Models of Plant Resistance Deployment

Evolution and spread of multiadapted pathogens in a spatially heterogeneous environment

Pathogen adaptation to multiple selective pressures challenges our ability to control their spread. Here we analyze the evolutionary dynamics of pathogens spreading in a heterogeneous host population where selection varies periodically in space. We study both the transient dynamics taking place at the front of the epidemic and the long-term evolution far behind the front. We identify five types of epidemic profiles arising for different levels of spatial heterogeneity and different costs of adaptation. In particular, we identify the conditions where a generalist pathogen carrying multiple adaptations can outrace a coalition of specialist pathogens. We also show that finite host populations promote the spread of generalist pathogens because demographic stochasticity enhances the extinction of locally maladapted pathogens. But higher mutation rates between genotypes can rescue the coalition of specialists and speed up the spread of epidemics for intermediate levels of spatial heterogeneity. Our work provides a comprehensive analysis of the interplay between migration, local selection, mutation, and genetic drift on the spread and on the evolution of pathogens in heterogeneous environments. This work extends our fundamental understanding of the outcome of the competition between two specialists and a generalist strategy (single- vs. multiadapted pathogens). These results have practical implications for the design of more durable control strategies against multiadapted pathogens in agriculture and in public health.

Approximate Bayesian Computation applied to time series of population genetic data disentangles rapid genetic changes and demographic variations in a pathogen population

Adaptation can occur at remarkably short timescales in natural populations, leading to drastic changes in phenotypes and genotype frequencies over a few generations only. The inference of demographic parameters can allow understanding how evolutionary forces interact and shape the genetic trajectories of populations during rapid adaptation. Here we propose a new Approximate Bayesian Computation (ABC) framework that couples a forward and individual-based model with temporal genetic data to disentangle genetic changes and demographic variations in a case of rapid adaptation. We test the accuracy of our inferential framework and evaluate the benefit of considering a dense versus sparse sampling. Theoretical investigations demonstrate high accuracy in both model and parameter estimations, even if a strong thinning is applied to time series data. Then, we apply our ABC inferential framework to empirical data describing the population genetic changes of the poplar rust pathogen following a major event of resistance overcoming. We successfully estimate key demographic and genetic parameters, including the proportion of resistant hosts deployed in the landscape and the level of standing genetic variation from which selection occurred. Inferred values are in accordance with our empirical knowledge of this biological system. This new inferential framework, which contrasts with coalescent-based ABC analyses, is promising for a better understanding of evolutionary trajectories of populations subjected to rapid adaptation.

Do Growers Using Solo Fungicides Affect the Durability of Disease Control of Growers Using Mixtures and Alternations? The Case of Spot-Form Net Blotch in Western Australia

Growers often use alternations or mixtures of fungicides to slow down the development of resistance to fungicides. However, within a landscape, some growers will implement such resistance management methods, whereas others do not, and may even apply solo components of the resistance management program. We investigated whether growers using solo components of resistant management programs affect the durability of disease control in fields of those who implement fungicide resistance management. We developed a spatially implicit semidiscrete epidemiological model for the development of fungicide resistance. The model simulates the development of epidemics of spot-form net blotch disease, caused by the pathogen Pyrenophora teres f. maculata. The landscape comprises three types of fields, grouped according to their treatment program, with spore dispersal between fields early in the cropping season. In one field type, a fungicide resistance management method is implemented, whereas in the two others, it is not, with one of these field types using a component of the fungicide resistance management program. The output of the model suggests that the use of component fungicides does affect the durability of disease control for growers using resistance management programs. The magnitude of the effect depends on the characteristics of the pathosystem, the degree of inoculum mixing between fields, and the resistance management program being used. Additionally, although increasing the amount of the solo component in the landscape generally decreases the lifespan within which the resistance management program provides effective control, situations exist where the lifespan may be minimized at intermediate levels of the solo component fungicide. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Effects of pathogen reproduction system on the evolutionary and epidemiological control provided by deployment strategies for two major resistance genes in agricultural landscapes

Resistant cultivars are of value for protecting crops from disease, but can be rapidly overcome by pathogens. Several strategies have been proposed to delay pathogen adaptation (evolutionary control), while maintaining effective protection (epidemiological control). Resistance genes can be (i) combined in the same cultivar (pyramiding), (ii) deployed in different cultivars sown in the same field (mixtures) or in different fields (mosaics), or (iii) alternated over time (rotations). The outcomes of these strategies have been investigated principally in pathogens displaying pure clonal reproduction, but many pathogens have at least one sexual event in their annual life cycles. Sexual reproduction may promote the emergence of superpathogens adapted to all the resistance genes deployed. Here, we improved the spatially explicit stochastic model landsepi to include pathogen sexual reproduction, and we used the improved model to investigate the effect of sexual reproduction on evolutionary and epidemiological outcomes across deployment strategies for two major resistance genes. Sexual reproduction favours the establishment of a superpathogen when single mutant pathogens are present together at a sufficiently high frequency, as in mosaic and mixture strategies. However, sexual reproduction did not affect the strategy recommendations for a wide range of mutation probabilities, associated fitness costs, and landscape organisations.

Imaging with spatio-temporal modelling to characterize the dynamics of plant-pathogen lesions

Within-host spread of pathogens is an important process for the study of plant-pathogen interactions. However, the development of plant-pathogen lesions remains practically difficult to characterize beyond the common traits such as lesion area. Here, we address this question by combining image-based phenotyping with mathematical modelling. We consider the spread of Peyronellaea pinodes on pea stipules that were monitored daily with visible imaging. We assume that pathogen propagation on host-tissues can be described by the Fisher-KPP model where lesion spread depends on both a logistic growth and an homogeneous diffusion. Model parameters are estimated using a variational data assimilation approach on sets of registered images. This modelling framework is used to compare the spread of an aggressive isolate on two pea cultivars with contrasted levels of partial resistance. We show that the expected slower spread on the most resistant cultivar is actually due to a significantly lower diffusion coefficient. This study shows that combining imaging with spatial mechanistic models can offer a mean to disentangle some processes involved in host-pathogen interactions and further development may allow a better identification of quantitative traits thereafter used in genetics and ecological studies.

Modeling the Impact of Proportion, Sowing Date, and Architectural Traits of a Companion Crop on Foliar Fungal Pathogens of Wheat in Crop Mixtures

Diversification of cropping systems is a lever for the management of epidemics. However, most research to date has focused on cultivar mixtures, especially for cereals, even though crop mixtures can also improve disease management. To investigate the benefits of crop mixtures, we studied the effect of different crop mixture characteristics (i.e., companion proportion, sowing date, and traits) on the protective effect of the mixture. We developed a SEIR (Susceptible, Exposed, Infectious, Removed) model of two damaging wheat diseases (Zymoseptoria tritici and Puccinia triticina), which were applied to different canopy components, ascribable to wheat and a theoretical companion crop. We used the model to study the sensitivity of disease intensity to the following parameters: wheat-versus-companion proportion, companion sowing date and growth, and architectural traits. For both pathogens, the companion proportion had the strongest effect, with 25% of companion reducing disease severity by 50%. However, changing companion growth and architectural traits also significantly improved the protective effect. The effect of companion characteristics was consistent across different weather conditions. After decomposing the dilution and barrier effects, the model suggested that the barrier effect is maximized for an intermediate proportion of companion crop. Our study thus supports crop mixtures as a promising strategy to improve disease management. Future studies should identify real species and determine the combination of host and companion traits to maximize the protective effect of the mixture. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

The proportion of resistant hosts in mixtures should be biased towards the resistance with the lowest breaking cost

Current agricultural practices facilitate emergence and spread of plant diseases through the wide use of monocultures. Host mixtures are a promising alternative for sustainable plant disease control. Their effectiveness can be partly explained by priming-induced cross-protection among plants. Priming occurs when plants are challenged with non-infective pathogen genotypes, resulting in increased resistance to subsequent infections by infective pathogen genotypes. We developed an epidemiological model to explore how mixing two distinct resistant varieties can reduce disease prevalence. We considered a pathogen population composed of three genotypes infecting either one or both varieties. We found that host mixtures should not contain an equal proportion of resistant plants, but a biased ratio (e.g. 80 : 20) to minimize disease prevalence. Counter-intuitively, the optimal ratio of resistant varieties should contain a lower proportion of the costliest resistance for the pathogen to break. This benefit is amplified by priming. This strategy also prevents the invasion of pathogens breaking all resistances.

The socio-economic challenges of managing pathogen evolution in agriculture

Genetic resistance forms the foundation of infectious disease management in crops. However, rapid pathogen evolution is causing the breakdown of resistance and threatening disease control. Recent research efforts have identified strategies for resistance gene deployment that aim to disrupt pathogen adaptation and prevent breakdown. To date, there has been limited practical uptake of such strategies. In this paper, we focus on the socio-economic challenges associated with translating applied evolutionary research into scientifically informed management strategies to control pathogen adaptation. We develop a conceptual framework for the economic valuation of resistance and demonstrate that in addition to various direct benefits, resistance delivers considerable indirect and non-market value to farmers and society. Incentives for stakeholders to engage in stewardship strategies are complicated by the uncertain timeframes associated with evolutionary processes, difficulties in assigning ownership rights to genetic resources and lack of governance. These interacting biological, socio-economic and institutional complexities suggest that resistance breakdown should be viewed as a wicked problem, with often conflicting imperatives among stakeholders and no simple cause or solution. Promoting the uptake of scientific research outcomes that address complex issues in sustainable crop disease management will require a mix of education, incentives, legislation and social change. This article is part of the theme issue 'Infectious disease ecology and evolution in a changing world'.

Understanding the effect of component proportions on disease control in two-component cultivar cereal mixtures using a pathogen dispersal scaling hypothesis

A field experiment was carried out to determine the importance of component cultivar proportions to spring barley mixture efficacy against rhynchosporium or scald symptoms caused by the splash-dispersed pathogen Rhynchosporium commune. A larger effect than expected was observed of small amounts of one component on another for reducing disease overall, but relative insensitivity to proportion as amounts of each component become more similar. An established theoretical framework, the 'Dispersal scaling hypothesis', was used to model the expected effect of mixing proportions on the spatiotemporal spread of disease. The model captured the unequal effect of mixing different proportions on disease spread and there was good agreement between predictions and observations. The dispersal scaling hypothesis therefore provides a conceptual framework to explain the observed phenomenon, and a tool to predict the proportion of mixing at which mixture performance is maximized.

Multiple scenarios for sexual crosses in the fungal pathogen Zymoseptoria tritici on wheat residues: Potential consequences for virulence gene transmission

Little is known about the impact of host immunity on sexual reproduction in fungal pathogens. In particular, it is unclear whether crossing requires both sexual partners to infect living plant tissues. We addressed this issue in a three-year experiment investigating different scenarios of Zymoseptoria tritici crosses according to the virulence ('vir') or avirulence ('avr') of the parents against a qualitative resistance gene. Co-inoculations ('vir × vir', 'avr × vir', 'avr × avr') and single inoculations were performed on a wheat cultivar carrying the Stb16q resistance gene (Cellule) and a susceptible cultivar (Apache), in the greenhouse. We assessed the intensity of asexual reproduction by scoring disease severity, and the intensity of sexual reproduction by counting the ascospores discharged from wheat residues. As expected, disease severity was more intense on Cellule for 'vir × vir' co-inoculations than for 'avr × vir' co-inoculations, with no disease for 'avr × avr'. However, all types of co-inoculation yielded sexual offspring, whether or not the parental strains caused plant symptoms. Parenthood was confirmed by genotyping (SSR markers), and the occurrence of crosses between (co-)inoculated and exogenous strains (other strains from the experiment, or from far away) was determined. We showed that symptomatic asexual infection was not required for a strain to participate in sexual reproduction, and deduced from this result that avirulent strains could be maintained asymptomatically "on" or "in" leaf tissues of plants carrying the corresponding resistant gene for long enough to reproduce sexually. In two of the three years, the intensity of sexual reproduction did not differ between the three types of co-inoculation in Cellule, suggesting that crosses involving avirulent strains are not anecdotal. We discuss the possible mechanisms explaining the maintenance of avirulence in Z. tritici populations and the potential impact of particular resistance deployments such as cultivar mixtures for limiting resistance breakdown.

The Characterization of Pathotypes in Grapevine Downy Mildew Provides Insights into the Breakdown of Rpv3, Rpv10, and Rpv12 Factors in Grapevines

We describe a standard method for characterizing the virulence profile of Plasmopara viticola, the causal agent of grapevine downy mildew. We used 33 European strains to inoculate six grapevine varieties carrying the principal factors for resistance to downy mildew (Rpv1, Rpv3.1, Rpv3.2, Rpv5, Rpv6, Rpv10, and Rpv12) and the susceptible Vitis vinifera 'Chardonnay'. For each interaction, we characterized the level of sporulation by image analysis and the intensity of the grapevine hypersensitive response by visual score. We propose a definition for the breakdown of grapevine quantitative resistances combining these two traits. Among the 33 strains analyzed, 28 are virulent on at least one resistance factor. We identified five different pathotypes across the 33 strains analyzed: two pathotypes overcoming a single resistance factor (vir3.1 and vir3.2) and three complex pathotypes overcoming multiple resistance factors (vir3.1,3.2; vir3.2,12; vir3.1,3.2,10). Our findings confirm the widespread occurrence of P. viticola strains overcoming the Rpv3 haplotypes (28 strains). We also detected the first breakdown of resistance to the Rpv10 by a strain from Germany and the breakdown of Rpv12 factors by a strain from Hungary. The pathotyping method proposed here and the associated differential host range lay the groundwork for the early detection of resistance breakdown in grapevines. This approach will also facilitate the monitoring of the evolution of P. viticola populations at large spatial scales. This is an essential step forward to promoting durable management of the resistant grapevine varieties currently available.

Multi-stage resistance to Zymoseptoria tritici revealed by GWAS in an Australian bread wheat diversity panel

Septoria tritici blotch (STB) has been ranked the third most important wheat disease in the world, threatening a large area of wheat production. Although major genes play an important role in the protection against Zymoseptoria tritici infection, the lifespan of their resistance unfortunately is very short in modern wheat production systems. Combinations of quantitative resistance with minor effects, therefore, are believed to have prolonged and more durable resistance to Z. tritici. In this study, new quantitative trait loci (QTLs) were identified that are responsible for seedling-stage resistance and adult-plant stage resistance (APR). More importantly was the characterisation of a previously unidentified QTL that can provide resistance during different stages of plant growth or multi-stage resistance (MSR). At the seedling stage, we discovered a new isolate-specific QTL, QSt.wai.1A.1. At the adult-plant stage, the new QTL QStb.wai.6A.2 provided stable and consistent APR in multiple sites and years, while the QTL QStb.wai.7A.2 was highlighted to have MSR. The stacking of multiple favourable MSR alleles was found to improve resistance to Z. tritici by up to 40%.

How the epidemiology of disease-resistant and disease-tolerant varieties affects grower behaviour

Population-scale effects of resistant or tolerant crop varieties have received little consideration from epidemiologists. When growers deploy tolerant crop, population-scale disease pressures are often unaffected. This only benefits growers using tolerant varieties, selfishly decreasing yields for others. However, resistant crop can reduce disease pressure for all. We coupled an epidemiological model with game theory to understand how this affects uptake of control. Each time a grower plants a new crop, they must decide whether to use an improved (i.e. tolerant/resistant) or unimproved variety. This decision is based on strategic-adaptive expectations in our model, with growers comparing last season's profit with an estimate of what is expected from the alternative crop. Despite the positive feedback loop promoting use of a tolerant variety whenever it is available, a mixed unimproved- and tolerant-crop equilibrium can persist. Tolerant crop can also induce bistability between a scenario in which all growers use tolerant crop and the disease-free equilibrium, where no growers do. However, due to 'free-riding' by growers of unimproved crop, resistant crop nearly always exists in a mixed equilibrium. This work highlights how growers respond to contrasting incentives caused by tolerant and resistant varieties, and the distinct effects on yields and population-scale deployment.

Future of Bacterial Disease Management in Crop Production

Bacterial diseases are a constant threat to crop production globally. Current management strategies rely on an array of tactics, including improved cultural practices; application of bactericides, plant activators, and biocontrol agents; and use of resistant varieties when available. However, effective management remains a challenge, as the longevity of deployed tactics is threatened by constantly changing bacterial populations. Increased scrutiny of the impact of pesticides on human and environmental health underscores the need for alternative solutions that are durable, sustainable, accessible to farmers, and environmentally friendly. In this review, we discuss the strengths and shortcomings of existing practices and dissect recent advances that may shape the future of bacterial disease management. We conclude that disease resistance through genome modification may be the most effective arsenal against bacterial diseases. Nonetheless, more research is necessary for developing novel bacterial disease management tactics to meet the food demand of a growing global population.

Field and Landscape Risk Factors Impacting Flavescence Dorée Infection: Insights from Spatial Bayesian Modeling in the Bordeaux Vineyards

Flavescence dorée (FD) is a quarantine disease threatening European vineyards. Its management is based on mandatory insecticide treatments and the uprooting of infected plants identified during annual surveys. Field surveys are currently not optimized because the drivers affecting FD spread in vineyard landscapes remain poorly understood. We collated a georeferenced dataset of FD detection, collected from 34,581 vineyard plots over 5 years in the South West France wine region. Spatial models fitted with integrated nested Laplace approximation were used to identify local and landscape factors affecting FD detection and infection. Our analysis highlights the importance of sampling period on FD detection and of local practices and landscape context on FD infection. At field scale, altitude and cultivar choice were the main factors affecting FD infection. In particular, the odds ratio of FD infection in fields planted with the susceptible Cabernet Sauvignon, Cabernet Franc, or Muscadelle varieties were approximately twice those in fields planted with the less susceptible Merlot. Field infection was also affected by the field's immediate surroundings (within a circle with a radius of 150 to 200 m), corresponding to landscapes of 7 to 12 ha. In particular, the probability of FD infection increased with the proportions of forest and urban land and with the proportion of susceptible cultivars, demonstrating that the cultivar composition impacts FD epidemiology at landscape scale. The satisfactory predictive performance of the model for identifying districts with a prevalence of FD detection >10% of the fields suggests that it could be used to target areas in which future surveys would be most valuable.

Improving sustainable crop protection using population genetics concepts

Growing genetically resistant plants allows pathogen populations to be controlled and reduces the use of pesticides. However, pathogens can quickly overcome such resistance. In this context, how can we achieve sustainable crop protection? This crucial question has remained largely unanswered despite decades of intense debate and research effort. In this study, we used a bibliographic analysis to show that the research field of resistance durability has evolved into three subfields: (i) 'plant breeding' (generating new genetic material), (ii) 'molecular interactions' (exploring the molecular dialogue governing plant-pathogen interactions) and (iii) 'epidemiology and evolution' (explaining and forecasting of pathogen population dynamics resulting from selection pressure(s) exerted by resistant plants). We argue that this triple split of the field impedes integrated research progress and ultimately compromises the sustainable management of genetic resistance. After identifying a gap among the three subfields, we argue that the theoretical framework of population genetics could bridge this gap. Indeed, population genetics formally explains the evolution of all heritable traits, and allows genetic changes to be tracked along with variation in population dynamics. This provides an integrated view of pathogen adaptation, in particular via evolutionary-epidemiological feedbacks. In this Opinion Note, we detail examples illustrating how such a framework can better inform best practices for developing and managing genetically resistant cultivars.

Specificity of Resistance and Tolerance to Cucumber Vein Yellowing Virus in Melon Accessions and Resistance Breaking with a Single Mutation in VPg

Cucumber vein yellowing virus (CVYV) is an emerging virus on cucurbits in the Mediterranean Basin, against which few resistance sources are available, particularly in melon. The melon accession PI 164323 displays complete resistance to isolate CVYV-Esp, and accession HSD 2458 presents a tolerance, i.e., very mild symptoms despite virus accumulation in inoculated plants. The resistance is controlled by a dominant allele Cvy-1¹, while the tolerance is controlled by a recessive allele cvy-2, independent from Cvy-1¹. Before introducing the resistance or tolerance in commercial cultivars through a long breeding process, it is important to estimate their specificity and durability. Upon inoculation with eight molecularly diverse CVYV isolates, the resistance was found to be isolate-specific because many CVYV isolates induced necrosis on PI 164323, whereas the tolerance presented a broader range. A resistance-breaking isolate inducing severe mosaic on PI 164323 was obtained. This isolate differed from the parental strain by a single amino acid change in the VPg coding region. An infectious CVYV cDNA clone was obtained, and the effect of the mutation in the VPg cistron on resistance to PI 164323 was confirmed by reverse genetics. This represents the first determinant for resistance-breaking in an ipomovirus. Our results indicate that the use of the Cvy-1¹ allele alone will not provide durable resistance to CVYV and that, if used in the field, it should be combined with other control methods such as cultural practices and pyramiding of resistance genes to achieve long-lasting resistance against CVYV.

Host mixtures for plant disease control: benefits from pathogen selection and immune priming

Multiline and cultivar mixtures are highly effective methods for agroecological plant disease control. Priming‐induced cross protection, occurring when plants are challenged by avirulent pathogen genotypes and resulting in increased resistance to subsequent infection by virulent ones, is one critical key to their lasting performance against polymorphic pathogen populations. Strikingly, this mechanism was until recently absent from mathematical models aiming at designing optimal host mixtures. We developed an epidemiological model to explore the effect of host mixtures composed of variable numbers of single‐resistance cultivars on the equilibrium prevalence of the disease caused by pathogen populations polymorphic for virulence complexity. This model shows that a relatively large amount of resistance genes must be deployed to achieve low disease prevalence, as pathogen competition in mixtures tends to select for intermediate virulence complexity. By contrast, priming significantly reduces the number of plant genotypes needed to drop disease prevalence below an acceptable threshold. Given the limited availability of resistance genes in cultivars, this mechanism of plant immunity should be assessed when designing host mixtures.

An epi-evolutionary model for predicting the adaptation of spore-producing pathogens to quantitative resistance in heterogeneous environments

We have modeled the evolutionary epidemiology of spore-producing plant pathogens in heterogeneous environments sown with several cultivars carrying quantitative resistances. The model explicitly tracks the infection-age structure and genetic composition of the pathogen population. Each strain is characterized by pathogenicity traits determining its infection efficiency and a time-varying sporulation curve taking into account lesion aging. We first derived a general expression of the basic reproduction number R 0 for fungal pathogens in heterogeneous environments. We show that the evolutionary attractors of the model coincide with local maxima of R 0 only if the infection efficiency is the same on all host types. We then studied the contribution of three basic resistance characteristics (the pathogenicity trait targeted, resistance effectiveness, and adaptation cost), in interaction with the deployment strategy (proportion of fields sown with a resistant cultivar), to (i) pathogen diversification at equilibrium and (ii) the shaping of transient dynamics from evolutionary and epidemiological perspectives. We show that quantitative resistance affecting only the sporulation curve will always lead to a monomorphic population, whereas dimorphism (i.e., pathogen diversification) can occur if resistance alters infection efficiency, notably with high adaptation costs and proportions of the resistant cultivar. Accordingly, the choice of the quantitative resistance genes operated by plant breeders is a driver of pathogen diversification. From an evolutionary perspective, the time to emergence of the evolutionary attractor best adapted to the resistant cultivar tends to be shorter when resistance affects infection efficiency than when it affects sporulation. Conversely, from an epidemiological perspective, epidemiological control is always greater when the resistance affects infection efficiency. This highlights the difficulty of defining deployment strategies for quantitative resistance simultaneously maximizing epidemiological and evolutionary outcomes.

Optimizing Plant Disease Management in Agricultural Ecosystems Through Rational In-Crop Diversification

Biodiversity plays multifaceted roles in societal development and ecological sustainability. In agricultural ecosystems, using biodiversity to mitigate plant diseases has received renewed attention in recent years but our knowledge of the best ways of using biodiversity to control plant diseases is still incomplete. In term of in-crop diversification, it is not clear how genetic diversity per se in host populations interacts with identifiable resistance and other functional traits of component genotypes to mitigate disease epidemics and what is the best way of structuring mixture populations. In this study, we created a series of host populations by mixing different numbers of potato varieties showing different late blight resistance levels in different proportions. The amount of naturally occurring late blight disease in the mixture populations was recorded weekly during the potato growing seasons. The percentage of disease reduction (PDR) in the mixture populations was calculated by comparing their observed late blight levels relative to that expected when they were planted in pure stands. We found that PDR in the mixtures increased as the number of varieties and the difference in host resistance (DHR) between the component varieties increased. However, the level of host resistance in the potato varieties had little impact on PDR. In mixtures involving two varieties, the optimum proportion of component varieties for the best PDR depended on their DHR, with an increasing skewness to one of the component varieties as the DHR between the component varieties increased. These results indicate that mixing crop varieties can significantly reduce disease epidemics in the field. To achieve the best disease mitigation, growers should include as many varieties as possible in mixtures or, if only two component mixtures are possible, increase DHR among the component varieties.