# Combining wheat with multi-species prairie strips reduces the development of Septoria tritici blotch

Lisa Besson, Olivier Duchene, Gaël Alvarez, Thomas Becu, Sébastien Fontaine, Nicolas Gross, Magali Joannin, Audrey Niboyet, Stéphane Sauvat, Emma Schwartz, et al.

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Lisa Besson, Olivier Duchene, Gaël Alvarez, Thomas Becu, Sébastien Fontaine, et al. Combining wheat with multi-species prairie strips reduces the development of Septoria tritici blotch. 2025. hal-05351353

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Distributed under a Creative Commons Attribution 4.0 International License
Combining wheat with multi-species prairie strips
reduces the development of Septoria tritici blotch

**Lisa Besson**¹, **Olivier Duchene**², **Gaël Alvarez**¹,³, **Thomas Becu**¹, **Sébastien Fontaine**¹, **Nicolas Gross**¹,
**Magali Joannin**⁷, **Audrey Niboyet**⁵,⁶, **Stéphane Sauvat**³, **Emma Schwartz**¹, **Gaëlle Marliac**³,⁴

**Affiliations :**

¹INRAE, Unité Mixte de Recherche Ecosystème Prairial, VetAgro Sup, Université Clermont
Auvergne, 5 chemin de Beaulieu, 63000 Clermont-Ferrand, France

²ISARA, Research Unit Agroecology and Environment, 23 Rue Jean Baldassini, 69007 Lyon, France

³VetAgro Sup, Universite Clermont Auvergne, 89 Av. de l'Europe, 63370 Lempdes, France

⁴INRAE, GDEC, Universite Clermont Auvergne, 5 chemin de Beaulieu, 63000 Clermont-Ferrand,
France

⁵Sorbonne Université, Université Paris Cité, UPEC, CNRS, INRAE, IRD, Institut d'Ecologie et des
Sciences de l'Environnement de Paris, 75005, Paris, France

⁶AgroParisTech, 91120, Palaiseau, France

⁷INRAE, UE PHACC, 5 chemin de Beaulieu, 63000 Clermont-Ferrand, France

**Corresponding author:**

E-mail address: [lisa.besson@inrae.fr](mailto:lisa.besson@inrae.fr)

**Keywords:**

Zymoseptoria tritici / Wheat / Diversification / Strip Intercropping / Prairies/ Disease
regulation/ Rhizosphere microbiome
Abstract

Crop diversification, notably through intercropping, is known for regulating disease development in agroecosystems. However, intercropping usually involves two or three species, limiting its potential to enhance ecosystem multifunctionality. This study investigates the impact of “agroprairies”, a cropping system design consisting of alternating narrow strips of wheat and multispecies prairies (~12 species), on the development of *Zymoseptoria tritici*, the causal agent of Septoria tritici blotch (STB), a major foliar disease in wheat.

Two complementary field experiments were conducted. The first one monitored the incidence and severity of STB under natural epidemic conditions across combinations of four wheat cultivars and four prairie mixtures with contrasted functional traits. The second experiment used above-canopy inoculation on a subset of treatments to assess disease dynamics under high disease pressure, independent of physical barrier effects. Rhizosphere microbiomes were analyzed before inoculation to evaluate potential enrichment in taxa associated with plant defense and disease suppression.

Under natural conditions, STB incidence and severity were reduced by 52% and 41%, respectively, in two agroprairie types. Prairie biomass and specific leaf area (SLA) were identified as key factors associated with this reduction. When the disease was inoculated above the canopy, this reduction effect disappeared, highlighting the dominant role of physical barriers in disease regulation. However, one agroprairie treatment still reduced disease progression by 18%, suggesting the involvement of additional regulatory mechanisms. Microbiome analysis revealed shifts in bacterial beta diversity and an enrichment of potentially beneficial taxa in the wheat rhizosphere, although no direct link with disease regulation was established.

# 1 Introduction

Septoria tritici blotch (STB), caused by the ascomycete fungus *Zymoseptoria tritici* (Sánchez-Vallet et al., 2015) is one of the most damaging leaf diseases of wheat (Suarez-Fernandez and De Francesco,
2024), the most widely grown small grain cereal in the world (Balfourier et al., 2019; Erenstein et al., 2022). The fungal pathogen disperses via wind in the acrospore (sexual) form from distant infected wheat debris and then via rain splash from the bottom to the top of the plant in the pycnidiospores (asexual) form (Shaw, 1987; Steinberg, 2015; Suffert et al., 2011). The risk of contamination extends from the two-node stage to the flowering stage. After contamination, the development of STB causes progressive leaf necrosis, which can lead to yield losses of up to 20% in the absence of effective disease management strategies (Fones and Gurr, 2015). The most common methods of managing this disease are using resistant wheat cultivars or applying fungicides (Sánchez-Vallet et al., 2015). However, *Z. tritici* evolves rapidly, and can develop resistance to these practices (Jørgensen et al., 2022; McDonald and Mundt, 2016). Furthermore, concerns about the environmental and health impacts of pesticides have led to the European Green Deal that aims to reduce pesticide use by 50% by 2030 (Boix-Fayos and de Vente, 2023).

Crop diversification at the field scale is an effective way to limit disease development (Vialatte et al., 2025). Among the crop diversification methods, intercropping is one of the most effective approaches (Beillouin et al., 2021) and several studies showed a reduction of leaf diseases in intercropping compared to monocrops (De Long et al., 2023; Zhang et al., 2019). For example, Zhang et al., (2019) demonstrated that wheat-faba bean intercrops reduced yellow rust and powdery mildew by 34% and 20% respectively.

Yet, current intercropping systems typically involve two or three plant species. However, studies in grassland ecosystems have demonstrated that the maximum efficiency of reducing disease severity can be achieved with much richer plant communities (12 plant species in Mitchell et al., 2002). If we consider functions other than disease regulation, such as biomass production, soil carbon storage and nutrient retention, as well as their resistance to climatic fluctuations, the level of diversification required to maximize ecosystem functions is likely even more important (Gross et al., 2017; Newberger et al., 2023; Wagg et al., 2014).

To address the challenge of increasing plant diversity at the field scale and improving ecosystem functioning, including disease regulation, a novel cropping system called “agroprairie” was designed.
In reference to agroforestry, agroprairies consist of a permanent, diversified grassland plant community that is arranged in narrow, alternating strips with annual crops (Figure 1, **Erreur ! Source du renvoi introuvable.C**). To foster and test the coexistence of a large number of species in the prairie strips over years, we used different mixtures of species commonly found in natural or semi-natural prairies of the studied area, and differently positioned on two different plant functional dimensions. The first dimension relates to resource use strategy of plants, that distinguishes "resource-acquisitive" and "resource-conservative" plant species (Grime, 2006). Resource-acquisitive species are characterized by rapid growth, high tissue turnover, litter chemistry that promotes microbial decomposition, and high nutrient uptake from the soil (Henneron et al., 2020). In contrast, "resource-conservative" species are characterized by slow growth (Lambers and Poorter, 1992), low tissue turnover, litter chemistry resistant to microbial decomposition and low nutrient uptake from soil (Henneron et al., 2020; Lambers and Poorter, 1992). The second plant functional dimension also used to differentiate plant communities of the prairie strips was their ability to fix atmospheric N, i.e the proportion of legumes in the plant community.

The strip intercropping of wheat with multi-species prairie can play a role in regulating disease development through various mechanisms known from intercropping systems (Figure 1). The first mechanism involved is the interference with spore dispersal by wind or rain in relation to barrier effect of companion plants. This mechanism is often the first suggested in intercropping field studies (Luo et al., 2022; Zhang et al., 2019). Second, host morphology and physiology alteration in response to the intercropping practice can also modify disease development, modifying microclimate and resources for pathogen (Richard et al., 2013; Vidal et al., 2017). This includes, for example, changes in the host plant's architecture and the leaf nitrogen (Gómez-Rodríguez et al., 2007; Wu et al., 2024). A third possible mechanism is the host sensitivity/defense modification partly linked with the recruitment of a specific microbiome in the host plant generated by intercropping (Mao et al., 2024; H. Yu et al., 2019; Zhu et al., 2024). A key site for microbiome-related changes appears to be the plant rhizosphere, where microbial communities are believed to control diseases by triggering defense responses or producing bioactive compounds (Berendsen et al., 2018; H. Liu et al., 2020). These microbial communities may
act locally within the rhizosphere (H. Liu et al., 2020; Liu et al., 2023) or subsequently colonize other plant tissues (Dini-Andreote, 2020). However, the role of rhizosphere microbiome in regulating plant responses to pathogens is much less understood than physical or physiological aspects in the field, even if studies reported positive effects of diversification practices on microbial diversity (Granzow et al., 2017; Jin et al., 2020; Pang et al., 2022; H. Yu et al., 2019).

The main objective of this study is to determine whether the development of STB in wheat is influenced by the agroprairie design, i.e. the strip intercropping of wheat with multispecies prairie, and to investigate the potential mechanisms involved in the regulation of STB. To do so, we have combined two complementary experiments. The first study aimed to identify how changes in STB incidence and severity under natural epidemic conditions relate to the use of the agroprairie design and to specific prairie plant communities and wheat cultivars. Our hypotheses were that the contrasting biomasses, heights and leaf traits of the intercropped prairie strips would generate different barrier effect efficiencies (Figure 1). Also, because wheat cultivars were chosen for their contrasting disease sensitivities and phenotype (e.g. maximum height), we expected different disease incidence and severity outcomes, depending on wheat cultivar response to intercropping situations.

The second experiment involved above canopy inoculation of *Z. tritici* and focused on the most sensitive wheat cultivar and the two most contrasting prairie plant communities, in order to assess how agroprairie design influences STB dynamics when the potential barrier effect of the prairie strip is ineffective. The two prairie plant communities selected were expected to exhibit the most contrasting plant architectures, based on their high proportion of sown legumes and opposing resource acquisition strategies. We hypothesized that prairie species differing in resource-use strategies and litter chemistry would harbor distinct rhizosphere microbiomes, thereby shaping the wheat rhizosphere microbiome. We therefore focused our analysis on the wheat rhizosphere microbiome, with particular attention to microbial taxa known to contribute to plant defense and pathogen suppression (Figure 1).
# 2 Material and methods

## 2.1 Site description

The two experiments were carried out in 2023-2024 at the INRAe Crouel Research Station, in Clermont-Ferrand in France (45°46'42.75"N, 3°8'48.79"E, elevation 331 m a.s.l). The soil is a calcareous silty clay-loam soil. Soil characteristics are indicated in Table A.2. Mean annual temperatures was 14 and 13°C in 2023 and 2024 respectively. Mean annual precipitation was 518 and 736 mm in 2023 and 2024, respectively. Before the experiments, a maize was cultivated in 2022, a winter wheat in 2021 and a soybean in 2020.

## 2.2 Agroprairie design and field management

"Agroprairie" design consisted of micro-plots (3m x 3.5m) with four alternating strips of prairie and wheat, measuring 37.5 cm and 30 cm in width, respectively. Within a wheat strip, 3 rows of wheat were sown. Prairie strips were sown in October 2022 and a spring wheat was cultivated in the agroprairie treatments in spring 2023 before monitoring campaigns start in fall 2023. Winter wheat was sown on November 25, 2023, at a density of 300 seeds m⁻². No fertilizers and no pesticides were applied to be able to observe only the effects of prairie strips on wheat disease development and wheat belowground microbiome. Prairie strips were managed by mowing. Three cuts were performed during the experiments: in October 2023, March 2024 and July 2024. The wheat in agroprairie was systematically compared to a control treatment, that was wheat grown in monocrop (Figure A.1). To assess the impact of wheat-prairie strip intercropping on the STB development, two complementary experiments were carried out.

## 2.3 Experiment 1: Effect of agroprairies on STB incidence and severity under natural epidemic conditions

### 2.3.1 Design of agroprairies treatments

The different agroprairies treatments were obtained through the combination of a given prairie plant community and a given wheat cultivar. Two different prairie plant communities, named A and B,
were made by grouping 15 plant species. The first plant community assembled consisted of species with
low specific leaf area (SLA) and high leaf dry matter content (LDMC), so being more "conservative"
species. On the contrary, the second plant community assembled consisted of species with high SLA
and low LDMC, so more "acquisitive" species (Table A.1). Each plant community were composed of
species with contrasting growth form (grasses, legumes and other forbs, Table A.1). Species selection
was based on trait values gathered from the TRY database (Kattge et al., 2020). Three legume species
were selected to be in each plant community, and represented either 5% or 50% of the seeds sown mass
as to get the two plant communities divided in two sub-groups, named A1, A2, B1 and B2, with low or
high legume abundance (Table A.1). In May 2024, near to the peak biomass, observations of species
presence and leaf traits measurements enabled us to check the correspondence between what was sown
and what was actually growing (see section 2.3.2).

Four cultivars of winter wheat (Triticum aestivum) were chosen among a panel of 39 cultivars that were phenotyped for 15 functional traits, including aboveground and belowground traits (Dubs et al., 2023). The four cultivars selected were chosen to differ in terms of yield potential, maximal height, and investment in aboveground (relative growth rate, RGR) and belowground growth (specific root length, SRL and root dry matter content, RDMC) (Table A.3). Two landrace cultivars (high maximal height) were finally selected: "Rouge de Bordeaux" (high SRL, RDMC), "Barbu de Champagne" (high SRL), as well as to two elite cultivars (high yield): "Rubisko" (high SRL), "Skerzzo" (high RGR, RDMC)). These four cultivars also varied in their sensitivity to STB (Table A.3).

Overall, sixteen different agroprairie treatments were studied, resulting from the combination
of four prairie plant communities and four wheat cultivars. We added four control treatments with the
four wheat cultivars as monocrops (pure stand), and all treatments were repeated four times in a
randomized complete block design made of eighty plots in total (Figure 2A, 2D).

2.3.2 Biomass, height and traits measurements of intercropped prairie strips

Biomass measurement of the intercropped prairies was done on July 15, 2024. Two linear meters
of prairies around one wheat strip where cut in each plot, 5 cm from the base. All the samples were then
dried in an oven at 60°C for 48 hours. The prairie biomass was weighted to evaluate the dry matter
production. Vegetation heights were measured at the field on June 10, 2024 with a meter and 2
repetitions per plots. To consider plant community dynamic and possible species invasion in the prairie,
a botanical survey was conducted at the maximum biomass production and each present species in each
experimental plot on April 23, 2024. Moreover, to take into account species traits plasticity in response
to the growth conditions, 10 individuals of each species were phenotyped for four morphological and
one chemical functional traits (i.e., height, leaf area (LA), specific leaf area (SLA), leaf dry matter
content (LDMC), and leaf N content (LNC)), following standardized procedures as described in Pérez-
Harguindeguy et al., (2013). LA, SLA, LDMC, and LNC were measured on plant material collected on
May 28, 2024, and the height of each species was measured on June 13, 2024, near to maximum plant
biomass. Finally, the community weighted mean trait values were calculated for each functional trait as
proxies of the functional structure of the plant community, based on the botanical survey conducted for
each subplot (Table A.4).

2.3.3 Wheat height and nitrogen concentration of the upper leaves (Nup) measurements

Wheat vegetation heights were measured at the field with a meter on June 10, 2024 just before
leaf senescence with 2 repetitions per plots. Nitrogen concentration of the upper leaves (Nup) of wheat
was measured on April 22, 2024 at the Zadok stage 32. Six wheat leaves were randomly selected on
each plot, and cut on 15 cm from the extremity of leaf. Samples of dried wheat leaves were ground in a
vibrating ball mill and analyzed for their “bulk” elemental composition (%C and %N concentrations)
using an Elemental Analyzer (Elementar Unicube CN Trace).

2.3.4 Disease scoring

To quantify the STB development across the different treatments, disease assessments started at
the second node stage on April 24, 2024 (Zadok stage 32). Then, notations of leaf incidence and severity
were visually assessed on five notations dates: May 06, May 17, May 27, June 10 and June 25, 2024 on
the three last leaf layers separately, on two randomly chosen linear meters per plots. The incidence is
the number of plants that showed STB symptoms on one linear meter of plants divided by the mean
number of plants counted after emergence in February 2024. The severity is the % of leaf surface with necrosis.

## 2.4 Experiment 2: Effect of agroprairies on STB dynamics after inoculation and on the wheat rhizosphere microbiome

In this experiment, the most STB-sensitive cultivar, Rubisko, considered fairly sensitive (Table A.3), was grown both as a monocrop and in agroprairie designs, intercropped with two prairie plant communities (A2 and B2), which were expected to exhibit the greatest contrast in aerial architecture. An inoculated treatment and a control treatment without inoculation were implemented. A total of six treatments were studied, repeated four times in a randomized complete block design for a total of twenty-four plots (Figure 2A, 2D).

### 2.4.1 Inoculum preparation and field inoculation

Z. tritici strain IPO 09415 was transferred onto a YG liquid, incubated at 17°C under a 140 rpm agitation during seven days. 200 µl of this suspension was streaked onto YPD agar, and also incubated at 17°C during four days. The fungal material was transferred to a 0,05% twin water to obtain a concentrated spore suspension, without filtration. The suspension was then diluted in water to obtain a concentration of 2.10⁶ spores/ml, corresponding to a high disease pressure level. Finally, the spore suspension was stored into a milk-glycerol mixture at -20°C. The inoculation took place on May 16, 2024, at flowering stage with no windy conditions. The inoculation zone was in the center of the plots, covering an area measuring 2x1.75m to avoid any dispersal to neighboring plots. The inoculum was sprayed evenly over the canopy using a sprayer 30 cm above the wheat. In total 145mL of solution was sprayed per plots. Throughout the trial, the plots were irrigated using sprinklers from June 6, 2024 to July 7, 2024 to maintain adequate humidity levels and ensure proper development of STB.

### 2.4.2 Disease scoring

Three disease notations of incidence and severity were recorded visually at the field on May 29, June 7 and June 17, 2024. Twenty-five leaves per plots were randomly chosen and assessed in inoculated and non-inoculated plots. One leaf sampling and visual notation on scanned leaf were done on June 14
only on inoculated plots. As a very large quantity of spores had been sprayed on inoculated plots, the disease was likely to develop rapidly on all the plants and leaves, in contrast to natural conditions of epidemic, where the disease develops less strongly and not necessarily on all leaf stages. We therefore aimed to precisely quantify disease dynamics by integrating both incidence and severity on the two upper leaf layers across all dates into a single key metric. To achieve this, STB was evaluated according to a standard six-level grading system based on the leaf area covered with spores: 0 = no spore spots; 1=<5% of the leaf area; 2 = 6 to 25% of the leaf area; 3 = 26 to 50%; 4 = 51 to 75%; and 5 = >75% of the leaf area (ICARDA, 1986). The disease index described by Zhang et al. (2023) was calculated for each observation date using the following formula, which accounts for both disease incidence and severity:

$$ Disease index = \frac{\sum (\text{number of diseased leaves in each level} \times \text{value of the corresponding level})}{\text{total leaf number investigated} \times \text{value of the highest level}} \times 100 $$

To precisely quantify disease progression over time following inoculation, the area under the disease progress curve (AUDPC) (Campbell and Madden, 1990) was calculated based on the disease index values:

$$ AUDPC = \sum_{i=1}^{n} \frac{1}{2}(X_{i} + X_{i-1})(t_{i} - t_{i-1}) $$

where $X_i$ and $X_{i-1}$ represent the disease index during the last and previous survey, respectively. $t_i$ and $t_{i-1}$ indicate the time of the last survey and the time of the previous survey, respectively.

### 2.4.3 Rhizosphere sampling and DNA extraction

Rhizospheric samples were taken before inoculation on April 24 from all the plots of the experiment 2 during the booting period. To extract the soil rhizosphere from wheat plants, four composite wheat plants per plots were uprooted carefully and shaken to only keep the adherent soil on the roots. To extract the soil rhizosphere from prairie plants, four composite cores were collected in the strips as close as possible to the sampled wheat plants using a root auger (10 cm wide and 15 cm deep), and then pooled. The protocol to separate root and rhizosphere samples was adapted from Simmons et al., (2018).
samples were washed with buffer solution (900mL sterile H₂O + 6.075g KH₂PO₄ + 7.875g K₂HPO₄ + 900µL Triton X-100) and a firmly shaken with a sonification. Roots were then separated from the solution, and washed with sterilized water. In the same time, rhizospheric soil solution was centrifugated. Then supernatant was removed for keeping only rhizospheric soil. Each sample was immediately stored at -80°C for DNA extraction. Later, DNA was extracted using the Fast DNAtm SPIN Kit for Soil (MP Biomedicals) according to the manufacturer's instructions. Briefly, 200mg of each sample was grinded in detergent to disrupt and dissolve membranes. After centrifugation with a solution to precipitate proteins, the supernatant was mixed with a DNA-binding silica matrix. The remaining solution was washed and filtered to obtain only the product of extraction in DNase- and Pyrogen-Free Water. The purity and concentration of the extracts were measured with a NanoDrop™ spectrometer (Ozyme).

### 2.4.4 PCR amplification and 16S and ITS-28S rRNA gene amplicon sequencing

The 27F (5'-AGRGTTYGATYMTGGCTCAG-3') and 1492R (5'-RGYTACCTTGTTACGACTT -3') was used to amplify the V1-V9 variable region of the bacterial 16S rRNA gene (PacBio, 2022) and the ITS1cattal (5'-ACCWGCGGARGGATCATTA-3') and LR5_TW14ngs (5'-TCCTGAGGGAAACTTCG-3') was used to amplify the ITS-28S of fungus rRNA gene (Tedersoo and Anslan, 2019; Tedersoo and Lindahl, 2016). Both the forward and reverse of the two pairs of primers were tailed with sample specific PacBio barcode sequences to allow for multiplexed sequencing.

The KAPA HiFi Hot Start DNA Polymerase (KAPABiosystems) was used for PCR amplification. For 16S rRNA amplification, after initial denature step (95°C for 3 minutes), 23 cycles of PCR amplification, with denaturing at 95 °C for 30 s, annealing at 57 °C for 30 s and extension at 72 °C for 60 s. For ITS-28S amplification, after initial denature step (95°C for 15 minutes), 30 cycles of PCR amplification, with denaturing at 95 °C for 30 s, annealing at 55 °C for 30 s and extension at 72 °C for 60 s.

Amplified DNAs pool was processed with the SMRTbell Express Template Prep Kit 2.0 (PacBio, USA), following the Procedure & Checklist - Amplification of Full-Length 16S Gene with Barcoded Primers for Multiplexed SMRTbell® Library Preparation and Sequencing protocol (Part Number 101-599-700
Version 04, PACBIO). The libraries were prepared, and sequenced in a PacBio Sequel II by the Gentyane platform at INRAE (France).

## 2.5 Statistical analysis

### 2.5.1 Experiment 1

All statistical analyses were conducted with R 4.4.2. (R Core Team, 2024).

First, a non-binomial generalized linear model was applied to raw incidence (number of infected plants) data to compare the different incidence results between wheat cultivars in monocrop. For this, the glmmTMB function from the glmmTMB package (Brooks et al., 2017) was used to test the effects of the block, the notation repetition, and the wheat cultivar. The total number of plants on a linear meter was included as an offset in the model, the model family was "nbinom2", using the parameter ziformula= ~1 to consider zero inflation. To validate this model, the plot function from the DHARMa package (Hartig, 2016) was used to test the homogeneity of the variances, the normality and the independence of the residuals from the model. The effect of cultivars on maximum height and leaf nitrogen concentration in monocrop was also tested using linear models, with only the cultivar as an effect. To ensure that the four prairie plant communities were differentiated according to their growth strategy and their percentage of legumes, a PCA was carried out on mean SLA, LDMC, LA, LNC and height described in Material and methods 2.3.2 using the PCA function in the FactorMineR package (Husson et al., 2025). To quantify the effect of agroprairies on the STB development, a linear model was used on the log response ratio (log RR) (Hedges et al., 1999) of the incidence and the severity in response variable using lm function. The log RR was calculated as log (X in agroprairies / mean (X in monocrop)), where X is the incidence or severity in a unique agroprairie, and mean X is the average incidence or severity of the four replicates in each monocrop. A logRR of <0 indicates a better STB regulation in agroprairie compared to monocrop. The last notation date (June 10) before wheat senescence was chosen to calculate this metric.

The effect of the categorical variables describing the intercropped prairie strips (A1, A2, B1, B2) was tested, with the block also included, in order to quantify the effect of wheat-prairie intercropping
independently. Then, to explore the origin of the potential effects of wheat-prairie strip intercropping on the log RR of incidence and severity, a second linear model was selected. This model was built following a forward selection logic: starting with categorical variables such as intercropped prairie type (A1, A2, B1, B2) and wheat cultivar, and then decomposing them into continuous quantitative variables characteristic of these categories. The factors tested included wheat cultivar, wheat height, Nitrogen concentration of the upper leaves (Nup), prairie height, prairie biomass, prairie specific leaf area (SLA), and the proportion of legumes in the associated prairies. Interactions between these variables were also tested. All tested models, along with their corresponding Akaike Information Criterion (AIC) values are detailed in Table A.5. To assess multicollinearity among explanatory variables in the selected model, we computed the Generalized Variance Inflation Factor (GVIF) and used the adjusted form $GVIF^{2}/(1/(2 \cdot Df))$, detailed in Table A.6, which accounts for the degrees of freedom associated with each variable (Fox and Weisberg, 2018).

## 2.5.2 Experiment 2

The effect of wheat-prairie strip intercropping on disease dynamics in inoculated plots was quantified using the AUDPC as the response variable, along with the effects of the associated prairie type and of the block. The same approach was applied to non-inoculated plots, using log RR as the response variable as indicated above. For all the selected models, residuals normality was checked using the Shapiro test, homoscedasticity using the Bartlett test and the independence of the residuals was checked graphically. Then analyses of variance were performed using the car package. For linear models with only fixed effects, Type II ANOVAs were performed. Then, post-hoc analyses of treatments were conducted using Tukey's HSD test. In the case of the analyses of logRR as the response variable, a one-sample Wilcoxon test was used to assess which treatment had a logRR significantly different than zero. For linear models including interactions, Type III ANOVAs were applied.

Concerning microbial analysis, after demultiplexing and removal of the barcodes, the reads were processed with the package DADA2 (Callahan et al., 2016). The primers were removed, and the reads were filtered to keep only fragments between 1000 and 1600bp. The sequences were dereplicated and the errors were corrected thanks to the learnerrors function of the package. The sequences were then
denoised and assigned a taxonomy according to the Silva database for bacteria and the Unite database for the fungi. For the microbiome analysis, the resulting data were treated with the phyloseq package (McMurdie and Holmes, 2013). The sequences were filtered to keep only bacterial and fungal sequences. Alpha diversity analysis was assessed using the Shannon and inverse Simpson indices.

Beta diversity analysis were performed using PCOA with the Aitchison distance. Then PERMANOVAs were performed for each plot using the adonis2 function from the vegan package (Oksanen et al., 2013) with 999 permutations. To investigate the relationship between rhizospheric microbiome diversity (alpha and beta diversity) and STB development, two Principal Component Analyses (PCAs) were performed using the same package described above. The first PCA included the Shannon index, the values of the first PCoA axes for both fungi and bacteria, and AUDPC values under inoculated conditions. The second PCA included the first PCoA axes for fungi and bacteria, along with STB incidence values at the final scoring date under non-inoculated conditions.

To investigate taxa involved in pathogen inhibition and induced systemic resistance (ISR), a list of bacterial and fungal genera, and species within these genera, was compiled. This list was based on literature reviews describing the general role of the rhizosphere microbiome in plant disease control. This list was used as a basis for analyzing the microbiome dataset (Table A.7; H. Liu et al., 2020; Pieterse et al., 2014; Qu et al., 2020; Yang et al., 2023; K. Yu et al., 2019).

From this list, we examined whether these taxa were present in our dataset. For those detected, the effect of the different treatments on their abundance was analyzed, following verification of the assumptions required for linear modeling using the methods described above. To meet these assumptions, data were transformed when necessary using the Boxcox function of the MASS package (Ripley et al., 2025). When assumptions were not met, even after transformation, data were analyzed using a Kruskal-Wallis test followed by post hoc Wilcoxon tests with Bonferroni adjustment.

# 3 Results
## 3.1 Experiment 1

### 3.1.1 Prairies growth and traits

At the peak biomass, prairies A1 and A2 were dominated by higher grass cover and lower legume cover. Prairie B1 exhibited higher forb cover and moderate legume cover, while prairie B2 was characterized by a predominance of legumes (Table A.4). On the PCA, the traits of prairies A1 and A2 was closer to “conservative-strategy” (high leaf dry matter content) (Figure 4A) than prairies B1 and B2 which were closer to “acquisitive-strategy” (high leaf area) (Figure 4A, Table A.4). Prairies B1 and A2 also differed in plant height, with A2 being significantly taller than B1 (Figure 4A). These differences in traits between intercropped prairies led to contrasted aerial biomasses, with the lowest average biomass of 556 g/m² for prairie A1, followed by prairie A2 with 684 g/m², and prairies B1 and B2 with 1400 g/m² (p-value < 0.0001; Figure 4B). A nonlinear relationship was observed between prairie biomass and legume proportion: prairie biomass increased up to approximately 30% legume cover, beyond which prairie biomass showed no further increase (Figure A.4B).

### 3.1.2 Wheat cultivar STB sensitivity, growth and leaf nitrogen content

STB presence was observed on the second leaf from the top of the plant, and was very rarely observed on the plant's first leaf (Figure A.2). Therefore, all following results only concern STB development on the second leaf. The wheat cultivar Rubisko was the most infected cultivar, with 17% of plants on average having STB symptoms. The cultivar Rouge de Bordeaux had 13% of infected plants on average, while Skerzzo and Barbu de Champagne had only 4 and 3%, respectively (Figure 3A). As expected, wheat cultivars exhibited contrasting plant heights but similar nitrogen concentrations in the upper leaves (Figure 3B, 3C).

### 3.1.3 The effect of agroprairie design on STB incidence and severity in natural epidemic conditions

Regarding disease progression in wheat, the incidence of STB did not increase much until the wheat flowering stage, with an average incidence of no more than 3% (Figure 5A). After flowering and before the leaf senescence began, the logRR of incidence and severity differed significantly between treatments (Figure 5B and 5C; p-value = 0.002 and p-value = 0.01, respectively). At this date, the
incidence of the disease varied between, on average, 4% and 10% of infected plants, with wheat grown
in monocrop and agroprairies A1 and A2 being more infected than wheat grown in agroprairie B1 and
B2 (Figure 5A). The logRR of the incidence was significantly lower than zero for agroprairies B1 and
B2 without significant difference between the two prairie types (Figure 5B; p-value < 0.001) indicating
a better STB regulation in agroprairies B1 and B2 compared to monocrop, with an average incidence
reduction of 52% across all cultivars. In contrast, the agroprairies A1 and A2 had no significant effect
on the log RR of disease incidence. Regarding disease severity, agroprairies B1 and B2 showed
significantly negative logRR values (Figure 5C; p-value = 0.004 and p-value = 0.001, respectively),
corresponding to an average severity reduction of 41%.

The model that best explained STB incidence and severity differences between agroprairie
treatments included an effect of prairie biomass and SLA (Table 1). Higher prairie biomass and higher
SLA were associated with a lower logRR, corresponding to a greater disease reduction in the agroprairie
compared to monocrop (Figure A.4A). A significant interaction between legume percentage and prairie
biomass was detected in explaining the log RR of STB incidence, with the interaction showing a positive
effect on logRR (p = 0.03). This indicates that, with similar biomass, prairies with more legumes tend
to regulate STB development less effectively.

Compared to monocrops, wheat grown in agroprairie design showed higher nitrogen
concentration in the upper leaves across all cultivars, except for Rouge de Bordeaux (Figure A.3A; p-
value < 0.05). The effect of strip intercropping on plant height depended on the wheat cultivar (Figure
A.3B; p-value < 0.05). For instance, Rubisko was significantly taller in strip intercropping with prairie
B2 than in monocrop (Figure A.3C; p-value < 0.05). However, none of these phenotypic changes were
found to be linked with disease indicators, and the interaction between wheat cultivars and the
agroprairie design effects had no significant effect (Table A.5).

## 3.2 Experiment 2

3.2.1 The effect of agroprairie design on STB progression after inoculation
In the control treatments (not inoculated), an average of 41% and 28% of the wheat plants were infected, in monocrop and agroprairies settings (Figure 6A). As in the first experiment, wheat intercropped with prairie strip B2 had significantly lower disease incidence and severity than wheat intercropped with prairie strip A2 (p-value < 0.05), although both prairie strips reduced disease compared to the monocrop situation (Figure A.5).

In inoculated treatments, 100% of the wheat plants were infected by STB (Figure 6A), and necrosis represented an average of 80% of the noted leaf surface at the end of the growing season (Figure 6B). In contrast to not inoculated treatments, the agroprairie A2 enabled better disease regulation compared to agroprairie B2. The area under disease progression curve (AUDPC), which integrates both incidence and severity to summarize overall disease progression over time on the two upper leaf stages, showed a mean reduction of 18% of disease progression compared to monocrop (Figure 6C).

### 3.2.2 Rhizosphere microbial diversity of the prairie plant communities

The bacterial phyla identified in the prairie rhizospheric soil were dominated by *Proteobacteria*, *Acidobacteriota* and *Bacteroidota*. The fungal phyla of soil rhizosphere were dominated by *Ascomycota* and *Basidiomycota* (Figure A.6). No significant differences were observed between the two intercropped prairie plant communities for the rhizosphere fungal and bacterial alpha diversity (Shannon and InvSimpson indices) (Figure A.7B). Regarding rhizosphere beta diversity, we observed significant differences of fungal and bacterial communities between prairie A2 and B2 (Figure A.8, p-value <0.001).

### 3.2.3 Changes in the wheat rhizosphere microbiome between agroprairie treatments

The bacterial phyla identified in the wheat rhizosphere were similar to those in the intercropped prairie strip (Figure A.6). No significant differences were observed for the rhizosphere fungal and bacterial alpha diversity (Shannon and InvSimpson indices) between treatments (Figure A.7A). Concerning wheat rhizosphere microbiome beta diversity, differences in bacterial communities were found in wheat grown in monocrop and in agroprairies (Figure 7A, p-value = 0.003). Despite the differences in
community structure, we did not detect any influence of microbial diversity indices (alpha or beta) on
disease indicators (incidence, severity or AUDPC) under natural or inoculated conditions (Figure A.9).

Our literature review identified 18 bacterial and 7 fungal genera involved in the plant induced systemic
resistance or in pathogen inhibition effect (Table A.7). Our experiment revealed that 11 of these bacterial
genera were present in the wheat rhizosphere (Figure 7B, Table A.7). Among these genera,
Pseudomonas was significantly more abundant in wheat grown in agroprairie A2 than in monocrop
(Figure 7C; p-value = 0.03). At the species level (Table A.7), Pseudomonas fluorescens, Pseudomonas
putida, Pseudomonas brassicacearum and Stenotrophomonas maltophilia were present in our samples.
Only Pseudomonas brassicacearum was significantly more abundant in the wheat rhizosphere when
intercropped with prairie strip A2. Although the disease development appeared to slow down with this
agroprairie treatment (Figure 7C; p-value = 0.03), we could not find a significant correlation between
disease development and Pseudomonas brassicacearum abundance.

Concerning fungi, the only genera identified in our samples that match those known to be involved in
plant-induced systemic resistance or pathogen inhibition (Table A.7) was Penicillium. However, no
significant differences in abundance were observed among our experimental treatments (Figure A.10B).

4 Discussion

In this study, we hypothesized that Septoria tritici blotch (STB) could be regulated differently in
agroprairie compared to monocrop designs, and within the different agroprairies depending on the wheat
cultivar and the prairie plant community used. We anticipated that discrepancies would emerge
depending on the effectiveness of specific regulatory mechanisms, among which is i) a barrier effect
that limits the dispersal of pathogens; ii) modifications of host morphology and physiology; iii)
alterations in host plant sensitivity resulting from changes in its rhizospheric microbiome (Figure 1).

4.1 Reduction of STB incidence and severity with agroprairies under natural epidemic conditions
An important finding of this study is the average reduction of 52% in STB incidence and 41% in STB severity observed in two of the four tested agroprairies (B1, B2) compared to monocrop under natural conditions of pathogen development (experiment 1). The positive role of plant diversification for controlling STB has been studied in wheat cultivar mixtures (e.g. Kristoffersen et al., 2020; Orellana-Torrejon et al., 2022; Vidal et al., 2020) but much less in intercropping situations, especially in open field conditions.

Another key finding of our study is the variable effect of the agroprairie design on STB severity and incidence depending on the prairie plant community used. We demonstrated that the reduction of STB severity and incidence were maximized in agroprairie treatments with high prairie strip biomass and mean specific leaf area (SLA) (Table 1). The role of the prairie biomass in explaining STB regulation in our models suggests the efficiency of the physical barrier effect in preventing the spore dispersal. This is supported by the fact that *Z.tritici* is a hemibiotrophic specific pathogen that can disperse horizontally via wind over long distances and vertically via rain (Suffert et al., 2011), and is therefore highly susceptible to the barrier effect (X. Liu et al., 2020; Ratnadass et al., 2012). This consistent barrier effect is further supported by the fact that its efficiency with high-biomass prairies was no longer observed in experiment 2 with disease inoculation above the canopy. The role of intercropping in spore dispersal interference was notably shown under controlled and inoculated conditions (Bannon and Cooke, 1998), were a wheat-clover intercropping system proved to reduce horizontal spore dispersal by 33% and vertical spore dispersal by 63% compared to a monocrop.

Furthermore, the agroprairie design consistently regulated STB incidence and severity, regardless of the wheat cultivar studied. This consistency is noteworthy because wheat cultivars expressed different levels of disease sensitivity in monocrop treatments, and were affected by intercropping, as demonstrated by changes in plant height and leaf nitrogen concentration (Figure A.3). These phenotypic changes could otherwise impact disease dynamics (Julian Maywald et al., 2023; Vidal et al., 2017).

Finally, the interaction between biomass and legume abundance in prairie strips and its effect on STB incidence suggests that the presence of legumes alters the barrier effect through morphological and architectural changes in the plant community. Alternatively, it may affect other regulatory mechanisms,
such as changes in the microclimate, microbiome, and resource availability, which may influence the STB development and wheat sensitivity (Boudreau, 2013; Duchene et al., 2017). However, the precise origin of this interaction remains unclear, and further research is needed to understand this effect.

## 4.2 Effect of agroprairies on STB progression after inoculation

Inoculation above the canopy with the fungal pathogen *Z. tritici* resulted in infection of all plants in both monocrop and agroprairie designs by the end of the growing season (Figure 6A), with an average of 80% leaf exhibiting necrosis (Figure 6B). Thus, the regulating effect of disease dispersion by agroprairies observed under non-inoculated conditions, especially in situations with high biomass prairie strips, was no longer operational. However, the AUDPC analysis revealed differences in the progression extent of the disease on wheat plants between treatments (Figure 6C). Interestingly, the agroprairie A2 with low biomass prairie strips significantly slowed the progression of STB, reducing the AUDPC by 18% compared to the wheat monocrop (Figure 6C). The A2 prairie strips produced over 50% less biomass than the B2 strips in the experiment 1 (Figure 4B) and exhibited similar differences in experiment 2. These results suggest that mechanisms other than the physical effect of prairie biomass were at play, and that prairie biomass contributes to STB regulation in agroprairies only under certain disease pressure and infection levels.

## 4.3 Increased beneficial microorganisms in the rhizosphere, while not correlated to STB indicators

In this study, we focused on the wheat rhizosphere microbiome, as it is particularly responsive to environmental changes induced by crop diversification and is considered to play a key role in the induction of plant defense mechanisms and pathogen suppression (H. Liu et al., 2020; Qu et al., 2020).

In our situations, the relative abundances of bacterial and fungal phyla observed in the wheat rhizosphere were consistent with those commonly reported in the literature (Trivedi et al., 2020). The
distinct beta diversity of the microbial communities found in the agroprairie and monocrop treatments
in experiment 2 (Figure 7A) strengthened our general understanding of the influence of plant community
composition and traits on microbial communities (Figure 7A, Table A.1, Table A.4). This observation
is consistent with previous studies reporting shifts in the wheat rhizosphere or endophytic root
microbiome when intercropped (Granzow et al., 2017; H. Yu et al., 2019) or in contact with certain
weeds (Ricono et al., 2025). Interestingly in our case, the genera *Pseudomonas* was significantly more
abundant in the wheat rhizosphere when intercropped in the A2 prairie strips (Figure 7C). Several studies
showed that the enrichment of *Pseudomonas spp.* can occur in a plant to play biocontrol role such as
induced systemic resistance or direct pathogen inhibition (Yang et al., 2023; K. Yu et al., 2019). For
example, *P. brassicacearum* has been studied for its role in plant immunity (K. Yu et al., 2019) and for
the production of lipo peptides very efficient against the development of many pathogens (Bahmani et
al., 2021; Berry et al., 2010; Mandryk-Litvinkovich et al., 2017). Although we observed no effect on
disease regulation under our field conditions with massive disease inoculation or in natural epidemic
conditions, these changes in microbial communities are likely crucial for developing a more resilient
growing environment for crops (Fadiji et al., 2023; Fang et al., 2025).

## 4.4 Research perspectives

Under our experimental conditions, the vegetation barrier created by two of the four tested
agroprairies proved highly effective in reducing the spread of STB under natural epidemic pressure.
This effectiveness of disease reduction by agroprairies is likely to vary depending on local climate and
disease pressure. For instance, with regard to climate, the orientation of prairie strips relative to
prevailing winds and the width of the strips may influence the strength of the barrier effect. Strips
oriented perpendicular to the prevailing wind direction could be more effective at limiting the dispersal
of *Z.tritici* spores than those aligned with the wind. Varying the widths of wheat and grass strips could
also be tested to determine the minimum strip width beyond which the prairie strip is no longer effective
at intercepting pathogen spores.
Furthermore, it is also important to note that in our non-inoculated experiment, disease pressure was moderate. In regions with higher natural disease pressure, the capacity of strip intercropping of wheat and prairie to reduce STB may be more limited, and the regulation effects observed in this study may not be as pronounced. Nevertheless, our study demonstrated that wheat-prairie strip intercropping provided disease protection across four different wheat cultivars. These results suggest that the protective role of agroprairies could potentially be extended to a broader range of cultivars and combined with other control strategies, such as the selection of resistance genes. Although some variation in cultivar sensitivity was observed, the cultivars used in this study were not highly sensitive to STB (Table A.3). We therefore recommend conducting further experiments to assess both the effectiveness and limitations of wheat-prairie strip intercropping in reducing STB and other wheat pathogens impact under diverse climatic conditions, particularly when using highly sensitive cultivars.

On the other hand, we observed that plant traits indicative of growth strategy of the intercropped prairie (SLA) could be linked to STB regulation (Table 1). This highlights the importance of studying the role and distribution of plant functional traits in ecosystem functioning, particularly with regard to pathogen regulation (Le Bagousse-Pinguet et al., 2021),

Finally, our study highlighted the complexity of disentangling specific mechanisms (e.g., microbiome shifts) from more overarching processes (e.g., barrier effects) involved in disease reduction within a field study manipulating diversified agroecosystems. Progress in this field requires balancing field research, which captures macroscopic and systemic phenomena, with more targeted, mechanistic and reductionist studies that help identify specific processes driving these effects.

## Acknowledgements

We gratefully acknowledge Laurent Falchetto, Clément Fenaux, Jérome Gayton, and Julien Laurencon for their contribution in the setup and the management of the field experiment. We thank gratefully Anne-Marie Destombes, Leslie Pluchino, Marina Compain, Laurence Andanson, Basile Manneveau and Evan Techer for their contribution in the management of the field experiment, the collection and
analyzes of samples. We also express our sincere thanks to Florence Cambon for her help in the
inoculum preparation, Perrine Brassier for the illustration (Cf Figure 1), Cyrille Saintenac and Claire
Neema for their advices. We thank Nathalie Mouthier for her contribution in the co-design of the field
experiment.

Funding sources

This project was supported by the Region Auvergne-Rhône-Alpes (Project CULTIVDUR - Pack Ambition Recherche 2021), the I-SITE Clermont (Project AGROPRAIRIES - CIR Axe 2), the Agence Nationale pour la Recherche (Project MoBiDiv - ANR 20-PCPA-0006) and the European Commission (Project SOILRES - Project number 101218994- HORIZON-MISS-2024-SOIL).

Declaration of competing interests

The authors declare that they have no known competing financial interests or personal relationships
that could have appeared to influence the work reported in this paper.

CRediT authorship contribution statement

**Lisa Besson:** Conceptualization, Methodology, Formal analysis, Investigation, Data Curation, Writing - Original Draft, Writing - Review & Editing, Visualization, Supervision

Olivier Duchene: Conceptualization, Methodology, Writing - Original Draft, Writing - Review & Editing, Funding acquisition

Gaël Alvarez: Methodology, Resources, Writing - Review & Editing, Funding acquisition

Thomas Becu: Investigation, Writing - Review & Editing

Sébastien Fontaine: Conceptualization, Methodology, Investigation, Resources, Writing - Original Draft, Writing - Review & Editing, Funding acquisition

Nicolas Gross: Methodology, Investigation, Writing - Review & Editing, Funding acquisition

Magali Joannin: Investigation, Resources

Audrey Niboyet: Conceptualization, Methodology, Writing - Review & Editing, Funding acquisition

Stéphane Sauvat: Investigation, Resources

Emma Schwartz: Formal analysis, Investigation, Data Curation, Writing - Review & Editing
Tables

<table><caption>Table 1: Results of the selected linear models chosen with the AIC criteria explaining the logRR of the incidence and the severity of STB in different types of agroprairies. % Legumes: Percentage of legumes in the associated prairie; Prairie biomass: Total aerial biomass of the associated prairie (g.m-2); SLA: Mean specific leaf area of the associated prairie (cm.g-1). Significant p-values are indicated in bold.</caption><thead><tr><th rowspan="3"></th><th colspan="6">Response variables:</th></tr><tr><th colspan="3">log RR of STB incidence values<br>adjusted R<sup>2</sup> = 0.36</th><th colspan="3">log RR of STB severity values<br>adjusted R<sup>2</sup> = 0.29</th></tr><tr><th>df</th><th>F df,53</th><th>p-value</th><th>df</th><th>F df,53</th><th>p-value</th></tr></thead><tbody><tr><td>% Legumes</td><td>1</td><td>1.79</td><td>0.18</td><td>1</td><td>0.77</td><td>0.38</td></tr><tr><td>Prairie biomass</td><td>1</td><td>15.81</td><td><strong>&lt;0.001</strong></td><td>1</td><td>11.27</td><td><strong>0.001</strong></td></tr><tr><td>SLA</td><td>1</td><td>4.52</td><td><strong>0.04</strong></td><td>1</td><td>4.43</td><td><strong>0.04</strong></td></tr><tr><td>Wheat cultivar</td><td>3</td><td>1.29</td><td>0.28</td><td>3</td><td>1.97</td><td>0.13</td></tr><tr><td>% Legumes:Prairie biomass</td><td>1</td><td>5.28</td><td><strong>0.03</strong></td><td>1</td><td>3.17</td><td>0.08</td></tr><tr><td>Block</td><td>3</td><td>4.97</td><td><strong>0.004</strong></td><td>3</td><td>3.27</td><td><strong>0.02</strong></td></tr></tbody></table>
Figures

<figure><img src="image_2.png" /><figcaption>Figure 1: Disease regulation mechanisms potentially involved in a wheat-prairie strip cropping</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>Figure 2: Aerial view of the two experiments at INRAe of Clermont Ferrand (experiment 1: E1 and experiment 2: E2)(A). Agroprairie including Rubisko at tillering stage and prairie A1 (high grass and low legume cover) (B), Agroprairie including Rubisko at bolting stage and prairie B2 (low grass and high legume cover) (C). Description scheme of the treatment combinations in experiments 1 and 2. Rub, Rubisko; Skz, Skerzzo; Rdb, Rouge de Bordeaux; Bch, Barbu de Champagne. M, Monocrop; A1, A2, B1 and B2, type of prairie combined with wheat (D).</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>Figure 3: Septoria tritici blotch (STB) incidences levels (A), maximum height (B) and nitrogen concentration in the upper leaves (C) in monocrops of the four different wheat cultivars. bch, Barbu de Champagne; rdb, Rouge de Bordeaux; rub, Rubisko; skz, Skerzzo. The p-values indicate the results of the cultivar effect from the negative binomial model (A) or linear models (B, C). Different letters indicate significant differences between the wheat cultivars. Mean values and standard errors are shown (n = 4).</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Figure 4: Principal component analysis of the mean value of functional traits in prairies strips, as a function of prairie type. (A1, A2, B1, B2). SLA, Specific leaf area; LNC, Leaf nitrogen content; LA, Leaf area; LDMC, Leaf dry matter content; H, Height (A). Mean aerial biomass between the different associated prairie types. Values are expressed as the mean ± standard error (n = 16). Different lowercase letters represent significant differences between treatments at p-value < 0,05 (B).</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>Figure 5: Evolution of the STB incidence between treatments. Wheat-mono, Wheat monocrop; Wheat-AP, Wheat in agroprairies (A). logRR of STB incidence (B) and severity (C) as a function of the type of agroprairie 198 days after sowing in the different types of agroprairies (A1, A2, B1, B2). B2, agroprairies with the highest legume rate; B1, agroprairies with higher Forbs and medium Legumes rate, A2 and A1, highest grass rate and lower legumes cover. Different lowercase letters represent significant differences between treatments at p-value < 0.05. Red stars represent indicates the values significantly lower than 0 (p-value < 0.05).</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Figure 6: Mean disease incidence (A) and severity (B) progression on the second leaf layer in inoculated and natural conditions. Area under disease progress curve in inoculated conditions, considering the disease index, calculated as a weighted sum of incidence and severity in inoculated conditions. Values are expressed as the mean ± standard deviation (n=4). Treatments: Wheat-mono, wheat monocrop; Wheat-AP-A2, wheat in agroprairie A2; Wheat-AP-B2, wheat in agroprairie B2. Different lowercase letters represent significant differences between treatments at p-value < 0.05 (C).</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Figure 7: Principal coordination analysis (PCoA) based on Aitchison distances of fungi and bacteria of wheat rhizosphere community structure (A). Heat map presenting the different bacterial genera observed in rhizosphere samples of the experiment 2, and which are known to be involved in plant induced systemic resistance and pathogen inhibition. A Z-score greater than 0 indicates enrichment of the bacterial genera (B). Bacterial taxa abundances in wheat rhizosphere between the different treatments. The abundances values are centered and log transformed. Values are expressed as the mean ± standard error (n = 4). Different lowercase letters represent significant differences between treatments at p-value < 0,05. There were chosen by performing a Wilcoxon test with the Bonferroni adjustment method. The relative abundance is expressed as centered log ratio (clr) (C). Treatments: Wheat-mono, wheat monocrop; Wheat-AP-A2, wheat in agroprairie A2; Wheat-AP-B2, wheat in agroprairie B2.</figcaption></figure>
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# Supplementary material

## Tables

<table><caption>Table A.1: Description of the four types of prairies used, with each species sown and their abundance in % of seed mass.</caption><thead><tr><th rowspan="3"></th><th rowspan="3">Functional type</th><th rowspan="3">Species name</th><th colspan="2">Abundance of legumes when sowing</th></tr><tr><th>low</th><th>high</th></tr><tr><th>Prairie A1</th><th>Prairie A2</th></tr></thead><tbody><tr><td rowspan="10">a priori known as acquisitive species</td><td rowspan="5">Forbs</td><td>Bellis perennis L., 1753</td><td>0,3</td><td>0,15</td></tr><tr><td>Centaurea Jacea gr.</td><td>2,6</td><td>2</td></tr><tr><td>Crepis biennis L., 1753</td><td>1,35</td><td>1</td></tr><tr><td>Silene dioica (L.) Clairv., 1811</td><td>0,75</td><td>0,5</td></tr><tr><td>Veronica hederifolia L., 1753</td><td>5</td><td>3</td></tr><tr><td rowspan="3">Legumes</td><td>Medicago lupulina L., 1753</td><td>3</td><td>30</td></tr><tr><td>Trifolium campestre Schreb., 1804</td><td>1,5</td><td>0,2</td></tr><tr><td>Trifolium repens L., 1753</td><td>0,5</td><td>20</td></tr><tr><td rowspan="7">Grass</td><td>Agrostis capillaris L., 1753</td><td>1</td><td>0,5</td></tr><tr><td>Arrhenaterrum elatiuis</td><td>7</td><td>5</td></tr><tr><td>Dactylis glomerata L., 1753</td><td>20</td><td>9</td></tr><tr><td>Holcus Lanatus L., 1753</td><td>23</td><td>12</td></tr><tr><td>Lolium perenne L., 1753</td><td>16</td><td>9,65</td></tr><tr><td>Poa trivialis L., 1753</td><td>8</td><td>2</td></tr><tr><td>Schedonorus pratensis., 1812</td><td>10</td><td>5</td></tr></tbody></table>

<table><thead><tr><th rowspan="3"></th><th rowspan="3">Functional type</th><th rowspan="3">Species name</th><th>low</th><th>high</th></tr><tr><th>Prairie B1</th><th>Prairie B2</th></tr><tr><th>% of seed mass</th><th>% of seed mass</th></tr></thead><tbody><tr><td rowspan="10">a priori known as conservative species</td><td rowspan="5">Forbs</td><td>Carum carvi L., 1753</td><td>3</td><td>2,2</td></tr><tr><td>Centaurea jacea L., 1753</td><td>1,5</td><td>1,25</td></tr><tr><td>Daucus carota L., 1753</td><td>1,25</td><td>1</td></tr><tr><td>Knautia arvensis (L.) Coult., 1828</td><td>2,5</td><td>2,2</td></tr><tr><td>Plantago lanceolata L., 1753</td><td>2,85</td><td>1,45</td></tr><tr><td rowspan="2">Legumes</td><td>Silene latifolia Poir., 1789</td><td>1</td><td>0,8</td></tr><tr><td>Silene nutans L., 1753</td><td>0,4</td><td>0,3</td></tr><tr><td rowspan="3">Grass</td><td>Lotus corniculatus L., 1753</td><td>2</td><td>15</td></tr><tr><td>Medicago sativa L., 1753</td><td>1</td><td>10</td></tr><tr><td>Trifolium pratense L., 1753</td><td>2</td><td>25</td></tr><tr><td rowspan="5">Grass</td><td>Bromopsis erecta (Huds.) Fourr., 186.</td><td>15,5</td><td>10</td></tr><tr><td>Festuca ovina gr.</td><td>15</td><td>5</td></tr><tr><td>Festuca rubra L., 1753</td><td>37</td><td>15</td></tr><tr><td>Phleum pratense L., 1753</td><td>10</td><td>8</td></tr><tr><td>Poa pratensis subsp. pratensis L., 171</td><td>5</td><td>2,8</td></tr></tbody></table>
<table><caption>Table A.2: Soil chemical properties of the experiment (measurements were made in February 2023).</caption><thead><tr><th>Soil chemical parameters</th><th></th></tr></thead><tbody><tr><td>Bulk density</td><td>1,03 g/cm3</td></tr><tr><td>Clay (&lt;2 µm)</td><td>346,70 g/kg</td></tr><tr><td>Coarse silt (20/50 µm)</td><td>69,75 g/kg</td></tr><tr><td>Fine silt (2/20 µm)</td><td>406,80 g/kg</td></tr><tr><td>Coarse sand (200/2000 µm)</td><td>109,45 g/kg</td></tr><tr><td>Fine sand (50/200 µm)</td><td>67,30 g/kg</td></tr><tr><td>Total carbon</td><td>38,27 g/kg</td></tr><tr><td>CaCO<sub>3</sub></td><td>31,95 g/kg</td></tr><tr><td>Total nitrogen (N)</td><td>2,25 g/kg</td></tr><tr><td>C organic estimated</td><td>34,44 g/kg</td></tr><tr><td>Organic matter</td><td>59,58 g/kg</td></tr><tr><td>Phosphorus (P<sub>2</sub>O<sub>5</sub>)</td><td>0,04 g/kg</td></tr><tr><td>Calcium (Ca)</td><td>42,94 cmol+/kg</td></tr><tr><td>Iron (Fe)</td><td>0,01 cmol+/kg</td></tr><tr><td>Magnesium (Mg)</td><td>3,69 cmol+/kg</td></tr><tr><td>Potassium (K)</td><td>0,73 cmol+/kg</td></tr><tr><td>Sodium (Na)</td><td>0,19 cmol+/kg</td></tr><tr><td>Cation exchange capacity (CEC)</td><td>46,04 cmol+/kg</td></tr><tr><td>pH</td><td>8,17</td></tr><tr><td>C/N</td><td>15,32</td></tr></tbody></table>
<table><caption>Table A.3: Four wheat cultivars were selected for this study based on traits measured on a panel of 39 wheat cultivars in a previous study (Dubs et al., 2023). Selection was based on key traits contrasting yield with maximum plant height, and aboveground growth (RGR) with belowground traits (SRL, and RDMC). RGR: Relative growth rate, SRL: Specific root length, RDMC: Root dry matter content. The cultivars were also selected for their varying levels of sensitivity to STB, while all remained slightly to moderately susceptible to the disease. The values shown represent the mean for each trait.</caption><thead><tr><th>Cultivar</th><th>RGR<br>(mg.day<sup>-1</sup>)</th><th>SRL (m.g<sup>-1</sup>)</th><th>RDMC<br>(mg.g<sup>-1</sup>)</th><th>Maximal<br>height (cm)</th><th>Yield (g.m<sup>-2</sup>)</th><th>STB<br>sensitivity<br>(%)</th></tr></thead><tbody><tr><td>Rubisko</td><td>5,70</td><td>28,91</td><td>63,53</td><td>80,00</td><td>257</td><td>31,25</td></tr><tr><td>Skerzzo</td><td>12,40</td><td>18,36</td><td>91,86</td><td>91,00</td><td>229</td><td>25,00</td></tr><tr><td>Rouge de Bordeaux</td><td>13,94</td><td>17,98</td><td>92,13</td><td>155,00</td><td>148</td><td>18,75</td></tr><tr><td>Barbu de Champagne</td><td>7,15</td><td>27,89</td><td>75,11</td><td>153,50</td><td>164</td><td>27,50</td></tr></tbody></table>
<table><caption>Table A.4: Average abundance of grasses, legumes and forbs (cover, %) and of community functional traits (weighted average of species traits) of prairies in association with wheat. LA, Leaf area; SLA, Specific leaf area; LDMC, Leaf dry matter content; LNC, Leaf Nitrogen Content; LCC: Leaf Carbon Content.</caption><thead><tr><th></th><th>Prairie type</th><th>A1</th><th>A2</th><th>B1</th><th>B2</th></tr></thead><tbody><tr><td rowspan="2">Legumes cover (%)</td><td>mean</td><td>3,1</td><td>5,9</td><td>27,7</td><td>63,8</td></tr><tr><td>s.e.</td><td>0,6</td><td>1,0</td><td>2,2</td><td>2,4</td></tr><tr><td rowspan="2">Grass cover (%)</td><td>mean</td><td>93,3</td><td>88,7</td><td>47,6</td><td>31,3</td></tr><tr><td>s.e.</td><td>1,2</td><td>1,2</td><td>2,5</td><td>2,4</td></tr><tr><td rowspan="2">Forbs cover (%)</td><td>mean</td><td>3,6</td><td>5,4</td><td>24,7</td><td>4,9</td></tr><tr><td>s.e.</td><td>1,0</td><td>1,1</td><td>1,9</td><td>1,2</td></tr><tr><td rowspan="2">Height (cm)</td><td>mean</td><td>93</td><td>108</td><td>75</td><td>96</td></tr><tr><td>s.e.</td><td>1,0</td><td>1,2</td><td>1,5</td><td>1,2</td></tr><tr><td rowspan="2">LA (cm²)</td><td>mean</td><td>6,2</td><td>7,0</td><td>16,1</td><td>16,6</td></tr><tr><td>s.e.</td><td>0,2</td><td>0,3</td><td>0,7</td><td>0,5</td></tr><tr><td rowspan="2">SLA (cm² g⁻¹)</td><td>mean</td><td>186</td><td>215</td><td>190</td><td>244</td></tr><tr><td>s.e.</td><td>2,4</td><td>2,8</td><td>2,5</td><td>2,7</td></tr><tr><td rowspan="2">LDMC (gDM g⁻¹)</td><td>mean</td><td>26,3</td><td>25,4</td><td>22,2</td><td>22,8</td></tr><tr><td>s.e.</td><td>0,2</td><td>0,2</td><td>0,3</td><td>0,2</td></tr><tr><td rowspan="2">LNC (%)</td><td>mean</td><td>1,5</td><td>2,0</td><td>2,6</td><td>4,0</td></tr><tr><td>s.e.</td><td>0,02</td><td>0,02</td><td>0,07</td><td>0,03</td></tr><tr><td rowspan="2">LCC (%)</td><td>mean</td><td>41,2</td><td>41,5</td><td>43,2</td><td>44,5</td></tr><tr><td>s.e.</td><td>0,04</td><td>0,05</td><td>0,06</td><td>0,05</td></tr><tr><td rowspan="2">Leaf C/N</td><td>mean</td><td>28,4</td><td>21,6</td><td>19,8</td><td>11,7</td></tr><tr><td>s.e.</td><td>0,2</td><td>0,1</td><td>0,3</td><td>0,1</td></tr></tbody></table>
<table><caption>Table A.5: Tested models explaining the log response ratio (log RR) of STB incidence and severity. Selected models with the lowest AIC criteria are highlighted in bold.</caption><thead><tr><th>Model</th><th>Response variable</th><th>Included variables</th><th>AIC</th></tr></thead><tbody><tr><td>M1</td><td></td><td>Cultivar, agroprairie type, block</td><td>193,2</td></tr><tr><td>M2</td><td></td><td>Cultivar, wheat height, wheat leaf nitrogen content, agroprairie type, block</td><td>193,6</td></tr><tr><td>M3</td><td></td><td>Cultivar, mean prairie SLA, prairie legume percentage, prairie height, block</td><td>197</td></tr><tr><td>M4</td><td></td><td>Cultivar, prairie biomass, block</td><td>192,5</td></tr><tr><td>M5</td><td></td><td>Cultivar, prairie biomass, prairie legume percentage, block</td><td>194,5</td></tr><tr><td>M6</td><td></td><td>Cultivar, prairie biomass, mean prairie SLA, prairie legume percentage, block</td><td>192,3</td></tr><tr><td>M7</td><td></td><td>Cultivar, agroprairie type, cultivar*agroprairie type, block</td><td>203,8</td></tr><tr><td>M8</td><td>log RR of STB incidence</td><td>Cultivar, mean prairie SLA, prairie legume percentage, mean prairie SLA*prairie legume percentage, block</td><td>197,7</td></tr><tr><td>M9</td><td></td><td>Cultivar, mean prairie SLA, prairie legume percentage, mean prairie height, mean_height*mean_SLA, block</td><td>194,6</td></tr><tr><td>M10</td><td></td><td>Cultivar, prairie legume percentage, prairie biomass, prairie biomass*Mean legume percentage, block</td><td>191,5</td></tr><tr><td>M11</td><td></td><td>Cultivar, prairie legume percentage, prairie biomass, mean prairie SLA, prairie biomass*Mean legume percentage, block</td><td>188,2</td></tr><tr><td>M12</td><td></td><td>Cultivar, prairie legume percentage, prairie biomass, mean prairie SLA, prairie biomass*mean prairie SLA, block</td><td>189,2</td></tr><tr><td>M13</td><td></td><td>Cultivar, prairie legume percentage, prairie biomass, mean prairie SLA, Mean legume percentage*mean prairie SLA, block</td><td>193,4</td></tr><tr><td>M14</td><td></td><td>Cultivar, mean prairie SLA, prairie legume percentage, mean prairie height, mean prairie SLA*prairie legume percentage, mean_height*mean_SLA, block</td><td>196,4</td></tr><tr><td>M15</td><td></td><td>Cultivar, prairie legume percentage, prairie biomass, mean prairie SLA, prairie biomass*mean prairie SLA, prairie biomass*prairie legume percentage, prairie legume percentage*mean prairie SLA, block</td><td>191,8</td></tr><tr><td>M1</td><td></td><td>Cultivar, agroprairie type, block</td><td>144,3</td></tr><tr><td>M2</td><td></td><td>Cultivar, wheat height, wheat leaf nitrogen content, agroprairie type, block</td><td>145,4</td></tr><tr><td>M3</td><td></td><td>Cultivar, mean prairie SLA, prairie legume percentage, prairie height, block</td><td>147,7</td></tr><tr><td>M4</td><td></td><td>Cultivar, prairie biomass, block</td><td>143,1</td></tr><tr><td>M5</td><td></td><td>Cultivar, prairie biomass, prairie legume percentage, block</td><td>145,1</td></tr><tr><td>M6</td><td></td><td>Cultivar, prairie biomass, mean prairie SLA, prairie legume percentage, block</td><td>142,7</td></tr><tr><td>M7</td><td></td><td>Cultivar, agroprairie type, cultivar*agroprairie type, block</td><td>155,2</td></tr><tr><td>M8</td><td>log RR of STB severity</td><td>Cultivar, mean prairie SLA, prairie legume percentage, mean prairie SLA*prairie legume percentage, block</td><td>147,4</td></tr><tr><td>M9</td><td></td><td>Cultivar, mean prairie SLA, prairie legume percentage, mean prairie height, mean_height*mean_SLA, block</td><td>146,7</td></tr><tr><td>M10</td><td></td><td>Cultivar, prairie legume percentage, prairie biomass, prairie biomass*Mean legume percentage, block</td><td>144,1</td></tr><tr><td>M11</td><td></td><td>Cultivar, prairie legume percentage, prairie biomass, mean prairie SLA, prairie biomass*Mean legume percentage, block</td><td>140,9</td></tr><tr><td>M12</td><td></td><td>Cultivar, prairie legume percentage, prairie biomass, mean prairie SLA, prairie biomass*mean prairie SLA, block</td><td>141,7</td></tr><tr><td>M13</td><td></td><td>Cultivar, prairie legume percentage, prairie biomass, mean prairie SLA, Mean legume percentage*mean prairie SLA, block</td><td>144,1</td></tr><tr><td>M14</td><td></td><td>Cultivar, mean prairie SLA, prairie legume percentage, mean prairie height, mean prairie SLA*prairie legume percentage, mean_height*mean_SLA, block</td><td>148,5</td></tr><tr><td>M15</td><td></td><td>Cultivar, prairie legume percentage, prairie biomass, mean prairie SLA, prairie biomass*mean prairie SLA, prairie biomass*prairie legume percentage, prairie legume percentage*mean prairie SLA, block</td><td>144,7</td></tr></tbody></table>
<table><caption>Table A.6: GVIF^(1/2*df)) of the selected model for explaining the logRR of the STB incidence and severity</caption><thead><tr><th rowspan="2"></th><th colspan="2">Response variables:<br>log RR of STB incidence/severity<br>values</th></tr><tr><th>df</th><th>GVIF^(1/(2*df))</th></tr></thead><tbody><tr><td>% Legumes</td><td>1</td><td>1,03</td></tr><tr><td>Prairie biomass</td><td>1</td><td>4,9</td></tr><tr><td>SLA</td><td>1</td><td>2,01</td></tr><tr><td>Bloc</td><td>3</td><td>1,7</td></tr><tr><td>Wheat cultivar</td><td>3</td><td>1,01</td></tr><tr><td>% Legumes:Prairie<br>biomass</td><td>1</td><td>5,4</td></tr></tbody></table>
<table><caption>Table A.7: Literature review of the principal bacterial and fungal genera involved in induced systemic resistance, systemic acquired resistance and/or antagonisms with fungal pathogens</caption><thead><tr><th>Kingdom</th><th>Genera</th><th>Species</th><th>Reference</th></tr></thead><tbody><tr><td rowspan="14">Bacteria</td><td>Bacillus</td><td>subtilis<br>amyloliquefaciens<br>mojavensis</td><td>(Qu et al., 2020)</td></tr><tr><td>Bradyrhizobium</td><td>japonicum</td><td></td></tr><tr><td>Burkholderia</td><td>phytofirmans</td><td>(Yu et al., 2019)</td></tr><tr><td>Chitinophaga</td><td></td><td>(Liu et al., 2020)</td></tr><tr><td>Chryseobacterium</td><td></td><td>(Yang et al., 2023)</td></tr><tr><td>Curtobacterium</td><td>flaccumfaciens</td><td>(Qu et al., 2020)</td></tr><tr><td>Flavobacterium</td><td></td><td>(Liu et al., 2020)</td></tr><tr><td>Lysinibacillus</td><td></td><td>(Qu et al., 2020)</td></tr><tr><td>Microbacterium</td><td></td><td>(Liu et al., 2020)</td></tr><tr><td>Paenibacillus</td><td>alvei<br>polymyxa</td><td>(Pieterse et al., 2014)</td></tr><tr><td>Piriformospora</td><td>indica</td><td>(Pieterse et al., 2014)</td></tr><tr><td>Pseudomonas</td><td></td><td>(Liu et al., 2020; Qu et al., 2020)</td></tr><tr><td></td><td>simiae<br>capeferrum<br>aeruginosa<br>brassicacearum</td><td>(Yu et al., 2019)</td></tr><tr><td></td><td>fluorescens<br>putida<br>piscium<br>protegens</td><td>(Yang et al., 2023)</td></tr><tr><td>Serratia</td><td>marcescens</td><td></td></tr><tr><td>Sinorhizobium</td><td>fredii</td><td>(Pieterse et al., 2014)</td></tr><tr><td>Sphingomonas</td><td></td><td>(Liu et al., 2020)</td></tr><tr><td>Stenotrophomonas</td><td>maltophilia<br>rhizophila</td><td>(Yang et al., 2023)</td></tr><tr><td>Streptomyces</td><td></td><td>(Qu et al., 2020)</td></tr><tr><td>Xanthomonas</td><td></td><td>(Liu et al., 2020)</td></tr><tr><td rowspan="8">Fungi</td><td>Clonostachys</td><td>rosea</td><td>(Yang et al., 2023)</td></tr><tr><td>Glomus</td><td>versiforme</td><td>(Yu et al., 2019)</td></tr><tr><td>Laccaria</td><td>bicolor<br>irregularis</td><td>(Yu et al., 2019)</td></tr><tr><td>Penicillium</td><td></td><td>(Pieterse et al., 2014)</td></tr><tr><td>Piriformospora</td><td></td><td>(Yu et al., 2019)</td></tr><tr><td>Rhizosphagus</td><td></td><td>(Qu et al., 2020)</td></tr><tr><td>Trichoderma</td><td>harzianum<br>virens<br>atroviride<br>asperellum</td><td>(Pieterse et al., 2014; Yang et al., 2023)</td></tr><tr><td></td><td></td><td>(Yang et al., 2023)</td></tr></tbody></table>

Figures
<figure><img src="image_9.png" /><figcaption>Figure A.1: Schematic representation of experimental plots, an agroprairie with 4 alternative strips of prairie and wheat. Wheat strips are composed of 3 lines (A) and a wheat monocrop (B).</figcaption></figure>
<figure><img src="image_10.png" /></figure>

Figure A.2: Average STB incidence observed in experiment 1 at the time when the most symptoms were observed, 198 days after sowing. First leaf layer: First wheat leaf from the top of the plant; Second leaf layer: Second wheat leaf from the top of the plant.
<figure><img src="image_11.png" /><figcaption>Figure A.3: LogRR of wheat leaf nitrogen in the upper leaves depending on wheat cultivar (A). LogRR of wheat maximum height depending on wheat cultivar (B). Wheat maximum height of the cultivar Rubisko depending on treatments. Values are expressed as the mean ± standard error (n = 4) (C) rub, Rubisko; skz, Skerzzo; bch, Barbu de Champagne; rdb, Rouge de Bordeaux, Wheat-mono, wheat monocrop; Wheat-AP-A1, wheat in agroprairie A1; Wheat-AP-A2, wheat in agroprairie A2; Wheat-AP-B1, wheat in agroprairie B1; Wheat-AP-B2, wheat in agroprairie B2. Different lowercase letters represent significant differences between treatments (p-value < 0,05).</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>Figure A.4: Evolution of the log RR of the STB incidence or severity depending on prairie biomass (g.m⁻²) and prairie specific leaf area (SLA, cm.g⁻¹) in different agroprairies type. The p-value corresponds to the significance of the group structuring in the form of ellipses, which considers both the log RR of the incidence or severity of STB and the biomass or SLA of associated prairie. B2, agroprairies with the highest legume rate; B1, agroprairies with higher Forbs and medium Legumes rate, A2 and A1, highest grass rate and lower legumes cover (A). Evolution of the associated prairie legume proportion relative to total prairie biomass (g·m⁻²) (B).</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>Figure A.5: logRR of STB incidence and severity as a function of the type of agroprairie on June 17th in the experiment 2. B2, agroprairies with a high legume rate and low grass rate; A2, agroprairies with a high grass rate and a low legumes rate. Different lowercase letters represent significant differences between treatments (p-value < 0,05). Red stars indicate the values significantly lower than 0 (p-value < 0,05).</figcaption></figure>
<figure><img src="image_14.png" /><figcaption>Figure A.6: Relative abundance of bacterial (panel A) and fungal (panel B) phylum in plant rhizosphere; Wheat-mono, wheat rhizosphere in monocrop; Wheat-AP-A2, wheat rhizosphere in agroprairie A2; Wheat-AP-B2, wheat rhizosphere in agroprairie B2; Prairie-AP-A2, prairie rhizosphere in agroprairie A1; Prairie-AP-B2, prairie rhizosphere in agroprairie B2.</figcaption></figure>
<figure><img src="image_15.png" /><figcaption>Figure A.7: Bacterial and fungal Shannon and inverse Simpson indices of wheat rhizosphere. Wheat-mono, wheat monocrop rhizosphere; Wheat-AP-A2, wheat rhizosphere in agroprairie A2; Wheat-AP-B2, wheat rhizosphere in agroprairie B2 (A); Bacterial and fungal Shannon and inverse Simpson indices of associated prairie rhizosphere. Prairie-AP-A2, prairie rhizosphere in agroprairie A1; Prairie-AP-B2, prairie rhizosphere in agroprairie B2 (B). No significant differences were observed between wheat and prairie treatments.</figcaption></figure>
<figure><img src="image_16.png" /><figcaption>Figure A.8: Principal coordination analysis (PCoA) based on Aitchison distances of fungi (a) and bacteria (b) of associated prairie rhizosphere community structure. Treatments: Prairie-AP-A2, prairie rhizosphere in agroprairie A2; Prairie-AP-B2, wheat in agroprairie A2; prairie rhizosphere in agroprairie B2</figcaption></figure>
<figure><img src="image_17.png" /><figcaption>Figure A.9: Principal component analysis presenting different alpha-diversity indices and disease values in inoculated disease conditions and natural disease conditions (Experiment 2). In inoculated conditions: AUDPC, Area under disease progress curve; In natural conditions: Incidence_second_leaf_layers, Incidence 198 days after sowing; Shannon_index_fungi and Shannon_index_bacteria, bacteria and fungi alpha diversity index; PCOA_1_Fungi, PCOA_1_Bacteria, First Coordinates values of the PCOA for bacterial and fungal beta diversity.</figcaption></figure>
<figure><img src="image_18.png" /><figcaption>Figure</figcaption></figure>

A.10:

Bacterial genera *Sphingomonas* (A) and fungal genera *Penicillium* (B) abundances between the different treatments. Values are expressed as the mean ± standard error (n = 4). Treatments: Wheat-mono, wheat monocrop rhizosphere; Wheat-AP-A2, wheat rhizosphere in agroprairie A2; Wheat-AP-B2, wheat rhizosphere in agroprairie B2. Different lowercase letters represent significant differences between treatments (p-value < 0,05). Differences between treatments are not significant for Penicillium. The relative abundance is expressed as centered log ratio (clr).