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This paper presents HydroShoot, a leaf-based functional-structural plant model (FSPM) that simulates gas exchange rates of complex plant canopies under water deficit conditions. HydroShoot is built assuming that simulating both the hydraulic structure of the shoot together with the energy budget of individual leaves is the asset for successfully scaling-up leaf to canopy gas exchange rates. HydroShoot includes three interacting modules: hydraulic, which calculates the distribution of xylem water potential across shoot hydraulic segments; energy, which calculates the complete energy budget of individual leaves; and exchange, which calculates net carbon assimilation and transpiration rates of individual leaves. HydroShoot was evaluated on virtual and real grapevines having strongly contrasted canopies, under well-watered and water deficit conditions. It captured accurately the impact of canopy architecture and soil water status on plant-scale gas exchange rates and leaf-scale temperature and water potential. Both shoot hydraulic structure and leaf energy budget simulations were, as postulated, required to adequately scaling-up leaf to canopy gas exchange rates. Notwithstanding, simulating shoot hydraulic structure was found more necessary to adequately performing this scaling task than simulating leaf energy budget. That is, the intra-canopy variability of leaf water potential was a better predictor of the reduction of whole plant gas exchange rates under water deficit than the intra-canopy variability of leaf temperature. We conclude that simulating the shoot hydraulic structure is a prerequisite if FSPMs are to be used to assess gas exchange rates of complex plant canopies as those of grapevines. Finally, HydroShoot is available through the OpenAlea platform (https://github.com/openalea/hydroshoot) as a set of reusable modules. EEA Mendoza Fil: Albasha, Rami. Institut National de la Recherche Agronomique. LEPSE Montpellier; Francia Fil: Fournier, Christian. Institut National de la Recherche Agronomique. LEPSE Montpellier; Francia Fil: Pradal, Christophe. CIRAD-UMR AGAP; Francia Fil: Chelle, Michael. Institut National de la Recherche Agronomique. Ecosys; Francia Fil: Prieto, Jorge Alejandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Mendoza; Argentina. Fil: Louarn, Gaëtan. Institut National de la Recherche Agronomique; Francia Fil: Simonneau, Thierry. Institut National de la Recherche Agronomique. LEPSE Montpellier; Francia Fil: Lebon, Eric. Institut National de la Recherche Agronomique. Unité Mixte de Recherche; Francia

This study developed and used an advanced whole-plant grapevine model to unravel factors affecting water and carbon fluxes during fleshy fruit growth., The growth of fleshy fruits is still poorly understood as a result of the complex integration of water and solute fluxes, cell structural properties, and the regulation of whole plant source–sink relationships. To unravel the contribution of these processes to berry growth, a biophysical grape (Vitis vinifera L.) berry growth module was developed and integrated with a whole-plant functional–structural model, and was calibrated on two varieties, Cabernet Sauvignon and Sangiovese. The model captured well the variations in growth and sugar accumulation caused by environmental conditions, changes in leaf-to-fruit ratio, plant water status, and varietal differences, with obvious future application in predicting yield and maturity under a variety of production contexts and regional climates. Our analyses illustrated that grapevines strive to maintain proper ripening by partially compensating for a reduced source–sink ratio, and that under drought an enhanced berry sucrose uptake capacity can reverse berry shrinkage. Sensitivity analysis highlighted the importance of phloem hydraulic conductance, sugar uptake, and surface transpiration on growth, while suggesting that cell wall extensibility and the turgor threshold for cell expansion had minor effects. This study demonstrates that this integrated model is a useful tool in understanding the integration and relative importance of different processes in driving fleshy fruit growth.

This paper aims at presenting HydroShoot, a functional-structural plant model (FSPM) that is developed to simulate gas-exchange rates of complex plant canopies under water deficit conditions, by scaling up gas-exchange rates from the leaf to the canopy levels. The main hypothesis is that simulating both the hydraulic structure of the shoot together with the energy budget of individual leaves is the asset for successfully achieving this up-scaling task. HydroShoot was hence built as the ensemble of three interacting modules: hydraulic which calculates the distribution of xylem water potential across shoot hydraulic segments, energy which calculates the complete energy budget of individual leaves, and exchange which calculates net assimilation and transpiration rates of individual leaves. HydroShoot was coupled with irradiance interception and soil water balance models, and was evaluated on virtual and real grapevines having strongly contrasted canopies, under well-watered and water-deficit conditions. HydroShoot captured accurately the impact of canopy architecture and the varying soil water deficit conditions on plant-scale gas-exchange rates and leaf-scale temperature and water potential distributions. Both shoot hydraulic structure and leaf energy budget simulations were, as postulated, required to adequately scaling-up leaf to canopy gas-exchange rates. Notwithstanding, simulating the hydraulic structure of the shoot was found far more necessary to adequately performing this scaling task than simulating leaf energy balance. That is, the intra-canopy variability of leaf water potential was a better predictor of the reduction of whole plant gas-exchange rates under water deficit than the intra-canopy variability of leaf temperature. We conclude therefore that simulating the shoot hydraulic structure is a prerequisite if FSPM's are to be used to assess gas-exchange rates of complex plant canopies as those of grapevines. Finally HydroShoot is available through the OpenAlea platform (https://github.com/openalea/hydroshoot) as a set of reusable modules.

International audience; Functional-structural plant modeling (FSPM) is a fast and dynamic method to predict plant growth under varying environmental conditions. Temperature is a primary factor affecting the rate of plant development. In the present study, we used three different temperature treatments (10/14 degrees C, 18/22 degrees C, and 26/30 degrees C) to test the effect of temperature on growth and development of rapeseed (Brassica napus L.) seedlings. Plants were sampled at regular intervals (every 3 days) to obtain growth data during the length of the experiment (1 month in total). Total leaf dry mass, leaf area, leaf mass per area (LMA), width-length ratio, and the ratio of petiole length to leaf blade length (PBR), were determined and statistically analyzed, and contributed to a morphometric database. LMA under high temperature was significantly smaller than LMA under medium and low temperature, while leaves at high temperature were significantly broader. An FSPM of rapeseed seedlings featuring a growth function used for leaf extension and biomass accumulation was implemented by combining measurement with literature data. The model delivered new insights into growth and development dynamics of winter oilseed rape seedlings. The present version of the model mainly focuses on the growth of plant leaves. However, future extensions of the model could be used in practice to better predict plant growth in spring and potential cold damage of the crop.

Despite disease control management, each year part of crop harvest is lost due to plant diseases. Yellow spot is an important foliar wheat disease throughout the world. The fungus that causes the disease survives on wheat stubble and this is most commonly the source of primary infection (by ascospores) in a crop canopy in the next season. On infected leaves, lesions are formed, surrounded by yellow halos. After a latency period, conidia, the cause of secondary infection, are produced on lesions and are spread over long distances by wind. The secondary cycle can repeat several times through the season and results in the progression of the disease in the canopy. Weather conditions and the developmental stage of the crop play an important role in the progression and severity of disease in the crop canopy. To study the interactions between pathogen, climatic conditions and growing host crop, we developed an epidemiological model of Pyrenophora tritici-repentis, the fungal pathogen that causes yellow spot, and coupled it with an existing functional-structural plant model (FSPM) for cereal crops. An FSPM simulates mutual interactions between plant architecture (structure) and physiological processes (function) in plants at a (sub)organ scale, affected by environmental conditions. In our model, light interception and temperature determine the development and the growth of the cereal crop. Temperature, rainfall, relative humidity and wind data control the development of yellow spot. The pathogen submodel predicts maturation of ascospores and simulates production and wind dispersal of conidia across the canopy. Conidia are transported inside a virtual cone starting from a sporulating lesion and with the axis following the wind direction. Simulations demonstrated horizontal and vertical progression of the disease in the growing crop canopy. However, the upper leaves grew often away from the disease after the begin of stem elongation. In the future we will perform enhanced sensitivity analysis that should help us to identify the most (least) important parameters and so help in the process of model parameterisation. Epidemiological models coupled to models for plant architecture and growth under different climatic conditions are a promising tool to study the dynamics of plant-pathogen-environment interactions and their effect on crop yield. Furthermore, the coupled model can be used as a simulation tool to study the impact of different disease management approaches and lead to improved disease control. We will test the applicability of the model against field data on disease progression in spring wheat.

Despite disease control management, each year part of crop harvest is lost due to plant diseases. Yellow spot is an important foliar wheat disease throughout the world. The fungus that causes the disease survives on wheat stubble and this is most commonly the source of primary infection (by ascospores) in a crop canopy in the next season. On infected leaves, lesions are formed, surrounded by yellow halos. After a latency period, conidia, the cause of secondary infection, are produced on lesions and are spread over long distances by wind. The secondary cycle can repeat several times through the season and results in the progression of the disease in the canopy. Weather conditions and the developmental stage of the crop play an important role in the progression and severity of disease in the crop canopy. To study the interactions between pathogen, climatic conditions and growing host crop, we developed an epidemiological model of Pyrenophora tritici-repentis, the fungal pathogen that causes yellow spot, and coupled it with an existing functional-structural plant model (FSPM) for cereal crops. An FSPM simulates mutual interactions between plant architecture (structure) and physiological processes (function) in plants at a (sub)organ scale, affected by environmental conditions. In our model, light interception and temperature determine the development and the growth of the cereal crop. Temperature, rainfall, relative humidity and wind data control the development of yellow spot. The pathogen submodel predicts maturation of ascospores and simulates production and wind dispersal of conidia across the canopy. Conidia are transported inside a virtual cone starting from a sporulating lesion and with the axis following the wind direction. Simulations demonstrated horizontal and vertical progression of the disease in the growing crop canopy. However, the upper leaves grew often away from the disease after the begin of stem elongation. In the future we will perform enhanced sensitivity analysis that should help us to identify the most (least) important parameters and so help in the process of model parameterisation. Epidemiological models coupled to models for plant architecture and growth under different climatic conditions are a promising tool to study the dynamics of plant-pathogen-environment interactions and their effect on crop yield. Furthermore, the coupled model can be used as a simulation tool to study the impact of different disease management approaches and lead to improved disease control. We will test the applicability of the model against field data on disease progression in spring wheat.

Functional–structural plant modeling (FSPM) is a fast and dynamic method to predict plant growth under varying environmental conditions. Temperature is a primary factor affecting the rate of plant development. In the present study, we used three different temperature treatments (10/14°C, 18/22°C, and 26/30°C) to test the effect of temperature on growth and development of rapeseed (Brassica napus L.) seedlings. Plants were sampled at regular intervals (every 3 days) to obtain growth data during the length of the experiment (1 month in total). Total leaf dry mass, leaf area, leaf mass per area (LMA), width-length ratio, and the ratio of petiole length to leaf blade length (PBR), were determined and statistically analyzed, and contributed to a morphometric database. LMA under high temperature was significantly smaller than LMA under medium and low temperature, while leaves at high temperature were significantly broader. An FSPM of rapeseed seedlings featuring a growth function used for leaf extension and biomass accumulation was implemented by combining measurement with literature data. The model delivered new insights into growth and development dynamics of winter oilseed rape seedlings. The present version of the model mainly focuses on the growth of plant leaves. However, future extensions of the model could be used in practice to better predict plant growth in spring and potential cold damage of the crop.

Fruit quality and more specifically quality of the fiber in the fruit of cotton, depends on interactions between fruit position in the plant architecture, temperature and agronomical practices, such as sowing time, mulching with plastic film, and topping of the plant's main stem and branches. A functional and structural cotton model CottonXL for fiber quality (strength, length and micronaire) was implemented at the level of each individual fruit in relation to thermal time for optimizing cotton fiber quality by matching cotton management to the environment. The model may be used to address climate and land use change scenarios., In general, the quality of fruits depends on local conditions experienced by the fruit during its development. In cotton, fruit quality, and more specifically the quality of the fibre in the fruit, depends on interactions between fruit position in the plant architecture, temperature and agronomic practices, such as sowing time, mulching with plastic film and topping of the plant's main stem and branches. To quantify this response of cotton fibre quality to environment and management, we developed a simulation model of cotton growth and development, CottonXL. Simulation of cotton fibre quality (strength, length and micronaire) was implemented at the level of each individual fruit, in relation to thermal time (represented by physiological age of the fruit) and prevailing temperature during development of each fruit. Field experiments were conducted in China in 2007 to determine model parameters, and independent data on cotton fibre quality in three cotton producing regions in China were used for model validation. Simulated values for fibre quality closely corresponded to experimental data. Scenario studies simulating a range of management practices predicted that delaying topping times can significantly decrease fibre quality, while sowing date and film mulching had no significant effect. We conclude that CottonXL may be used to explore options for optimizing cotton fibre quality by matching cotton management to the environment, taking into account responses at the level of individual fruits. The model may be used at plant, crop and regional levels to address climate and land-use change scenarios.

Functional-structural plant models (FSPMs), combining the physiological function of a plant with its architecture, require precise and transparent specifications. This can be seen as a new challenge to the design of programming languages. Here we introduce, exemplarily for a model of young poplar trees, our new formalism of relational growth grammars (RGGs), which extend the well-known Lindenmayer (L-)systems to a specific sort of node- and edge-labelled graph grammars. The model has been written in the programming language XL, which extends standard Java by rule-based programming with RGGs and overcomes many of the disadvantages of L-systems. RGGs can bridge different scales: In our model, morphogenetic rules in L-system style are combined with rules describing a regulatory network of hormone biosynthesis and rules updating photosynthate concentrations of shoot modules, all in one and the same formalism.

Quality aspects of cut chrysanthemum, such as weight of the plant and number and size of flowers, have been widely studied. However, these are seldom integrated in a single model. A functional-structural model, GreenLab, was used to simulate the geometrical features of chrysanthemum with underlying rules on biomass production and allocation. In this paper, model calibration was conducted based on data from a climate room experiment. Chrysanthemum ‘Reagan Improved' was grown at 16°C and 380 μmol m-2 s-1 PAR, with 14 days long day (LD) period (19 h of light), followed by short day (SD) period (11 h of light) until harvest. Detailed measurements included weight and size of leaves and internodes in the main stem, and diameter of flowers, weight of all leaves and that of stems for side shoots. Non-linear least square method was applied to fit the parameters of the GreenLab model, such as the sink strength of the organs. New features introduced into GreenLab are: (1) number of primordia to fill the gap between development and growth; (2) delay function of growth to simulate the top-down flowering sequence; (3) additional sink of growing internodes on main stem to simulate their strong growth after terminal-bud removal; (4) two phases for individual flower growth. Realistic 3D chrysanthemum was simulated, which is a starting point for introducing effects of varying cultivation conditions.

The thesis is devoted to a unified variational approach for diverse applications, such as parameter calibration, optimal control and data assimilation, based on plant architecture and functioning. The mathematical formulation of the functional-structural plant model GreenLab is completed by the introduction of an empirical formula on environmental factors to mimic photosynthesis. A soil water balance submodel has been integrated into GreenLab to descript the dynamic soil-plant system. The dynamics formulation enables efficient numerical solutions for the variational systems by introducing the corresponding adjoint model. Differentiation algorithms are employed to derive adjoint code by hand in a systematic way directly from GreenLab source code. This variational approach is followed to solve an optimal control problem of sunflower water supply for better fruit production. Data assimilation concept is introduced to decrease the model uncertainties both in initial conditions and external model parameters. The defined problems and optimal control techniques proposed in this thesis reveal possible agronomic applications., La thèse est consacrée à une approche variationnelle unifiée pour des applications diverses, telles que l'identification de paramètres, la contrôle optimal et l'assimilation de données, pour la modélisation de l'architecture et du fonctionnement des plantes. La formulation mathématique du modèle fonction-structure de plantes GreenLab est réalisé par l'introduction d'une formule empirique sur des facteurs environnementaux pour modèliser la photosynthèse. Un sous-modèle d'équilibre de l'eau dans le sol a été ajouté dans GreenLab pour le système dynamique de sol-plantes. La formulation dynamique permet d'obtenir des solutions numériques efficaces pour les systèmes variationnels en utilisant le modèle d'adjoint correspondant. Les algorithmes de différentiation sont utilisées pour différentier le code GreenLab d'une manière systématique afin d'obtenir le code d'adjoint. L'approche variationnelle est utilisée pour résoudre un problème de d'approvisionnement optimal d'eau pour le tournesol et pour une meilleure production de fruits. Le concept de l'assimilation de données est utilisé pour diminuer les incertitudes sur la condition initiale et les paramètres externes de modèles. Les resultats sur les problèmes étudiés montrent que les concepts d'assimilation de données et de contrôle optimal sont utilisables en agronomie.