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.

Analyses of microstructural variation in the human striatum using non-negative matrix factorization.

The striatum is a major subcortical connection hub that has been heavily implicated in a wide array of motor and cognitive functions. Here, we developed a normative multimodal, data-driven microstructural parcellation of the striatum using non-negative matrix factorization (NMF) based on multiple magnetic resonance imaging-based metrics (mean diffusivity, fractional anisotropy, and the ratio between T1- and T2-weighted structural scans) from the Human Connectome Project Young Adult dataset (n = 329 unrelated participants, age range: 22-35, F/M: 185/144). We further explored the biological and functional relationships of this parcellation by relating our findings to motor and cognitive performance in tasks known to involve the striatum as well as demographics. We identified 5 spatially distinct striatal components for each hemisphere. We also show the gain in component stability when using multimodal versus unimodal metrics. Our findings suggest distinct microstructural patterns in the human striatum that are largely symmetric and that relate mostly to age and sex. Our work also highlights the putative functional relevance of these striatal components to different designations based on a Neurosynth meta-analysis.

A functional structural model of grass development based on metabolic regulation and coordination rules.

Shoot architecture is a key component of the interactions between plants and their environment. We present a novel model of grass, which fully integrates shoot morphogenesis and the metabolism of carbon (C) and nitrogen (N) at organ scale, within a three-dimensional representation of plant architecture. Plant morphogenesis is seen as a self-regulated system driven by two main mechanisms. First, the rate of organ extension and the establishment of architectural traits are regulated by concentrations of C and N metabolites in the growth zones and the temperature. Second, the timing of extension is regulated by rules coordinating successive phytomers instead of a thermal time schedule. Local concentrations are calculated from a model of C and N metabolism at organ scale. The three-dimensional representation allows the accurate calculation of light and temperature distribution within the architecture. The model was calibrated for wheat (Triticum aestivum) and evaluated for early vegetative stages. This approach allowed the simulation of realistic patterns of leaf dimensions, extension dynamics, and organ mass and composition. The model simulated, as emergent properties, plant and agronomic traits. Metabolic activities of growing leaves were investigated in relation to whole-plant functioning and environmental conditions. The current model is an important step towards a better understanding of the plasticity of plant phenotype in different environments.

Adaptation of Biotrophic Leaf Pathogens to Fertilization-Mediated Changes in Plant Traits: A Comparison of the Optimization Principle to Invasion Fitness.

One of the conclusions of evolutionary ecology applied to agroecosystem management is that sustainable disease management strategies must be adaptive to overcome the immense adaptive potential of crop pathogens. In this context, knowledge of how pathogens adapt to changes in cultural practices is necessary. In this article we address the issue of the evolutionary response of biotrophic crop pathogens to changes in fertilization practices. For this purpose, we compare predictions of latent period evolution based on three empirical fitness measures (seasonal spore production, within-season exponential growth rate, and area under disease progress curves [AUDPCs]) with predictions based on the concept of invasion fitness from adaptive dynamics. We use pairwise invisibility plots to identify the evolutionarily stable strategies (ESSs) of the pathogen latent period. We find that the ESS latent period is in between the latent periods that maximize the seasonal spore production and the within-season exponential growth rate of the pathogen. The latent periods that maximize the AUDPC are similar to those of the ESS latent periods. The AUDPC may therefore be a critical variable to determine the issue of between-strain competition and shape pathogen evolution.

Does the Latent Period of Leaf Fungal Pathogens Reflect Their Trophic Type? A Meta-Analysis of Biotrophs, Hemibiotrophs, and Necrotrophs.

We performed a meta-analysis to search for a relation between the trophic type and latent period of fungal pathogens. The pathogen incubation period and the level of resistance of the hosts were also investigated. This ecological knowledge would help us to more efficiently regulate crop epidemics for different types of pathogens. We gathered latent period data from 103 studies dealing with 51 fungal pathogens of the three major trophic types (25 biotrophs, 15 hemibiotrophs, and 11 necrotrophs), representing 2,542 mean latent periods. We show that these three trophic types display significantly different latent periods. Necrotrophs exhibited the shortest latent periods (<100 degree-days [DD]), biotrophs had intermediate ones (between 100 and 200 DD), and hemibiotrophs had the longest latent periods (>200 DD). We argue that this relation between trophic type and latent period points to two opposing host exploitation strategies: necrotrophs mount a rapid destructive attack on the host tissue, whereas biotrophs and hemibiotrophs avoid or delay the damaging phase. We query the definition of hemibiotrophic pathogens and discuss whether the length of the latent period is determined by the physiological limits inherent to each trophic type or by the adaptation of pathogens of different trophic types to the contrasting conditions experienced in their interaction with the host.

Plant architecture and foliar senescence impact the race between wheat growth and Zymoseptoria tritici epidemics.

Background and Aims: In order to optimize crop management in innovative agricultural production systems, it is crucial to better understand how plant disease epidemics develop and what factors influence them. This study explores how canopy growth, its spatial organization and leaf senescence impact Zymoseptoria tritici epidemics. Methods: We used the Septo3D model, an epidemic model of Septoria tritici blotch (STB) coupled with a 3-D virtual wheat structural plant model (SPM). The model was calibrated and evaluated against field experimental data. Sensitivity analyses were performed on the model to explore how wheat plant traits impact the interaction between wheat growth and Z. tritici epidemics. Key Results: The model reproduces consistently the effects of crop architecture and weather on STB progress on the upper leaves. Model sensitivity analyses show that the effects of plant traits on epidemics depended on weather conditions. The simulations confirm the known effect of increased stem height and stem elongation rate on limiting STB progress on upper leaves. Strikingly, the timing of leaf senescence is one of the most influential traits on simulated STB epidemics. When the green life span duration of leaves is reduced by early senescence, epidemics are strongly reduced. Conclusions: We introduce the notion of a 'race' for the colonization of emerging healthy host tissue between the growing canopy and the developing epidemics. This race is 2-fold: (1) an upward race at the canopy scale where STB must catch the newly emerging leaves before they grow away from the spore sources; and (2) a local race at the leaf scale where STB must use the resources of its host before it is caught by leaf apical senescence. The results shed new light on the importance of dynamic interactions between host and pathogen.

Modelling interaction dynamics between two foliar pathogens in wheat: a multi-scale approach.

Background and Aims: Disease models can improve our understanding of dynamic interactions in pathosystems and thus support the design of innovative and sustainable strategies of crop protections. However, most epidemiological models focus on a single type of pathogen, ignoring the interactions between different parasites competing on the same host and how they are impacted by properties of the canopy. This study presents a new model of a disease complex coupling two wheat fungal diseases, caused by Zymoseptoria tritici (septoria) and Puccinia triticina (brown rust), respectively, combined with a functional-structural plant model of wheat. Methods: At the leaf scale, our model is a combination of two sub-models of the infection cycles for the two fungal pathogens with a sub-model of competition between lesions. We assume that the leaf area is the resource available for both fungi. Due to the necrotic period of septoria, it has a competitive advantage on biotrophic lesions of rust. Assumptions on lesion competition are first tested developing a geometrically explicit model on a simplified rectangular shape, representing a leaf on which lesions grow and interact according to a set of rules derived from the literature. Then a descriptive statistical model at the leaf scale was designed by upscaling the previous mechanistic model, and both models were compared. Finally, the simplified statistical model has been used in a 3-D epidemiological canopy growth model to simulate the diseases dynamics and the interactions at the canopy scale. Key Results: At the leaf scale, the statistical model was a satisfactory metamodel of the complex geometrical model. At the canopy scale, the disease dynamics for each fungus alone and together were explored in different weather scenarios. Rust and septoria epidemics showed different behaviours. Simulated epidemics of brown rust were greatly affected by the presence of septoria for almost all the tested scenarios, but the reverse was not the case. However, shortening the rust latent period or advancing the rust inoculum shifted the competition more in favour of rust, and epidemics became more balanced. Conclusions: This study is a first step towards the integration of several diseases within virtual plant models and should prompt new research to understand the interactions between canopy properties and competing pathogens.

IPSIM-Web, An Online Resource for Promoting Qualitative Aggregative Hierarchical Network Models to Predict Plant Disease Risk: Application to Brown Rust on Wheat.

A qualitative pest modeling platform, named Injury Profile Simulator (IPSIM), provides a tool to design aggregative hierarchical network models to predict the risk of pest injuries, including diseases, on a given crop based on variables related to cropping practices as well as soil and weather environment at the field level. The IPSIM platform enables modelers to combine data from various sources (literature, survey, experiments, and so on), expert knowledge, and simulation to build a network-based model. The overall structure of the platform is fully described at the IPSIM-Web website ( www6.inra.fr/ipsim ). A new module called IPSIM-Wheat-brown rust is reported in this article as an example of how to use the system to build and test the predictive quality of a prediction model. Model performance was evaluated for a dataset comprising 1,788 disease observations at 13 French cereal-growing regions over 15 years. Accuracy of the predictions was 85% and the agreement with actual values was 0.66 based on Cohen's κ. The new model provides risk information for farmers and agronomists to make scientifically sound tactical (within-season) decisions. In addition, the model may be of use for ex post diagnoses of diseases in commercial fields. The limitations of the model such as low precision and threshold effects as well as the benefits, including the integration of different sources of information, transparency, flexibility, and a user-friendly interface, are discussed.

Crop Fertilization Impacts Epidemics and Optimal Latent Period of Biotrophic Fungal Pathogens.

Crop pathogens are known to rapidly adapt to agricultural practices. Although cultivar resistance breakdown and resistance to pesticides have been broadly studied, little is known about the adaptation of crop pathogens to fertilization regimes and no epidemiological model has addressed that question. However, this is a critical issue for developing sustainable low-input agriculture. In this article, we use a model of life history evolution of biotrophic wheat fungal pathogens in order to understand how they could adapt to changes in fertilization practices. We focus on a single pathogen life history trait, the latent period, which directly determines the amount of resources allocated to growth and reproduction along with the speed of canopy colonization. We implemented three fertilization scenarios, corresponding to major effects of increased nitrogen fertilization on crops: (i) increase in nutrient concentration in leaves, (ii) increase of leaf lifespan, and (iii) increase of leaf number (tillering) and size that leads to a bigger canopy size. For every scenario, we used two different fitness measures to identify putative evolutionary responses of latent period to changes in fertilization level. We observed that annual spore production increases with fertilization, because it results in more resources available to the pathogens. Thus, diminishing the use of fertilizers could reduce biotrophic fungal epidemics. We found a positive relationship between the optimal latent period and fertilization when maximizing total spore production over an entire season. In contrast, we found a negative relationship between the optimal latent period and fertilization when maximizing the within-season exponential growth rate of the pathogen. These contrasting results were consistent over the three tested fertilization scenarios. They suggest that between-strain diversity in the latent period, as has been observed in the field, may be due to diversifying selection in different cultural environments.

A modelling framework to simulate foliar fungal epidemics using functional-structural plant models.

BACKGROUND AND AIMS: Sustainable agriculture requires the identification of new, environmentally responsible strategies of crop protection. Modelling of pathosystems can allow a better understanding of the major interactions inside these dynamic systems and may lead to innovative protection strategies. In particular, functional-structural plant models (FSPMs) have been identified as a means to optimize the use of architecture-related traits. A current limitation lies in the inherent complexity of this type of modelling, and thus the purpose of this paper is to provide a framework to both extend and simplify the modelling of pathosystems using FSPMs. METHODS: Different entities and interactions occurring in pathosystems were formalized in a conceptual model. A framework based on these concepts was then implemented within the open-source OpenAlea modelling platform, using the platform's general strategy of modelling plant-environment interactions and extending it to handle plant interactions with pathogens. New developments include a generic data structure for representing lesions and dispersal units, and a series of generic protocols to communicate with objects representing the canopy and its microenvironment in the OpenAlea platform. Another development is the addition of a library of elementary models involved in pathosystem modelling. Several plant and physical models are already available in OpenAlea and can be combined in models of pathosystems using this framework approach. KEY RESULTS: Two contrasting pathosystems are implemented using the framework and illustrate its generic utility. Simulations demonstrate the framework's ability to simulate multiscaled interactions within pathosystems, and also show that models are modular components within the framework and can be extended. This is illustrated by testing the impact of canopy architectural traits on fungal dispersal. CONCLUSIONS: This study provides a framework for modelling a large number of pathosystems using FSPMs. This structure can accommodate both previously developed models for individual aspects of pathosystems and new ones. Complex models are deconstructed into separate 'knowledge sources' originating from different specialist areas of expertise and these can be shared and reassembled into multidisciplinary models. The framework thus provides a beneficial tool for a potential diverse and dynamic research community.

Modelling the effect of wheat canopy architecture as affected by sowing density on Septoria tritici epidemics using a coupled epidemic-virtual plant model.

BACKGROUND AND AIMS: The relationship between Septoria tritici, a splash-dispersed disease, and its host is complex because of the interactions between the dynamic plant architecture and the vertical progress of the disease. The aim of this study was to test the capacity of a coupled virtual wheat-Septoria tritici epidemic model (Septo3D) to simulate disease progress on the different leaf layers for contrasted sowing density treatments. METHODS: A field experiment was performed with winter wheat 'Soissons' grown at three contrasted densities. Plant architecture was characterized to parameterize the wheat model, and disease dynamic was monitored to compare with simulations. Three simulation scenarios, differing in the degree of detail with which plant variability of development was represented, were defined. KEY RESULTS: Despite architectural differences between density treatments, few differences were found in disease progress; only the lower-density treatment resulted in a slightly higher rate of lesion development. Model predictions were consistent with field measurements but did not reproduce the higher rate of lesion progress in the low density. The canopy reconstruction scenario in which inter-plant variability was taken into account yielded the best agreement between measured and simulated epidemics. Simulations performed with the canopy represented by a population of the same average plant deviated strongly from the observations. CONCLUSIONS: It was possible to compare the predicted and measured epidemics on detailed variables, supporting the hypothesis that the approach is able to provide new insights into the processes and plant traits that contribute to the epidemics. On the other hand, the complex and dynamic responses to sowing density made it difficult to test the model precisely and to disentangle the various aspects involved. This could be overcome by comparing more contrasted and/or simpler canopy architectures such as those resulting from quasi-isogenic lines differing by single architectural traits.

Local dispersal of Puccinia triticina and wheat canopy structure.

The development of dynamic models jointly to simulate host growth and disease spread necessitates a precise description of pathogen dispersal in relation to canopy structure. In this study, we measured disease spread from a single infected leaf positioned at different heights in wheat canopies. The resulting lesion distribution was described along crop rows and over three leaf layers. The spore sources, although limited to a single leaf, nearly saturated the host surface accessible to the spores. Most of the lesions were found within 30 to 40 cm of the source. The vertical position of the source influenced the lesion distribution and the steepness of the disease gradients. The leaf layer and the wheat row that contained the spore source were the most infected. Close to the source, a few heavily infected leaves produced steep disease gradients, whereas spore diffusion resulted in shallower gradients along the adjacent rows and on the other leaf layers. Depending on the precision needed, the lesion distribution can be described either at the level of leaf layers or by dispersal gradients for each row and leaf layer.

Aggressiveness components and adaptation to a host cultivar in wheat leaf rust.

Experimental evidence on the capacity of pathogen populations to quantitatively adapt to their hosts and on the life traits that are involved is lacking at this time. In this article, we identified a situation in which a leaf rust pathotype (P1) was found at a high frequency on a widely grown cultivar (Soissons) and we tested the hypothesis that P1 was more aggressive on Soissons than other virulent pathotypes (P2 and P3). Several components of the pathogen life cycle were measured on adult wheat plants in two different experiments under greenhouse conditions: latent period, spore production per lesion and per unit of sporulating tissue, uredinium size, and lesion life span. Regardless of the component, pathotype P1 was repeatedly found to be more aggressive than at least one of the other two pathotypes, with differences of 5 to 51%. Breaking down spore production per lesion into uredinium size and spore production per square millimeter of sporulating tissue showed that the three pathotypes presented different aggressiveness profiles, suggesting different development constraints for the pathogen, either for its growth capacity into host tissues or its ability to exploit the host resources for spore production. Although leaf rust pathotypes present a clonal structure, quantitative differences were found for aggressiveness traits within a pathotype.

Wheat leaf rust uredospore production and carbon and nitrogen export in relation to lesion size and density.

ABSTRACT To develop mechanistic yield loss models for biotrophic fungi, we need better account for the export of dry matter, carbon, and nitrogen from the leaf into the spores. Three experiments in controlled environment chambers were performed to study the dynamics of uredospores production of Puccinia triticina on seedling leaves of wheat in relation to time, lesion density, and sporulating surface area. The detrimental effect of lesion density on the sporulation capacity of brown rust lesions was confirmed. When lesion density increased, spores production per lesion strongly decreased. However, our results showed that increasing lesion density also greatly reduces lesion size. A model was developed to summarize these relationships. Our main conclusion is that the density effect on spore production per lesion is accounted for by lesion size. When sporulation was related to the sporulating surface area, it became independent of density. As well, carbon and nitrogen contents of the spores were independent of lesion density. Our data suggest that when nitrogen available in the host is limiting, spore production is reduced but nitrogen content of spores tend to remain stable.

Wheat Leaf Rust Uredospore Production on Adult Plants: Influence of Leaf Nitrogen Content and Septoria tritici Blotch.

ABSTRACT Leaf rust uredospore production and lesion size were measured on flag leaves of adult wheat plants in a glasshouse for different lesion densities. We estimated the spore weight produced per square centimeter of infected leaf, per lesion, and per unit of sporulating area. Three levels of fertilization were applied to the plants to obtain different nitrogen content for the inoculated leaves. In a fourth treatment, we evaluated the effect of Septoria tritici blotch on leaf rust uredospore production. The nitrogen and carbon content of the spores was unaffected or marginally affected by lesion density, host leaf nitrogen content, or the presence of Mycosphaerella graminicola on the same leaf. In leaves with a low-nitrogen content, spore production per lesion was reduced, but lesion size was unaffected. A threshold effect of leaf nitrogen content in spore production was however, evident, since production was similar in the medium- and high-fertilizer treatments. In leaves inoculated with M. graminicola and Puccinia triticina, the rust lesions were smaller and produced fewer spores. The relationships among rust lesion density, lesion size, and uredospore production were fitted to a model. We determined that the density effect on spore production resulted mainly from a reduction in lesion size, the spore production per unit of sporulating surface being largely independent of lesion density. These results are consistent with those obtained previously on wheat seedlings. The main difference was that the sporulation period lasted longer in adult leaves.

Coupling a 3D virtual wheat (Triticum aestivum) plant model with a Septoria tritici epidemic model (Septo3D): a new approach to investigate plant-pathogen interactions linked to canopy architecture.

This work initiates a modelling approach that allows us to investigate the effects of canopy architecture on foliar epidemics development. It combines a virtual plant model of wheat (Triticum aestivum L.) with an epidemic model of Septoria tritici which is caused by Mycosphaerella graminicola, a hemi-biotrophic, splashed-dispersed fungus. Our model simulates the development of the lesions from the infected lower leaves to the healthy upper leaves in the growing canopy. Epidemics result from the repeated successions of lesion development (during which spores are produced) and spores dispersal. In the model, canopy development influences epidemic development through the amount of tissue available for lesion development and through its effects on rain penetration and droplets interception during spore dispersal. Simulations show that the impact of canopy architecture on epidemic development differs between canopy traits and depends on climate. Phyllochron has the strongest effect, followed by leaf size and stem elongation rate.

Modelling wheat growth and yield losses from late epidemics of foliar diseases using loss of green leaf area per layer and pre-anthesis reserves.

BACKGROUND AND AIMS: Crop protection strategies, based on preventing quantitative crop losses rather than pest outbreaks, are being developed as a promising way to reduce fungicide use. The Bastiaans' model was applied to winter wheat crops (Triticum aestivum) affected by leaf rust (Puccinia triticina) and Septoria tritici blotch (STB; Mycosphaerella graminicola) under a range of crop management conditions. This study examined (a) whether green leaf area per layer accurately accounts for growth loss; and (b) whether from growth loss it is possible to derive yield loss accurately and simply. Methods Over 5 years of field experiments, numerous green leaf area dynamics were analysed during the post-anthesis period on wheat crops using natural aerial epidemics of leaf rust and STB. Key Results When radiation use efficiency (RUE) was derived from bulk green leaf area index (GLAI), RUE(bulk) was hardly accurate and exhibited large variations among diseased wheat crops, thus extending outside the biological range. In contrast, when RUE was derived from GLAI loss per layer, RUE(layer) was a more accurate calculation and fell within the biological range. In one situation out of 13, no significant shift in the RUE(layer) of diseased crops vs. healthy crops was observed. A single linear relationship linked yield to post-anthesis accumulated growth for all treatments. Its slope, not different from 1, suggests that the allocation of post-anthesis photosynthates to grains was not affected by the late occurring diseases under study. The mobilization of pre-anthesis reserves completely accounted for the intercept value. Conclusions The results strongly suggest that a simple model based on green leaf area per layer and pre-anthesis reserves can predict both growth and yield of wheat suffering from late epidemics of foliar diseases over a range of crop practices. It could help in better understanding how crop structure and reserve management contribute to tolerance of wheat genotypes to leaf diseases.

Quantification of the effects of Septoria tritici blotch on wheat leaf gas exchange with respect to lesion age, leaf number, and leaf nitrogen status.

Quantification of the damaging effects of pathogens on diseased plants and inclusion of these damaging functions in crop simulation models is of great importance for a more complete understanding of yield response to diseases. In this study, the effect of Septoria tritici blotch (STB) on net photosynthetic and dark respiration rates of wheat flag leaves was quantified. Bastiaans' model: Y=(1-x)beta was used to characterize the relationship between relative leaf photosynthesis (Y, considering Ynet and Ygross) and STB severity (with x the proportion of the diseased area). The value of beta indicates whether the effect of disease on photosynthesis is larger (beta >1), lower (beta <1), or equal (beta =1) to the proportion of visible diseased area. In the experimental conditions used here, leaf nitrogen content (in a range from 0.18 to 0.24 mg cm(-2)), and leaf number (flag and second leaves) did not significantly influence the effect of STB on leaf gas exchange. By contrast, damage depended strongly on the developmental stages of the STB lesions. STB lesions had no effect on inoculated leaves before visible symptoms appeared. Chlorotic symptoms had less effect on leaf net photosynthetic rate than could be accounted for by the visible diseased area (betanet=0.81). The effect of necrotic lesions on the leaf net photosynthetic capacity was slightly greater than that accounted for by visible symptoms (betanet=1.35). Our results suggest that the effect of the necrotic symptoms on the net photosynthesis expressed by betanet >1 is due to a combination of a decrease in the gross photosynthesis (betagross still >1) and to an increase in the dark respiration rate (betagross

Wheat leaf photosynthesis loss due to leaf rust, with respect to lesion development and leaf nitrogen status.

In wheat (Triticum aestivum cv. Soissons) plants grown under three different fertilisation treatments, we quantified the effect of leaf rust (Puccinia triticina) on flag leaf photosynthesis during the whole sporulation period. Bastiaans' model: Y = (1 - x)beta was used to characterize the relationship between relative leaf photosynthesis (Y) and disease severity (x). The evolution of the different types of symptoms induced by the pathogen (sporulating, chlorotic and necrosed tissues) was evaluated using image analysis. The beta-values varied from 2 to 11, 1.4-2, and 0.8-1 during the sporulation period, when considering the proportion of sporulating, sporulating + necrotic, and total diseased area, respectively. Leaf nitrogen (N) content did not change the effect of the disease on host photosynthesis. We concluded that leaf rust has no global effect on the photosynthesis of the symptomless parts of the leaves and that the large range in the quantification of leaf rust effect on the host, which is found in the literature, can be accounted for by considering the different symptom types. We discuss how our results could improve disease assessments and damage prediction in a wheat crop.

Analysis and modelling of effects of leaf rust and Septoria tritici blotch on wheat growth.

A model to predict Septoria tritici blotch (STB) and leaf rust effects on wheat growth was constructed and evaluated in two steps. At the leaf scale, Bastiaans' approach that predicts the relative photosynthesis of a wheat leaf infected with a single disease, was extended to the case of two diseases, one biotrophic and one necrotrophic by considering the leaf rust-STB complex. A glasshouse experiment with flag leaves inoculated either singly with one disease or with two diseases combined was performed to check the leaf damage model. No interaction of the two diseases on photosynthesis loss was observed when they occurred simultaneously on the same leaf. In a second step, the single-leaf model was extended to the canopy scale to model the effects of the leaf rust-STB complex on the growth of a wheat crop. The model predicts the effects of disease on the growth of an affected crop relative to the growth of a healthy crop. The canopy model accounted for different contributions to photosynthetic activity of leaf layers, derived from their position in the canopy and their natural leaf senescence. Treatments differing in nitrogen fertilization, microclimatic conditions, and wheat cultivars were implemented in a field experiment to evaluate the model. The model accurately estimated the effect of disease on crop growth for each cultivar, with differences from experimental values lower than 10%, which suggests that this model is well suited to aid an understanding of disease effects on plant growth. A reduction in green leaf area was the main effect of disease in these field experiments and STB accounted for more than 70% of the reduction in plant growth. Simulations suggested that the production of rust spores may result in a loss of biomass from diseased crops and that stem photosynthesis may need to be considered in modelling diseased crop growth.