# Potential of undersown species identity versus diversity to manage disease in crops

Seraina Lisa Cappelli, Luiz Alberto Domeignoz Horta, Stephanie Gerin, Jussi Heinonsalo, Annalea Lohila, Krista Raveala, Bernhard Schmid, Rashmi Shrestha, Mikko Johannes Tiusanen, Paula Thitz, et al.

<figure><img src="image_1.png" /></figure>

## To cite this version:

Seraina Lisa Cappelli, Luiz Alberto Domeignoz Horta, Stephanie Gerin, Jussi Heinonsalo, Annalea Lohila, et al.. Potential of undersown species identity versus diversity to manage disease in crops. Functional Ecology, 2024, 38 (7), pp.1497 - 1509. 10.1111/1365-2435.14592. hal-04676509

HAL Id: hal-04676509

https://hal.inrae.fr/hal-04676509v1

Submitted on 23 Aug 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_3.png" /></figure>

# Potential of undersown species identity versus diversity to manage disease in crops

Seraina Lisa Cappelli<fnref n="1" />,²,³ ID

Luiz Alberto Domeignoz Horta²,⁴ ID

Stephanie Gerin⁵ ID

Jussi Heinonsalo⁶ ID

Annalea Lohila⁵,⁷ ID

Krista Raveala<fnref n="1" /> ID

Bernhard Schmid⁸ ID

Rashmi Shrestha⁹ ID

Mikko Johannes Tiusanen²,¹⁰ ID

Paula Thitz<fnref n="1" /> ID

Anna-Liisa Laine<fnref n="1" /> ID

**Correspondence**

Seraina Lisa Cappelli

Email: scappell@umn.edu

**Funding information**

Maj ja Tor Nesslingin Säätiö; Research Council of Finland, Grant/Award Number: 327222

Handling Editor: Katie Field

**Abstract**

1. In the absence of chemical control with its negative side effects, fungal pathogens can cause large yield losses, requiring us to develop agroecosystems that are inherently disease resistant. Grassland biodiversity experiments often find plant species diversity to reduce pathogen pressure, but whether incorporating high biodiversity levels in agricultural fields have similar effects remains largely unknown.

2. We tested if undersown plant species diversity could reduce barley disease, and whether the effect was mediated through above- or below-ground mechanisms, by combining an agricultural field trial with a soil transplant experiment.

3. As predicted, barley disease decreased in the presence of undersown plants. Undersown species richness had no effect, but their abundance led to early season disease reduction. Above-ground mechanisms underpinned this disease reduction. Barley yield slightly decreased with increasing undersown species richness, and undersown species varied in their impact on yield.

4. We identified two undersown species, *Trifolium repens* and *T. hybridum*, that contributed most to disease reduction and had the potential to increase barley yield. Furthermore, our results indicate that above-ground mechanisms caused this. We show that agroecosystem functioning can be improved without trade-offs on yield by targeted selection of undersown species.

**KEYWORDS**

agroecosystem, barley, dilution effect, disease, diversification, undersowing

This is an open access article under the terms of the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2024 The Author(s). *Functional Ecology* published by John Wiley & Sons Ltd on behalf of British Ecological Society.

<figure><img src="image_4.png" /></figure>

<footnote n="1">¹Faculty of Biological and Environmental Sciences, Research Centre for Ecological Change, Organismal and Evolutionary Biology Research Programme, University of Helsinki, Helsinki, Finland; ²Department of Evolutionary Biology and Environmental Studies, University of Zürich, Zürich, Switzerland; ³Ecology, Evolution & Behavior, University of Minnesota, St. Paul, Minnesota, USA; ⁴University Paris-Saclay, INRAE, AgroParisTech, UMR EcoSys, Palaiseau, France; ⁵Finnish Meteorological Institute, Climate System Research, Helsinki, Finland; ⁶Department of Forest Sciences, Faculty of Agriculture and Forestry, University of Helsinki, Helsinki, Finland; ⁷Institute for Atmospheric and Earth System Sciences (INAR Physics), University of Helsinki, Helsinki, Finland; ⁸Remote Sensing Laboratories, Department of Geography, University of Zürich, Zürich, Switzerland; ⁹Department of Microbiology, Faculty of Agriculture and Forestry, University of Helsinki, Helsinki, Finland and ¹⁰Department of Environmental Systems Science, ETH Zürich, Zürich, Switzerland</footnote>
# 1 | INTRODUCTION

Pathogenic fungi are ubiquitous (Fisher et al., 2012) and responsible for 70% of all known plant diseases (Carris et al., 2012) causing approximately 12% of yield losses in crops, despite growing use of fungicides (Oerke, 2006; Savary et al., 2019; Walters et al., 2012). Intensive fungicide use promotes resistant pathogen evolution (Lucas et al., 2015), poses human health risks (Lushchak et al., 2018) and impacts the environment (Meena et al., 2020; Zubrod et al., 2019). Monoculture yield decline over time is often associated with the accumulation of above- or below-ground pathogens (Bassi et al., 2023). Alternative fungal pathogen control is needed to reduce negative impacts of fungicides and maintain their efficacy.

Intercropping, undersowing and other diversification practices are potentially powerful tools to increase genetic, species and functional diversity to mitigate negative impacts of intensive farming, maintain ecosystem functioning and improve food security (Brooker et al., 2015; Cappelli et al., 2022; Finney & Kaye, 2017; IPBES, 2019; Kleijn et al., 2019; McAlvay et al., 2022). In particular, undersowing with perennial plant communities may provide ecosystem functions after crops are harvested, for example by serving as ground cover (Valkama et al., 2015). Further, undersowing can incorporate species with a greater trait and phylogenetic divergence than most crops, which may increase the benefits for ecosystem functioning (Fu et al., 2019; Porter & Sachs, 2020; Zhu et al., 2019). Species richness and functional diversity in agricultural fields may also reduce disease pressure in crops (Finckh et al., 2000; Karjalainen, 1986; Larkin, 2015; Trenbath, 1993), aligning with general findings of decreased disease with increasing species richness in ecological diversity experiments (Liu et al., 2016; Mitchell et al., 2003; Rottstock et al., 2014). In mixed cropping systems, especially cereal crops benefit from reduced disease pressure (Bedoussac et al., 2015; Zhang et al., 2019). For example, intercropping barley with different legume species decreased net blotch disease by 50% or more in a study in Denmark (Henrik Hauggaard-Nielsen et al., 2008).

Mechanisms such as decreased and intercepted pathogen dispersal, decreased host susceptibility or induced resistance reduce disease pressure in diverse plant communities (Civitello et al., 2015; Keesing et al., 2006) and contribute to reduced disease pressure in mixed cropping systems (Finckh et al., 2000; Trenbath, 1993). Disease amplification with increasing richness is possible, but less commonly observed (e.g. Ocimati et al., 2018; Power & Mitchell, 2004). Above-ground, non-host species in the plant community can physically intercept dispersing spores. For example, wheat plants have been shown to intercept spore dispersal between barley plants of a barley pathogen (Burdon & Chilvers, 1977). The canopy structure created by different plant communities can affect the microclimate in the vegetation, which can alter pathogen population dynamics. Mixing tall and short varieties of rice created a canopy that dried out more effectively, which reduced how long leaves remained wet and decreased rice blast infection (Zhu et al., 2000). Neighbouring plants can also suppress pathogens through allelopathic effects on pathogen growth (Gómez-Rodríguez et al., 2003), or by triggering defence reactions in uninfected neighbouring plants (Wenda-Piesik

et al., 2010). With increasing plant species diversity also pathogen diversity increases while pathogen abundance decreases (Rottstock et al., 2014). Being exposed to non or low-virulent microbes can trigger induced defence pathways, which can then prime the crop to be resistant against its own pathogens (Finckh et al., 2000). Exposing barley plants to two fungi that are non-pathogenic for barley reduced colonization success of net blotch causing fungus *Pyrenophora teres* by more than two thirds (Lyngs Jørgensen et al., 1998).

Similar mechanisms can also occur below-ground. Soils of diverse plant communities host a higher diversity of mycorrhizal fungi, than those of species-poor communities (Guzman et al., 2021) and mycorrhiza can prime crop plants to be more resistant to diseases (Pozo & Azcón-Aguilar, 2007). Volatile organic compounds of pathogen-exposed barley roots slowed the growth of other barley pathogens (Fiers et al., 2013; Kaddes et al., 2016). Another below-ground mechanism by which neighbouring plants can affect the health of a focal species is through plant nutritional status, which can be improved in mixtures due to complementary resource use between plant species (Ashton et al., 2010; Gómez-Rodríguez et al., 2003; McKane et al., 2002). For example, intercropping with legumes improves cereal nutrition with nitrogen (Bedoussac et al., 2015) which can increase resistance against certain pathogens (Dordas, 2008; Tripathi et al., 2022). The soil microbial community is an important driver of soil-mediated disease suppression. Diverse plant communities harbour diverse soil microbial communities, which can promote disease resistance in plants through the production of toxins and metabolites, by competitive exclusion of pathogens, by triggering systemic defences in the hosts, or by improving plant health through increased stress resistance (Bollmann-Giolai et al., 2022; Garbeva et al., 2004). More diverse plant communities can host larger (Eisenhauer et al., 2010), more diverse (Maciá-Vicente et al., 2023) and more beneficial soil microbial communities (Stefan et al., 2021) than monocultures.

With higher plant species richness, it is likely that more of those mechanisms occur simultaneously, and that total disease suppression increases both via above- and below-ground mechanisms. Different plant species likely vary in their contribution to disease suppression in neighbouring plants, as has been shown for root pathogens in grasslands (Ampt et al., 2022). Disentangling the contribution of above and below-ground mechanisms to disease reduction with increasing species richness in a crop field can guide research-based agroecosystem designs.

In an agricultural context, impacts of diversification should carefully also consider effects on yield. Intercropping often increases total mixture yield compared with the average monoculture yield of the species used in the mixture (Martin-Guay et al., 2018), in the same way as it does in biodiversity experiments (Balvanera et al., 2006). However, the per-area yield of a single crop in mixture is usually lower than of the corresponding monoculture (Abu-Bakar et al., 2014; Bedoussac et al., 2015; Hauggaard-Nielsen et al., 2001). Therefore, despite all beneficial effects such as disease suppression, intercrops or undersown species can compete with the main crop and reduce its yield. Different neighbour species vary in the
magnitude of this competitive impact (Abu-Bakar et al., 2014; Bedoussac et al., 2015). Especially legume intercrops have less negative impact on cereal yields than non-leguminous species (Abu-Bakar et al., 2014; Duchene et al., 2017). Similarly, undersowing with legumes reduces barley yield less than undersowing with grasses (Karlsson-Strese et al., 1998; Kunelius et al., 1992). This might be because legumes suppress other species that would compete more for soil nitrogen with a main crop, because they can access atmospheric nitrogen (Hauggaard-Nielsen et al., 2001). Intercropping has positive impacts on soil quality, including water retention, and the effects of this on yield may become apparent only in the long-term and particularly during drought episodes (Brooker et al., 2015).

In this manuscript, we tested if undersown diversity reduces foliar disease in barley and if such diversity effects are driven by undersowing in general or by particular undersown species. Furthermore, we asked whether above- or below-ground mechanisms were underpinning the effect of undersown diversity on foliar disease (H1 in Figure 1). We tested if undersown diversity changed the soil fungal communities towards a disease-suppressive community composition (H2 in Figure 1). Finally, we tested the impact of the undersown diversity and single undersown species on barley yield (H3 in Figure 1).

## 2 | MATERIALS AND METHODS

### 2.1 | TWINWIN field experiment

The TWINWIN experiment was established in 2019 in Helsinki (60°13'N/25°01'E) to study the effects of undersown plant species and their diversity on above- and below-ground

agroecosystem functioning. The 30 years annual mean temperature was $5.97 \pm 0.77^\circ C$ and the annual precipitation $682 \pm 115$ mm (Finnish Meteorological Institute, 2019). Barley (Hordeum vulgare L. var. Harbinger) was sown with $80 \text{ kg ha}^{-1} \text{ year}^{-1}$ nitrogen at the end of May or beginning of June (date varied between years) in 56 out of 60 plots of $40 \text{ m}^2$ arranged in four blocks (Figure 2d). Four plots remained without plants, to study vegetation effects on soil functions (not used in this study). Barley grew alone with (4 plots) and without herbicide (8 plots) as control (undersown species richness = 0). In the other plots, barley grew with undersown plant communities, which represented undersown richness levels one (eight single undersown species, each replicated in three plots), two (10 unique mixtures of two undersown species), four (six unique mixtures of four undersown species) and eight (one mixture of all eight undersown species, replicated in four plots) (Figure 2a). The eight undersown species differed in their ability to fix nitrogen and in their rooting depth: *Trifolium repens* L., *T. hybridum* L. (shallow rooting, nitrogen fixing), *T. pratense* L., *Medicago sativa* L. (deep rooting, nitrogen fixing), *Lolium multiflorum* Lam., *Phleum pratense* L. (shallow rooting, non-nitrogen fixing), *Festuca arundinacea* Schreb., *Cichorium intybus* L. (deep rooting, non-nitrogen fixing, Figure 2b). The species compositions in two and four species mixtures varied in functional diversity (number of functional types: ability to fix nitrogen × rooting depth) within their species richness levels (Figure 2c). Undersown plants were sown 1 week after barley in rows between the barley. 6 weeks after barley sowing, we clipped weeds taller than barley at ground level. In early September, the barley was harvested to measure *barley yield* (H3 in Figure 1). Plots were tilled in spring before sowing for the next season. No permit was needed for this experiment.

<figure><img src="image_5.png" /><figcaption>FIGURE 1 Hypotheses (H) on above- and below-ground mechanisms by which plant diversity affects barley disease, yield and the composition of soil fungal communities, based on operational taxonomic units (OTU). The undersown plant community will impact disease and yield via paths A–C, but for yield we only test their net effect. In italics are examples of processes that may underlie those mechanisms.</figcaption></figure>

**Hypotheses:**

**H1** Undersown richness and/or functional diversity will decrease barley foliar disease by a) above-ground (path A) and b) below-ground mechanisms (path B) and c) in a plant species-specific manner.

**H2** Undersown richness will create soil fungal communities with higher fungal OTU richness, including increased richness of potentially pathogenic and potentially mutualistic fungal OTUs, decreased potentially pathogenic fungal OTU abundance and increased potentially mutualistic fungal OTU abundance. (path C)

**H3** The undersown plant community will impact barley yield, but the direction and magnitude will vary due to multiple competing mechanisms leading to plant-species specific effect sizes, with legumes having a more positive impact.
<figure><img src="image_6.png" /><figcaption>FIGURE 2 TWINWIN experiment design: (a) Three control and four undersowing treatments. (b) The eight undersown species vary in rooting depth and N-fixation capacity, leading to four different ‘functional types’. (c) The undersown communities vary in richness and functional diversity (number of functional types of undersowns in a plot). (d) The 60 plots are arranged in four blocks (see also Table S1).</figcaption></figure>

To verify H1 (Figure 1), we assessed barley leaf damage by leaf spots on the top three leaves (if available) of 20 and 10 haphaz- ardly selected barley stems per plot in mid-July and August 2020 respectively. Per leaf, the damage was classified as 0 (0% leaf area damage), 1 (<10%), 2 (10%–25%), 3 (25%–50%) or 4 (50%–100%) (adapted from Dirzo & Domínguez, 2010). Net blotch (Pyrenophora teres Drechs. 1923), a hemibiotrophic fungal pathogen of barley (Liu et al., 2011) was dominant and its presence was recorded. We also visually estimated the relative abundances of all species in all plots at the end of July. All data used for this study were collected in 2020.

## 2.2 | Soil transplant experiment

To evaluate how diversity affects disease through above- and below-ground mechanisms (Figure 1, H1a,b), we did a reciprocal soil transplant experiment. We grew barley plants in pots contain- ing soil originating from different TWINWIN plots (collected in early June 2020) in the greenhouse and reciprocally placed the pots back into the different field plots (late June). We used a subset of the plots and undersown species: *L. multiflorum*, *F. arundinacea*, *T. hy- bridum*, and *M. sativa*, one from each functional type. We used soil from barley monoculture plots, the plots with either one of the four undersown species alone, these four undersown species together, and the plots with barley and eight undersown species, leading to seven different soil types, varying in undersown species richness

(hereafter soil-origin richness). Pots with each soil type were placed back into the original plots (seven different environment types vary- ing in undersown species richness (hereafter environment richness)). We replicated each soil by environment type combination five times (245 pots, details in Supplementary methods).

To assess soil-origin and environment richness impact on disease (Figure 1, H1a,b), we measured pathogen leaf damage on the tallest tiller per pot as described above, 1 and 5 weeks after placing the pots in the field, which is one and 2 months after sowing the seeds to the pots.

To test whether soil-origin richness changed soil fungal communi- ties (Figure 1, H2), we sequenced the fungal communities (ITS4–ITS7 region) in a random subset of 231 pots representing each treatment at the end of the experiment. We chose to sequence a subset of samples to ensure a deep sequencing depth within a single sequenc- ing run while still having enough replicates of each treatment. If we had included all the samples, we would have run the risk of having too many samples for a single sequencing run and therefore a shal- low sequencing depth. Rather than checking for overall differences in the soil fungal community composition, we characterized them based on metrics related to disease and disease prevention (Figure 1, H2): We used the FUNGuild database (Nguyen et al., 2016) to check for each operational taxonomic unit (OTU) if it was potentially patho- genic or potentially symbiotic. Then, we calculated OTU Shannon diversity index, the relative abundance of potentially pathogenic OTU reads (contribution of pathogens to total fungal abundance), the proportion of potentially pathogenic OTUs (contribution of pathogens
to total fungal diversity) and checked for the presence of potentially symbiotic fungi for each pot (details in Supplementary methods).

## 2.3 | Statistical analysis

See Table 1.

## 2.4 | TWINWIN field experiment

To test the hypothesis of undersown diversity leading to reduced barley disease in the TWINWIN main experiment (Figure 1, H1), we ran a series of cumulative link mixed effects models (Ordinal package, Christensen, 2019). Cumulative link mixed effects models account for the ordered categorical nature of the response variable disease that was scored in ordered percent damage ranges (see above, Ananth & Kleinbaum, 1997). We corrected for block and leaf position (highest, second highest, third highest: indication of leaf age) by fitting these terms before any other fixed effects and included plot and plant individual as random effects. To check whether undersown richness and/or functional diversity explain disease, we fit four models with different combinations of the two metrics as fixed effects and compared AIC values of those models. For this and all following analyses the logarithm of undersown richness plus one was used, because this allowed us to include control plots with undersown richness=0. The model including undersown richness alone had the lowest AIC value (Table S2) and therefore we continued with a model that did not include undersown functional diversity (model 1). To test whether any diversity effect was driven by herbicide treatment in the plots without undersown species (zero undersown richness) or by undersowing as such, we fitted contrasts for herbicide treatments (with vs. without, model 2) or herbicide (with vs. without) and undersowing (0 vs. >0) treatments (model 3) before fitting undersown richness (testing the richness effect for plots with

richness >0). Although not relevant for this study, herbicide treatment was a part of the overall experimental design and thus included in the analysis. To test for particular species effects (Figure 1, H1b), we used model 1 as baseline and added a presence/absence term for each undersown species (model 4). The model was simplified by stepwise removal of non-significant presence/absence terms using likelihood ratio tests. To account for variable establishment success of the different undersown species in different plots, presence/absence data were replaced with species relative abundances and the relative abundance of weeds and barley were included as covariates in a further model (model 5). This model was also simplified.

To analyse effects of the undersown treatments on barley yield in the TWINWIN main experiment (H3), yield was analysed in response to undersown richness and/or functional diversity, again controlling for block (model 6), using linear models. Again, we used AIC comparisons to find the best combination of undersown diversity metrics and the model with undersown richness alone had the lowest AIC value (Table S11) and was used for detailed analysis (model 1). Then, we fitted two additional models in which we first corrected for herbicide treatment (model 7) or for herbicide and undersowing treatments (model 8). Furthermore, we checked if disease reduction by undersown richness explained a potential diversity effect, by fitting the proportion of leaves with high disease (>10% leaf area damaged) as covariate before fitting undersown richness, which was not the case (data not shown).

To test the effects of single undersown species on yield (H3), we fit another linear model with a treatment variable that distinguishes between plots in which barley was grown alone, alone with herbicide, together with any of the undersown species (undersown diversity of 1 per species), and with two, four and eight undersown species. Again, block was fit before treatment (model 9). The design of the pot experiment did not allow to test for effects of functional diversity and did not provide enough power to test for species level effects. We analysed pairwise differences between treatments with Tukey's HSD post hoc test (emmeans package, Lenth, 2022).

<table><caption>TABLE 1 Replication statement.</caption><thead><tr><th></th><th>Variable of interest</th><th>Scale of inference</th><th>Scale at which the factor of interest is applied</th><th>Number of replicates at the appropriate scale</th></tr></thead><tbody><tr><td rowspan="2">TWINWIN main experiment</td><td>Disease</td><td>Leaf per individual</td><td>Plot</td><td>3 leaves of 20 and 10 individuals per plot in July and August respectively. See Figure 2a for details on replicates per undersowing treatments. Total 56 out of 60 plots used</td></tr><tr><td>Yield</td><td>Plot</td><td></td><td>See Figure 2a for details on replicates per undersowing treatments Total 56 out of 60 plots used</td></tr><tr><td rowspan="2">Soil transplant experiment</td><td>Disease</td><td>Leaf</td><td>Pot</td><td>3 leaves of one plant per pot, 5 replicates per environment x soil-origin combination. Total 245 pots</td></tr><tr><td>Soil fungal community metrics</td><td>Pot</td><td></td><td>4-5 replicates per environment x soil-origin combination. Total 231 pots</td></tr></tbody></table>
## 2.5 | Soil transplant experiment

Disease in the soil transplant experiment was analysed per time point using cumulative link mixed effects models (ordinal package, Christensen, 2019) with leaf position, soil-origin richness, environment richness and the interaction between the two as fixed effects and plot as random effect (model 10, H1a,b). For this and all following analyses the logarithm of environment and soil-origin richness plus one were used.

To test whether the treatments affected the soil fungal community composition in the soil transplant experiment we analysed all metrics in response to environment and soil-origin richness and their interaction, including plot as random effect (H2). OTU Shannon diversity was analysed with a linear mixed effects model (model 11, lme4 package, Bates et al., 2015). The proportion of potentially pathogenic OTUs and the relative abundance of potentially pathogenic OTU reads were analysed with generalized linear mixed models with beta-binomial distribution (models 12 and 13, glmmTMB package, Brooks et al., 2017). We analysed presence/absence of potentially symbiotic OTUs with a generalized linear mixed effects model with a binomial distribution (model 14, lme4 package, Bates et al., 2015). We also used cumulative link models to test if the functional community metrics explained disease levels, but none of them had significant effects (results not shown). All analyses were done in R (Version 4.0.2, R Core Team, 2022). The data used in this manuscript are publicly available (Cappelli et al., 2024; Domeignoz Horta et al., 2024).

# 3 | RESULTS

## 3.1 | TWINWIN field experiment

Ninety-six per cent of the 3312 leaves and 100% of the 1120 surveyed barley individuals in the TWINWIN experiment showed some signs of disease when we assessed disease in July 2020. We found distinct symptoms of net blotch disease caused by *Pyrenophora teres* on 44% of the individuals. Likely, its proportion was higher, but we could not with certainty identify the damage as net blotch when the lesions were small. It is likely that pathogens causing other leaf spot diseases like *Cochliobolus sativus* were present, too. In August 2020, all assessed leaves (total 1666) showed signs of infection and most (93%) were in the highest damage category, which made detailed analysis impossible. The following results are based on the disease measurements in July.

Undersown richness explained barley foliar disease better than undersown functional diversity (Table S2) and we therefore did not include functional diversity in the final models. After correcting for block and leaf position, undersown richness had a marginally significant negative effect on disease (p=0.067; Tables S3 and S4; Figure S2, model 1). This effect was largely explained by the difference between plots with and without undersown species (Tables S3

and S4; Figure S3c, model 3). Undersowing reduced the probability of heavy damaged (>10% of the leaf area damaged) from 70.38% to 18.95% (p=0.007; Tables S3 and S4; Figure S3b, model 3, H1). When including the presence of single undersown species instead of herbicide and undersowing, the negative diversity effect of undersown richness on barley disease remained significant (p<0.001; Tables S5 and S6, model 4). The two species that increased barley disease when present, were *Phleum pratense* (probability of >10% leaf area damage increased from 55.31% to 63.74%, p<0.001) and *Lolium multiflorum* (probability of >10% leaf area damage increased from 54.28% to 67.79%, p=0.027; Tables S5 and S6; Figure S4, model 4, H1c). When we corrected for single species abundances, undersown richness had no significant effect on barley disease (p=0.180). Instead, with increasing abundance of five out of the eight species, all of the nitrogen-fixing species and *Cichorium intybus*, barley disease significantly decreased, while the abundance of the other three species had no significant effect (Tables S7 and S8; Figure 3, model 5, H1c).

*Barley yield* was on average $2.246 \pm 0.580\text{tha}^{-1}$. Undersown richness explained yield better than undersown functional diversity (Table S9) and the following models therefore only include undersown richness. Undersown richness reduced yield from $2.422\text{tha}^{-1}$ in the absence of undersowns to $1.952\text{tha}^{-1}$ when all eight species were undersown, but this was only marginally significant (p=0.073; Tables S10 and S11; Figure S5, model 6, H3). The diversity effect was largely explained by herbicide and undersowing (Tables S10 and S11, models 7 and 8). The model analysing yield in response to a categorical treatment variable explained more variance in yield (R$^2$=0.527, model 9) than the models analysing it in response to undersown richness (R$^2$=0.227, model 6). There was one undersown species that had a large and statistically significant negative impact on yield: undersowing with *Medicago sativa* reduced yield by 50% compared with barley growing alone with (-54%) or without (-49%) herbicide (Table S12; Figure S6, model 9). The other undersowing treatments had smaller impacts on yield: undersowing with *Trifolium hybridum* (+6% compared with barley alone, -3% compared with barley with herbicide), *Trifolium repens* (+4%, -5%), *Trifolium pratense* (-11%, -19%), *Lolium multiflorum* (-11%, -19%), *Phleum pratense* (-13%, -21%), *Festuca arundinacea* (+7%, -3%), *Cichorium intybus* (-24%, -31%), two species (-11%, -17%), four species (-23%, -30%) and eight species (-9%, -17%) but none of those differences were statistically significant (Table S12; Figure S6, model 9, H3b).

## 3.2 | Soil transplant experiment

In the soil transplant experiment, leaves were less likely to be damaged by disease in early July when the pots were placed into a more species-rich environment (p=0.006, Figure 4a). Soil-origin richness had the opposite effect, but this was not statistically significant (p=0.43, Figure 4b). The interaction between environment and soil-origin richness was not statistically significant (p=0.44, Tables S13
<figure><img src="image_7.png" /><figcaption>**FIGURE 3** Probability of a barley leaf to fall into different damage categories as a function of the abundance of *Trifolium repens*, *Trifolium hybridum*, *Medicago sativa*, *Trifolium pratense* and *Cichorium intybus*, predicted by the simplified cumulative link mixed model (model 5). The fitted lines match the realized abundances of the species and are given with 95% confidence limits. The significances of the effects are indicated with *p* values and additionally highlighted with stars (*p* = 0.01–0.05, **p** = 0.01–0.001, ***p*** < 0.001).</figcaption></figure>

and S14, model 10a). In early August, none of the treatments had a statistically significant effect on disease (Tables S13 and S14, model 10b, H1a,b).

The Shannon diversity of the fungal OTUs in the pot soils increased marginally with soil-origin richness, but only when the pots were placed in a plot with high environment richness (*p* = 0.084; Tables S15 and S16; Figure S7, H2). The proportion of potentially pathogenic OTUs decreased both with soil origin (*p* < 0.001; Tables S17 and S18; Figure S8a) and environment richness (*p* = 0.039; Tables S17 and S18; Figure S8b), but the relative abundance of potentially pathogenic OTU reads was not affected by soil origin (*p* = 0.40; Tables S19 and S20; Figure S9a) or environment richness (*p* = 0.98; Tables S19 and S20; Figure S9b, H2). The probability for the occurrence of potentially symbiotic OTUs in the pot greatly increased with soil-origin richness (*p* < 0.001; Tables S21 and S22; Figure S10a) but did not change with environment richness (*p* = 0.69; Tables S21 and S22; Figure S10b, H2).

4 | DISCUSSION

Here, we tested whether undersown plant species can be used as a sustainable disease control in an agroecosystem. We observed reduced barley foliar disease in undersown plots of the TWINWIN experiment as predicted by H1 (Figures S2 and S3). The reduced foliar disease in the pot experiment could only be attributed to environment, but not soil-origin diversity (Figure 4), suggesting that, and as predicted by H1a, above-ground mechanisms exclusively drive disease in this system. Single undersown species varied in their contribution to disease reduction in the TWINWIN experiment (Figure 3), consistent with H1c, and the presence of two species was even attributed to increased disease. Later in the season, all barley leaves in both experiments were heavily diseased, independent of the treatments. Our results highlight that the addition of few undersown species can promote desired ecosystem functions and enhance sustainable agriculture, but the outcome depends on the identity of the added species.
<figure><img src="image_8.png" /><figcaption>FIGURE 4 Probability of a barley leaf in the soil transplant experiment to fall into a damage category with (a) increasing environment richness and (b) soil-origin richness in July 2020. The significances of the effects are indicated with *p* values and additionally highlighted with stars (ns, *p* > 0.05, *p* = 0.01–0.05, **p** = 0.01–0.001, ***p* < 0.001).</figcaption></figure>

Undersowing decreased barley foliar disease early in the season and this effect became—although only marginally significant—stronger with increasing undersown richness (Figures S2 and S3), as predicted by H1. Biodiversity experiments (Liu et al., 2016; Mitchell et al., 2003; Rottstock et al., 2014) and intercropping studies generally find similar effects of diversity (Kunelius et al., 1992). Undersown richness was the better predictor of barley disease than undersown functional diversity, which is also consistent with findings from a grassland experiment (Rottstock et al., 2014). However, the root traits used to create the gradient of functional diversity were not primarily chosen to optimize foliar disease reduction. Diversifying traits linked to defence against pathogens could still be a promising approach (Garrett et al., 2009). The effect of undersown diversity was explained mostly by undersowing per se, meaning that undersowing single or only few species can provide adequate disease reduction. This is consistent with ecological theory and the results from diversity experiments indicating largest benefits of diversification at low diversity (Cardinale et al., 2012; Isbell et al., 2017). The need for only few species should simplify the adoption of undersowing in agroecosystems.

Most of the species that reduced barley disease were nitrogen-fixing legumes (Figure 3), which is consistent with observations from intercropping studies (Hauggaard-Nielsen et al., 2008; Zhang et al., 2019). Legumes in crop rotations and crop mixtures enhance the nutritional status of the other crops (Angus et al., 2015; Iverson et al., 2014 but see Boetzl et al., 2023). Nitrogen fertilization has been linked to reduced pressure by certain diseases (Dordas, 2008; Tripathi et al., 2022), which may explain the reducing effects of the undersown legumes on barley foliar disease. However, the missing soil effects in our soil transplant experiment do not support such soil-mediated mechanisms. Above-ground mechanisms, like physical interference with growth and dispersal are therefore more likely to explain the disease reduction by the

non-grass undersowns. Canopy structure, especially density, has been shown to reduce spore dispersal by rain splashes (Schoeny et al., 2008; Vidal et al., 2017) which is how *Phyrenophora teres* ascospores disperse (Liu et al., 2011). The results of Rottstock et al. (2014) suggest canopy density and improved space filling are key components limiting spore dispersal with increasing plant diversity. Our results suggest, that leaf shape might contribute to this, too. It is possible that the thin narrow leaves with upward orientation of grasses are not as well suited to capture dispersing spores as the broader leave shapes of the forbs. Furthermore, the forbs which are only distantly related to barley, might have exposed the barley plants to microbes that are not able to successfully infect barley, as most fungal pathogens infect only closely related hosts (Klenke, 2015). However, those microbes, non-pathogenic for barley, might still have challenged the barleys immune system, leading to increased defence against *Pyrenophora teres*, as has been observed by Lyngs Jørgensen et al. (1998). In contrast, closely related neighbours can contribute to disease risk, by increasing the number of potential hosts (Parker et al., 2015). *Pyrenophora teres* can also infect *Phleum pratense* and *Lolium multiflorum* (Farr & Rossman, 2023). The stubble born pathogen *P. teres* (Liu et al., 2011) might overwinter on the alternative host, which could explain why *P. pratense* and *L. multiflorum* presence increased barley foliar disease (Figure S4).

Impacts of single undersown species on yield corresponding to our study have been detected in meta-analyses of other Nordic studies, where undersown legumes increased yield by 6% but non-legumes decreased it by 3% (Valkama et al., 2015). However, our results regarding the yield-reducing impacts of *Medicago sativa* (a legume not included by Valkama et al., 2015) contrast with results from other legumes, indicating that not all legumes promote yield, as was suggested also earlier by (Abdalla et al., 2019). *M. sativa* visibly outgrew barley in many plots where it was present. Yield losses
with increasing undersown richness and in plots where barley was grown with *M. sativa* suggest that mechanisms such as competition between barley and undersown species may overrule potential yield gains through disease suppression. To fully disentangle, the competition-related undersowing effects from those related to disease suppression, a fungicide treatment could be included in future studies.

This study showed that undersown species can suppress crop diseases similarly as intercropping (Finckh et al., 2000; Hauggaard-Nielsen et al., 2008; Trenbath, 1993) but only early in the season and without large impact on yield. The variety of barley used here is susceptible to net blotch (Jalli & Vuorinen, 2007; Ohralahti, 2013) which might explain why we did not detect reduction effects of undersowing later in the season. Undersowing might be able to contribute to prolonged disease suppression with positive impacts on yield if it were combined with additional disease control measures, like using resistant cultivars. The shallow rooting legumes *Trifolium repens* and *T. hybridum* reduced disease most strongly and yield was, although not statistically significant, higher in their presence compared with the barley monocultures. Those two species might also have other beneficial effects: Boetzl et al. (2023) showed that undersown clover mixtures increased pollinator abundance and suppressed weeds.

Contrary to our expectations (H1b), only environmental richness and not soil-origin richness had a statistically significant effect on foliar disease in the pot experiment (Figure 4), suggesting that above-ground mechanisms contributed to disease reduction exclusively in this study system. Processes like physical spore interception above-ground (Vidal et al., 2017), microclimatic conditions favouring barley health (Zhu et al., 2000) or allelopathic effects (Gómez-Rodríguez et al., 2003; Wenda-Piesik et al., 2010) must have been more important drivers of disease than below-ground mechanisms, like improved nutritional status (Bedoussac et al., 2015), root allelopathy (Fiers et al., 2013; Kaddes et al., 2016) or effects through changes in the soil microbial community (Moya et al., 2018; Pozo & Azcón-Aguilar, 2007). However, we did not evaluate below-ground disease symptoms. Future studies could focus more on below-ground diseases and mechanisms driving them.

We found a shift towards a soil fungal community that should promote barley health with increasing soil-origin richness: OTU richness (Figure S7) and the presence of potentially symbiotic fungi (Figure S10) increased while the proportion of potentially pathogenic fungi decreased (Figure S8). Only the relative abundance of potentially pathogenic OTUs did not changed with soil-origin richness. Despite this and the generally acknowledged positive effects of soil microbial diversity for plant health (Bollmann-Giolai et al., 2022; Garbeva et al., 2004), soil-origin richness did not significantly reduce foliar disease in the pot experiment. Focusing on symptoms specific to soilborne diseases might have revealed different patterns (Zhang et al., 2019), especially since we observed a lower proportion of potentially pathogenic OTUs at higher undersown richness. Furthermore, diversity effects in experiments commonly become stronger over time (e.g., Huang et al., 2018; Lange et al., 2023; Meyer et al., 2016; Reich et al., 2012) as soil microbial communities build up

over years (Morriën et al., 2017). The observed undersown diversity related soil microbial community differences might affect above-ground disease symptoms only after a longer experiment duration.

## 5 | CONCLUSIONS

Undersowing barley with few effective species can reduce disease pressure via above-ground mechanisms, and hence can provide an alternative pest control to pesticides. Our study did not provide evidence of disease suppression via below-ground mechanisms, but it should be noted that these may only manifest after a time lag. Jointly, our results show that agroecosystem functioning, in this case disease control, can be improved without trade-offs on yield by selecting undersown species that can provide the desired ecosystem function. Establishing direct links between diversification schemes, microbial responses (both above and below-ground) and yield across different environmental conditions and crops offers an exciting future venue of research and is needed to guide sustainable agricultural practices.

## AUTHOR CONTRIBUTIONS

J.H., A.L. B.S. and A.-L.L. conceived the ideas and designed methodology for TWINWIN, S.L.C. and A.-L.L. for the soil transplant experiment. S.L.C., L.A.D.H., S.G., K.R. and R.S collected the data. S.L.C analysed the data with input of L.A.D.H., B.S., M.J.T., P.T. and A.-L.L. S.L.C. led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication. Our study has co-authors from several different countries, including scientists based in the Finland where the study was carried out. All authors were engaged throughout the research process to ensure that the diverse sets of perspectives they represent was considered. Whenever relevant, literature published by scientists from the region was cited and we considered relevant work published in Finnish. The study is part of a larger network (https://www.bsag.fi/en/carbon-action-en/), which incorporates the perspective of researchers, policy makers and local farmers to develop sustainable agricultural practices and serves as a portal for knowledge transfer between the different stakeholders.

## ACKNOWLEDGEMENTS

We thank Alma Oksanen and Heidi Blom for all their help with the experiments. We are grateful to the research farm staff for maintaining the TWINWIN experiment. Funding was provided by the Nessling Foundation and the Academy of Finland (STN MULTA; 327222) to A-L.L, A.L. and J.H.

## CONFLICT OF INTEREST STATEMENT

We do not have any conflict of interests regarding the submission of this manuscript.

## DATA AVAILABILITY STATEMENT

The analysed data used for this manuscript are available on Dryad Digital Repository https://doi.org/10.5061/dryad.gtht76hvc and the
sequences from the soil transplant experiment are on NCBI under the code PRJNA1108597, released on 14 May 2024 (http://www.ncbi.nlm.nih.gov/bioproject/1108597).

## ORCID

Seraina Lisa Cappelli ID https://orcid.org/0000-0002-8141-404X

Luiz Alberto Domeignoz Horta ID https://orcid.org/0000-0003-4618-6253

Stephanie Gerin ID https://orcid.org/0000-0001-7734-9161

Jussi Heinonsalo ID https://orcid.org/0000-0001-8516-1388

Annalea Lohila ID https://orcid.org/0000-0003-3541-672X

Bernhard Schmid ID https://orcid.org/0000-0002-8430-3214

Rashmi Shrestha ID https://orcid.org/0000-0002-6827-9269

Mikko Johannes Tiusanen ID https://orcid.org/0000-0002-9361-0777

Paula Thitz ID https://orcid.org/0000-0001-5843-9284

Anna-Liisa Laine ID https://orcid.org/0000-0002-0703-5850

## REFERENCES

Abdalla, M., Hastings, A., Cheng, K., Yue, Q., Chadwick, D., Espenberg, M., Truu, J., Rees, R. M., & Smith, P. (2019). A critical review of the impacts of cover crops on nitrogen leaching, net greenhouse gas balance and crop productivity. *Global Change Biology*, 25(8), 2530–2543. https://doi.org/10.1111/gcb.14644

Abu-Bakar, M., Ahmad, R., Ehsanullah, & Zahri, Z. A. (2014). Comparison of barley-based intercropping system for productivity and net economic return. *International Journal of Agriculture and Biology*, 16(6), 1183–1188.

Ampt, E. A., van Ruijven, J., Zwart, M. P., Raaijmakers, J. M., Termorshuizen, A. J., & Mommer, L. (2022). Plant neighbours can make or break the disease transmission chain of a fungal root pathogen. *New Phytologist*, 233(3), 1303–1316. https://doi.org/10.1111/nph.17866

Ananth, C. V., & Kleinbaum, D. G. (1997). Regression models for ordinal responses: A review of methods and applications. *International Journal of Epidemiology*, 26(6), 1323–1333. https://doi.org/10.1093/ije/26.6.1323

Angus, J. F., Kirkegaard, J. A., Hunt, J. R., Ryan, M. H., Ohlander, L., & Peoples, M. B. (2015). Break crops and rotations for wheat. *Crop and Pasture Science*, 66(6), 523. https://doi.org/10.1071/CP14252

Ashton, I. W., Miller, A. E., Bowman, W. D., & Suding, K. N. (2010). Niche complementarity due to plasticity in resource use: Plant partitioning of chemical N forms. *Ecology*, 91(11), 3252–3260. https://doi.org/10.1890/09-1849.1

Balvanera, P., Pfisterer, A. B., Buchmann, N., He, J.-S., Nakashizuka, T., Raffaelli, D., & Schmid, B. (2006). Quantifying the evidence for biodiversity effects on ecosystem functioning and services. *Ecology Letters*, 9(10), 1146–1156. https://doi.org/10.1111/j.1461-0248.2006.00963.x

Bassi, L., Hennecke, J., Albracht, C., Bröcher, M., Solbach, M. D., Schaller, J., Doan, V. C., Wagner, H., Eisenhauer, N., Ebeling, A., Meyer, S. T., van Dam, N. M., & Weigelt, A. (2023). Uncovering the secrets of monoculture yield decline: Trade-offs between leaf and root chemical and physical defence traits in a grassland experiment. *Oikos*, 2024, e10061. https://doi.org/10.1111/oik.10061

Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. *Journal of Statistical Software*, 67(1). https://doi.org/10.18637/jss.v067.i01

Bedoussac, L., Journet, E.-P., Hauggaard-Nielsen, H., Naudin, C., Corre-Hellou, G., Jensen, E. S., Prieur, L., & Justes, E. (2015). Ecological

principles underlying the increase of productivity achieved by cereal-grain legume intercrops in organic farming. A review. *Agronomy for Sustainable Development*, 35(3), 911–935. https://doi.org/10.1007/s13593-014-0277-7

Boetzl, F. A., Douhan Sundahl, A., Friberg, H., Viketoft, M., Bergkvist, G., & Lundin, O. (2023). Undersowing oats with clovers supports pollinators and suppresses arable weeds without reducing yields. *Journal of Applied Ecology*, 60(4), 614–623. https://doi.org/10.1111/1365-2664.14361

Bollmann-Giolai, A., Malone, J. G., & Arora, S. (2022). Diversity, detection and exploitation: Linking soil fungi and plant disease. *Current Opinion in Microbiology*, 70, 102199. https://doi.org/10.1016/j.mib.2022.102199

Brooker, R. W., Bennett, A. E., Cong, W.-F., Daniell, T. J., George, T. S., Hallett, P. D., Hawes, C., Iannetta, P. P. M., Jones, H. G., Karley, A. J., Li, L., McKenzie, B. M., Pakeman, R. J., Paterson, E., Schöb, C., Shen, J., Squire, G., Watson, C. A., Zhang, C., ... White, P. J. (2015). Improving intercropping: A synthesis of research in agronomy, plant physiology and ecology. *New Phytologist*, 206(1), 107–117. https://doi.org/10.1111/nph.13132

Brooks, M., Kristensen, K., Benthem, K., Magnusson, A., Berg, C., Nielsen, A., Skaug, H. J., Mächler, M., & Bolker, B. (2017). glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. *The R Journal*, 9(2), 378. https://doi.org/10.32614/RJ-2017-066

Burdon, J. J., & Chilvers, G. A. (1977). The effect of barley mildew on barley and wheat competition in mixtures. *Australian Journal of Botany*, 25(1), 59. https://doi.org/10.1071/BT9770059

Cappelli, S. L., Domeignoz Horta, L. A., Gerin, S., Heinonsalo, J., Lohila, A., Raveala, K., Schmid, B., Shrestha, R., Tiusanen, M. J., Thitz, P., & Laine, A.-L. (2024). Data from: The potential of undersown species identity vs. diversity to manage disease in crops. *Dryad Digital Repository*, https://doi.org/10.5061/dryad.gtht76hvc

Cappelli, S. L., Domeignoz-Horta, L. A., Loaiza, V., & Laine, A.-L. (2022). Plant biodiversity promotes sustainable agriculture directly and via belowground effects. *Trends in Plant Science*, 27(7), 674–687. https://doi.org/10.1016/j.tplants.2022.02.003

Cardinale, B. J., Duffy, A., Gonzalez, A., Hooper, D. U., Perrings, C., Venail, P., Narwani, A., Mace, G. M., Tilman, D., Wardle, D. A., Kinzig, A. P., Daily, G. C., Loreau, M., Grace, J. B., Larigauderie, A., Srivastava, D. S., & Naeem, S. (2012). Biodiversity loss and its impact on humanity. *Nature*, 486(7401), 59–67. https://doi.org/10.1038/nature11148

Carris, L. M., Little, C. R., & Stiles, C. M. (2012). *Introduction to fungi*. The plant health instructor. https://www.apsnet.org/edcenter/disan dpath/fungalasco/intro/Pages/IntroFungi.aspx

Christensen, R. H. B. (2019). ordinal: Regression models for ordinal data (version 2019.12-10) [Cran Repository]. https://CRAN.R-project.org/package=ordinal

Civitello, D. J., Cohen, J., Fatima, H., Halstead, N. T., Liriano, J., McMahon, T. A., Ortega, C. N., Sauer, E. L., Sehgal, T., Young, S., & Rohr, J. R. (2015). Biodiversity inhibits parasites: Broad evidence for the dilution effect. *Proceedings of the National Academy of Sciences of the United States of America*, 112(28), 8667–8671. https://doi.org/10.1073/pnas.1506279112

Dirzo, R., & Domínguez, C. A. (2010). Plant-herbivore interactions in Mesoamerican tropical dry forests. In S. H. Bullock, H. A. Mooney, & E. Medina (Eds.), *Seasonally dry tropical forests* (pp. 304–325). Cambridge University Press. https://doi.org/10.1017/CBO9780511753398.012

Domeignoz Horta, L. A., Cappelli, S. L., Gerin, S., Heinonsalo, J., Lohila, A., & Raveala, K. (2024). TwinTwin Experiment, Cappelli et al., 2024 Functional ecology. Raw sequence reads. https://www.ncbi.nlm.nih.gov/bioproject/1108597

Dordas, C. (2008). Role of nutrients in controlling plant diseases in sustainable agriculture. A review. *Agronomy for Sustainable
Development, 28(1), 33-46. https://doi.org/10.1051/agro:2007051

Duchene, O., Vian, J.-F., & Celette, F. (2017). Intercropping with legume for agroecological cropping systems: Complementarity and facilitation processes and the importance of soil microorganisms. A review. *Agriculture, Ecosystems & Environment*, 240, 148-161. https://doi.org/10.1016/j.agee.2017.02.019

Eisenhauer, N., Beßler, H., Engels, C., Gleixner, G., Habekost, M., Milcu, A., Partsch, S., Sabais, A. C. W., Scherber, C., Steinbeiss, S., Weigelt, A., Weisser, W. W., & Scheu, S. (2010). Plant diversity effects on soil microorganisms support the singular hypothesis. *Ecology*, 91(2), 485-496. https://doi.org/10.1890/08-2338.1

Farr, D. F., & Rossman, A. Y. (2023). *Fungal databases: U.S. National Fungus Collections, ARS, USDA*. https://nt.ars-grin.gov/fungaldatabases/

Fiers, M., Lognay, G., Fauconnier, M.-L., & Jijakli, M. H. (2013). Volatile compound-mediated interactions between barley and pathogenic fungi in the soil. *PLoS One*, 8(6), e66805. https://doi.org/10.1371/journal.pone.0066805

Finckh, M. R., Gacek, E. S., Goyeau, H., Lannou, C., Merz, U., Mundt, C. C., Munk, L., Nadziak, J., Newton, A., de Vallaille-Pope, C., & Wolfe, M. S. (2000). Cereal variety and species mixtures in practice, with emphasis on disease resistance. *Agronomie*, 20(7), 813-837. https://doi.org/10.1051/agro:2000177

Finney, D. M., & Kaye, J. P. (2017). Functional diversity in cover crop polycultures increases multifunctionality of an agricultural system. *Journal of Applied Ecology*, 54(2), 509-517. https://doi.org/10.1111/1365-2664.12765

Finnish Meteorological Institute. (2019). *Index of index/geodata/ilmatiede*. http://www.nic.funet.fi/index/geodata/ilmatiede/

Fisher, M. C., Henk, D. A., Briggs, C. J., Brownstein, J. S., Madoff, L. C., McCraw, S. L., & Gurr, S. J. (2012). Emerging fungal threats to animal, plant and ecosystem health. *Nature*, 484(7393), 186-194. https://doi.org/10.1038/nature10947

Fu, Y.-B., Peterson, G. W., Horbach, C., Konkin, D. J., Beiles, A., & Nevo, E. (2019). Elevated mutation and selection in wild emmer wheat in response to 28 years of global warming. *Proceedings of the National Academy of Sciences of the United States of America*, 116(40), 20002-20008. https://doi.org/10.1073/pnas.1909564116

Garbeva, P., van Veen, J. A., & van Elsas, J. D. (2004). Microbial diversity in soil: Selection microbial populations by plant and soil type and implications for disease suppressiveness. *Annual Review of Phytopathology*, 42, 243-270. https://doi.org/10.1146/annurev.phyto.42.012604.135455

Garrett, K. A., Zúñiga, L. N., Roncal, E., Forbes, G. A., Mundt, C. C., Su, Z., & Nelson, R. J. (2009). Intraspecific functional diversity in hosts and its effect on disease risk across a climatic gradient. *Ecological Applications*, 19(7), 1868-1883. https://doi.org/10.1890/08-0942.1

Gómez-Rodríguez, O., Zavaleta-Mejia, E., González-Hernández, V., Rivera-Muñoz, M., & Cárdenas-Soriano, E. (2003). Allelopathy and microclimatic modification of intercropping with marigold on tomato early blight disease development. *Field Crops Research*, 83(1), 27-34. https://doi.org/10.1016/S0378-4290(03)00053-4

Guzman, A., Montes, M., Hutchins, L., DeLaCerda, G., Yang, P., Kakouridis, A., Dahlquist-Willard, R. M., Firestone, M. K., Bowles, T., & Kremen, C. (2021). Crop diversity enriches arbuscular mycorrhizal fungal communities in an intensive agricultural landscape. *New Phytologist*, 231(1), 447-459. https://doi.org/10.1111/nph.17306

Hauggaard-Nielsen, H., Ambus, P., & Jensen, E. S. (2001). Interspecific competition, N use and interference with weeds in pea-barley intercropping. *Field Crops Research*, 70(2), 101-109. https://doi.org/10.1016/S0378-4290(01)00126-5

Hauggaard-Nielsen, H., Jørnsgaard, B., Kinane, J., & Jensen, E. S. (2008). Grain legume-cereal intercropping: The practical application of diversity, competition and facilitation in arable and organic cropping systems. *Renewable Agriculture and Food Systems*, 23(1), 3-12. https://doi.org/10.1017/S1742170507002025

Huang, Y., Chen, Y., Castro-Izaguirre, N., Baruffol, M., Brezzi, M., Lang, A., Li, Y., Härdtle, W., von Oheimb, G., Yang, X., Liu, X., Pei, K., Both, S., Yang, B., Eichenberg, D., Assmann, T., Bauhus, J., Behrens, T., Buscot, F., ... Schmid, B. (2018). Impacts of species richness on productivity in a large-scale subtropical forest experiment. *Science*, 362(6410), 80-83. https://doi.org/10.1126/science.aat6405

IPBES. (2019). Summary for policymakers of the global assessment report on biodiversity and ecosystem services. Zenodo, https://doi.org/10.5281/zenodo.3553579

Isbell, F., Gonzalez, A., Loreau, M., Cowles, J., Diaz, S., Hector, A., Mace, G. M., Wardle, D. A., O'Connor, M. I., Duffy, J. E., Turnbull, L. A., Thompson, P. L., & Larigauderie, A. (2017). Linking the influence and dependence of people on biodiversity across scales. *Nature*, 546(7656), 65-72. https://doi.org/10.1038/nature22899

Iverson, A. L., Marín, L. E., Ennis, K. K., Gonthier, D. J., Connor-Barrie, B. T., Remfert, J. L., Cardinale, B. J., & Perfecto, I. (2014). REVIEW: Do polycultures promote win-wins or trade-offs in agricultural ecosystem services? A meta-analysis. *Journal of Applied Ecology*, 51(6), 1593-1602. https://doi.org/10.1111/1365-2664.12334

Jalli, M., & Vuorinen, M. (2007). Ohran taudit kuriin. *Koetoiminta Ja Käytäntö*, 64, 3-4.

Kaddes, A., Parisi, O., Berhal, C., Ben Kaab, S., Fauconnier, M.-L., Nasraoui, B., Jijakli, M. H., Massart, S., & de Clerck, C. (2016). Evaluation of the effect of two volatile organic compounds on barley pathogens. *Molecules* (Basel, Switzerland), 21(9), 1124. https://doi.org/10.3390/molecules21091124

Karjalainen, R. (1986). Spring wheat mixtures in northern crop production: Ability of mixtures to buffer disease development and yield loss caused by *Septoria nodorum*. *Agricultural and Food Science*, 58(1), 33-42. https://doi.org/10.23986/afsci.72218

Karlsson-Strese, E.-M., Rydberg, I., Becker, H. C., & Umaerus, M. (1998). Strategy for catch crop development II. Screening of species undersown in spring barley (*Hordeum vulgare* L.) with respect to catch crop growth and grain yield. *Acta Agriculturae Scandinavica Section B: Soil and Plant Science*, 48(1), 26-33. https://doi.org/10.1080/09064719809362475

Keesing, F., Holt, R. D., & Ostfeld, R. S. (2006). Effects of species diversity on disease risk. *Ecology Letters*, 9(4), 485-498. https://doi.org/10.1111/j.1461-0248.2006.00885.x

Kleijn, D., Bommarco, R., Fijen, T. P. M., Garibaldi, L. A., Potts, S. G., & van der Putten, W. H. (2019). Ecological intensification: Bridging the gap between science and practice. *Trends in Ecology & Evolution*, 34(2), 154-166. https://doi.org/10.1016/j.tree.2018.11.002

Klenke, F. (2015). *Pflanzenparasitische Kleinpilze: Bestimmungsbuch für Brand-, Rost-, Mehltau-, Flagellatenpilze und Wucherlingsverwandte in Deutschland, Österreich, der Schweiz und Südtirol*. Springer Berlin Heidelberg.

Kunelius, H. T., Johnston, H. W., & MacLeod, J. A. (1992). Effect of undersowing barley with Italian ryegrass or red clover on yield, crop composition and root biomass. *Agriculture, Ecosystems & Environment*, 38(3), 127-137. https://doi.org/10.1016/0167-8809(92)90138-2

Lange, M., Eisenhauer, N., Chen, H., & Gleixner, G. (2023). Increased soil carbon storage through plant diversity strengthens with time and extends into the subsoil. *Global Change Biology*, 29, 2627-2639. https://doi.org/10.1111/gcb.16641

Larkin, R. P. (2015). Soil health paradigms and implications for disease management. *Annual Review of Phytopathology*, 53, 199-221. https://doi.org/10.1146/annurev-phyto-080614-120357

Lenth, R. V. (2022). *emmeans: Estimated marginal means, aka least-squares means*. https://CRAN.R-project.org/package=emmeans

Liu, X., Lyu, S., Zhou, S., & Bradshaw, C. J. A. (2016). Warming and fertilization alter the dilution effect of host diversity on disease severity. *Ecology*, 97(7), 1680-1689. https://doi.org/10.1890/15-1784.1

Liu, Z., Ellwood, S. R., Oliver, R. P., & Friesen, T. L. (2011). Pyrenophora teres: Profile of an increasingly damaging barley pathogen.
Molecular Plant Pathology, 12(1), 1–19. https://doi.org/10.1111/j.1364-3703.2010.00649.x

Lucas, J. A., Hawkins, N. J., & Fraaije, B. A. (2015). The evolution of fungicide resistance. *Advances in Applied Microbiology*, 90, 29–92. https://doi.org/10.1016/bs.aambs.2014.09.001

Lushchak, V. I., Matviyishn, T. M., Husak, V. V., Storey, J. M., & Storey, K. B. (2018). Pesticide toxicity: A mechanistic approach. *EXCLI Journal*, 17, 1101–1136. https://doi.org/10.17179/excli2018-1710

Lyngs Jørgensen, H. J., Lübeck, P. S., Thordal-Christensen, H., de Neergaard, E., & Smedegaard-Petersen, V. (1998). Mechanisms of induced resistance in barley against *Drechslera teres*. *Phytopathology*, 88(7), 698–707. https://doi.org/10.1094/PHYTO.1998.88.7.698

Maciá-Vicente, J. G., Francioli, D., Weigelt, A., Albracht, C., Barry, K. E., Buscot, F., Ebeling, A., Eisenhauer, N., Hennecke, J., Heintz-Buschart, A., van Ruijven, J., & Mommer, L. (2023). The structure of root-associated fungal communities is related to the long-term effects of plant diversity on productivity. *Molecular Ecology*, 32, 3763–3777. https://doi.org/10.1111/mec.16956

Martin-Guay, M.-O., Paquette, A., Dupras, J., & Rivest, D. (2018). The new Green Revolution: Sustainable intensification of agriculture by intercropping. *Science of the Total Environment*, 615, 767–772. https://doi.org/10.1016/j.scitotenv.2017.10.024

McAlvay, A. C., DiPaola, A., D'Andrea, A. C., Ruelle, M. L., Musulishvili, M., Halstead, P., & Power, A. G. (2022). Cereal species mixtures: An ancient practice with potential for climate resilience. A review. *Agronomy for Sustainable Development*, 42(5). https://doi.org/10.1007/s13593-022-00832-1

McKane, R. B., Johnson, L. C., Shaver, G. R., Nadelhoffer, K. J., Rastetter, E. B., Fry, B., Giblin, A. E., Kielland, K., Kwiatkowski, B. L., Laundre, J. A., & Murray, G. (2002). Resource-based niches provide a basis for plant species diversity and dominance in arctic tundra. *Nature*, 415(6867), 68–71. https://doi.org/10.1038/415068a

Meena, R., Kumar, S., Datta, R., Lal, R., Vijayakumar, V., Brtnicky, M., Sharma, M., Yadav, G., Jhariya, M., Jangir, C., Pathan, S., Dokulilova, T., Pecina, V., & Marfo, T. (2020). Impact of agrochemicals on soil microbiota and management: A review. *Land*, 9(2), 34. https://doi.org/10.3390/land9020034

Meyer, S. T., Ebeling, A., Eisenhauer, N., Hertzog, L., Hillebrand, H., Milcu, A., Pompe, S., Abbas, M., Bessler, H., Buchmann, N., de Luca, E., Engels, C., Fischer, M., Gleixner, G., Hudewenz, A., Klein, A. M., de Kroon, H., Leimer, S., Loranger, H., ... Weisser, W. W. (2016). Effects of biodiversity strengthen over time as ecosystem functioning declines at low and increases at high biodiversity. *Ecosphere*, 7(12), e01619. https://doi.org/10.1002/ecs2.1619

Mitchell, C. E., Reich, P. B., Tilman, D., & Groth, J. V. (2003). Effects of elevated CO₂ nitrogen deposition, and decreased species diversity on foliar fungal plant disease. *Global Change Biology*, 9(3), 438–451. https://doi.org/10.1046/j.1365-2486.2003.00602.x

Morriën, E., Hannula, S. E., Snoek, L. B., Helmsing, N. R., Zweers, H., de Hollander, M., Soto, R. L., Bouffaud, M. L., Buée, M., Dimmers, W., Duyts, H., Geisen, S., Girlanda, M., Griffiths, R. I., Jørgensen, H. B., Jensen, J., Plassart, P., Redecker, D., Schmelz, R. M., ... van der Putten, W. H. (2017). Soil networks become more connected and take up more carbon as nature restoration progresses. *Nature Communications*, 8, 14349. https://doi.org/10.1038/ncomms14349

Moya, P., Girotti, J. R., Toledo, A. V., & Noemi, S. M. (2018). Antifungal activity of Trichoderma VOCs against *Pyrenophora teres*, the causal agent of barley net blotch. *Journal of Plant Protection Research*, 58(1), 45–53. https://doi.org/10.24425/119115

Nguyen, N. H., Song, Z., Bates, S. T., Branco, S., Tedersoo, L., Menke, J., Schilling, J. S., & Kennedy, P. G. (2016). FUNGuild: An open annotation tool for parsing fungal community datasets by ecological guild. *Fungal Ecology*, 20, 241–248. https://doi.org/10.1016/j.funeco.2015.06.006

Ocimati, W., Were, E., Groot, J. C. J., Tittonell, P., Nakato, G. V., & Blomme, G. (2018). Risks posed by intercrops and weeds as

alternative hosts to Xanthomonas campestris pv. musacearum in Banana fields. *Frontiers in Plant Science*, 9, 1471. https://doi.org/10.3389/fpls.2018.01471

Oerke, E. C. (2006). Crop losses to pests. *Journal of Agricultural Science*, 144, 31–43. https://doi.org/10.1017/S0021859605005708

Ohralahti, K. (2013). *Yield response to fungicide treatment of barley varieties with different disease resistance* (Master's thesis). University of Helsinki. https://helda.helsinki.fi/bitstream/handle/10138/39667/Gradu_Kalle_Ohralahti.pdf?sequence=1&isAllowed=y

Parker, I. M., Saunders, M., Bontrager, M., Weitz, A. P., Hendricks, R., Magarey, R., Suiter, K., & Gilbert, G. S. (2015). Phylogenetic structure and host abundance drive disease pressure in communities. *Nature*, 520(7548), 542–544. https://doi.org/10.1038/nature14372

Porter, S. S., & Sachs, J. L. (2020). Agriculture and the disruption of plant-microbial symbiosis. *Trends in Ecology & Evolution*, 35(5), 426–439. https://doi.org/10.1016/j.tree.2020.01.006

Power, A. G., & Mitchell, C. E. (2004). Pathogen spillover in disease epidemics. *The American Naturalist*, 164(5), S79-S89. https://doi.org/10.1086/424610

Pozo, M. J., & Azcón-Aguilar, C. (2007). Unraveling mycorrhiza-induced resistance. *Current Opinion in Plant Biology*, 10(4), 393–398. https://doi.org/10.1016/j.pbi.2007.05.004

R Core Team. (2022). *R: A language and environment for statistical computing* [computer software]. R Foundation for Statistical Computing. https://www.R-project.org/

Reich, P. B., Tilman, D., Isbell, F., Mueller, K., Hobbie, S. E., Flynn, D. F. B., & Eisenhauer, N. (2012). Impacts of biodiversity loss escalate through time as redundancy fades. *Science*, 336(6081), 589–592. https://doi.org/10.1126/science.1217909

Rottstock, T., Joshi, J., Kummer, V., & Fischer, M. (2014). Higher plant diversity promotes higher diversity of fungal pathogens, while it decreases pathogen infection per plant. *Ecology*, 95(7), 1907–1917. https://doi.org/10.1890/13-2317.1

Savary, S., Willocquet, L., Pethybridge, S. J., Esker, P., McRoberts, N., & Nelson, A. (2019). The global burden of pathogens and pests on major food crops. *Nature Ecology & Evolution*, 3(3), 430–439. https://doi.org/10.1038/s41559-018-0793-y

Schoeny, A., Menat, J., Darsonval, A., Rouault, F., Jumel, S., & Tivoli, B. (2008). Effect of pea canopy architecture on splash dispersal of *Mycosphaerella pinodes* conidia. *Plant Pathology*, 57(6), 1073–1085. https://doi.org/10.1111/j.1365-3059.2008.01888.x

Stefan, L., Hartmann, M., Engbersen, N., Six, J., & Schöb, C. (2021). Positive effects of crop diversity on productivity driven by changes in soil microbial composition. *Frontiers in Microbiology*, 12, 660749. https://doi.org/10.3389/fmicb.2021.660749

Trenbath, B. R. (1993). Intercropping for the management of pests and diseases. *Field Crops Research*, 34(3–4), 381–405. https://doi.org/10.1016/0378-4290(93)90123-5

Tripathi, R., Tewari, R., Singh, K. P., Keswani, C., Minkina, T., Srivastava, A. K., de Corato, U., & Sansinenea, E. (2022). Plant mineral nutrition and disease resistance: A significant linkage for sustainable crop protection. *Frontiers in Plant Science*, 13, 883970. https://doi.org/10.3389/fpls.2022.883970

Valkama, E., Lemola, R., Känkänen, H., & Turtola, E. (2015). Meta-analysis of the effects of undersown catch crops on nitrogen leaching loss and grain yields in the Nordic countries. *Agriculture, Ecosystems & Environment*, 203, 93–101. https://doi.org/10.1016/j.agee.2015.01.023

Vidal, T., Lusley, P., Leconte, M., de Vallavieille-Pope, C., Huber, L., & Saint-Jean, S. (2017). Cultivar architecture modulates spore dispersal by rain splash: A new perspective to reduce disease progression in cultivar mixtures. *PLoS One*, 12(11), e0187788. https://doi.org/10.1371/journal.pone.0187788

Walters, D. R., Avrova, A., Bingham, I. J., Burnett, F. J., Fountaine, J., Havis, N. D., Hoad, S. P., Hughes, G., Looseley, M., Oxley, S. J. P., Renwick, A., Topp, C. F. E., & Newton, A. C. (2012). Control of
foliar diseases in barley: Towards an integrated approach. *European Journal of Plant Pathology*, 133(1), 33-73. https://doi.org/10.1007/s10658-012-9948-x

Wenda-Piesik, A., Piesik, D., Ligor, T., & Buszewski, B. (2010). Volatile organic compounds (VOCs) from cereal plants infested with crown rot: Their identity and their capacity for inducing production of VOCs in uninfested plants. *International Journal of Pest Management*, 56(4), 377-383. https://doi.org/10.1080/09670874.2010.505668

Zhang, C., Dong, Y., Tang, L., Zheng, Y., Makowski, D., Yu, Y., Zhang, F., & van der Werf, W. (2019). Intercropping cereals with faba bean reduces plant disease incidence regardless of fertilizer input; a meta-analysis. *European Journal of Plant Pathology*, 154(4), 931-942. https://doi.org/10.1007/s10658-019-01711-4

Zhu, Y., Chen, H., Fan, J., Wang, Y., Li, Y., Chen, J., Fan, J. X., Yang, S., Hu, L., Leung, H., Mew, T. W., Teng, P. S., Wang, Z., & Mundt, C. C. (2000). Genetic diversity and disease control in rice. *Nature*, 406(6797), 718-722. https://doi.org/10.1038/35021046

Zhu, Y.-H., Weiner, J., Yu, M.-X., & Li, F.-M. (2019). Evolutionary agroecology: Trends in root architecture during wheat breeding. *Evolutionary Applications*, 12(4), 733-743. https://doi.org/10.1111/eva.12749

Zubrod, J. P., Bundschuh, M., Arts, G., Brühl, C. A., Imfeld, G., Knäbel, A., Payraudeau, S., Rasmussen, J. J., Rohr, J., Scharmüller, A., Smalling, K., Stehle, S., Schulz, R., & Schäfer, R. B. (2019). Fungicides: An

overlooked pesticide class? *Environmental Science & Technology*, 53(7), 3347-3365. https://doi.org/10.1021/acs.est.8b04392

## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

**Supplementary Information S1:** The supplementary information contains more methodological details about the TWINWIN experiment and the soil transplant experiment and supplementary result tables and figures.

**How to cite this article:** Cappelli, S. L., Domeignoz Horta, L. A., Gerin, S., Heinonsalo, J., Lohila, A., Raveala, K., Schmid, B., Shrestha, R., Tiusanen, M. J., Thitz, P., & Laine, A.-L. (2024). Potential of undersown species identity versus diversity to manage disease in crops. *Functional Ecology*, 38, 1497-1509. https://doi.org/10.1111/1365-2435.14592