypes (ideotypes iP1 and iP2 in Table 3.A) with the highest potential wheat yield. Conversely, its actual yield, weed tolerance (lowest yield loss due to weeds) and weed suppression (lowest field infestation by weeds) were worse than all other ideotypes of Table 3.A. Ideotypes aiming to maximise weed-infested wheat yield as response variable (Fig. 2) allowed identifying the best all-rounders. The iY1 and iY2 ideotypes not only produced the highest weed-infested wheat yield (+83 % for iY1 vs iP1), but also the best weed tolerance and weed suppression. Ideotypes aiming to minimise yield loss or field infestation resulted in similar or worse yield loss (+43 % for iT1 vs iY1) or similar or worse field infestation (+43 % for iS1 vs iY1). Moreover, the selected ideotypes were less well defined, with fewer parameter rules (e.g., 26 rules for the highest yielding iY1 vs. 12 for the most suppressive iS1, Table 4). The potential yield of these high-yielders was though lower than for the high-potential iP1 and iP2 ideotypes (–18 % for iY2 vs iP2, Table 3.A).

### 3.3.2. High-yielding ideotypes establish earlier and mature later than existing varieties

The best wheat yields in tilled weed-infested organic systems were obtained by two contrasting parameter combinations. Compared to the three existing varieties, the highest yielding ideotype, iY1 (11.5 t/ha, Table 3.A) presented a delayed germination but with fewer cohorts (parameter gb ≥1.11 in line [2] in Table 4), an earlier emergence for shallow seeds (low parameter in [3]), less seedling loss (low [4]) and a faster ground cover (high [5]).

A narrower root system (low [6]) allowed more above-ground biomass, and thus more leaf area for light interception and photosynthesis (note that water and nitrogen were unlimiting after emergence in these simulations). Ideotype iY1 was also taller per unit biomass than most existing varieties (large [12]), i.e., plants were tall and slim rather than stocky. Both their width and their height were more homogeneous (low [9] and [10]), albeit with shorter plants (low [13]) and narrower plants per unit biomass during reproduction (low [11]). Leaf area was

concentrated more toward the top of the plant (large [14]), which compensated for the slightly weaker shading response before reproduction, i.e., a lower ability to produce thinner larger leaves when shaded (low [16]). From flowering onwards, iY1 had a stronger shading response (large [18]), and the harvest index of its heavy plants was larger (large [19]). iY1 also photosynthesized better in cool conditions than the three existing varieties (low [21]) and was also more frost-tolerant during early stages (low [27]).

Most importantly, iY1 flowered and matured later than most of the three existing varieties used here (higher [24] and [25]), which required sowing before 6 November to allow a sufficiently long period of biomass accumulation. This ability was crucial as shown by ideotype iY3, which was defined by exactly the same rules as iY1 (see section E.2.1 online), except that it flowered earlier (FloweringTT in [1110, 1650]). This resulted in 34 % reduction in yield, resulting from a 68 % increase in yield loss and a 10 % drop in yield potential.

### 3.3.3. Later-emerging ideotypes needed more heat tolerance and shading response

Ideotype iY2 presented an even lower yield (6.9 t/ha, Table 3) than iY3. It differed from iY1 mainly in terms of phenology: there were no rules ensuring a fast germination and emergence (no rules in lines [1] and [3]in Table 4) or a late flowering (no [24] rule). Instead of being more efficient in cool conditions in terms of photosynthesis and frost tolerance (high [21] and [26]–[27]), iY2 was better adapted to heat (high [23]). Its main advantage compared to iY1 were its strong shade response, with the ability to increase leaf area per leaf biomass when shaded by neighbour plants (high [17]).

### 3.3.4. Early establishment and winter hardiness make the difference in the presence of weeds

The best ideotype for potential yield production (iP1 in Table 4) had many parameter rules in common with the best ideotype for weed-infested yield (iY1) but differed less from existing varieties. The main differences concerned germination, emergence and establishment, with iP1 emerging later (larger [3]) and less well (larger [4]), with a slower ground cover development (lower [5]).

Plant morphology was also less competitive against weeds, with possibly less biomass allocated above-ground (no rule restricting root-system width in [6]), a more heterogeneous canopy (large [9] and no [10]), no topward leaf area (no [14]) and, most importantly, no shade response (no [16]–[18]). iP1 also photosynthesized less well in cool conditions (larger [21]), and was more frost-sensitive ([26]–[27] rules
<caption>**Table 4**</caption>

The FLORSYS parameters driving the key beneficial wheat variety characteristics (lines) and defining wheat ideotypes, i.e., optimal combinations of wheat parameters (columns) in tilled organic wheat identified with classification and regression trees, aiming to maximise potential wheat yield (from weed-free simulations), actual wheat yield (from weed-infested simulations), weed tolerance (low yield loss due to weeds) or weed suppression (low field infestation by weeds). Rules in green improve yield, weed tolerance / suppression compared to some (standard font) or all (bold) existing varieties, those in red show trade-offs, i.e., rules that potentially decrease performance and result from correlations with beneficial characteristics and/or have no influence in the given cropping system type. White cells show rules respected by current varieties. Only the parameters that were among the 10 most influential parameters for at least one indicator and whose Variable Importance Predictor cumulated over all indicators ≥ 0.001 were shown. In blue, parameters whose values were constant for the three existing varieties.

<table><thead><tr><th rowspan="2">Beneficial wheat characteristics</th><th colspan="3">Corresponding FLORSYS parameters</th><th rowspan="2">Potential yield<br>iP1</th><th colspan="2">Actual yield</th><th rowspan="2">Weed tolerance<br>iT1</th><th rowspan="2">Weed suppression<br>iS1</th></tr><tr><th>Name</th><th>Meaning</th><th>Unit</th><th>iY1</th><th>iY2</th></tr></thead><tbody><tr><td colspan="9"><strong>A. Germination, emergence and establishment</strong></td></tr><tr><td>[1] Early and fast germination</td><td>baseTempG</td><td>Base temperature for germination</td><td>°C</td><td>≥-0.78</td><td>≥-0.09</td><td></td><td></td><td></td></tr><tr><td>[2] Single-cohort but late germination</td><td>gb</td><td>Shape parameter for germination progress<sup>●</sup></td><td>none</td><td><strong>≥0.94</strong></td><td><strong>≥1.11</strong></td><td>&lt;1.11</td><td><strong>≥1.11</strong></td><td></td></tr><tr><td>[3] Fast pre-emergent growth for shallow seeds</td><td>lb</td><td>Shape parameter for pre-emergent shoot growth<sup>●</sup></td><td>none</td><td>&lt;3.82</td><td><strong>2.59</strong></td><td></td><td></td><td>&lt;3.43</td></tr><tr><td>[4] Low seedling loss in tilled (fine-earth) soil</td><td>betaFineEarth</td><td>Seedling mortality in uncompacted soil structure</td><td>sdI·sdI<sup>-1</sup><br>-ln(mm<sup>-1</sup>)</td><td>[-0.71;<br>-0.53]</td><td><strong>&lt;0.64</strong></td><td><strong>&lt;0.67</strong></td><td></td><td></td></tr><tr><td>[5] Fast ground cover</td><td>RGR</td><td>Relative growth rate after emergence</td><td>ln(cm<sup>2</sup>)·°<br>Cday<sup>-1</sup></td><td>&lt;0.024</td><td><strong>≥0.022</strong></td><td><strong>≥0.023</strong></td><td></td><td>≥0.018</td></tr><tr><td colspan="9"><strong>B. Root system</strong></td></tr><tr><td>[6] Limited root system → more above-ground biomass</td><td>Emax</td><td>Potential maximal width of the root system</td><td>mm</td><td></td><td><strong>&lt;258</strong></td><td><strong>&lt;274</strong></td><td></td><td></td></tr><tr><td>[7] More water take-up → less water for weed germination / emergence</td><td>rateDepth</td><td>Speed of root system depth extension</td><td>mm·day<sup>-1</sup></td><td></td><td>&lt;19.6</td><td></td><td><strong>≥18.1</strong></td><td></td></tr><tr><td>[8] Root growth tolerant to soil compaction</td><td>soilPen</td><td>Resistance to soil compaction (0=none, 1=total)</td><td>cm·cm<sup>-1</sup></td><td>≥0.87</td><td>≥0.92</td><td></td><td></td><td>≥0.82</td></tr><tr><td colspan="9"><strong>C. Above-ground plant morphology (see section B.1 online for more explanations on morphology parameters)</strong></td></tr><tr><td>[9] Homogeneous plant height (reproduction)</td><td>b_HBRlate</td><td>Sensitivity of plant height to above-ground plant biomass<sup>●</sup></td><td>none</td><td>≥0.11</td><td>&lt;0.12</td><td></td><td></td><td>&lt;0.13</td></tr><tr><td>[10] Homogeneous plant width (emergence)</td><td>b_WBRearly</td><td>Sensitivity of plant width to above-ground plant biomass<sup>●</sup></td><td>none</td><td></td><td>&lt;0.48</td><td>&lt;0.52</td><td></td><td>≥0.42</td></tr><tr><td>[11] Good ground cover until maturity</td><td>WBRlate</td><td>Plant width biomass ratio<sup>●</sup> if no shading (reproduction)</td><td>cm/g</td><td>&lt;16.2</td><td>&lt;17.6</td><td></td><td></td><td>≥12.4</td></tr><tr><td>[12] Tall per unit biomass (vegetative growth)</td><td>HBRmid</td><td>Plant height biomass ratio (vegetative growth) if no shading</td><td>cm/g</td><td>≥21.9</td><td>≥22.4</td><td></td><td></td><td>≥22</td></tr><tr><td>[13] Tall during reproduction</td><td>max_height</td><td>Maximum plant height</td><td>cm</td><td>[82.3;<br>121]</td><td><strong>&lt;92.1</strong></td><td><strong>&lt;89.2</strong></td><td></td><td>&lt;109</td></tr><tr><td>[14] Leaf area toward plant top (emergence)</td><td>b_RLHearly</td><td>Shape of leaf area distribution along plant height<sup>●</sup></td><td>none</td><td></td><td>≥2.52</td><td>≥2.21</td><td></td><td></td></tr><tr><td>[15] Thin/large leaves (emergence)</td><td>SLAearly</td><td>Specific Leaf Area (emergence) if no shading</td><td>cm<sup>2</sup>·g<sup>-1</sup></td><td>≥152</td><td>&lt;179</td><td></td><td>≥168</td><td></td></tr><tr><td>[16] Thinner / larger leaves when shaded</td><td>mu_SLAearly</td><td>Increase in specific leaf area if shaded (emergence)</td><td>none</td><td></td><td>&lt;0.12</td><td>≥0.076</td><td></td><td></td></tr><tr><td>[17] High harvest index of heavy plants</td><td>mu_SLAlate</td><td>Same (reproduction)</td><td>none</td><td></td><td>&lt;0.34</td><td><strong>≥0.39</strong></td><td></td><td></td></tr><tr><td>[18] High harvest index of heavy plants</td><td>shapeHI</td><td>Same (reproduction)</td><td>none</td><td></td><td>≥0.21</td><td>&lt;0.28</td><td></td><td>&lt;0.7</td></tr><tr><td>[19] High harvest index of heavy plants</td><td>shapeHI</td><td>Sensitivity of harvest index to plant biomass<sup>●</sup></td><td>none</td><td><strong>≥1.1</strong></td><td><strong>≥1.02</strong></td><td></td><td></td><td></td></tr><tr><td colspan="9"><strong>D. Photosynthesis</strong></td></tr><tr><td>[20] High light interception and shading per unit leaf area</td><td>k_coef</td><td>Light extinction coefficient</td><td>none</td><td></td><td>&lt;0.87</td><td></td><td></td><td>≥0.66</td></tr><tr><td>[21] Efficient photosynthesis in cool conditions</td><td>tPhoto2</td><td>Temperature above which net photosynthesis is maximum</td><td>°C</td><td>&lt;13.1</td><td><strong>&lt;10</strong></td><td><strong>≥12.7</strong></td><td><strong>&lt;11</strong></td><td><strong>&lt;10.7</strong></td></tr><tr><td>[22] Efficient photosynthesis in hot conditions</td><td>tPhoto3</td><td>Temperature below which photosynthesis is maximum</td><td>°C</td><td>[15.3;<br>17.6]</td><td>&lt;18.4</td><td></td><td></td><td></td></tr><tr><td>[23] Efficient photosynthesis in hot conditions</td><td>tPhoto4</td><td>Temperature above which photosynthesis stops</td><td>°C</td><td>&lt;41.5</td><td><strong>≥41.3</strong></td><td><strong>≥40.9</strong></td><td></td><td></td></tr><tr><td colspan="9"><strong>E. Phenology</strong></td></tr><tr><td>[24] Late flowering onset → more wheat biomass prod.</td><td>FloweringTT</td><td>Time from plant emergence to flowering onset</td><td>°C days</td><td>≥1610</td><td>≥1650</td><td></td><td>≥1456</td><td></td></tr><tr><td>[25] Late maturity onset → more wheat biomass prod.</td><td>TTflo</td><td>Time from flowering onset to onset of grain filling</td><td>°C days</td><td></td><td><strong>≥956</strong></td><td><strong>[760,<br>956]</strong></td><td><strong>≥876</strong></td><td></td></tr><tr><td colspan="9"><strong>F. Frost tolerance</strong></td></tr><tr><td>[26] Frost tolerant (emergence)</td><td>tFrostEarly1</td><td>Max temperature causing frost-related biomass loss</td><td>°C</td><td>[-7.7;<br>-5.6]</td><td>&lt;6.8</td><td><strong>≥5.9</strong></td><td></td><td>&lt;5.4</td></tr><tr><td>[27] Frost tolerant (reproduction)</td><td>tFrostEarly3</td><td>Max temperature killing all plants through frost</td><td>°C</td><td>&lt;23.3</td><td><strong>&lt;26.2</strong></td><td><strong>&lt;24.8</strong></td><td></td><td></td></tr><tr><td>[28] Frost tolerant (reproduction)</td><td>tFrostLate1</td><td>Max temperature causing frost-related biomass loss</td><td>°C</td><td>&lt;4.0</td><td>≥5.9</td><td></td><td><strong>&lt;4.7</strong></td><td>&lt;4.0</td></tr></tbody></table>

● The higher this value, the later the germination or growth onset but the higher the germination or growth speed

● if b_WBR (resp. b_HBR) is 0, plant width (resp. plant height) is constant irrespective of plant biomass; the larger b_WBR (resp. b_HBR), the more plant width (resp. plant height) increases with plant biomass (see section B.1 online for more details)

● Plant width / above-ground plant biomass (see section B.1 online for more details)

● The lower this parameter, the more uniformly leaf area is distributed along plant height (see section B.1 online for more details)

● Harvest index for a plant = meanHI · ABMshapeHI-1, with ABM = above-ground plant biomass. So, if shapeHI = 1, harvest index does not depend on plant biomass. If shapeHI > 1, harvest index of heavy plants (e.g., highly tillering or tall plants, long growing season) > harvest index of light plants (and vice-versa for shapeHI < 1)
allow higher damaging temperature) than iY1. Such characteristics allowed earlier emerging and frost-tolerant weeds to take advantage in weed-infested simulations whereas in weed-free simulations, neighbouring wheat plants compensated for plant loss by increasing biomass production in the surviving plants.

### 3.3.5. The all-round winner was a virtual variety

As already identified in Fig. 3, the virtual variety *Virtual1* was an all-round winner. It was the only variety answering to the best ideotype for maximising yield of weed-infested wheat (iY1, Table 3.B), weed tolerance (iT1) and suppression (iS1) and the second-best for maximising potential yield (iP1). The high performance of *Virtual1* in average over all flowering timings as in Fig. 3 also showed that it performed well over all cropping systems, regardless of their management and the resulting flowering timing.

This was though not the case of all varieties answering to the ideotype rules. For instance, variety *Virtual10* fitted the rules of the ideotype with the highest yield potential (iP2 in Table 3.B). But potential yield averaged over all flowering precocities (Fig. 3) was only a third of iP2's yield, with its stringent flowering rule (≥1760 °C days, section E.2.1 online), forcing an even later flowering than for iY1 and iP1 ([20] in Table 4). In other words, even the best emergence timing and plant morphology (which iP2 and *Virtual1* have in common) are nothing without adequate flowering and maturity timings (which differ between iP2 and *Virtual1*).

## 3.4. The ideotypes depend on the cropping system

### 3.4.1. What changes with undersown clover?

If wheat was undersown with clover, the wheat yield was lower but the weed tolerance and suppression of the wheat-clover intercrop was better than for a pure-stand wheat (iC1-iC2 vs iY1-iY2 in Table 5.A). A much smaller number of wheat characteristics were relevant in undersown wheat (iC1 in Table 6), indicating that the presence of clover made the choice of the wheat variety less important. The best ideotype iC1 was similar to existing varieties in terms of germination staggering (i.e., the number of emergence cohorts, [2]), plant width per above-ground biomass during reproduction [16] and flowering duration [19]. But its flowering was later than in existing varieties and in iY1 [18], and it was also more frost-tolerant after emergence [23].

### 3.4.2. What changes if there is no tillage?

If there was no tillage in organic wheat (without undersowing), two contrasting ideotypes allowed a high production with good weed tolerance and suppression if they were combined with early sowing and

late harvesting (iN1 and iN2 in Table 5).

The first of these ideotypes, iN1, succeeded because of its early and highly covering establishment, which was earlier and better than for the high-yielding iY1. Indeed, iN1 presented a very low base temperature for germination and emergence growth (line [1] in Table 6), in addition to low seedling loss [3] as well as fast pre-[4] and post-emergent growth [5]. This configuration cancelled out the rather uncompetitive plant morphology of iN1 with its small plants and leaves [10]–[13]. These features though made the plants less damageable by mechanical weeding, and plants grew wider per unit biomass during reproduction (which suppressed late-emerging weeds, [16]). The early crop establishment, together with early sowing (Table 5), also cancelled out the post-emergence frost sensitivity [23] and the inefficient photosynthesis in cool conditions [21] as wheat plants were already well grown at winter onset.

The other ideotype for untilted wheat, iN2, bet everything on above-ground plant morphology, with tall plants [12][13], a large leaf area [10][11] and a long growth period [18][19]. Again, early sowing (Table 5) was essential to compensate for a low winter hardiness ([23] in Table 6).

In summary, in untilted fields, the ideotypes needed to establish and cover the ground very fast (to outdistance and smother weeds), or to grow very tall above the weeds (to intercept radiation and shade weeds).

### 3.4.3. What changes in conventional systems?

If wheat was sprayed with herbicides (iH1-iH2 in Table 5), field infestation was always lower than with the other ideotypes. The best ideotype iH1 was very similar to the best no-till ideotype iN1 (see section E.2.2 online) and similarly needed early sowing and late harvesting to be efficient. Ideotype iH3 was similar to the high-yield organic iY1, but with slower emergence and establishment.

Many of the optimal traits identified for iH1-iH2 allowed a better herbicide efficiency to the detriment of competitiveness against weeds (i.e., smaller/thicker leaves and stocky plants in iH1, slow establishment in iH3). This explains why the most weed-tolerant varieties in organic systems (where competitiveness against weeds is crucial) perform less well in herbicide-sprayed systems (where herbicide efficiency is crucial).

## 3.5. The key role of flowering timing

The time from plant emergence to flowering onset (*FloweringTT*) was the most influential wheat parameter in all cropping-system types and regardless of the production or management target (Table 4, Table 6). Wheat yield potential (from weed-free simulations) increased with the

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

Performance of the three best wheat ideotypes maximizing weed-infested yield per cropping-system type identified with classification and regression trees, together with the wheat varieties answering to the rules determining each ideotype. The rules for these ideotypes are in section E.2.2 online.

<table><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th rowspan="2"></th><th colspan="2">Reference: Organic tilled wheat (same as in Table 3)</th><th colspan="6">What changes if ...</th></tr><tr><th>iY1</th><th>iY2</th><th>iC1</th><th>iC2</th><th>iN1</th><th>iN2</th><th>iH1</th><th>iH3</th></tr></thead><tbody><tr><td colspan="11"><strong>A. Average ideotype performance</strong></td></tr><tr><td>Potential wheat yield</td><td>Yield from weed-free simulations</td><td>t/ha</td><td>15.7</td><td>14</td><td>10.2</td><td>11.8</td><td>17.5</td><td>19.7</td><td>17.2</td><td>15.5</td></tr><tr><td>Actual wheat yield</td><td>Yield from weeds-inclusive simulations</td><td>t/ha</td><td>11.5</td><td>7.6</td><td>7.1</td><td>6</td><td>13.6</td><td>17.3</td><td>12.1</td><td>7</td></tr><tr><td>Wheat yield loss</td><td>Relatively to weed-free yield</td><td>%</td><td>28</td><td>47</td><td>36</td><td>54</td><td>28</td><td>12</td><td>33</td><td>57</td></tr><tr><td>Field infestation</td><td>Field infestation by weeds</td><td>t/ha</td><td>1.31</td><td>1.9</td><td>1.7</td><td>2.3</td><td>0.7</td><td>1.1</td><td>0.43</td><td>0.21</td></tr><tr><td colspan="11"><strong>B. Varieties (Cap = Caphorn, Céz = Cézanne, Orv = Orvantis) answering to the rules determining each ideotype</strong></td></tr><tr><td colspan="11"><strong>Virtual varieties corresponding to the ideotype rules<sup>®</sup></strong></td></tr><tr><td></td><td></td><td></td><td><strong>1</strong></td><td>3 11</td><td><strong>12 15</strong></td><td><strong>1</strong></td><td>3</td><td>10</td><td>3</td><td><strong>1</strong></td></tr><tr><td>Restrictions on management</td><td>Sow before</td><td></td><td>6Nov</td><td></td><td>6Nov</td><td></td><td>20 Oct</td><td>12 Oct</td><td>20 Oct</td><td>30 Oct</td></tr><tr><td></td><td>Harvest after</td><td></td><td></td><td>28 Jun</td><td></td><td>28 Jun</td><td>8 Jul</td><td>3 Jul</td><td>19 Jul</td><td>30 Jun</td></tr></tbody></table>

®in bold, the two best varieties in terms of yield averaged over all organic cropping systems and flowering precocities, Fig. 3
<caption>**Table 6**</caption>

Summary of wheat ideotype characteristics in comparison to the three current varieties and consistency with literature reports with similar (=), contrasting (≠) or inconclusive (?) conclusions (details in section F.3 online). Detailed rules of ideotypes iY1, iP1, iT1 and iS1 are in Table 3, for the other ideotypes, see section E.2.2 online. Rules in green improve yield, weed tolerance / suppression compared to some (standard) or all (bold) existing varieties, those in red show trade-offs resulting from correlations with beneficial characteristics. In blue, parameters whose values were constant for the three existing varieties. Note that parameter values must be combined to be efficient.

<table><thead><tr><th rowspan="2">Wheat parameters (and comparison to current varieties)</th><th rowspan="2">Min-max values in the 3 real varieties</th><th colspan="3">Reference cropping system</th><th colspan="4">Weed-infested wheat yield depending on cropping system</th><th rowspan="2">Literature (number of papers)</th></tr><tr><th>Potential (weed-free) yield iP1</th><th>Tolerance T1</th><th>Suppression iS1</th><th>Reference iY1</th><th>Clover iC1</th><th colspan="2">No-till</th></tr></thead><tbody><tr><td colspan="10"><strong>A. Germination and emergence</strong></td></tr><tr><td>[1] Base temperature</td><td>1°C</td><td>Similar</td><td></td><td></td><td>Similar</td><td></td><td>Lower</td><td></td><td></td></tr><tr><td>[2] Germination cohorts</td><td>0.93<sup>●</sup></td><td>Fewer</td><td>Fewer</td><td></td><td>Fewer</td><td>Similar</td><td></td><td></td><td>=3</td></tr><tr><td>[3] Pre-emergent growth after germination</td><td>3.2<sup>●</sup></td><td>Similar</td><td></td><td>Similar</td><td>Faster</td><td></td><td>Faster</td><td></td><td></td></tr><tr><td>[4] Seedling loss in uncompacted soil (vs average soil structure)</td><td>-0.63 sdl·sdl<sup>-1</sup><br/>·ln(mm<sup>-1</sup>)</td><td>Similar</td><td></td><td></td><td>Less</td><td></td><td>Less</td><td></td><td></td></tr><tr><td>[5] Post-emergence ground cover: relative leaf-area growth rate</td><td>0.019–0.022<br/>ln(cm<sup>2</sup>) ·°Cday<sup>-1</sup></td><td>Similar</td><td></td><td>Similar</td><td>Faster</td><td></td><td>Faster</td><td>Faster</td><td>=19</td></tr><tr><td colspan="10"><strong>B. Root system</strong></td></tr><tr><td>[6] Root system width<sup>●</sup></td><td>293–308 mm</td><td>Similar</td><td>Narrower</td><td></td><td>Narrower</td><td></td><td>Similar</td><td>Narrower</td><td>?1</td></tr><tr><td>[7] Root system depth growth</td><td>15.2–15.3 mm day<sup>-1</sup></td><td></td><td>Faster</td><td></td><td>Similar</td><td></td><td></td><td></td><td></td></tr><tr><td>[8] Root growth in compacted soil</td><td>0.96–1 cm cm<sup>-1</sup></td><td>Similar</td><td></td><td>Similar</td><td>Similar</td><td></td><td>Worse</td><td></td><td></td></tr><tr><td colspan="10"><strong>C. Above-ground plant morphology</strong></td></tr><tr><td>[9] Leaf area distribution along plant height</td><td>1.75–2.74<sup>●</sup></td><td>Similar</td><td>Larger</td><td></td><td>Higher</td><td></td><td>Higher</td><td>Lower</td><td></td></tr><tr><td>[10] Leaf area per leaf biomass (emergence)</td><td>154–170 cm<sup>2</sup>·g<sup>-1</sup></td><td>Similar</td><td></td><td></td><td>Similar</td><td></td><td>Smaller</td><td>Larger</td><td>=3</td></tr><tr><td>[11] Shading response at different stages (thinner larger leaves)</td><td>0.061–0.859<sup>●</sup></td><td></td><td>Weaker</td><td>Weaker</td><td>Similar / Stronger</td><td></td><td>Weaker / Similar</td><td>Stronger</td><td></td></tr><tr><td>[12] Plant height per biomass (vegetative)</td><td>21.9–23.0 cm·g<sup>-1</sup></td><td>Taller</td><td></td><td>Taller</td><td>Taller</td><td></td><td>Smaller</td><td>Taller</td><td>=12</td></tr><tr><td>[13] Maximum plant height</td><td>99–108 cm</td><td>Similar</td><td></td><td>Smaller</td><td>Smaller</td><td></td><td>Smaller</td><td>Taller</td><td>?3</td></tr><tr><td>[14] Plant height homogeneity (repro.)</td><td>0.094–0.168<sup>●</sup></td><td>Worse</td><td></td><td>Better</td><td>Better</td><td></td><td>Worse</td><td>Worse</td><td></td></tr><tr><td>[15] Plant width homogeneity (emergence)</td><td>0.31–0.67<sup>●</sup></td><td></td><td></td><td>Worse</td><td>Better</td><td></td><td>Better</td><td></td><td></td></tr><tr><td>[16] Plant width per biomass (reproduction)</td><td>7.2–21.2 cm·g<sup>-1</sup></td><td>Narrower</td><td></td><td>Larger</td><td>Narrower</td><td>Similar</td><td>Larger</td><td></td><td>=6</td></tr><tr><td>[17] Harvest index in heavy plants</td><td>1.0<sup>●</sup></td><td>Higher</td><td></td><td></td><td>Higher</td><td></td><td></td><td>Higher</td><td></td></tr><tr><td colspan="10"><strong>D. Phenology</strong></td></tr><tr><td>[18] Flowering onset (°C days)</td><td>1100–3184 °Cdays</td><td>>1610</td><td>>1460</td><td></td><td>>1650</td><td>>2250</td><td>>1570</td><td>>1730</td><td>=1 ≠3</td></tr><tr><td>[19] Maturity onset vs flowering onset</td><td>750 °Cdays</td><td></td><td>Later</td><td></td><td>Later</td><td>Similar</td><td></td><td>Later</td><td>≠5</td></tr><tr><td colspan="10"><strong>E. Photosynthesis</strong></td></tr><tr><td>[20] Leaf extinction</td><td>0.80</td><td></td><td></td><td>Similar</td><td>Similar</td><td></td><td>Lower</td><td>Lower</td><td></td></tr><tr><td>[21] Photosynthesis efficiency</td><td>...cool</td><td>12°C<sup>●</sup></td><td>Similar</td><td>Better</td><td>Better</td><td></td><td>Worse</td><td>Worse</td><td></td></tr><tr><td>[22] when weather is ...</td><td>...hot</td><td>40°C<sup>●</sup></td><td>Similar</td><td>Better</td><td>Similar</td><td></td><td>Better</td><td>Similar</td><td></td></tr><tr><td colspan="10"><strong>F. Frost tolerance</strong></td></tr><tr><td>[23] Frost tolerance</td><td>After emergence</td><td>-25°C<sup>●</sup></td><td>Similar</td><td></td><td>Similar</td><td>Better</td><td>Better</td><td>Worse</td><td>Worse</td></tr><tr><td>[24]</td><td>During reproduction</td><td>-5°C<sup>●</sup></td><td></td><td>Better</td><td>Similar</td><td></td><td>Similar</td><td>Worse</td><td>Worse</td></tr></tbody></table>

● The higher this parameter, the fewer germination cohorts there are

● The higher this parameter, the slower pre-emergent growth is just after germination.

● A narrow root system means more above-ground biomass and light interception, a wider system less soil water available for weed germination and emergence

● The higher this parameter, the more uneven and upward leaf area is distributed.

● The higher this parameter, the thinner and larger leaves become on shaded plants.

● The closer to zero, the more homogeneous plant heights or widths are.

● Values > 1 indicate a higher harvest index in heavy vs light plants, and vice-versa for values < 1

● Temperature above which photosynthesis is optimal

● Temperature above which photosynthesis stops

● Temperature below which all plants die through frost

● Temperature below which plants lose biomass due to frost

lateness of flowering before reaching a sill of slightly more than 5 t/ha in average at approximately 1800°C days (Fig. 4.A). Note that these late-flowering varieties needed adapted sowing and harvest dates allowing full maturity at harvest. The actual (weed-infested) wheat yield followed a similar pattern for early flowering at a lower yield level but yield decreased in crops flowering after 2000°C days (Fig. 4.B). Indeed, the later flowering onset also allowed more time for weeds to accumulate biomass (Fig. 4.C) and thus hinder crop growth.

### 3.6. The most influential wheat management techniques

Table 7 summarizes the most influential wheat management

techniques. Tillage was essential for weed tolerance and suppression, by triggering weed seed germination during fallow to empty the weed seed bank, burying weed seeds too deeply for emergence and destroying weed plants during fallow. However, timing was crucial to avoid having triggered seeds emerge after crop sowing (e.g., positive correlation between time from plough/tillage to sowing and weed tolerance/suppression) and/or delaying crop sowing too much for optimal growth (i.e., negative correlation between tillage November-March and potential yield). The regular placement of the wheat plants (via a narrow interrow) was more important than a high crop plant density. Mechanical weeding was only helpful if well timed, i.e., early enough to avoid crop damage but late enough to destroy most early emerging
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Variation in wheat yield potential (A) and weed suppression (B) with flowering lateness of wheat varieties (time from emergence to flowering onset), and the resulting wheat yield in weed-infested fields (C). Dots are all wheat years from the 7 organic situations × 400 cropping systems × 10 weather repetitions × 15 years simulated with FLORSYS. Blue lines show polynomial regressions with confidence intervals in grey.</figcaption></figure>
<caption>**Table 7**</caption>

The key wheat management techniques driving potential yield, weed tolerance (i.e., low yield loss due to weeds) and weed suppression (i.e., low field infestation by weed biomass) in wheat crops. The 10 most influential techniques per indicator were selected based on relative variable importance predictors in classification and regression trees (CART) analysing indicators as a function of wheat parameters and crop management techniques. Probability cells were coloured in green (an increase in technique values results in an increased indicator value) and red (an increase in technique values results in a decreased indicator value).

<table><thead><tr><th colspan="4">Management technique</th><th colspan="3">Effect on <sup>o</sup></th><th rowspan="2">Explanation</th></tr><tr><th>Technique</th><th>Unit</th><th>Min</th><th>Max</th><th>Potential yield</th><th>Weed tolerance</th><th>Weed suppression</th></tr></thead><tbody><tr><td>Superficial tillage (disregarding rolling)</td><td>per year</td><td>0</td><td>10</td><td></td><td></td><td>+</td><td>Tillage empties weed seed bank by stimulating germination during summer fallow ("false seed bed technique"), destroys weed plants prior to crop sowing</td></tr><tr><td>Superficial tillage during April-October</td><td>per year</td><td>0</td><td>10</td><td></td><td>+</td><td>+</td><td></td></tr><tr><td>Tillage during November-March</td><td>per year</td><td>0</td><td>10</td><td>-</td><td></td><td></td><td>Comes with very late sowing, shortening the crop duration and crop biomass accumulation</td></tr><tr><td>Time from ploughing to sowing</td><td>days</td><td>1</td><td>124</td><td></td><td>-</td><td></td><td>Leaves more time for weeds to re-infest the field after the last tillage operation cleaned out all weeds</td></tr><tr><td>Time from last tillage to sowing</td><td>days</td><td>0</td><td>107</td><td></td><td>-</td><td>-</td><td></td></tr><tr><td>Last tillage date</td><td></td><td>2Sep</td><td>28Nov</td><td>-</td><td>+</td><td></td><td>Leaves less time for weed germination</td></tr><tr><td>Sowing date</td><td></td><td></td><td></td><td></td><td>-</td><td></td><td>Leaves more and tillage during fallow</td></tr><tr><td>Interrow width if row sowing</td><td>cm</td><td>4</td><td>72</td><td>-</td><td></td><td></td><td>Shortens the wheat growth duration</td></tr><tr><td>Mechanical weeding operations</td><td>per year</td><td>0</td><td>5</td><td>-</td><td></td><td>+</td><td>Less light interception by wheat, more space/light available for weeds</td></tr><tr><td>Mechanically weeded field area</td><td>%</td><td>0</td><td>100</td><td>-</td><td></td><td></td><td>Destroys weed plants inside the crop, but damage to the crop</td></tr><tr><td>Mean tractor speed during mech. weeding</td><td>km/h</td><td>0</td><td>18</td><td>-</td><td></td><td></td><td>More damage to the crop</td></tr><tr><td>Time from sowing to 1<sup>st</sup> mechanical weeding operation</td><td>days</td><td>2</td><td>273</td><td></td><td></td><td>-</td><td>Weeds are larger and more difficult to destroy</td></tr><tr><td>Time from last mechanical weeding to harvest</td><td>days</td><td>4</td><td>248</td><td>+</td><td>-</td><td></td><td>Less damage to the crop, but last weeding operation too early to destroy late-emerging weeds</td></tr><tr><td>Harvest date</td><td></td><td>8Jun</td><td>14Aug</td><td></td><td>+</td><td>-</td><td>Leaves more time for crop biomass production, but also for weeds to grow &amp; reproduce</td></tr><tr><td>Herbicides (during fallow and on crop)</td><td>per year</td><td>0</td><td>4</td><td></td><td></td><td>+</td><td>More weeds are destroyed → more space and nutrients for wheat</td></tr><tr><td>Herbicides sprayed on wheat</td><td>per year</td><td>0</td><td>4</td><td></td><td></td><td>+</td><td></td></tr></tbody></table>

<sup>o</sup>An increase in a wheat trait leads most likely to an increase (+) or to a decrease (-) in yield, weed tolerance or suppression.

weeds.

The timing of sowing and harvest was also complicated, because early sowings and late harvests (which were needed for the beneficial effects of late-flowering varieties shown in Fig. 4) allowed both more crop biomass production and more weed reproduction (and thus more infestation in future crops). Moreover, delayed sowing was beneficial for weed tolerance by leaving more time for weed seeds to germinate during fallow, but only if accompanied by the last tillage operation to clean the field of all weeds.

# 4. Discussion

## 4.1. What is new?

In the present paper, we applied the approach developed by Colbach et al. (2022) for pea to wheat. This approach combined detailed experimental measurements characterizing varieties from a previous study (Colbach et al., 2020b) with a simulation study to test actual and virtual varieties in virtual field networks, in order to identify ideotypes of varieties and cropping systems for agroecological weed management. We already discussed the advantages and limits of this approach in our previous articles (Colbach et al., 2020b, 2022), in terms of variability of investigated situations, use of parameters reflecting intrinsic plant

properties that vary little with the environment, and lower risk of confusion effects.

Several steps had to be adapted in the present study compared to the previous study (Colbach et al., 2022). The simulation steps were the same as for pea study except that the focus was on organic cropping systems rather than on conventional herbicide-sprayed systems. Moreover, as model parameters were only available for three existing wheat varieties (compared to seven pea varieties in the previous study), we included more virtual varieties and used a larger range of values for drawing the variety parameters. The idea was to investigate a larger range of potential varieties. This worked insofar as the yields simulated for the virtual varieties relatively to the existing varieties varied much more here for wheat (min and max of virtual-variety weed-free yields were 21 % and 212 % of min and max of existing-variety yields, and 35 %-310 % for weed-infested yield) than for pea in the previous study (60 %-68 %, 73 %-76 %, (Colbach et al., 2022) and section F.2 online), and this even though the pea study included both spring and winter varieties.

Compared to previous studies on wheat (see Section 4.2.2), our study produced several new types of results: (1) we investigated a larger range of variety parameters covering the whole life-cycle from seed to plant to seed production whereas past studies mostly focused on a small number of traits, (2) the parameters investigated here are variety-inherent
constants which are close to biophysical processes and do not depend on the environment where past studies usually measure more integrative variety traits that vary among trials, (3) we evaluated both real and virtual varieties, which allowed us to go beyond the realm of past breeding history, and thus could identify ideotypes which do not yet exist to guide future breeding schemes, (4) key traits and ideotypes were determined both over all situations and according to different types of organic cropping systems whereas past studies usually focussed on organic vs conventional at best.

We identified ideotypes adapted to broad cropping system types (e. g., tilled organic, untilled organic). Few rules applied to all ideotypes. These generic wheat rules concerned emergence (e.g., low seedling loss) and establishment (e.g., fast post-emergence ground cover) as well as flowering precocity. In contrast, the "generalist" pea rules all concerned morphological characteristics (Colbach et al., 2022). However, these "generalist" pea rules only concerned spring varieties whereas the present study only looked at winter wheat varieties. The importance of emergence and establishment over morphology in winter wheat is consistent with field reports of crop and weed emergence dates having more effect than plant-plant proximity on wheat and weed yields (Fahad et al., 2015).

What makes the work of crop breeders even more difficult is the importance of interactions among parameters. Indeed, contrasting parameter values can lead to the same performance in terms of wheat yield or weed tolerance/suppression, depending on which other wheat parameter values they are combined with. The most prominent example was a large root-system extension, which was only beneficial to reduce weed germination when it was associated with plants investing the lower above-ground biomass into wide rather than tall plants as in ideotype IN1 adapted to organic no-till.

## 4.2. Are the results consistent with previous studies?

### 4.2.1. The simulated variety behaviour is consistent with literature

Our results are conditional on the prediction quality of FLORSYS, which was shown to be adequate in a previous study (Section 2.1.5). Here, we specifically searched the literature for wheat studies. A large-scale field trial (Fontaine et al., 2012) testing 15 wheat varieties (among which Caphorn) with organic management reported a variety behaviour consistent with our simulations (details in section F.1 online). The range of the observed yields and weed tolerance were though smaller than simulated here: 6.5-9.1 t/ha and 2.3-3.5 t/ha for weed-free and weed-infested yields, respectively, with 31-47 % yield loss. This was the result of our virtual varieties which were expressly created to extend the variety range. Both in the trial and in our simulations, Caphorn ranked in the middle of tested variety pool but its yields were roughly 75 % larger in the trial than in our simulations, and the yield loss was 40 % less. This could be due to the small number of locations (2) and years (2-3) in the field study, whereas our simulation study started with three contrasting and often quite dense weed floras which evolved over 15 years in thousands of cropping systems, and covered 10 weather repetitions, with 3-4 wheat crops per repetition.

Despite these disparities, both the field trial and our simulations showed the same negative trend between weed-induced yield loss and weed-free yields, and a positive correlation between weed-infested and weed-free yields (details in section F.1 online). The two yield types were though much less correlated in the field trial ($R^2$ = 0.14, compared to our 0.79). Possibly, our protocol for creating virtual variety missed a whole "dimension" of intra-species variability for wheat, as we started from only three existing varieties. The low correlation between potential and actual yield are well known in wheat (Andrew et al., 2015) as well as for other species, and are often explained by a trade-off between community performance and competitiveness (Denison et al., 2003).

### 4.2.2. Our results agree with the rare literature on wheat traits

It was difficult to compare the present conclusions on wheat variety

parameters with literature, because field trials mostly focused on a small number of traits, which moreover often differed from the variety parameters used in FLORSYS. For instance, tillering ability is a major trait considered in wheat varieties (Andrew et al., 2015; van der Meulen and Chauhan, 2017) but is not used explicitly in FLORSYS. We therefore tried to find analogies to show for instance that the better weed-infested yield of wheat varieties with taller plants per unit biomass in our simulations is consistent with the increased competitiveness of taller varieties in literature (see review by Lemerle et al., 2001; Bertholdsson, 2005; Andrew et al., 2015; Kucek et al., 2021). The two traits are though fundamentally different insofar as the FLORSYS parameter is a species-intrinsic stage-dependent potential ability (i.e., in unstressed conditions) and plant height is a situation-dependent trait integrating past growth resulting from species ability and resource availability.

When the simulations could be compared to literature reports (Table 6, details in section F.3 online), our conclusions were often consistent with literature (as for pre-emergent growth speed, post-emergent ground cover, and frost tolerance) or literature reported no effects or contrasting conclusions (e.g., root system, plant height). The sole exception was flowering precocity. In literature, an earlier heading, flowering or maturity onset is generally associated with high weed suppression (Huel and Hucl, 1996; Bertholdsson, 2005; Mason et al., 2008; Reid et al., 2009a; Piliksere et al., 2013; Worthington et al., 2015; Zerner et al., 2016), although not always (Coleman et al., 2001). Conversely, our ideotypes all required a later flowering than most of the three existing varieties used here, and also later maturity onset. Actually, later flowering/maturity was only beneficial if associated with the fast and homogeneous establishment as well as plant morphology optimizing light interception (Table 6). In average, Fig. 4.C confirmed that earlier varieties were more weed-suppressive. Moreover, ideotypes were selected here mostly for maximising weed-infested yield, which results from the combination of weed suppression, weed tolerance and yield potential. However, the latter greatly increased with late flowering (Fig. 4.A) and is usually not monitored in trials screening for weed-suppressive varieties.

## 4.3. Implications for farmers and crop breeders

### 4.3.1. The ideal variety in a nutshell

The identification of the parameters relevant for yield and weed control are a major stepping-stone toward decision support for farmers to choose varieties adapted to different cropping-system types. The take-home message in the present study was the importance of late-flowering, late-maturing and winter-hardy wheat varieties with early, highly covering and homogeneous plant establishment, possibly to the detriment of root development. The latter is though only valid if nitrogen and water are non-limiting after crop emergence. This limitation is crucial, particularly in organic systems where nitrogen is often insufficient (Osman et al., 2012; Rempelos et al., 2023). If crop establishment is too late and/or unsatisfactory, a homogeneously tall canopy with heavy plants with a high harvest index is essential. No single beneficial parameter ensures a good variety performance; the latter is always the result of a combination of several complementary parameters.

The ideotypes require adapted sowing and harvesting dates, and their weed suppression can be improved with narrower interrows. Because of the high plasticity of wheat growth, sowing densities are less influential. In any case, a single lever such as competitive varieties is insufficient to manage weeds in organic systems. Low or zero herbicide systems must indeed combine many "little hammers" (Liebman and Gallandt, 1997). Among these, tillage was shown here to be a crucial technique, without which weed control would be impossible in herbicide-free systems (Colbach and Cordeau, 2022). As for mechanical weeding, timing is essential to ensure a good efficacy (Lundkvist, 2009; Rasmussen et al., 2010).

In practice, it seems more relevant for wheat breeders to define different ideotypes according to the cropping systems and production
situations considered. Subsequently, some traits common to several ideotypes could be prioritized (Lever et al., 2022). However, including these targeted traits in future breeding schemes already considering a number of complex traits could be a difficult task, especially because these targeted traits are potentially characterized by antagonistic effects with other important traits or/and highly dependent on environmental conditions and agronomic practices ((Liebman and Gallandt, 1997; Debaeke et al., 2024).

### 4.3.2. How to identify candidate varieties?

The next step will be to screen the existing wheat varieties and to identify those which are the closest to the prioritized ideotypes. These are described by many detailed parameters, which were essential to identify precise seed or plant abilities needed for a good crop performance. However, many of these parameters are not routinely measured during the different steps of breeding schemes (Zhao et al., 2006) and are estimated in controlled conditions (e.g., Gardarin et al., 2010) or on individual plants in garden plots (Colbach et al., 2020b). This would mean retesting all varieties with the help of, e.g., high-throughput phenotyping platforms in greenhouse or in fields (Jeudy et al., 2016; Brichet et al., 2017; Aharon et al., 2020). Another, faster avenue would be to link the complicated FLORSYS parameters to state variables that are easy to measure in variety trials, following the same principle as Colbach et al. (2020b) used at the species scale.

In practice, it was difficult to match FLORSYS parameters to measurable traits in field conditions. However, we attempted to identify proxies for some parameters and compared the FLORSYS results to the available literature (see last column of Table 6 and details in section F.3 online). At this stage, there are consistent trends between some sets of FLORSYS parameters and results from the literature, especially regarding traits associated with early vigour. These traits, although studied in the literature, have so far been little integrated into wheat breeding schemes. One of the reasons is related to the potential reduction in grain yield in weed-free conditions. However, recent studies do not confirm this usual hypothesis. For instance, a comparison of a competitive near-isogenic line (NIL) with its recurrent parent characterized by different high ground cover did not show difference in grain yield and thousand kernel weight (Aharon et al., 2021), while a comparison of high-vigour lines with commercial wheat varieties used as parents showed a slight reduction or no difference in grain yield in weed-infested field (Hendriks et al., 2022). Based on knowledge from both the present FLORSYS simulations and the literature, we therefore suggest to prioritize a few well-chosen traits associated with early vigour in breeding schemes.

### 4.3.3. Shift the focus from yield potential to weed competitiveness

Breeding for weed tolerance or/and weed suppression requires that farmers and breeders shift their focus from yield potential toward weed competitiveness. Indeed, the latter has usually not been considered a priority for breeders in their breeding schemes (Andrew et al., 2015). Breeders must also focus more on germination and emergence timing and speed, and not simply on post-emergence competitiveness as in the past. Indeed, our simulations as well as field trials demonstrate that timing trumps competitiveness (Fahad et al., 2015). However, very few studies investigate intra-species variability in traits, (e.g., base temperature for germination, Dürr et al., 2015), and below-ground plant traits are even less known (Lever et al., 2022) and studied mostly under controlled conditions (e.g., Colombo et al., 2022).

The differences in the best ideotypes for organic (iY1) and herbicide-sprayed wheat (iH1) identified here might also explain why varieties selected for conventional cropping systems often underperform in organic ones, justifying different breeding schemes (e.g., Reid et al., 2009b; Kirk et al., 2012), or combining information provided from both organic and low-input cropping systems (Le Campion et al., 2014; Muellner et al., 2014). Indeed, iH1 presented many features (e.g., small thick leaves, low shade response, small plants per unit biomass) that facilitate both herbicide and light penetration into the canopy as the

latter are highly correlated (Kim et al., 2011). In the absence of herbicides, the high light penetration though greatly favours weed growth and thus wheat yield loss.

In practice, wheat breeding schemes for varieties sown in conventional and organic cropping systems are at least partially different. However, some traits relevant to the different ideotypes could be integrated into all schemes, among which are traits associated with early vigour, as shown by the importance of fast and homogeneous emergence and post-emergence ground cover here and also in the literature (Lever et al., 2022).

### 4.4. From theory to practice: what remains to be done?

The wheat-parameter rankings and the optimal parameter combinations identified here are a first essential step for decision support to both wheat breeders and farmers, proposing varieties for agroecological weed management depending on the cropping-system type, performance goal and/or weed flora. However, before attempting to develop a decision support system to guide these end users, several avenues still need to be explored. The present simulations mostly disregarded plant-plant competition for soil resources, and our team is presently running a new simulation study focusing on competition for nitrogen, thanks to the new FLORSYS submodel including crop-weed competition for nitrogen (Moreau et al., 2021). This will also allow considering harvest quality in terms of protein content. The range of investigated virtual varieties needs to be improved by starting from a larger pool of existing wheat varieties, which means parameterizing more wheat varieties for FLORSYS.

Finally, the present simulation study was run in a single geographical region, albeit with different weather series and weed floras. As variety performance has been shown to vary among regions and years (McDonald, 2003; Jacob et al., 2017), our study must be repeated in other pedoclimates (including climate-change scenarios) to adapt the selection and farming guidelines. These will probably vary, as they do here among cropping systems, particularly considering climate change. In the latter case, tolerance to heat and drought will likely be more influential as, e.g., drought events will increase in frequency and severity (Brisson et al., 2010).

## 5. Conclusion

The present sensitivity analysis of wheat production and biological weed regulation to wheat variety and crop management identified the key parameters that drive potential yield, weed tolerance and weed suppression in wheat, depending on cropping system. These are pointers for breeding wheat varieties in agroecological cropping systems aiming to regulate weeds by biological interactions. We also produced rules to guide farmers to choose the best wheat variety, depending on the cropping system. Further research is needed to resolve the trade-off between wheat parameters that promote potential yield and those driving weed tolerance and suppression, to investigate the role of competition for soil resources, and to link model parameters to variables routinely measured during crop breeding schemes.

### CRediT authorship contribution statement

**Pierre Lebreton:** Writing – original draft, Visualization, Software, Formal analysis.
**Delphine Moreau:** Writing – review & editing, Visualization, Conceptualization.
**Rémi Perronne:** Writing – review & editing, Project administration, Funding acquisition, Conceptualization.
**Nathalie Colbach:** Writing – review & editing, Visualization, Supervision, Methodology, Formal analysis, Conceptualization.

### Declaration of Competing Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.

## Acknowledgements

This project was supported by INRAE, the French projects MOBIDIV (ANR-20-PCA-0006) financed by the Agence Nationale de la Recherche, COPRAA which was supported by the Office Français de la Biodiversité OFB as part of an Ecophyto call launched by the French Ministries in charge of Ecology, Agriculture, Health and Research, FSOV 2020 Q - Blé-Adventices-III (2021–2024) funded by the Plant Breeding Support Fund FSOV, and the Mix-it! project funded by the Swiss Federal Office for Agriculture (BLW).

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.eja.2024.127501.

## Data availability

Data will be made available on request.

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