# Crop gains induced by diversification exceed crop losses to diseases and weeds in a low-input rice cultivation system

Koloina Rahajaharilaza, Cyrille Violle, Bertrand Muller, Kirsten vom Brocke, Jean Benoît Morel, Rémi Pélissier, Elsa Ballini, Delphine Luquet, Perle Ramavovololona, Florian Fort, et al.

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Koloina Rahajaharilaza, Cyrille Violle, Bertrand Muller, Kirsten vom Brocke, Jean Benoît Morel, et al.. Crop gains induced by diversification exceed crop losses to diseases and weeds in a low-input rice cultivation system. Basic and Applied Ecology, 2025, 84, pp.81-91. 10.1016/j.baae.2025.03.001. hal-05004259

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# Crop gains induced by diversification exceed crop losses to diseases and weeds in a low-input rice cultivation system

Koloina Rahajaharilaza<sup>a,b,c</sup>, Cyrille Violle<sup>c</sup>, Bertrand Muller<sup>b,d</sup>, Kirsten vom Brocke<sup>b,d</sup>, Jean Benoît Morel<sup>c</sup>, Rémi Pelissier<sup>f</sup>, Elsa Ballini<sup>f,g</sup>, Delphine Luquet<sup>d</sup>, Perle Ramavovololona<sup>a,g</sup>, Florian Fort<sup>h</sup>, Lucie Mahaut<sup>i,w</sup>

## ARTICLE INFO

### Keywords:

- Varietal mixture

- Functional traits

- Agrobiodiversity

- Multifunctionality

## ABSTRACT

Crop losses to pests, or crop gains to diversification: what matters most? This is a pivotal question for the acceptance of crop diversification solutions by farmers and breeders. At the intraspecific level in particular, the magnitude of beneficial effects of mixing varieties on crop yield compared to disease attacks and weed invasion in the absence of pesticide remains questioned. Here, we quantified for the first time the relative importance of different types of biotic interactions – crop-crop interactions, crop-fungi interactions and crop-weed interactions, on grain yields in a region of the world where alternative agricultural practices are urgently needed to face the multiple pressures induced by global changes. We evaluated the performance of ten upland rice varieties of Madagascar highlands using a field trial where all varieties were grown in pure stands and in bi-variety mixtures. Grain production and disease attacks by *Pyricularia oryzae* were assessed in all varieties and plots. Weed biomass was recorded in all plots. We also measured plant traits involved in plant-plant interactions - plant height and flowering time in particular - for all plots and varieties to identify generic assembly rules for optimizing the performance of varietal mixtures. We found strong positive effects of rice mixture on both crop yield and pathogen resistance while weed biomass remained constant and high whatever the experimental condition. When statistically controlling for one pressure, we demonstrated that the beneficial effects of crop-crop interactions on yield largely overcame the negative effects of crop-fungi and crop-weed interactions. The average height of varieties in mixtures as well as their height plasticity had strong predictive power of crop yields, confirming the great potential of trait-based approaches to design innovative cropping system. Overall, the predominant, positive effect of varietal mixtures over disease and weed pressures represents a key argument for crop diversification.

## Introduction

Beyond abiotic stress and resource availability, crop yield is strongly conditioned by biotic interactions, including disease attacks and weed invasion (Oerke 2006; Horvath et al. 2023). Agrochemical inputs such as pesticides and fertilizers have long been used to attenuate crop losses to

diseases and weeds. This solution has gained more and more criticisms because of the environmental and human health damages it generates (Tilman et al. 2002). In addition, the use of pesticides has conducted to the emergence of pathogen resistance, which has in turn resulted in severe yield losses (Brown 2002). Finally, the massive use of agro-chemical inputs represents a poverty trap for smallholder agriculture in

<footnote><sup>a</sup> University of Antananarivo, Faculté des Sciences, Ecole Doctorale Sciences de la Vie et de l'Environnement (ED SVE), Antananarivo, Madagascar</footnote>

<footnote><sup>b</sup> AGAP, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France</footnote>

<footnote><sup>c</sup> CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France</footnote>

<footnote><sup>d</sup> CIRAD, UMR AGAP, F-34398 Montpellier, France</footnote>

<footnote><sup>e</sup> PHIM Plant Health Institute, Université de Montpellier, INRAE, CIRAD, Institut Agro, IRD, Montpellier, France</footnote>

<footnote><sup>f</sup> PHIM Plant Health Institute, Université de Montpellier, Institut Agro, CIRAD, INRAE, IRD, Montpellier, France</footnote>

<footnote><sup>g</sup> Dispositif en Partenariat Système de Production d'Altitudes Durable, Antsirabe, Madagascar</footnote>

<footnote><sup>h</sup> CEFE, Univ Montpellier, CNRS, EPHE, IRD, Institut Agro, Montpellier, France</footnote>

<footnote><sup>i</sup> CEFE, Univ Montpellier, CNRS, EPHE, IRD, INRAE, Montpellier, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: lucie.mahaut@cefe.cnrs.fr (L. Mahaut).</footnote>
developing countries since the environmental degradation they caused often overcomes their positive effects on yields (Tittonell & Giller 2013). The implementation of nature-based solutions, rooted on ecological knowledge of the functioning of natural ecosystems (Altieri et al. 2012), has been proposed as an effective way to increase crop yield and to decrease disease attacks and weed invasion, while limiting the use of agricultural inputs (Eggermont et al. 2015). One increasingly popular solution is the diversification of crops (species and/or varieties) within the field (Prieto et al. 2015; Litrico & Violle 2015; Barot et al. 2017) based on the postulate that crop-crop interactions favor resource partitioning between them, and in doing so directly increase crop yields. Meanwhile, crop diversity acts as a barrier against pathogens and weeds (Petit et al. 2015; Stenberg 2017; Gaba et al. 2018; Pélissier et al. 2021b), and can as such exert an indirect, positive effect on crop yields. Crop diversity can notably help to overcome the well-known growth-defense trade-off (Huot et al. 2014), which impedes the selection of varieties that simultaneously maximize their growth and their resistance to pathogens. However, whether crop gains due to diversification are strong enough to counterbalance the negative effects of disease attacks and weed invasion on yields in the absence of pesticides, remained to be demonstrated. This question is a priority for the widespread acceptance of crop diversification by farmers and breeders, and for its subsequent implementation in a large set of cropping systems.

The idea of increasing field-level diversity as a lever of crop yield improvement comes from a long history of ecological research dedicated to study the effect of (plant) biodiversity on ecosystem productivity in natural systems (Tilman et al. 2014). As such, theoretical and experimental studies have led to the consensus of a positive relationship between plant diversity and ecosystem productivity (Hooper et al. 2005; Cardinale et al. 2012), even if *in-situ* studies have emphasized more complex relationships (Adler et al. 2011). Resource-use complementarity is considered as a key mechanism driving this relationship (Barry et al. 2019): genetically- or phenotypically-diverse ecosystems should have a more complete utilization of the total amount of resources available in the system than less diverse ones since they are likely composed of species (and/or genotypes) displaying different resource-use strategies (Hooper et al. 2005; Barot et al. 2017). Recent evidence suggests that these positive diversity effects do not emerge from 'pure' resource-use complementarity processes but rather from the relaxation of conspecific or congenetic competition (Mahaut et al. 2020, 2023; Dee et al. 2023). Regardless of the nature of the mechanisms involved, as expected from ecological theories, mixing crop species has been shown to favour crop yield in several cropping systems (Prieto et al. 2015; Beillouin et al. 2021; Smith et al. 2023; Schöb et al. 2023). Interestingly, more and more evidence has also pointed out positive diversity effects when mixing varieties from a single species at the field level (varietal mixtures hereafter) (Montazeaud et al. 2020b, 2022; Cissé et al. 2023; Dubs et al. 2023), validating the role of genetic diversity for the regulation of ecosystem functions as stated by theory (Hughes et al. 2008). This finding is even more promising for agriculture as intraspecific systems are more tractable to manage than interspecific ones, notably when considering harvesting constraints. As such, varietal mixtures are increasingly considered as a viable strategy in cereals (Finckh et al. 2000; Montazeaud et al. 2020b; McAlvay et al. 2022; Stefan et al. 2025) for which the cultivation of pure stands has long been the gold standard for yield maximization. Nonetheless, metanalyses suggest that diversity effects are significant but subtle (e.g., 2.2 %-gain of crop yield in (Reiss & Drinkwater 2018)), which questions their potential to counterbalance expected major negative effects of other types of biotic interactions such as disease attacks and weed invasion (Oerke 2006) and which might arise in absence (or reduction) of pesticide use.

Ecological theory also predicts beneficial effects of plant biodiversity on the limitation of disease attacks and weed invasion. The first mostly relies on the dilution effect hypothesis: diverse mixtures are composed of different hosts for pathogens, which slows down their spatial spread since pathogens are most often host-specific (Jones & Dangl 2006;

Pélissier et al. 2021b). In cereals, this led to successful pest management strategies based on the mixtures of varieties displaying different resistance genes (Zhu et al. 2000), which in turn limits the selection of resistant pathogens. Interestingly, recent studies also emphasized additional beneficial effects of crop mixtures (Montazeaud et al. 2022; Pélissier et al. 2023), including the production of pathogen-killer secondary compounds induced by crop-crop interactions (Pélissier et al. 2021a). A second beneficial effect relies on the diversity-invasibility hypothesis (Elton 2020): diverse mixtures are likely to better resist invasion by weeds because they occupy more ecological niches, which let less vacant niches for new incomers (weeds in that case) (Clark 2013). This hypothesis received some validation in natural or experimental communities composed of wild plant species (Kennedy et al. 2002; Bezemer & van der Putten 2007). In an agricultural context, it has been demonstrated that cover crop mixtures can promote weed suppression, too (Sabbagh et al. 2020; Hofmeijer et al. 2021). It remains hardly tested in intraspecific crop mixtures for which we can expect lower effects since conspecific genotypes are likely to share similar ecological niches (Pakeman et al. 2020). However, weed impacts, as well as disease attacks, can still be preponderant in crop mixtures, irrespective of the beneficial direct effects of crop-crop interactions on yield. Finally, disease pressures can also promote the positive effects of crop diversity on crop yield. Indeed, it has been hypothesised that positive plant-plant interactions are greater under stressful growing conditions. Originally, this hypothesis has been stated for harsh climate conditions (Bertness & Callaway 1994), then for resource-limited conditions (Harpole and Tilman 2007; Maestre et al. 2009). In both cases, stressful conditions should favour resource partitioning and facilitation between species or genotypes. More recently, this hypothesis has been extended to stress caused by biotic interactions (Guerrero-Ramírez & Eisenhauer 2017), disease pressures in particular (van Ruijven et al. 2020). Experimental studies suggested a stronger positive effects of plant diversity on productivity under high disease pressures, in both natural and cultivated systems (Maron et al. 2011; Schnitzer et al. 2011; Wang et al. 2023; Pichon et al. 2024). Altogether, this set of (independent) evidence emphasizes the pressing need to jointly quantify the relative importance of the impacts of different types of biotic interactions on crop gains and losses, and specifically to quantify the potential of crop-crop interactions to buffer crop losses to diseases and weeds.

Provided remaining positive effects of crop diversity on crop yield after accounting for the negative impacts caused by diseases and weeds, it is critical to identify the most efficient crop assemblages and the underlying assembly rules (Barot et al. 2017). Theory suggests that genetic diversity is a good proxy for niche diversity so that crop yield should be maximized in mixtures composed of genetically-distant genotypes (Hughes et al. 2008). This hypothesis received little experimental support though (Turner et al. 2020; Stefan et al. 2024). One reason is that plant-plant interactions are controlled by multiple phenotypic dimensions (Kraft et al. 2015) that cannot be summarized by a single genetic distance between competing plants. Trait-based approaches, which has been very popular in ecology for the last two decades, offer a more viable perspective since different plant traits have been identified as proxies for plant's niche axes (Violle & Jiang 2009) and competitive ability (Gaudet & Keddy 1988; Goldberg & Landa 1991; Goldberg 1996; Navas & Violle 2009; Violle et al. 2009). In particular, plant height has long been demonstrated as a trait linked to light depletion and thus to plant competition (Gaudet and Keddy 1988; Violle et al. 2009): the taller plants, the stronger competitors. Phenology, which is often quantified by flowering time as a soft proxy, has also been considered as a candidate interaction trait since two species or genotypes with different phenologies are more likely to use resources differently (in time) (Elzinga et al. 2007). Among-plant trait means and trait distances are thus two metrics that are usually used to characterized plant assemblages on a phenotypic basis (Ricotta & Moretti 2011) so as to evaluate the mechanisms underlying plant diversity effects on productivity (Roscher et al. 2012; Garnier et al. 2016). A repeated caveat of this
approach is the lack of quantification of trait plasticity in response to plant competition (Turcotte & Levine 2016; Pérez-Ramos et al. 2019), while phenotypic plasticity is likely a strong contributor of plant diversity effects (Zuppinger-Dingley et al. 2014; Forsman 2015; Gawilnowski et al. 2024). In this study, thanks to an ambitious experimental effort, we measured plant traits in both pure stands and varietal mixtures so as to test the effect of variety's trait values, trait distance between interacting varieties and trait plasticity on crop yield modulation.

Here, we quantified the respective effects of varietal mixtures, disease attacks and weed invasion on crop yield in upland rice cropping systems of the Madagascar highlands. Upland rice has been popularized in the central highlands of the country to compensate the low production of lowland rice (Breumier et al. 2018). Yet, rice cultivation in Madagascar highlands is challenged by high incidence of pests and diseases, soil degradation, and unpredictable weather conditions (Gerardeaux et al. 2012; Raboin et al. 2014). Quantifying the potential of varietal mixture to overcome yield losses caused by pests is therefore particularly pressing for the sustainability of agriculture in this region.

## Material and methods

### Study site and experimental design

The experiment was conducted in the Central Highlands of Madagascar in the Vakinankaratra region (19°40'00.82'S 46°32'11.49'E) at 900–1100m altitude. The region is characterized by a tropical high-altitude climate (a warm rainy season from mid-October to April and a cooler dry season from May to September). The average total rainfall is 200mm, with the highest amounts falling in December and January (approx. 300mm). The average temperature during the growing season is 25 °C (minimum: 12 °C and maximum: 35 °C). Rainfed crops are grown in Ferralsols on Tanety (slopes of hills targeted for rainfed cropping practices), characterized by a pH of ca. 5.1 and a cation exchange capacity lower of 16 cmol.kg⁻¹. These properties reduce nutrient availabilities in the soil, as well as its ability to retain basic cations on its adsorbent complex, resulting in deficiencies in several elements such as phosphorus, calcium, magnesium and nitrogen (Raminoarison et al. 2020).

We selected ten upland rice varieties from a tropical japonica rice panel, namely: PRIMAVERA, EARLY MUTANT IAC 165, IAC 25,

IR53236–275–1, DOURADO PRECOCE, CIRAD 392 (FOFIFA 152), CIRAD 394 (FOFIFA 154), CIRAD 488 (FOFIFA 161), IRAT 380 and IRAT 109. We selected these varieties based on their contrasted ecophysiological characteristics (Courtois et al. 2013; Rakotoson et al. 2021) to maximize a priori trait diversity. This pool also includes resistant genotypes to blast (Pyricularia oryzae), which is the main disease in upland rice cropping systems of Madagascar. These ten varieties were grown in pure stand and in every possible bi-variety mixture for a total of 55 combinations (45 binary mixtures and 10 pure stands). We chose a block design to account for field heterogeneity (Yau 1997). The 55 combinations were sown in plots of 1.6m x 1.4m and randomly organized into five blocks, for a total of 275 plots (Fig. 1). Each plot was made of 56 hills and each hill was hand-sewn with eight seeds of a given variety. For the varietal mixture plots, hills were organized in check-board pattern by alternating hills of each variety (Fig. 1). The spacing between hills was 20 cm. We applied a cattle manure of about 6t DM. ha⁻¹, which corresponds to farming practices in the region. The manure was characterized by: 30.5% organic matter (17% carbon), 0.6% nitrogen, 0.1% phosphorus, 0.9% basic cation (calcium, magnesium, potassium) and a pH = 7.49. We did not apply any pesticide nor herbicide.

### Plant trait and yield measurements

We measured plant traits related to plant phenology and competitive ability on all varieties in all experimental conditions (pure stands and mixtures). More specifically, we characterized the date for which ~50% of the plants flowered in a plot (hereafter flowering date, in Julian days). We also measured maximum plant height of five plants per plot and variety at peak of vegetative biomass production and computed mean variety height in each plot. At maturity, we harvested all hills in all plots per variety. Harvests were done by hand using a specialized curved knife for harvesting, known as "antsim-bilona," which is used by Malagasy farmers. We separated the grain and vegetative aboveground biomass components. We carefully separated the grains from the panicles and measured grain yield as grain dry biomass after drying them for 48 hours at 60C°. We did not consider vegetative aboveground biomass since the plants, after having filled their grain, became senescent.

<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Sampling design. We grew ten rice varieties, both in pure stands and in all 45 possible binary mixtures. These 55 combinations were replicated in 5 blocks for a total of 275 plots. Each plot is made of 56 hills and each hill is sown with 8 seeds of a given variety.</figcaption></figure>
## Assessment of weed and disease pressures

Throughout the experiment, Striga spp.– the principal weed of upland rice cropping system which is a parasitic plant– and *Pyricularia oryzae* (Zhang et al. 2016; Qi et al. 2019) infestation were recorded. Striga spp. plants were counted in each plot before each weeding operation, which occurred three times during the experiment. We computed the total number of Striga spp. plants in each plot by summing these three replicates. *Pyricularia oryzae* on leaves was assessed during flowering stage. Two parameters were considered to evaluate blast resistance: the incidence and the severity of the disease. This was done by screening all 56 hills of every plot at flowering stage. To assess blast incidence, we counted the number of hills infected by the pathogen in each plot. We considered a hill to be infected if it had at least five lesions. Then, for each variety in the plot, we calculated the percentage of hills that had more than five lesions to determine blast incidence. To assess the disease severity, we assessed total leaf lesion area. In the field, we measured the area of each lesion on all leaves from all individuals. We used a printed model of leaf lesion surface caused by rice blast to visually determine the area damaged on each leaf, then summing them for each individual. Then, we summed these measurements to obtain the total leaf lesion area in cm² for a single hill. We divided the total leaf lesion area by the area of a healthy mature leaf of the same hill of the same variety. Accordingly, disease severity is expressed in “number of equivalent leaves”. Leaf area was evaluated using a SunScan (Probe type SS1 v1.02R) at flowering time. After calculating the number of equivalent leaves for each individual hill, we averaged these values to obtain a single value of disease severity per variety and per plot. This value represents the mean disease severity for a given variety in a plot, allowing for a standardized comparison across varieties.

## Calculation of the Land Equivalent Ratio (LER) and Net effect ratio of disease (NERDI)

We estimated the benefit of mixing varieties for crop yield using the Land Equivalent Ratio (LER), which quantifies the relative land area required under pure stand to produce the same grain yield as under crop mixture according to the formula:

$$LER = \frac{\mathbf{y}_{A,mix}}{\mathbf{y}_{A,pure}} + \frac{\mathbf{y}_{B,mix}}{\mathbf{y}_{B,pure}} \qquad (1)$$

where $Y_{A,mix}$ and $Y_{B,mix}$ are the yields of two varieties (A and B) sown in mixture and $Y_{A,pure}$ and $Y_{B,pure}$ the yields of the same varieties (A and B) grown in pure stand. A LER value of 1.0 (0.5/1 + 0.5/1), indicates no difference in yield between the varietal mixture and the average of their single variety, whereas values greater than 1.0 indicate a yield advantage for varietal mixtures. We computed LER for each binary mixture separately for each block.

Mirroring the LER, to compare the ability of varietal mixtures to resist to *Pyricularia oryzae*, we computed the NERDI (Net effect ratio of disease) based on differences in disease (DI) incidence and severity (Li et al. 2023; Rahajaharilaza et al. 2023). This index measures the difference between the observed disease infection and the expected mixture infection based on pure crop infection and the variety proportions in the mixture, according to the following formula:

$$NERDI_{(AB)} = \frac{p_A \cdot DI_{A,mix} + p_B \cdot DI_{B,mix}}{p_A \cdot DI_{A,pure} + p_B \cdot DI_{B,pure}} \qquad (2)$$

NERDI<sub>(AB)</sub> is the net effect ratio of disease of the mixture of varieties A and B. The ratio is between the observed disease (DI) incidence or severity in the mixtures and the expected disease incidence or disease severity based on the infection levels in pure stands. $p_A$ and $p_B$ are the proportion of the variety A and B in the mixture (here, p=0.5). DI<sub>mix</sub> is the disease incidence or disease severity of the variety A or B in mixed plots and DI<sub>pure</sub> the average disease infections in pure stands per block

for the variety. A NERDI value above 1 indicates higher disease in mixtures, while a NERDI value less than 1 indicates a higher resistance performance in the mixture than in the pure stand.

## Computation of mean trait values, trait differences and trait plasticity

We computed different facets of functional diversity based on plant maximal height and flowering date for each binary mixture plots. First, we quantified the averaged trait value between the two mixed varieties. Second, we computed the absolute difference between trait values of the mixed varieties. Finally, we quantified trait plasticity for each variety in each plot mixture by calculating the deviation between trait value in the mixture and trait value in the pure stand of the same bock. We ran a linear regression model between trait value in the mixture and trait value in the monoculture to account for the potential block effect and used the residuals of this model to quantify trait deviation of each variety. Then, we calculated the mean deviation between the two varieties to obtain a single value per mixture plot, as a measure of trait plasticity.

## Statistical analyses

Although disease incidence and severity can bring complementary information about disease pressure, we found a strong positive correlation between both indices (r = 0.53, P<0.001; Supplementary Figure S1). Therefore, we focused on disease incidence in the following analysis for a sake of clarity.

We first computed the average yield, disease incidence and weed biomass for each variety grown as pure stands to test whether a given variety can simultaneously be productive and resistant to pests. We compared the agronomical performances of the ten varieties using non-parametrical Kruskal-Wallis tests.

Second, we evaluated the effects of crop-crop interactions, disease incidence and weed biomass on crop yields. We classified each plot with respect to its cultivation type (pure stand or mixture), disease incidence (low or high) and weed biomass (low/high). We used the median of disease incidence and weed biomass throughout the experiment to perform this classification. Then, we used a multi-factor ANOVA to test the effect of cultivation type, disease incidence and weed biomass on crop yields using the *lm* function in R. This model also included the factor ‘block’ (1, 2, 3, 4 or 5) to test for potential confounding effect on crop yield. In addition, we tested whether averaged disease incidence and weed biomass differed between mixture plots and pure-stands using *t*-test.

Third, we quantified the relationship between the LER of grain biomass and the NERDI of blast incidence to test whether a given varietal mixture can simultaneously provide higher yield and higher disease resistance than expected from the pure stands of its components. We identified productive AND resistant mixtures as those having a LER > 1 and a NERDI (1; productive mixtures with LER > 1; resistant mixtures with NERDI < 1 and finally unproductive AND non-resistant mixtures with LER < 1 and NERDI > 1. We quantified the correlation between LER and NERDI using Pearson correlation index. We did not run similar analysis for weed biomass since we did not find any significant effect of crop mixture on weed pressure (details in the results section).

Finally, we ran multiple linear regressions to test the effects of mean trait values, trait differences and trait plasticity on mixture performance (both LER and NERDI). We built a first ‘null’ model (M0) that only accounted for the block effect on LER/NERDI. Then, for each measure of mixture performance, we built six alternative models (M1<sub>k</sub>) by adding the mean, distance and deviation of plant height and flowering date to the null model. We evaluated the importance of each facet of functional diversity for mixture performance by comparing the AIC value between M0 and each M1<sub>k</sub> (Maire et al. 2018; Mahaut et al. 2020). The stronger the difference in AIC values, the stronger the influence of the tested facet of functional diversity.
## Results

Despite substantial variations, our results indicated no significant difference in terms of grain biomass (Kruskal test; chi-squared = 14.75, df = 9, P = 0.10) between the ten varieties grown in pure stand (Fig. 2).

However, we reported significant difference in terms of disease incidence (Kruskal test; chi-squared = 30.58, df = 9, P < 0.001). By ordering varieties with respect to their average grain biomass in pure stand, we found that the varieties with the highest grain biomass (Early Mutant IAC 165 and IRAT 380) were not the one with the highest disease

<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Averaged genotype's performance in mono-genotypic stands. a) Grain yield; b) Disease incidence; c) Weed biomass. Genotypes are ordered by mean grain biomass.</figcaption></figure>
incidence (IR53236–275–1 and Dourado Precoce) (Fig. 2). Finally, weed pressure did not vary across the varieties grown in pure stands (Kruskal test; chi-squared = 1.19, df = 9, P = 0.99).

By comparing grain biomass with respect to the type of plot, the level of disease incidence and of weed invasion, we found higher grain yield in mixtures than in pure stands (P < 0.001; Supplementary Table 1; Fig. 3) and higher grain yield in plots with low level of disease incidence (P = 0.04; Supplementary Table 1; Fig. 3). Conversely, we reported no significant difference between grain biomass in plots with low and high level of weed biomass (P = 0.52; Supplementary Table 1; Fig. 3). In addition, we found that disease incidence was significantly lowered in mixture plots than in pure stands (t-test, mean disease incidence in mixture = 45.04; mean disease incidence in pure stands = 57.24, P=0.001) while weed pressure did not significantly vary between mixture and pure-stands (t-test, mean weed biomass in mixture = 22.68; mean disease incidence in pure stands = 23.08, P=0.888).

Mean LER was 1.14 (95% confidence interval = [1.11; 1.17]), indicating a significant, average increase of grain yield of 14% in crop mixtures compare to pure stands. Similarly, mean NERDI was 0.80 (95%

confidence interval = [0.77; 81], indicating a significant, average increase of disease resistance of 20% in crop mixtures. Although most of the mixture plots displayed both higher grain yield (LER>1) and disease resistance (NERDI<1) than pure stands (Fig. 4), we did not find any significant correlation between LER and NERDI (r=-0.08, P = 0.21).

Finally, we found that plasticity of plant height, followed by mean plant height, were by far the main facets of functional diversity that drove grain yields in mixture (Table 1). Mean, distance and plasticity of flowering time had no significant effects on LER (Table 1). In addition, we reported any significant effect of functional diversity on disease resistance (Supplementary Table S2).

Discussion

Crop diversification is often considered as a nature-based solution to mitigate the effects of climate changes on cropping systems (Barot et al. 2017; Miralles-Wilhelm 2023), which is a particularly pressing demand in countries such as Madagascar. The magnitude of its mitigating effects remains a matter of debate though, and the uncertainty surrounding this

<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Grain biomass in different growth conditions. Left: raw data. Horizontal line shows median value. Right: partial effects computed from a multi-factor ANOVA. Vertical lines show confidence interval 95%. *** P<0.001; * P<0.05; ns: no significant difference.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Fig. 4.** Relationship between the LER for grain yield and NERDI of disease incidence. LER > 1 indicates higher yield in mixture compare to mono-genotypic stand. NERDI <1 indicates higher disease resistance in mixtures.</figcaption></figure>

<caption>**Table 1**

**Outputs of linear regressions quantifying the effects of traits on LER grain.**</caption>

Mean: mean trait value in the varietal mixture. Difference: absolute difference of trait values between the mixed genotypes. Deviation: a proxy of trait plasticity computed as the difference between genotype trait in pure stand and in mixture. Grey colour indicates significant effect.

<table><thead><tr><th>Metric</th><th>Trait</th><th>Coefficient</th><th>SD</th><th>P-value</th><th>r<sup>2</sup></th><th>ΔAIC</th></tr></thead><tbody><tr><td rowspan="2">Mean</td><td>Onset flowering</td><td>-0.014</td><td>0.007</td><td>0.063</td><td>0.11</td><td>1.57</td></tr><tr><td><strong>Height</strong></td><td><strong>0.008</strong></td><td><strong>0.001</strong></td><td><strong>&lt;0.001</strong></td><td><strong>0.25</strong></td><td><strong>41.84</strong></td></tr><tr><td rowspan="2">Difference</td><td>Onset flowering</td><td>0.029</td><td>0.016</td><td>0.062</td><td>0.11</td><td>8.66</td></tr><tr><td>Height</td><td>-0.014</td><td>0.011</td><td>0.207</td><td>0.11</td><td>6.70</td></tr><tr><td rowspan="2">Deviation</td><td>Onset flowering</td><td>-0.012</td><td>0.008</td><td>0.134</td><td>0.10</td><td>0.31</td></tr><tr><td><strong>Height</strong></td><td><strong>0.011</strong></td><td><strong>0.001</strong></td><td><strong>&lt;0.001</strong></td><td><strong>0.32</strong></td><td><strong>62.76</strong></td></tr></tbody></table>

question is one of the main reasons for limited acceptance of such a solution by farmers and breeders worldwide. In particular, it remains unclear how crop losses to diseases and weeds can be counterbalanced by crop gains by diversification, as well as whether crop diversification can both mitigate pest impacts and maximize resource utilization by crops. Here, using a full pairwise field trial under traditional low-input growing conditions in Madagascar, we found a strong effect of mixing rice varieties on both crop yield and disease resistance, while weed invasion did not vary with planting conditions. Importantly, we demonstrated for the first time a predominant effect of varietal mixtures on crop yield over disease impacts and weed invasion, which represents a key argument for crop diversification.

We found an average 14%-gain for grain production when mixing rice varieties, which is well above the gain generally reported in both natural and cultivated systems (Cardinale et al. 2012; Reiss & Drinkwater 2018). One widespread explanation for this mixture advantage is the higher level of resource-use complementarity between two different genotypes when grown together and the associated relaxation of intra-genotypic competition (Mahaut et al. 2023). Based on ecological theory, it has been also suggested that such gain should be higher under harsher, resource-limited conditions (Harpole & Tilman 2007; Brooker et al. 2021). However, this hypothesis has been hardly validated so far

since most experimental tests were performed in Western countries (Cardinale et al. 2012). The exceptionally-high gain of crop yield in mixtures might be explained by the stressful conditions (climate and resource availability) under which the field trial was performed. We also found an average reduction of 20% in the pressure of the *Pyricularia oryzae* disease, which is also much higher than the reduction usually reported in the literature (Zhu et al. 2000). The explanation for this reduction is often attributed to the barrier effect or dilution effect (Pélissier et al. 2021b) induced by the presence of resistant genotypes (e. g., Primavera here) in the mixture, as well as by a reduction in the epidemiological effect in susceptible varieties (Pélissier et al. 2021a) such as Dourado Precoce and IR53236.275.1. When representing mixture effects on both disease impacts and crop yield (namely NERDI incidence and LER grain) in a biplot graph, we highlighted a joint positive effect of mixing varieties on both field-scale performances. Such a representation is highly helpful to pinpoint varietal mixtures that maximize both performances, compared to the performances of pure stands. It also indicates that mixture effects are not always beneficial, but depend on genotypic composition. Indeed, we found a lack of correlation between both mixture effects, which highlights the fact that (i) there is no clear trade-off between mixture effects on yield and disease resistance and (ii) the most productive mixtures differ from mixtures having the highest disease resistance. Maximizing multiple functions through the combination of different genotypes in a field is always advanced as a grand promise of crop diversification (Litrico & Violle 2015; Barot et al. 2017; Gaba et al. 2018). Here, we showed that there is no single solution even for functions that are expected to strongly trade off (de Tombeur et al., 2023). Some recent methods have been proposed to identify the composition of (species) assemblages that can maximize different functions of an ecosystem as well as its multifunctionality (Jaillard et al. 2018, 2021). Applying these methods to crop diversification could be very valuable to find the set of assemblages of varieties that optimizes several agronomical functions.

We found no variation in the level of weed invasion, whatever the varietal composition (in pure stands or in mixture), while this is a primary cause of crop damages to deal with in rice cultures. High levels of biodiversity – taxonomic, phylogenetic or functional diversity – have long been advanced as a barrier to invaders (Kennedy et al. 2002) under the rationale that the more species, the more filled ecological niches, the
less niche space available for new species (Renault et al. 2022). Several studies have validated this diversity-invasibility linkage in experimental and natural ecosystems. There is a lack of demonstration of it at the intraspecific level though. One possible reason is that different genotypes of a single species, while potential highly phenotypically diverse, are still very similar in their niche requirements. Our results are in line with this assertion. The weed invasion status of upland rice cultures is quite specific though. Indeed, they are mostly invaded by Striga spp., which is a well-known parasite plant species of rice cultures. As such, the expected beneficial mechanisms of varietal mixtures are the same as those involved in resistance to fungi attacks, i.e. barrier or dilution effects induced by the mixture of resistant and non-resistant varieties. The rice phenotypes (and a *forteriori* genotypes) resistant to Striga are less known though (Samejima et al. 2016). In the future, mirroring what has been done by the very active field of plant disease epidemiology (Jones & Dangl 2006; Pélissier et al. 2021b), the structuration of research around plant parasite resistance from a crop diversification seems a priority.

One grand challenge of crop diversification is to find generic assembly rules - i.e., the identity and number of genotypes (or species) that can be mixed to maximize one or several field-scale functions - to be applied to any situation and cropping system (Barot et al. 2017; Wuest et al. 2021; Kopp et al. 2023). However, several recent studies have emphasized a strong context-dependence of biodiversity-ecosystem functioning relationships (Turnbull et al. 2016; Dee et al. 2023), which questions the universality of (still-unknown) assembly rules. The fact that we found that different crop functions are maximized by different combinations of varieties reinforces this doubt. One first step is to establish assembly rules function by function. Plant traits are usual suspects for this exercise since a given plant trait can be associated to one or several plant functions (Violle et al. 2007; de Bello et al. 2010). Trait-based approaches have been extensively applied in natural ecosystems (Garnier et al. 2016), with mixed results regarding the type of mechanisms that can be captured by plant traits (van der Plas et al. 2020). They have been more and more applied in an agricultural context for the last decade (Garnier & Navas 2012; Wood et al. 2015). The majority of applications focuses on biomass or grain production functions (Reiss & Drinkwater 2018; Dainese et al. 2019). In that case, it is expected that plant traits related to resource acquisition and use matter, with two underlying mechanisms: (i) crop yield is higher with the selection of genotypes characterized by traits associated to fast and efficient resource acquisition and use (Garnier & Navas 2012; Litrico & Violle 2015); (ii) crop yield is higher in assemblages composed of genotypes characterized by different resource use strategies so that between-genotype resource complementarity is maximized (Barot et al. 2017; Brooker et al. 2021). These two hypotheses can be tested with different trait-based metrics, the first being captured by the mean trait value among all components of the mixture (Grime 1998; Garnier et al. 2004, 2016), the second by the trait distance between these components (Mahaut et al. 2023; Dubs et al. 2023). Curiously though, there has been an over-emphasis on trait distances rather than on trait means in crop diversification studies (Montazeaud et al. 2020b; Kopp et al. 2023), while the effect of trait means on ecosystem productivity predominates over the effect of trait distances in ecological meta-analyses (Garnier et al. 2016). The scarce studies that combine both metrics found a strong joint effects of them on crop yield (Montazeaud et al. 2020b). Importantly, most of studies have used plant traits measured in pure stands only, with the postulate that trait changes do not depend on the genotype-genotype interactions. Here, we did not detect any effect of trait distance on crop yield, suggesting that mechanisms involving between-genotype (spatial and temporal) resource partitioning does not dominate, contrary to what is most commonly expected in crop diversification studies (Barot et al. 2017). Yet, resource partitioning might be better captured by difference in root traits (but see (Montazeaud et al. 2018), which were unfortunately impossible to measure here. Conversely, we found a strong positive effect of height mean on crop

yield, which is expected in most herbaceous ecosystems worldwide (Garnier et al. 2016; Montazeaud et al. 2020a). In addition, and for the first time, we also highlighted an even stronger effect of height plasticity on grain yield of mixtures: the more plastic genotypes perform on average better in mixtures, with subsequent greater productivity at the plot scale. Overall, our findings strongly suggest to more systematically consider the role of trait plasticity in crop diversification studies.

## Conclusion

Quantifying the effects of various types of biotic interactions on the performance of agroecosystems is of primary importance to develop innovative management strategies based on agrobiodiversity. We demonstrate for the first time that the benefit of plant-plant interactions in varietal mixture largely overcomes the negative effects of disease and weeds on grain yield. Our findings thus provide strong support for the diversification of crops, particularly in cropping systems that face multiple environmental pressures. Extending our approach to other types of biotic interactions such as plants - soil microbiome will bring additional insights to develop a comprehensive understanding of the role of biodiversity in regulating agroecosystem performance and in doing so, to design cropping systems that could optimise the management of agrobiodiversity. In addition, we show that simultaneously increasing grain yield and disease resistance requires sub-optimality for each function, mixtures with the highest yield differing from mixtures with the highest resistance. These results call for a change of paradigm for crop sciences for which the maximization of crop performances – primarily yield components – has long been the gold standard. The search for generic assembly rules should therefore shift from the identification of crop mixture that maximise individual functions toward mixtures that optimise multifunctionality. Trait-based approaches will be of particular value to this end given their great potential in capturing organisms’ response to, and effect on, biotic interactions.

## Funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the ANR MUSE (ANR-16-IDEX-0006 AMUSER).

## Ethical approval

Not applicable.

## Consent to participate

Not applicable.

## Consent for publication

Not applicable.

## Availability of data, material and code

The datasets and the code generated to analyse the data of the current study will be available in the FigShare repository after acceptance.

## Code availability

The code use to produce the analysis will be deposit on FigShare after acceptance.

## Author's contribution

KR: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Supervision, Writing original draft. CV:
Conceptualization, Funding acquisition, Project administration, Resources, Writing - original draft, review and editing. BM: Funding acquisition, Investigation, Project administration, Resources, Validation, Writing - review & editing. KB: Investigation, Writing - original draft. JBM: Investigation, Methodology, Writing - review & editing. RP: Methodology, Writing - review & editing. EB: Investigation, Methodology, Writing - review & editing. DL: Supervision, Project administration, Writing - review & editing. FF: Data curation, Formal analysis, Supervision, Validation, Writing - review & editing. PR: Supervision, Project administration, Resources, Writing - review & editing. LM: Conceptualization, Formal analysis, Investigation, Methodology, Software, Writing - original draft, review and editing. All authors read and approved the final manuscript.

## Declaration of competing interest

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.

## Acknowledgements

We would like to thank the DP SPAD - CIRAD Madagascar field team for their support in carrying out the experiments. We also thank Dr. Harinjaka Raveloson for his assistance in conducting disease evaluation.

## Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.baae.2025.03.001.

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