Christian Fournier

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 kinetics of plant canopy architecture¿concepts and applications

Most crop models simulate the crop canopy as an homogeneous medium. This approach enables modelling of mass and energy transfer through relatively simple equations, and is useful for understanding crop production. However, schematisation of an homogeneous medium cannot address the heterogeneous nature of canopies and interactions between plants or plant organs, and errors in calculation of light interception may occur. Moreover, conventional crop models do not describe plant organs before they are visible externally e.g young leaves of grasses. The conditions during early growth of individual organs are important determinants of final organ size, causing difficulties in incorporating effects of environmental stresses in such models. Limited accuracy in describing temporal source-sink relationships also contributes to difficulty in modelling dry matter distribution and paramaterisation of harvest indices. Functional-architectural modelling overcomes these limitations by (i) representing crops as populations of individual plants specified in three dimensions and (ii) by modelling whole plant growth and development from the behaviour of individual organs, based on sound models of organs such as leaves and internodes. Since individual plants consist of numerous organs, generic models of organ growth applicable across species are desirable. Consequently, we are studying the development of individual organs, and parameterising it in terms of environmental variables and plant characteristics. Models incorporating plant architecture are currently applied in education, using dynamic visual representation for teaching growth and development. In research, the 3D representation of plants addresses issues presented above and new applications including modelling of pesticide distribution, fungal spore dispersal through splashing and plant to plant heterogeneity.

A process-based model to simulate nitrogen distribution in wheat (Triticum aestivum) during grain-filling

Nitrogen (N) distribution among plant organs plays a major role in crop production and, in general, plant fitness to the environment. In the present study, a process-based model simulating N distribution within a wheat (Triticum aestivum L.) culm during grain filling was developed using a functional-structural approach. A model of turnover of the photosynthetic apparatus was used to describe the fluxes between a common pool of mobile N and each leaf lamina. Grain N accumulation within a time-step was modelled as the minimum between the quantity calculated by a potential function and the N available in the common pool. Nitrogen dynamics in the other organs (i.e. stem, chaff, root N uptake and remobilisation) were accounted for by forced variables. Using a unique set of six parameters, the model was able to simulate the observed N kinetics of each lamina and of the grains under a wide range of crop N supplies and for three cultivars. The time-course of the vertical gradient of lamina N during grain filling was realistically simulated as an emerging property of the local processes defined at the lamina scale. The model described in the present study offers new insight into the interactions between N metabolism, plant architecture and productivity.

Relative contributions of light interception and radiation use efficiency to the reduction of maize productivity under cold temperatures

Maize (Zea mays L.) is a chill-susceptible crop cultivated in northern latitude environments. The detrimental effects of cold on growth and photosynthetic activity have long been established. However, a general overview of how important these processes are with respect to the reduction of productivity reported in the field is still lacking. In this study, a model-assisted approach was used to dissect variations in productivity under suboptimal temperatures and quantify the relative contributions of light interception (PARc) and radiation use efficiency (RUE) from emergence to flowering. A combination of architectural and light transfer models was used to calculate light interception in three field experiments with two cold-tolerant lines and at two sowing dates. Model assessment confirmed that the approach was suitable to infer light interception. Biomass production was strongly affected by early sowings. RUE was identified as the main cause of biomass reduction during cold events. Furthermore, PARc explained most of the variability observed at flowering, its relative contributions being more or less important according to the climate experienced. Cold temperatures resulted in lower PARc, mainly because final leaf length and width were significantly reduced for all leaves emerging after the first cold occurrence. These results confirm that virtual plants can be useful as fine phenotyping tools. A scheme of action of cold on leaf expansion, light interception and radiation use efficiency is discussed with a view towards helping breeders define relevant selection criteria.

A comparative analysis of leaf shape of wheat, barley and maize using an empirical shape model

BACKGROUND AND AIMS The phenotypes of grasses show differences depending on growth conditions and ontogenetic stage. Understanding these responses and finding suitable mathematical formalizations are an essential part of the development of plant and crop models. Usually, a marked change in architecture between juvenile and adult plants is observed, where dimension and shape of leaves are likely to change. In this paper, the plasticity of leaf shape is analysed according to growth conditions and ontogeny. METHODS Leaf shape of Triticum aestivum, Hordeum vulgare and Zea mays cultivars grown under varying conditions was measured using digital image processing. An empirical leaf shape model was fitted to measured shape data of single leaves. Obtained values of model parameters were used to analyse the patterns in leaf shape. KEY RESULTS The model was able to delineate leaf shape of all studied species. The model error was small. Differences in leaf shape between juvenile and adult leaves in T. aestivum and H. vulgare were observed. Varying growth conditions impacted leaf dimensions but did not impact leaf shape of the respective species. CONCLUSIONS Leaf shape of the studied T. aestivum and H. vulgare cultivars was remarkably stable for a comparable ontogenetic stage (leaf rank), but differed between stages. Along with other aspects of grass architecture, leaf shape changed during the transition from juvenile to adult growth phase. Model-based analysis of leaf shape is a method to investigate these differences. Presented results can be integrated into architectural models of plant development to delineate leaf shape for different species, cultivars and environmental conditions.

OpenAlea: scientific workflows combining data analysis and simulation

Analyzing biological data (e.g., annotating genomes, assembling NGS data...) may involve very complex and interlinked steps where several tools are combined together. Scientific workflow systems have reached a level of maturity that makes them able to support the design and execution of such in-silico experiments, and thus making them increasingly popular in the bioinformatics community. However, in some emerging application domains such as system biology, developmental biology or ecology, the need for data analysis is combined with the need to model complex multi-scale biological systems, possibly involving multiple simulation steps. This requires the scientific workflow to deal with retro-action to understand and predict the relationships between structure and function of these complex systems. OpenAlea (openalea.gforge.inria.fr) is the only scientific workflow system able to uniformly address the problem, which made it successful in the scientific community. One of its main originality is to introduce higher-order dataflows as a means to uniformly combine classical data analysis with modeling and simulation. In this demonstration paper, we provide for the first time the description of the OpenAlea system involving an original combination of features. We illustrate the demonstration on a high-throughput workflow in phenotyping, phenomics, and environmental control designed to study the interplay between plant architecture and climatic change.

InfraPhenoGrid: A scientific workflow infrastructure for Plant Phenomics on the Grid

Plant phenotyping consists in the observation of physical and biochemical traits of plant genotypes in response to environmental conditions. Challenges , in particular in context of climate change and food security, are numerous. High-throughput platforms have been introduced to observe the dynamic growth of a large number of plants in different environmental conditions. Instead of considering a few genotypes at a time (as it is the case when phenomic traits are measured manually), such platforms make it possible to use completely new kinds of approaches. However, the data sets produced by such widely instrumented platforms are huge, constantly augmenting and produced by increasingly complex experiments, reaching a point where distributed computation is mandatory to extract knowledge from data. In this paper, we introduce InfraPhenoGrid, the infrastructure we designed and deploy to efficiently manage data sets produced by the PhenoArch plant phenomics platform in the context of the French Phenome Project. Our solution consists in deploying scientific workflows on a Grid using a middle-ware to pilot workflow executions. Our approach is user-friendly in the sense that despite the intrinsic complexity of the infrastructure, running scientific workflows and understanding results obtained (using provenance information) is kept as simple as possible for end-users.

How does pea architecture influence light sharing in virtual wheat-pea mixtures? A simulation study based on pea genotypes with contrasting architectures

Light sharing within virtual wheat-pea mixtures was influenced by the variability of pea’s architectural parameters affecting LAI and height. Light capture was affected by the development of leaflets, number of branches and phytomers and internode length.

Modelling the radiometric response of a dynamic, 3D structural model of Scots pine in the optical and microwave domains

A dynamic 3D structural model is used to simulate the structural growth stages of a Scots pine canopy from age 5 to 50 years. The 3D structural output of the model agrees with observed measures of Scots pine canopy structure. Needles are added to the structural model according to measured density and phyllotaxy (distribution). The 3D structural models are used to drive both optical and microwave models of canopy radiometric response. Simulated canopy radiometric response is compared with airborne hyperspectral reflectance data (HyMAP) and airborne synthetic aperture radar (ASAR) backscatter data, recorded during the SAR and Hyperspectral Airborne Campaign (SHAC) conducted over the UK during 2000. Simulations are shown to agree well in general with observations. This method is shown to be suitable for exploring the impact of canopy structure on the measured remotely sensed signal.

Modelling Nitrogen Distribution in Virtual Plants, as Exemplified by Wheat Culm During Grain Filling

Nitrogen is fundamental for plant growth. In cereals, growing grains represent a strong sink that triggers nitrogen remobilisation from vegetative organs and results in plant death. A better understanding of this mechanism would help in optimizing crop productivity while reducing fertilization. This work presents an experimental analysis and a process–based model of the spatiotemporal nitrogen distribution during grain filling in winter wheat culms. Nitrogen was distributed homogeneously within individual laminae and sheaths, but a strong gradient existed between organs at successive positions along the culm. During grain filling, the changes in nitrogen content of individual laminae and sheaths showed identical patterns, differing only by a scale factor. Modelling N content of each lamina as the result of the turnover of photosynthetic nitrogen and supposing that all organs share a single pool of mobile nitrogen allowed predicting the observed patterns with high accuracy. This offers new insight for modelling plant nitrogen economy.