Search

Your search keyword '"Christophe Pradal"' showing total 104 results
Start Over Author "Christophe Pradal"
104 results on '"Christophe Pradal"'

1. PhenoTrack3D: an automatic high-throughput phenotyping pipeline to track maize organs over time

Author

Benoit Daviet, Romain Fernandez, Llorenç Cabrera-Bosquet, Christophe Pradal, and Christian Fournier

Subjects
High-throughput phenotyping, Computer vision, Maize, Tracking, Sequence alignment, Plant physiology, Plant culture, SB1-1110, Biology (General), QH301-705.5
Abstract

Abstract Background High-throughput phenotyping platforms allow the study of the form and function of a large number of genotypes subjected to different growing conditions (GxE). A number of image acquisition and processing pipelines have been developed to automate this process, for micro-plots in the field and for individual plants in controlled conditions. Capturing shoot development requires extracting from images both the evolution of the 3D plant architecture as a whole, and a temporal tracking of the growth of its organs. Results We propose PhenoTrack3D, a new pipeline to extract a 3D + t reconstruction of maize. It allows the study of plant architecture and individual organ development over time during the entire growth cycle. The method tracks the development of each organ from a time-series of plants whose organs have already been segmented in 3D using existing methods, such as Phenomenal [Artzet et al. in BioRxiv 1:805739, 2019] which was chosen in this study. First, a novel stem detection method based on deep-learning is used to locate precisely the point of separation between ligulated and growing leaves. Second, a new and original multiple sequence alignment algorithm has been developed to perform the temporal tracking of ligulated leaves, which have a consistent geometry over time and an unambiguous topological position. Finally, growing leaves are back-tracked with a distance-based approach. This pipeline is validated on a challenging dataset of 60 maize hybrids imaged daily from emergence to maturity in the PhenoArch platform (ca. 250,000 images). Stem tip was precisely detected over time (RMSE

Published
2022
Full Text
View/download PDF

2. High-throughput and automatic structural and developmental root phenotyping on Arabidopsis seedlings

Author

Romain Fernandez, Amandine Crabos, Morgan Maillard, Philippe Nacry, and Christophe Pradal

Subjects
High-throughput phenotyping, Computer vision, Dynamic analysis, Root system architecture, Tracking, Plant culture, SB1-1110, Biology (General), QH301-705.5
Abstract

Abstract Background High-throughput phenotyping is crucial for the genetic and molecular understanding of adaptive root system development. In recent years, imaging automata have been developed to acquire the root system architecture of many genotypes grown in Petri dishes to explore the Genetic x Environment (GxE) interaction. There is now an increasing interest in understanding the dynamics of the adaptive responses, such as the organ apparition or the growth rate. However, due to the increasing complexity of root architectures in development, the accurate description of the topology, geometry, and dynamics of a growing root system remains a challenge. Results We designed a high-throughput phenotyping method, combining an imaging device and an automatic analysis pipeline based on registration and topological tracking, capable of accurately describing the topology and geometry of observed root systems in 2D + t. The method was tested on a challenging Arabidopsis seedling dataset, including numerous root occlusions and crossovers. Static phenes are estimated with high accuracy ( $$R^2=0.996$$ R 2 = 0.996 and $$0.923$$ 0.923 for primary and second-order roots length, respectively). These performances are similar to state-of-the-art results obtained on root systems of equal or lower complexity. In addition, our pipeline estimates dynamic phenes accurately between two successive observations ( $$R^2=0.938$$ R 2 = 0.938 for lateral root growth). Conclusions We designed a novel method of root tracking that accurately and automatically measures both static and dynamic parameters of the root system architecture from a novel high-throughput root phenotyping platform. It has been used to characterise developing patterns of root systems grown under various environmental conditions. It provides a solid basis to explore the GxE interaction controlling the dynamics of root system architecture adaptive responses. In future work, our approach will be adapted to a wider range of imaging configurations and species.

Published
2022
Full Text
View/download PDF

3. Functional–Structural Plant Modeling Highlights How Diversity in Leaf Dimensions and Tillering Capability Could Promote the Efficiency of Wheat Cultivar Mixtures

Author

Emmanuelle Blanc, Pierre Barbillon, Christian Fournier, Christophe Lecarpentier, Christophe Pradal, and Jérôme Enjalbert

Subjects
aerial architecture, competition for light, variety mixture, sensitivity analysis, functional traits, tillering plasticity, Plant culture, SB1-1110
Abstract

Increasing the cultivated diversity has been identified as a major leverage for the agroecological transition as it can help improve the resilience of low input cropping systems. For wheat, which is the most cultivated crop worldwide in terms of harvested area, the use of cultivar mixtures is spreading in several countries, but studies have seldom focused on establishing mixing rules based on plant architecture. Yet, the aerial architecture of plants and the overall canopy structure are critical for field performance as they greatly influence light interception, plant interactions and yield. The very high number of trait combinations in wheat mixtures makes it difficult to conduct experimentations on this issue, which is why a modeling approach appears to be an appropriate solution. In this study, we used WALTer, a functional structural plant model (FSPM), to simulate wheat cultivar mixtures and try to better understand how differences between cultivars in key traits of the aerial architecture influence mixture performance. We simulated balanced binary mixtures of cultivars differing for different critical plant traits: final height, leaf dimensions, leaf insertion angle and tillering capability. Our study highlights the impact of the leaf dimensions and the tillering capability on the performance of the simulated mixtures, which suggests that traits impacting the plants' leaf area index (LAI) have more influence on the performance of the stand than traits impacting the arrangement of the leaves. Our results show that the performance of mixtures is very variable depending on the values of the explored architectural traits. In particular, the best performances were achieved by mixing cultivars with different leaf dimensions and different tillering capability, which is in agreement with numerous studies linking the diversity of functional traits in plant communities to their productivity. However, some of the worst performances were also achieved by mixing varieties differing in their aerial architecture, which suggests that diversity is not a sufficient criterion to design efficient mixtures. Overall, these results highlight the importance of simulation-based explorations for establishing assembly rules to design efficient mixtures.

Published
2021
Full Text
View/download PDF

4. A robot-assisted imaging pipeline for tracking the growths of maize ear and silks in a high-throughput phenotyping platform

Author

Nicolas Brichet, Christian Fournier, Olivier Turc, Olivier Strauss, Simon Artzet, Christophe Pradal, Claude Welcker, François Tardieu, and Llorenç Cabrera-Bosquet

Subjects
Image-assisted phenotyping, Computer vision, Robot-assisted imaging, Machine learning, Maize, Water deficit, Plant culture, SB1-1110, Biology (General), QH301-705.5
Abstract

Abstract Background In maize, silks are hundreds of filaments that simultaneously emerge from the ear for collecting pollen over a period of 1–7 days, which largely determines grain number especially under water deficit. Silk growth is a major trait for drought tolerance in maize, but its phenotyping is difficult at throughputs needed for genetic analyses. Results We have developed a reproducible pipeline that follows ear and silk growths every day for hundreds of plants, based on an ear detection algorithm that drives a robotized camera for obtaining detailed images of ears and silks. We first select, among 12 whole-plant side views, those best suited for detecting ear position. Images are segmented, the stem pixels are labelled and the ear position is identified based on changes in width along the stem. A mobile camera is then automatically positioned in real time at 30 cm from the ear, for a detailed picture in which silks are identified based on texture and colour. This allows analysis of the time course of ear and silk growths of thousands of plants. The pipeline was tested on a panel of 60 maize hybrids in the PHENOARCH phenotyping platform. Over 360 plants, ear position was correctly estimated in 86% of cases, before it could be visually assessed. Silk growth rate, estimated on all plants, decreased with time consistent with literature. The pipeline allowed clear identification of the effects of genotypes and water deficit on the rate and duration of silk growth. Conclusions The pipeline presented here, which combines computer vision, machine learning and robotics, provides a powerful tool for large-scale genetic analyses of the control of reproductive growth to changes in environmental conditions in a non-invasive and automatized way. It is available as Open Source software in the OpenAlea platform.

Published
2017
Full Text
View/download PDF

5. AutoWIG: automatic generation of python bindings for C++ libraries

Author

Pierre Fernique and Christophe Pradal

Subjects
C++, Python, Automatic bindings generation, Electronic computers. Computer science, QA75.5-76.95
Abstract

Most of Python and R scientific packages incorporate compiled scientific libraries to speed up the code and reuse legacy libraries. While several semi-automatic solutions exist to wrap these compiled libraries, the process of wrapping a large library is cumbersome and time consuming. In this paper, we introduce AutoWIG, a Python package that wraps automatically compiled libraries into high-level languages using LLVM/Clang technologies and the Mako templating engine. Our approach is automatic, extensible, and applies to complex C++ libraries, composed of thousands of classes or incorporating modern meta-programming constructs.

Published
2018
Full Text
View/download PDF

6. Virtual Plants Need Water Too: Functional-Structural Root System Models in the Context of Drought Tolerance Breeding

Author

Adama Ndour, Vincent Vadez, Christophe Pradal, and Mikaël Lucas

Subjects
functional structural plant model, drought, phenotyping, root system architecture, plant development and physiology, breeding, Plant culture, SB1-1110
Abstract

Developing a sustainable agricultural model is one of the great challenges of the coming years. The agricultural practices inherited from the Green Revolution of the 1960s show their limits today, and new paradigms need to be explored to counter rising issues such as the multiplication of climate-change related drought episodes. Two such new paradigms are the use of functional-structural plant models to complement and rationalize breeding approaches and a renewed focus on root systems as untapped sources of plant amelioration. Since the late 1980s, numerous functional and structural models of root systems were developed and used to investigate the properties of root systems in soil or lab-conditions. In this review, we focus on the conception and use of such root models in the broader context of research on root-driven drought tolerance, on the basis of root system architecture (RSA) phenotyping. Such models result from the integration of architectural, physiological and environmental data. Here, we consider the different phenotyping techniques allowing for root architectural and physiological study and their limits. We discuss how QTL and breeding studies support the manipulation of RSA as a way to improve drought resistance. We then go over the integration of the generated data within architectural models, how those architectural models can be coupled with functional hydraulic models, and how functional parameters can be measured to feed those models. We then consider the assessment and validation of those hydraulic models through confrontation of simulations to experimentations. Finally, we discuss the up and coming challenges facing root systems functional-structural modeling approaches in the context of breeding.

Published
2017
Full Text
View/download PDF

7. Morphological Plant Modeling: Unleashing Geometric and Topological Potential within the Plant Sciences

Author

Alexander Bucksch, Acheampong Atta-Boateng, Akomian F. Azihou, Dorjsuren Battogtokh, Aly Baumgartner, Brad M. Binder, Siobhan A. Braybrook, Cynthia Chang, Viktoirya Coneva, Thomas J. DeWitt, Alexander G. Fletcher, Malia A. Gehan, Diego Hernan Diaz-Martinez, Lilan Hong, Anjali S. Iyer-Pascuzzi, Laura L. Klein, Samuel Leiboff, Mao Li, Jonathan P. Lynch, Alexis Maizel, Julin N. Maloof, R. J. Cody Markelz, Ciera C. Martinez, Laura A. Miller, Washington Mio, Wojtek Palubicki, Hendrik Poorter, Christophe Pradal, Charles A. Price, Eetu Puttonen, John B. Reese, Rubén Rellán-Álvarez, Edgar P. Spalding, Erin E. Sparks, Christopher N. Topp, Joseph H. Williams, and Daniel H. Chitwood

Subjects
plant biology, plant science, morphology, mathematics, topology, modeling, Plant culture, SB1-1110
Abstract

The geometries and topologies of leaves, flowers, roots, shoots, and their arrangements have fascinated plant biologists and mathematicians alike. As such, plant morphology is inherently mathematical in that it describes plant form and architecture with geometrical and topological techniques. Gaining an understanding of how to modify plant morphology, through molecular biology and breeding, aided by a mathematical perspective, is critical to improving agriculture, and the monitoring of ecosystems is vital to modeling a future with fewer natural resources. In this white paper, we begin with an overview in quantifying the form of plants and mathematical models of patterning in plants. We then explore the fundamental challenges that remain unanswered concerning plant morphology, from the barriers preventing the prediction of phenotype from genotype to modeling the movement of leaves in air streams. We end with a discussion concerning the education of plant morphology synthesizing biological and mathematical approaches and ways to facilitate research advances through outreach, cross-disciplinary training, and open science. Unleashing the potential of geometric and topological approaches in the plant sciences promises to transform our understanding of both plants and mathematics.

Published
2017
Full Text
View/download PDF

11. A root functional–structural model allows assessment of the effects of water deficit on water and solute transport parameters

Author

Fabrice Bauget, Virginia Protto, Christophe Pradal, Yann Boursiac, and Christophe Maurel

Subjects
Physiology, Plant Science
Abstract

Root water uptake is driven by a combination of hydrostatic and osmotic forces. Water transport was characterized in primary roots of maize seedlings grown hydroponically under standard and water deficit (WD) conditions, as induced by addition of 150 g l–1 polyethylene glycol 8000 (water potential= –0.336 MPa). Flow measurements were performed using the pressure chamber technique in intact roots or on progressively cut root system architectures. To account for the concomitant transport of water and solutes in roots under WD, we developed within realistic root system architectures a hydraulic tree model integrating both solute pumping and leak. This model explains the high spontaneous sap exudation of roots grown in standard conditions, the non-linearity of pressure–flow relationships, and negative fluxes observed under WD conditions at low external hydrostatic pressure. The model also reveals the heterogeneity of driving forces and elementary radial flows throughout the root system architecture, and how this heterogeneity depends on both plant treatment and water transport mode. The full set of flow measurement data obtained from individual roots grown under standard or WD conditions was used in an inverse modeling approach to determine their respective radial and axial hydraulic conductivities. This approach allows resolution of the dramatic effects of WD on these two components.

Published
2022

12. Spatio-temporal analysis of strawberry architecture: insights into the control of branching and inflorescence complexity

Author

Marc Labadie, Karine Guy, Marie-Noëlle Demené, Yves Caraglio, Gaetan Heidsieck, Amelia Gaston, Christophe Rothan, Yann Guédon, Christophe Pradal, Béatrice Denoyes, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Université de Montpellier (UM), Biologie du fruit et pathologie (BFP), Université de Bordeaux (UB)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Invenio, Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université de Montpellier (UM), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Scientific Data Management (ZENITH), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)

Subjects
plant architecture, Physiology, OpenAlea, [SDV]Life Sciences [q-bio], branching, multiscale tree graph, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Plant Science, Fragaria × ananassa, hidden hybrid Markov/semi-Markov chain
Abstract

Plant architecture plays a major role in flowering and therefore in crop yield. Attempts to visualize and analyse strawberry plant architecture have been few to date. Here, we developed open-source software combining two- and three-dimensional representations of plant development over time along with statistical methods to explore the variability in spatio-temporal development of plant architecture in cultivated strawberry. We applied this software to six seasonal strawberry varieties whose plants were exhaustively described monthly at the node scale. Results showed that the architectural pattern of the strawberry plant is characterized by a decrease of the module complexity between the zeroth-order module (primary crown) and higher-order modules (lateral branch crowns and extension crowns). Furthermore, for each variety, we could identify traits with a central role in determining yield, such as date of appearance and number of branches. By modeling the spatial organization of axillary meristem fate on the zeroth-order module using a hidden hybrid Markov/semi-Markov mathematical model, we further identified three zones with different probabilities of production of branch crowns, dormant buds, or stolons. This open-source software will be of value to the scientific community and breeders in studying the influence of environmental and genetic cues on strawberry architecture and yield.

Published
2023

14. Modelling the impact of proportion, sowing date, and architectural traits of a companion crop on foliar fungal pathogens of wheat in crop mixtures

Author

Sébastien Levionnois, Christophe Pradal, Christian Fournier, Jonathan Sanner, and Corinne Robert

Subjects
Plant Science, Agronomy and Crop Science
Abstract

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-vs.-companion proportion, companion sowing date and growth, and architectural traits. For both pathogens, the companion proportion had the strongest effect, with decreasing 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.

Published
2023

15. Towards smart and sustainable development of modern berry cultivars in Europe

Author

Elisa Senger, Sonia Osorio, Klaus Olbricht, Paul Shaw, Béatrice Denoyes, Jahn Davik, Stefano Predieri, Saila Karhu, Sebastian Raubach, Nico Lippi, Monika Höfer, Helen Cockerton, Christophe Pradal, Ebru Kafkas, Suzanne Litthauer, Iraida Amaya, Björn Usadel, Bruno Mezzetti, Forschungszentrum Jülich GmbH | Centre de recherche de Juliers, Helmholtz-Gemeinschaft = Helmholtz Association, Instituto de Hortofruticultura Subtropical y Mediterranea 'La Mayora' (IHSM), Universidad de Málaga [Málaga] = University of Málaga [Málaga]-Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Hansabred [Dresden], The James Hutton Institute, Biologie du fruit et pathologie (BFP), Université de Bordeaux (UB)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Norsk institutt for bioøkonomi=Norwegian Institute of Bioeconomy Research (NIBIO), Institute for BioEconomy [Sesto Fiorentino] (IBE | CNR), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), Natural Resources Institute Finland (LUKE), Julius Kühn-Institut - Federal Research Centre for Cultivated Plants (JKI), National Institute of Agricultural Botany (NIAB), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Université de Montpellier (UM), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Scientific Data Management (ZENITH), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Cukurova University, Heinrich Heine Universität Düsseldorf = Heinrich Heine University [Düsseldorf], Università Politecnica delle Marche [Ancona] (UNIVPM), and The authors acknowledge support from the European Union’s Horizon 2020 research and innovation program (grant agreement ID: 101000747)

Subjects
consumer preference, [SDV]Life Sciences [q-bio], métadonnées, plant genetic resources, Plant Science, Fragaria, F30 - Génétique et amélioration des plantes, génomique, Ressource génétique végétale, image analysis, Genetics, genomics, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Humans, amélioration génétique, QK, BreedingValue project, trait/genotype association, Cell Biology, Sustainable Development, metabolomics, Amélioration des plantes, Petits fruits, Propriété organoleptique, [SDV.BV.AP]Life Sciences [q-bio]/Vegetal Biology/Plant breeding, Valeur génétique, Plant Breeding, ddc:580, berry breeding, Fruit, Rubus, Vaccinium, Génotype, Genome-Wide Association Study
Abstract

International audience; Fresh berries are a popular and important component of the human diet. The demand for high-quality berries and sustainable production methods is increasing globally, challenging breeders to develop modern berry cultivars that fulfill all desired characteristics. Since 1994, research projects have characterized genetic resources, developed modern tools for high-throughput screening, and published data in publicly available repositories. However, the key findings of different disciplines are rarely linked together and only a limited range of traits and genotypes has been investigated. The Horizon2020 project BreedingValue will address these challenges by studying a broader panel of strawberry, raspberry and blueberry genotypes in detail, in order to recover the lost genetic diversity that has limited the aroma and flavor intensity of recent cultivars. We will combine metabolic analysis with sensory panel tests and surveys to identify the key components of taste, flavor and aroma in berries across Europe, leading to a high-resolution map of quality requirements for future berry cultivars. Traits linked to berry yields and the effect of environmental stress will be investigated using modern image analysis methods and modeling. We will also use genetic analysis to determine the genetic basis of complex traits for the development and optimization of modern breeding technologies such as molecular marker arrays, genomic selection and genome wide association studies. Finally, the results, raw data and metadata will be made publicly available on the open platform Germinate in order to meet FAIR data principles and provide the basis for sustainable research in the future.

Published
2022

16. Simulating the effects of water limitation on plant biomass using a 3D functional-structural plant model of shoot and root driven by soil hydraulics

Author

Renato K. Braghiere, Loïc Pagès, Frédéric Gérard, Christophe Pradal, Jochem B. Evers, Ecologie fonctionnelle et biogéochimie des sols et des agro-écosystèmes (UMR Eco&Sols), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), California Institute of Technology (CALTECH), University of California [Los Angeles] (UCLA), University of California (UC), Wageningen University and Research [Wageningen] (WUR), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Unité de recherche Plantes et Systèmes de Culture Horticoles (PSH), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), contract with the National Aeronautics and Space Administration (80NM0018D0004)., European Project: 727217,ReMIX(2017), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), University of California, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM)

Subjects
0106 biological sciences, 010504 meteorology & atmospheric sciences, Biomass, GroIMP, F62 - Physiologie végétale - Croissance et développement, Déficit hydrique du sol, Plant Science, Root system, Plant Roots, 01 natural sciences, Soil, santé des plantes, Water content, media_common, Transpiration, water deficit, 2. Zero hunger, U10 - Informatique, mathématiques et statistiques, ArchiSimple, Propriété hydraulique du sol, PE&RC, soil-plant interactions, Physiologie végétale, Droughts, Crop and Weed Ecology, Plant Shoots, water uptake, Stomatal conductance, Relation plante sol, media_common.quotation_subject, Biology, Competition (biology), Culture intercalaire, functional-structural plant models, Min3P, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, Absorption d'eau, 0105 earth and related environmental sciences, photosynthesis, Water, Original Articles, Interspecific competition, 15. Life on land, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Plant Leaves, Agronomy, Modélisation, Soil water, soil modelling, intercropping, 010606 plant biology & botany
Abstract

Background and AimsImproved modelling of carbon assimilation and plant growth to low soil moisture requires evaluation of underlying mechanisms in the soil, roots, and shoots. The feedback between plants and their local environment throughout the whole spectrum soil-root-shoot-environment is crucial to accurately describe and evaluate the impact of environmental changes on plant development. This study presents a 3D functional structural plant model, in which shoot and root growth are driven by radiative transfer, photosynthesis, and soil hydrodynamics through different parameterisation schemes relating soil water deficit and carbon assimilation. The new coupled model is used to evaluate the impact of soil moisture availability on plant productivity for two different groups of flowering plants under different spatial configurations.MethodsIn order to address different aspects of plant development due to limited soil water availability, a 3D FSP model including root, shoot, and soil was constructed by linking three different well-stablished models of airborne plant, root architecture, and reactive transport in the soil. Different parameterisation schemes were used in order to integrate photosynthetic rate with root water uptake within the coupled model. The behaviour of the model was assessed on how the growth of two different types of plants, i.e. monocot and dicot, is impacted by soil water deficit under different competitive conditions: isolated (no competition), intra, and interspecific competition.Key ResultsThe model proved to be capable of simulating carbon assimilation and plant development under different growing settings including isolated monocots and dicots, intra, and interspecific competition. The model predicted that (1) soil water availability has a larger impact on photosynthesis than on carbon allocation; (2) soil water deficit has an impact on root and shoot biomass production by up to 90 % for monocots and 50 % for dicots; and (3) the improved dicot biomass production in interspecific competition was highly related to root depth and plant transpiration.ConclusionsAn integrated model of 3D shoot architecture and biomass development with a 3D root system representation, including light limitation and water uptake considering soil hydraulics, was presented. Plant-plant competition and regulation on stomatal conductance to drought were able to be predicted by the model. In the cases evaluated here, water limitation impacted plant growth almost 10 times more than the light environment.

Published
2020

18. Phenotyping and modeling of root hydraulic architecture reveal critical determinants of axial water transport

Author

Yann Boursiac, Christophe Pradal, Fabrice Bauget, Mikaël Lucas, Stathis Delivorias, Christophe Godin, Christophe Maurel, Institut des Sciences des Plantes de Montpellier (IPSIM), Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Université de Montpellier (UM), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier, Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Scientific Data Management (ZENITH), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Simulation et Analyse de la morphogenèse in siliCo (MOSAIC), Inria Lyon, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Zenith, and Mosaic

Subjects
Arabidopsis thaliana, Physiology, [SDV]Life Sciences [q-bio], Arabidopsis, Phénotype, Conductance hydraulique des racines, Plant Science, Aquaporins, Plant Roots, Relation plante eau, Système racinaire, Xylem, Genetics, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Absorption d'eau, U10 - Informatique, mathématiques et statistiques, Water, Biological Transport, Physiologie végétale, F61 - Physiologie végétale - Nutrition, Modélisation
Abstract

Water uptake by roots is a key adaptation of plants to aerial life. Water uptake depends on root system architecture (RSA) and tissue hydraulic properties that, together, shape the root hydraulic architecture. This work investigates how the interplay between conductivities along radial (e.g. aquaporins) and axial (e.g. xylem vessels) pathways determines the water transport properties of highly branched RSAs as found in adult Arabidopsis (Arabidopsis thaliana) plants. A hydraulic model named HydroRoot was developed, based on multi-scale tree graph representations of RSAs. Root water flow was measured by the pressure chamber technique after successive cuts of a same root system from the tip toward the base. HydroRoot model inversion in corresponding RSAs allowed us to concomitantly determine radial and axial conductivities, providing evidence that the latter is often overestimated by classical evaluation based on the Hagen–Poiseuille law. Organizing principles of Arabidopsis primary and lateral root growth and branching were determined and used to apply the HydroRoot model to an extended set of simulated RSAs. Sensitivity analyses revealed that water transport can be co-limited by radial and axial conductances throughout the whole RSA. The number of roots that can be sectioned (intercepted) at a given distance from the base was defined as an accessible and informative indicator of RSA. The overall set of experimental and theoretical procedures was applied to plants mutated in ESKIMO1 and previously shown to have xylem collapse. This approach will be instrumental to dissect the root water transport phenotype of plants with intricate alterations in root growth or transport functions.

Published
2022

19. Connecting plant phenotyping and modelling communities: lessons from science mapping and operational perspectives

Author

Clément Saint Cast, Guillaume Lobet, Llorenç Cabrera-Bosquet, Valentin Couvreur, Christophe Pradal, François Tardieu, Xavier Draye, Interactions Sol Plante Atmosphère (UMR ISPA), Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Earth and Life Institute [Louvain-La-Neuve] (ELI), Université Catholique de Louvain = Catholic University of Louvain (UCL), Forschungszentrum Jülich GmbH | Centre de recherche de Juliers, Helmholtz-Gemeinschaft = Helmholtz Association, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Scientific Data Management (ZENITH), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Université de Montpellier (UM), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Earth and Life Institute - Environmental Sciences (ELIE), European Project: 731013 ,EPPN2020(2017), Ecophysiologie et Génomique Fonctionnelle de la Vigne (UMR EGFV), Université de Bordeaux (UB)-Institut des Sciences de la Vigne et du Vin (ISVV)-Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), European Project: 739514,EMPHASIS-PREP, and UCL - SST/ELI/ELIA - Agronomy

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, data and model integration, F40 - Écologie végétale, [SHS.INFO]Humanities and Social Sciences/Library and information sciences, Phénotype, Analyse de réseau, Genetics and Molecular Biology (miscellaneous), Plant Science, Biochemistry, Biochemistry, Genetics and Molecular Biology (miscellaneous), Analyse de système, F30 - Génétique et amélioration des plantes, Bibliometric analyses, modelling, plant phenotyping, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Modélisation environnementale, Modélisation des cultures, imaging, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [SDV.BV.AP]Life Sciences [q-bio]/Vegetal Biology/Plant breeding, C30 - Documentation et information, Modeling and Simulation, network, Bibliométrie, Plante, U30 - Méthodes de recherche, ddc:004, Banque de données, Agronomy and Crop Science, Génotype, Modèle mathématique
Abstract

Plant phenotyping platforms generate large amounts of high-dimensional data at different scales of plant organization. The possibility to use this information as inputs of models is an opportunity to develop models that integrate new processes and genetic inputs. We assessed to what extent the phenomics and modelling communities can address the issues of interoperability and data exchange, using a science mapping approach (i.e. visualization and analysis of a broad range of scientific and technological activities as a whole). In this paper, we (i) evaluate connections, (ii) identify compatible and connectable research topics and (iii) propose strategies to facilitate connection across communities. We applied a science mapping approach based on reference and term analyses to a set of 4332 scientific papers published by the plant phenomics and modelling communities from 1980 to 2019, retrieved using the Elsevier’s Scopus database and the quantitative-plant.org website. The number of papers on phenotyping and modelling dramatically increased during the past decade, boosted by progress in phenotyping technologies and by key developments at hardware and software levels. The science mapping approach indicated a large diversity of research topics studied in each community. Despite compatibilities of research topics, the level of connection between the phenomics and modelling communities was low. Although phenomics and modelling crucially need to exchange data, the two communities appeared to be weakly connected. We encourage these communities to work on ontologies, harmonized formats, translators and connectors to facilitate transparent data exchange.

Published
2022

20. RhizoDep - A 3D root functional model to simulate rhizodeposition processes

Author

Frédéric Rees, Bruno Andrieu, Loic Pages, Romain Barillot, Céline Richard-Molard, Claire Chenu, Christophe Pradal, Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Unité de recherche Plantes et Systèmes de Culture Horticoles (PSH), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), and Christophe Pradal

Subjects
[SDV.EE]Life Sciences [q-bio]/Ecology, environment, [SDE]Environmental Sciences, [SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/Agronomy, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2020

21. VersionClimber: Version Upgrades Without Tears

Author

Patrick Valduriez, Konrad Hinsen, Dennis Shasha, Sarah Cohen-Boulakia, Matthew J. Turk, Christophe Pradal, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Bioinformatique (LRI) (BioInfo - LRI), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Courant Institute of Mathematical Sciences [New York] (CIMS), New York University [New York] (NYU), NYU System (NYU)-NYU System (NYU), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM)

Subjects
conda, General Computer Science, Computer science, Cloud computing, [INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE], 02 engineering and technology, Logiciel, computer.software_genre, Index Terms Software Engineering, Software, 0202 electrical engineering, electronic engineering, information engineering, U10 - Informatique, mathématiques et statistiques, business.industry, Versioning, General Engineering, 020207 software engineering, Informatique, software deployment, Lexicographical order, Mac OS, Software deployment, Bounded function, Operating system, business, computer, Software versioning
Abstract

International audience; VersionClimber is an automated system to help update the package and data infrastructure of a software application based on priorities that the user has indicated (e.g. I care more about having a recent version of this package than that one). The system does a systematic and heuristically efficient exploration (using bounded upward compatibility) of a version search space in a sandbox environment (Virtual Env or conda env), finally delivering a lexicographically maximum configuration based on the user-specified priority order. It works for Linux and Mac OS on the cloud.

Published
2019

22. Apple tree adaptation to shade in agroforestry: an architectural approach

Author

Pierre-Eric Lauri, Frédéric C. Do, Lydie Dufour, Christophe Pradal, Benjamin Pitchers, Agrosystèmes Biodiversifiés (UMR ABSys), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Ecologie fonctionnelle et biogéochimie des sols et des agro-écosystèmes (UMR Eco&Sols), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Scientific Data Management (ZENITH), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), This work was supported by Fondation de France for a Ph.D. student grant and partly funded by the French DEPHY-EXPE program ALTO (Systèmes en ArboricuLture et Transition agrOécologique) and the Hérault department program PIRAT (Programme Interdisciplinaire de Recherche développement en AgroforesTerie)., Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM)

Subjects
0106 biological sciences, Secondary growth, F08 - Systèmes et modes de culture, [SDV]Life Sciences [q-bio], F62 - Physiologie végétale - Croissance et développement, Plant Science, Agroforesterie, F50 - Anatomie et morphologie des plantes, 01 natural sciences, Tolérance à l'ombre, vegetative shoots, biology, Agroforestry, Phenology, floral phenology, ombre, floral initiation, Malus, Shoot, [SDE]Environmental Sciences, shade, Phénologie, Plant Shoots, plant architecture, Specific leaf area, Growing season, Apple tree, Flowers, 010603 evolutionary biology, systèmes agroforestiers, Genetics, Adaptation, Ecology, Evolution, Behavior and Systematics, ComputingMethodologies_COMPUTERGRAPHICS, Morphologie végétale, plurispecific agrosystems, shoot allometry, 15. Life on land, biology.organism_classification, Plant Leaves, Light intensity, Fruit, Malus pumila, 010606 plant biology & botany
Abstract

Correction added on 07 May 2021, after first online publication: some minor wording changes have been made in the article; International audience; PREMISE: The expression of shade adaptation traits is expected to be stronger in low light and can be detrimental to flowering and yield. Our study focused on the expression of shade adaptation traits of apple trees (Malus domestica Borkh. ‘Dalinette’) in an agroforestry system.METHODS: The architecture of 45 apple trees in their third and fourth year was extensively described and analyzed at the tree scale and compared depending on the light quantity received during the growing season. Flower cluster phenology and the relation between leaf area and floral initiation were also investigated.RESULTS: The number of growing shoots and the leaf area were reduced by shade even if specific leaf area increased with increasing shade. Shade did not modify primary growth but did decrease secondary growth, so that apple tree shoots in shade were slender, with a lower taper and reduced number and proportion of flower clusters. The correlation between floral initiation and leaf area was high both in full and moderate light but not for apple trees in low light. Shade did not impact the date of bud burst and the early phenological stages of flower clusters, but it reduced the number of days at full bloom.CONCLUSIONS: Our results suggest that while the architecture of apple trees is modified by a reduction in light intensity, it is not until a reduction of 65% that the capability to produce fruit is impeded. These results could help optimize the design of apple-tree-based agroforestry systems.

Published
2021

23. Cache-aware scheduling of scientific workflows in a multisite cloud

Author

Esther Pacitti, François Tardieu, Christophe Pradal, Gaëtan Heidsieck, Patrick Valduriez, Daniel de Oliveira, Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Universidade Federal Fluminense [Rio de Janeiro] (UFF), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, ANR-16-CONV-0004,DIGITAG,Institut Convergences en Agriculture Numérique(2016), ANR-11-INBS-0012,PHENOME,Centre français de phénomique végétale(2011), ANR-11-INBS-0013,IFB (ex Renabi-IFB),Institut français de bioinformatique(2011), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)

Subjects
Multisite cloud, Exploit, Computer Networks and Communications, Computer science, Distributed computing, Workflow System, Cloud computing, 02 engineering and technology, Reuse, Scheduling (computing), Distributed Caching, processus, Workflow Scheduling, 0202 electrical engineering, electronic engineering, information engineering, Code (cryptography), Workflow scheduling, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, [INFO]Computer Science [cs], business.industry, U10 - Informatique, mathématiques et statistiques, Scientific Workflow, 020206 networking & telecommunications, Informatique, Workflow, Hardware and Architecture, 020201 artificial intelligence & image processing, Cache, business, Software
Abstract

International audience; Many scientific experiments today are performed using scientific workflows, which become more and more data-intensive. We consider the efficient execution of such workflows in a multisite cloud, leveraging heterogeneous resources available at multiple geo-distributed data centers. Since it is common for workflow users to reuse code or data from previous workflows, a promising approach for efficient workflow execution is to cache intermediate data in order to avoid re-executing entire workflows. However, caching intermediate data and scheduling workflows to exploit such caching in a multisite cloud is complex. In particular, workflow scheduling must be cache-aware, in order to decide whether reusing cache data or re-executing workflows entirely. In this paper, we propose a solution for cache-aware scheduling of scientific workflows in a multisite cloud. Our solution includes a distributed and parallel architecture and new algorithms for adaptive caching, cache site selection, and dynamic workflow scheduling. We implemented our solution in the OpenAlea workflow system, together with cache-aware distributed scheduling algorithms. Our experimental evaluation in a three-site cloud with a real application in plant phenotyping shows that our solution can yield major performance gains, reducing total time up to 42% with 60% of the same input data for each new execution.

Published
2021

24. Root phenotyping: important and minimum information required for root modeling in crop plants

Author

Hirokazu Takahashi, Christophe Pradal, Graduate School of Bioagricultural Sciences, Nagoya University, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM)

Subjects
0106 biological sciences, 0301 basic medicine, Root (linguistics), Legacy data, legacy data, root phenotyping, Phénotype, Plant Science, Root system, Agricultural engineering, Biology, F50 - Anatomie et morphologie des plantes, 7. Clean energy, 01 natural sciences, Système racinaire, Crop, 03 medical and health sciences, F01 - Culture des plantes, Genetics, root modeling, 2. Zero hunger, root system architecture, Root morphology, Rhizosphere, Morphologie végétale, Invited Review, 15. Life on land, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, root imaging, 030104 developmental biology, Rhizosphère, Imagerie, Modélisation, Root system architecture, Plante de culture, Agronomy and Crop Science, Racine, 010606 plant biology & botany
Abstract

International audience; As plants cannot relocate, they require effective root systems for water and nutrient uptake. Root development plasticity enables plants to adapt to different environmental conditions. Research on improvements in crop root systems is limited in comparison with that in shoots as the former are difficult to image. Breeding more effective root systems is proposed as the "second green revolution". There are several recent publications on root system architecture (RSA), but the methods used to analyze the RSA have not been standardized. Here, we introduce traditional and current root-imaging methods and discuss root structure phenotyping. Some important root structures have not been standardized as roots are easily affected by rhizosphere conditions and exhibit greater plasticity than shoots; moreover, root morphology significantly varies even in the same genotype. For these reasons, it is difficult to define the ideal root systems for breeding. In this review, we introduce several types of software to analyze roots and identify important root parameters by modeling to simplify the root system characterization. These parameters can be extracted from photographs captured in the field. This modeling approach is applicable to various legacy root data stored in old or unpublished formats. Standardization of RSA data could help estimate root ideotypes.

Published
2021

25. A modelling framework for the simulation of signal transport within 3D structure: application for the simulation of within-tree variability in floral induction in apple trees

Author

Belhassine, Fares, Fumey, Damien, Christophe Pradal, Chopard, Jérôme, Costes, Evelyne, Pallas, Benoît, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), ITK [Clapiers], Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Hochschule Geisenheim University, University of Hannover, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM)

Subjects
flowering, fruit trees, architecture, signalling, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation
Abstract

Introduction - Organ development and meristem fate is partly determined by endogenous signals moving within plants. These signals (e.g. hormones, sugar…) originate from organs considered as sources (roots, leaves, seeds…) and act on meristems to trigger developmental processes such as transition toward flowering. Functional structural plant model (FSPM) are of major interest as they are based on an explicit description of plant architecture needed for simulating transports within plants. Transport or fluxes have been modeled in FSPM with a special consideration on carbon allocation (Génard et al., 2008). Approaches for simulating hormone fluxes are scarce, adapted to plant with simple architecture and usually associated with a comprehensive knowledge on the processes to be simulated (Prusinkiewicz et al., 2009); thus limiting their adaptability in various contexts. In this study, we present a generic model for simulating signal fluxes and their impact on meristem fates in complex 3D tree structure. We applied this model to the simulation of within tree variability of floral induction in the apple tree, as a first case of study. Previous experiments (Belhassine et al., 2019) showed that floral induction in meristems occurs less often in the presence of fruit whereas it is favoured by the presence of leaves. Furthermore, the influences of fruit and leaves on the floral induction of meristems depend on the distances of those organs within the tree structure. Fruit and leaves were thus assumed to be sources of inhibiting signals (possibly gibberellins from seeds) and activating signals (possibly FT protein), respectively. However, the distance at which these signals move within the structure and the relative sensitivity of the meristems to these signals are highly difficult to estimate from experimental studies, only. Material and methods - The model uses libraries from the OpenAlea platform (Pradal et al., 2008). The model runs on 3D tree architectures coded in Multiscale Tree Graphs (MTG, Godin and Caraglio, 1999). It simulates transport of signals in both acropetal and basipetal directions and a decrease in signal quantity with the distances from the emitting sources. Making use of previous formalisms developed for carbon allocation models (Reyes et al., 2020), the decrease in signal quantity depends on the distance to the emitting source and an attenuation parameter (r) modulating the effect of distances. For r values close to 0, the signal is equally distributed among organs within the structure whereas it is transported at shorter distances when r increases. The amount of signal reaching each meristem then determines its probability to switch form a developmental stage to another This fate was determined with a sigmoidal function assuming two parameters, a transition parameter (t) and a shape parameter (v). t represents the amount of signal at which the probability of the meristem to switch to another developmental stage is equal to 50% and v represents the uncertainty in SAM fate for a given amount of signal. The model was used for simulating within tree variability in floral induction in apple tree. Fruit were considered as sources of inhibiting signal and leaves as sources of activating signal. Both signals were combined to determine the occurrence of floral induction in each shoot apical meristem of the tree. The model was calibrated on an original database of apple trees subjected to either leaf or fruit removal at different scales of plant organization (shoot, branch, tree) to modify the intensity of signals as well as the distances between the remaining leaves, fruit and meristems (Belhassine et al., 2019). 3D mock-ups built from digitizing data and measured floral induction in the different parts of the trees were confronted to model outputs for estimating the best combinations of parameter values. The model was validated on trees subjected both to leaf and fruit removal and to contrasted crop load conditions, testing two different functions to combine the impact of activating and inhibiting signals. Results and Discussion - Simulations performed on simple hypothetical structures showed model consistency to simulate signal effects as well as the decrease in signal amount with distances and its distribution in branching systems. Calibrations performed on apple trees for simulating within tree variability were highly relevant (R²>0.9 and RMSE

Published
2020

26. Toward Virtual Modelling Environments using Notebooks for Phenotyping and Simulation of Plant Development

Author

Frédéric Boudon, Vaillant, Jan, Christophe Pradal, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Fonctionnement agroécologique et performances des systèmes de cultures horticoles (UPR HORTSYS), Département Performances des systèmes de production et de transformation tropicaux (Cirad-PERSYST), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Hochschule Geisenheim University, University of Hannover, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM)

Subjects
FSPM, Phenotyping, Virtual Research Environment, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Simulation, Jupyter Notebooks
Abstract

Special issue in in silico Plants; International audience; Introduction - Functional-Structural Plant Models give new opportunities to understand plant growth and interaction with their environment. In the last decade, dedicated modelling platforms allowed the creation of a multitude of models, built upon series of dedicated tools for the representation, acquisition, analysis and simulation of plant growth and functioning. Such integrative platforms usually relied on software modules built using multiple computer languages or formalisms. While some efforts have been made for the distribution of these tools over multiple operating systems, the resulting models were usually poorly documented and organized. Models were either created as scripts or scientific workflows. The visual representation of workflows gives an overview of the modelled processes. However, their reuse by non-experts was usually limited to the modification of parameters. Reproducibility was also limited due to deployment and installation complexity on new computers with different configurations. Until recently, no cross-platform packaging suite was robust enough to manage modules built with multiple languages. In a new initiative to alleviate these problems, we explored, in the context of the OpenAlea platform (Pradal et al., 2008), the use of the Jupyter framework (Kluyver et al., 2016) to create virtual research environments for plant modelling. Material and Methods – Our approach is based on Jupyter Notebooks, an increasingly popular web-based interactive computational application. Notebooks allow to formalize modelling scenarios as “computational narrative” (Perkel, 2018) by combining code with clear documentation based on the markdown language that includes illustration and mathematical formula rendering capability. Users can customize the narratives by editing cells of code and execute these cells in any order to refine operations. Notebooks can combine different modelling languages such as Python, R or Julia. Jupyter, combined with web deployment services such as Binder or Google Colaboratory, allow users to manipulate modelling narratives within web browsers while executing them on the cloud. To ease cloud deployment, OpenAlea packages are built and distributed from the Conda management system over multiple platforms. This allows building deployable virtual environments from docker images. Continuous integration services, based on Travis-CI and AppVeyor, deliver new releases on code update after automatic compilation and tests. Jupyter Notebooks have been extended to allow interaction with FSPMs. Using this approach, computational narratives for plant modelling can be formalized as notebooks and can be re-executed with an up-to-date virtual research environment by the community. Results - Illustration of this approach will be presented on two modelling pipelines. First, we developed a software pipeline to characterize apple tree architecture from point clouds acquired using terrestrial laser scanner. For this, architectural traits of each tree were extracted from their scan and their heritability was then assessed. This example illustrates standard pipelines where analyses are combined and applied on a large dataset. Scalability can be achieved using distributed computation on the cloud (Heidsieck et al., 2019). For the second illustration, we integrate L-Py, a L-systems simulation package, into Jupyter Notebooks. By providing specific widgets, different steps of the simulation can be computed and the resulting 3D structures and associated properties can be iteratively visualized and analysed. 3D visualization of complex vegetal structures through client-server protocol was an important challenge. We developed optimized methods that minimize the size of data to exchange and allow interactive visualization of complex scenes. During the design stage, interaction and responsiveness are of main importance when exploring model parameters space. For this, graphical controls are generated within notebooks to allow tuning of parameters. A use case will be presented on the simulation of a mango tree over several growing cycles. Discussion and Conclusion - Our aim was to provide tools to deliver models as notebooks to improve reproducibility and dissemination of FSPM. Documented and interactive notebooks provide interesting didactic tools to teach or test different types of FSPMs. Good code practices are however necessary to generate appropriate notebooks (Rule et al., 2019). Database of models start being constituted (https://openalea.rtfd.io/en/latest/tutorials). Such an approach can provide a standard for model documentation published with scientific papers to enhance scientific reproducibility.

Published
2020

27. Simulating rhizodeposition as a function of shoot and root interactions within a new 3D Functional-Structural Plant Model

Author

Rees, Frédéric, Barillot, Romain, Gauthier, Marion, Pagès, Loïc, Christophe Pradal, Andrieu, Bruno, Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Unité de recherche Plantes et Systèmes de Culture Horticoles (PSH), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM)

Subjects
FSPM, [SDV.EE]Life Sciences [q-bio]/Ecology, environment, exudation, ArchiSimple, CN-Wheat, wheat, carbon allocation, [SDE]Environmental Sciences, [SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/Agronomy, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology
Abstract

International audience; Introduction Rhizodeposition, i.e. the release of organic materials by roots, represents a significant portion of plant’s carbon (C) budget, ranging from 5% to 15% of net photosynthesized C (Pausch and Kuzyakov, 2018). Various rhizodeposits can be released by roots, e.g. soluble exudates, secreted mucilage, sloughed cells, or volatile organic compounds. Despite their short lifetime, some of these products have been shown to favor plant growth, e.g. by increasing water and nutrient uptake. Among rhizodeposition processes, exudation has been suggested to depend on the concentration of carbohydrates inside the roots (Personeni et al., 2007). However, rhizodeposition not only depends on the availability of C in the roots, but also on the architecture of the root system, and many have shown that rhizodeposits are more concentrated in specific areas, such as root tips. Consequently, a Functional-Structural Plant Model (FSPM) would theoretically represent the best framework for simulating the spatial and temporal dynamics of rhizodeposition, as it can describe the evolution of both the metabolism and the architecture of the plant. The objective of this work is to create such a framework by coupling a whole-plant FSPM, a 3D root architectural model, and a new model simulating rhizodeposition.Modelling approachOur strategy has been to combine the FSPM CN-Wheat (Barillot et al., 2016), which describes the main processes of C and nitrogen (N) acquisition and transformation by an individual wheat plant and the 3D growth and development of its aerial organs, with the model ArchiSimple (Pagès et al., 2014) that simulates the development of the 3D root architecture for a range of plant species, and the new model RhizoDep, which calculates a full C balance in each part of a root system in order to simulate local rhizodeposition fluxes. The complementarity of the three models is illustrated in Figure 1: i) CN-Wheat is used to calculate the amount of C allocated from the shoots to the roots, ii) ArchiSimple provides the 3D structure of the root system, and iii) RhizoDep distributes the C provided by the shoots within the 3D root system and simulates the actual growth, respiration and rhizodeposition of each root element based on C availability. The major link between the three models lies in the exchange of C between aboveground and belowground tissues, which is driven by gradients of sucrose concentration in the different compartments of the plant.Preliminary results & short-term perspectivesThe coupling of the three models has been started using the OpenAlea platform and its Multiscale Tree Graph formalism (Pradal et al., 2008). First simulations were done using the allocation of C to the roots simulated by CN-Wheat as an input to the root model based on the effective coupling of ArchiSimple and RhizoDep. These simulations show how rhizodeposition is intrinsically dependent on the architecture of the root system and on the total amount of available C. For completing the coupling, several issues still need to be tackled, e.g. how N uptake and metabolism should be spatialized in a 3D root system, how it may be regulated by local C and N availability, and how rhizodeposition can modify soil N availability. However, this modelling approach has already led to a first prototype able to simulate rhizodeposition processes on a dynamic, 3D root system that is fully integrated within the functioning of the whole plant. Its refinement will offer unique opportunities to study the possible link between rhizodeposition and the environmental factors affecting plant growth, e.g. atmospheric CO2 concentration or soil N availability.

Published
2020

28. MTG as a standard representation of plants in FSPMs

Author

Christophe Pradal, Godin, Christophe, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Reproduction et développement des plantes (RDP), École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), MOSAIC (MOSAIC), Institut FRESNEL (FRESNEL), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL)

Subjects
plant architecture, multiscale data structure, graph traversal, simulation, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, modularity
Abstract

International audience; Introduction - Two decades ago, a mathematical formalism was introduced to represent the complexity of plant branching structures at different scales with a unified approach. Using Multiscale Tree Graph (MTG), it was possible to capture the topology and the geometry of a multitude of plant species at different scales in a precise manner. However, the use of this formalism was initially dedicated to quantitative acquisition and analysis of plant branching systems. Its use in mechanistic models of plant growth has progressively emerged in various works to support multiscale modeling. In this paper, we review how the MTG formalism has been applied to models of plant development and extend it to comply fully with the requirements of Functional-Structural Plant Models (FSPM) at different scales. Methods - Formalizing plant structure representation – To represent branching, different mathematical formalisms have been proposed such as Axial Tree (Prusinkiewicz et Lindenmeyer, 90), MTG (Godin and Caraglio, 1998), which define plant multiscale topological structures as a serie of nested tree graph, and more recently a structure-ofscales (Ong and Kurth, 2012) that, inspired from MTGs, models the scales as a partially ordered sets and allows to manage an extensive range of scales. Here we review how MTGs can be embedded in the context of FSPMs. This includes the possibility to grow MTG using developmental rules expressed for instance in the formalism of L-systems, and to encode the dynamic structure at different developmental stages as a time-series. We also discuss new extensions such as the possibility to manipulate continuous representations of plant properties at different scales. Generic algorithms - Various algorithms have been designed to manipulate MTGs in a generic manner. We defined multiscale traversal (basipetal or acropetal) and user-defined visitors to model flows through plant structure at different scales, interaction with the environment as well as 3D visualization. Other algorithms are provided such as scale insertion or removal (see Figure), or a merge operator that concatenates several MTGs. Universal coding of plants - MTGs correspond to computational data structures that can be described as simple bracketed strings. This notation is generic and provides simple means to describe any plant architecture as a universal string of characters. For Multiscale Axial Tree, a restricted family of MTGs, this notation corresponds to the lstring notation in the context of L-Systems (Boudon et al. 2012). This coding strategy has also been extended to encode dynamic multiscale structures. Results and Discussion In this section, we illustrate how the formalism of MTGs can be used in different FSPM applications. These applications concern acquisition and reconstruction, transport, interaction with the environment as well as simulation of plant growth. Mapping real plants to MTG - MTG can be built either: i) from a textual description of a plant structure provided manually or automatically using digitizing techniques, ii) from root phenotyping pipelines using the RSML format, and iii) from LIDAR or image shoot phenotyping methods available in OpenAlea. Modelling multiscale transport - One important application of FSPM is the simulation of resource transport between plant components inside the plant architecture. Here, we use multiscale traversal algorithms to solve the flow of resources or hormones. Our approach is computationally efficient in memory and time, and has been applied to model shoot and root hydraulic architecture (Albasha et al., 2019), as well as source/sink carbon allocation at different scales (Reyes et al., 2019). Interaction with the environment - Plant environment is usually represented as a separated spatial data structure (eg. 1D layers, grid of voxels, octree). This spatial structure models the geometric neighborhood of each plant entity. A mapping between these two structures is maintained to allow information transfer between the spatial and the topological structures. For example, the radiative balance or water uptake in a soil is computed using the spatial structure while the results are back-projected on the topological one. Simulation of growth - The MTG formalism is the central data structure of the OpenAlea platform. Connexions with several FSPM simulation frameworks (GroIMP, L-Py or LIGNUM) have been developed by using the MTG data-structure as a unifying representation of plants. MTG growth can also be carried out in a procedural, data-driven manner, by interpolating different stages to form a temporal MTG, like in the ADEL model. Conclusion - The MTG formalism has been extended to model and simulate FSPM at different scales. A package is available in OpenAlea with full documentation (https://mtg.readthedocs.io). Web tutorials are implemented using Jupyter Notebooks and are saved as interactive and reproducible documents with specific MTG and 3D widgets (see https://bit.ly/2QaYgVJ).

Published
2020

29. What structural plant modelling and image-based phenotyping can learn from each other?

Author

Fournier, Christian, Artzet, Simon, Tardieu, François, Christophe Pradal, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Hochschule Geisenheim University, University of Hannover, Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM)

Subjects
architecture, model-oriented phenotyping, high throughput phenotyping, maize, development, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation
Abstract

International audience; Introduction - High throughput phenotyping technologies have spread rapidly in the recent years to meet the demand for phenotyping of large panels of plants, covering a large genetic diversity and a large range of environmental conditions. Image-based technology, which allows following the architectural development of plant over time, is among the most popular, due to its simplicity, to a high degree of automation of the acquisition process, and to the richness of the information acquired. The automation of the analysis process is also actively developing (Ubbens et al., 2020), which offers unprecedentedly large and detailed dataset for plant modelling and for the development of new applications. Linking phenomics and crop modelling allows for example already to integrate the genetic variability of responses of plants to the environment, and to reason which combination of alleles is desirable for different pedo-climatic conditions, for present and future climate (Tardieu et al, 2017). By design, crop models however do not capture in details the architectural development of plants, that is the core data produced by image based phenomics. Using and adapting structural (functional) plant models for the analysis of such data will potentially minimise the loss of information, improve the modelling at fine scale and provide simulation tools that can be used as new source of information for crop modelling. Our objective is to experiment such a coupling for maize architectural development, and discuss how it may affect modelling and phenotyping. Materials and Methods - Multi-view images from a large phenotyping experiment (1600 plants, 40 days) performed on the PhenoArch platform https://www6.montpellier.inra.fr/lepse/M3P/PHENOARCH) are analysed with the Phenomenal image analysis pipeline (Artzet et al., 2019), which generates, for each plant, a sequence of 3D reconstructions at different stages of development (Figure 1A). Phenomenal also allow to segment the plant into smaller components (stem and individual leaves), and extract phenotypic feature such as leaf length, leaf width and leaf angles. We first use these data to parameterise, one-time point at a time, a static structural model of maize (Fournier et al., 2012) (Figure 1B). The different time points are then used together to estimate a dynamic model of plant development as a function of temperature (ADEL-maize, Fournier et al., 1998) (Figure 1C). To evaluate the quality of the representation of these two nested levels of simplification, virtual plants are illuminated with a light model and compared for their interception efficiency in several conditions (isolated plants and self-similar canopies, under clear sky and overcast conditions). We also assess how the raw phenotypic features extracted by Phenomenal compare to those simulated by the dynamic model. Result and discussion - Each modeling step results in a high level of compression of the data, the highest level being between the raw plant and the static model (from 1A to 1B). The first compression is essentially linked to the simplification of the specification of the geometry (from voxels to meshes) and to the use of construction rules. The second compression is linked to the use of parametric models that capture the evolution of the objects with time, but with simplifications. In terms of light interception, all models yield similar value for self-similar canopy simulation. For isolated plants, the static model produces interception values similar to raw data, but the dynamic model can have up to 50% difference on interception in zenithal direction. This is explained by an over simplification of the leaf reorientation patterns. Fitting the dynamic model allows to improve the different measurements of plant organs (lengths, width, ..). This is explained by the compilation of the repetition of the measurements over time. The dynamic model allows to get temporal leaf tracking and to identify artifacts in the segmentations. Combining raw data and leaf tracking finally allows to extract dynamics patterns of development, including the sequence of leaf reorientation, that could be used for modelling. Conclusions - Fitting a structural plant model to elaborated phenotyping data acquired in a platform was beneficial both for model improvement and data analysis. The improved model more closely matches the interception efficiency of observations and has more robust parameterisations. Data analysis was enriched with dynamical features and benefits from averaging of repeated measurements. As a result, we obtain a fully parameterised structural model for hundreds of genotypes. Foreseen application of such a model range from multi-genotype analysis of plant development, use in interpretation of phenotyping data in the field and ideotyping.

Published
2020

30. Co-simulation with OpenAlea and GroIMP for cross-platform functional-structural plant modelling

Author

Long, Qinqin, Christophe Pradal, Kurth, Winfried, Department Ecoinformatics, Biometrics and Forest Growth [Göttingen Univ], Georg-August-University [Göttingen], Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Hochschule Geisenheim University, University of Hannover, ANR-14-CE35-0017,FSPM Apple,Multiscale functional-structural plant modelling at the example of apple trees(2014), Georg-August-University = Georg-August-Universität Göttingen, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM)

Subjects
component-based, Openalea, interfacing, GroIMP, data exchange, interoperability, model connection, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation
Abstract

International audience; Introduction - Within the FSPM community, different teams of researchers have specialized on different processes. Thus there is an increasing wish to re-use the diverse simulation packages which were already created but which are usually implemented within different software environments, often not directly compatible with each other. The OpenAlea platform (Pradal et al., 2008) was developed as an environment to connect and reuse components with specific functionality in a scientific workflow environment. However, not all widely-used FSPM-related tools are already available from OpenAlea. In our work, we created an interface between OpenAlea and the FSPM platform GroIMP (Kniemeyer, 2008). The latter contains some dedicated tools, among them a simulator for light distribution and interception, based on stochastic path tracing. This radiation model is interesting due to its accuracy, its spectral capabilities and because it is already used in different applications. To demonstrate the technical usability of our interface, we took an established simulator for the growth and structural development of apple trees, MAppleT (Costes et al., 2008), which is already accessible from OpenAlea but which does not include a radiation model on its own. By exporting the generated tree structures from MAppleT via OpenAlea to GroIMP, we were able to employ GroIMP's light model on them and to reimport the structures with added information on "absorbed light" at phytomer level. Within OpenAlea, photosynthesis was then calculated and tentatively assumed effects on organ sizes could be visualized. Our conceptual contributions are a generic web architecture and the bidirectional matching between two different multiscale formalisms for topology and geometry in FSPMs. OpenAlea - OpenAlea emphasizes modularity and reuse by using a central data structure, the MTG (Godin and Caraglio, 1998). This enables indirect communication between the components that are integrated in the platform, using a blackboard architecture. It captures the multiscale organization of plant canopies, particularly its topology. Various properties can also be stored at the different scales. MTG vertices are topological elements that represent modular parts of a plant (e.g., axis, phytomer, organ). The neighborhood of each element is stored in the MTG as well as its associated properties. Geometrical elements are stored separately in an external scene graph for efficiency but are available from a property of the MTG. GroIMP - In GroIMP, a scene, including virtual plants, is represented as a rooted graph which can be an MTG in the sense of Godin and Caraglio (1998). At the same time, it has the semantics of a scene graph (a well-known data model in computer graphics). In contrast to the MTG in OpenAlea, it contains all information about the scene including geometry. Its nodes can represent geometrical objects (e.g., standing for plant organs), light sources, spatial transformations (e.g., rotations), or they are abstract nodes used purely for replacement purposes during development. The development of scenes, including plants, is modelled by parallel graph rewriting: Rules are applied by substituting in every timestep all instances of graphs which occur as left-hand side of a rule by the corresponding right-hand side. L-systems, operating on strings, can be subsumed as special cases under this formalism. The Interface - Although the data models of OpenAlea and GroIMP were both derived from the same mathematical concept, the implemented data structures of both platforms differ in several aspects. To bridge the gap between them, a data extractor from OpenAlea to GroIMP has first to combine the topological (MTG) with the geometrical information and to build a scene graph where the global positional information of each object is split into the transformation matrices of its predecessors (in the graph) and of itself. Furthermore, the scale information, represented by an indexing of nodes in OpenAlea, must be evaluated to build decomposition edges between all node pairs where a direct "is-part-of" relationship shall exist in the GroIMP graph. An extractor for the reverse data flow, from GroIMP to OpenAlea, faces another problem: since the GroIMP graph can contain cycles in the general case, a spanning tree has first to be derived within each scale level to be able to form a valid MTG on OpenAlea. Our graph model for data exchange is a canonical data model that makes the interoperability infrastructure independent from any specific FSPMs. It is a rooted, directed graph with typed nodes and thus more generic than an MTG. Technically, our connecting software tool consists of a client-side interface on top of OpenAlea and a server-side interface on top of GroIMP. An XML based data exchange format called XEG specified from the generic data exchange graph model is provided for the integration. Details are given by Long (2019). Results and Discussion - In a case study, we have applied our interface to provide an integration of the MAppleT model (Costes et al., 2008), that simulates apple tree growth and development based on stochastics and biomechanics and which is accessible via OpenAlea, with a light interception model based on stochastic pathtracing implemented within GroIMP. The objective was to get a bi-platform FSPM that simulates growth by taking local light interception into account. The workflow is as follows: Through the client-side interface the MTG generated by MAppleT is translated to an XEG graph, which is then packed to a message for transmission to the light interception model which resides remotely (on GroIMP). Through the server-side interface, the message is received, unpacked and translated into a GroIMP graph, forming the input of the light interception model. Then, update rules are applied which change a property "absorbed light" of nodes representing geometrical objects, according to the raytracing results. Through the server-side interface, the result is translated to a data frame in XEG packed to be sent back to OpenAlea (respectively, MAppleT) to complete the cross-platform simulation. Through the client-side interface, the data is unpacked and translated to Open- Alea as an MTG. Here, as growth in MAppleT is originally not based on light, we have as a first attempt applied an ad-hoc computation of biomass based directly on the intercepted light. The growth of an apple fruit then depends on the new biomass and thus on the light values from GroIMP. Botanically, this scenario is certainly not realistic since it disregards any translocation of assimilates, but it proves technical usability of the interface. Acknowledgements - Parts of this work were funded by DFG and ANR in the joint project "Multiscale functional-structural plant modelling at the example of apple trees", DFG grant number KU 847/11-1.

Published
2020

31. Distributed caching of scientific workflows in multisite cloud

Author

François Tardieu, Daniel de Oliveira, Christophe Pradal, Patrick Valduriez, Esther Pacitti, Gaëtan Heidsieck, Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Universidade Federal Fluminense [Rio de Janeiro] (UFF), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), France Grille Scientific Interest Group, Hartmann S., Küng J., Kotsis G., Tjoa A.M., Khalil I., HPDASC, ANR-11-INBS-0012,PHENOME,Centre français de phénomique végétale(2011), ANR-11-INBS-0013,IFB (ex Renabi-IFB),Institut français de bioinformatique(2011), ANR-16-CONV-0004,DIGITAG,Institut Convergences en Agriculture Numérique(2016), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro

Subjects
Multisite cloud, 050101 languages & linguistics, Computer science, Distributed computing, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], Cloud computing, 02 engineering and technology, Reuse, 0202 electrical engineering, electronic engineering, information engineering, Code (cryptography), 0501 psychology and cognitive sciences, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Workflow scheduling, business.industry, 05 social sciences, Scientific workflow, Distributed caching, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [SDV.BV.AP]Life Sciences [q-bio]/Vegetal Biology/Plant breeding, Workflow, 020201 artificial intelligence & image processing, Cache, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Workflow system, business
Abstract

Best Paper Award; International audience; Many scientific experiments are performed using scientific workflows, which are becoming more and more data-intensive. We consider the efficient execution of such workflows in the cloud, leveraging the heterogeneous resources available at multiple cloud sites (geo-distributed data centers). Since it is common for workflow users to reuse code or data from other workflows, a promising approach for efficient workflow execution is to cache intermediate data in order to avoid re-executing entire workflows. In this paper, we propose a solution for distributed caching of scientific workflows in a multisite cloud. We implemented our solution in the OpenAlea workflow system, together with cache-aware distributed scheduling algorithms. Our experimental evaluation on a three-site cloud with a data-intensive application in plant phenotyping shows that our solution can yield major performance gains, reducing total time up to 42% with 60% of same input data for each new execution.

Published
2020

32. Reuse of process-based models: Automatic transformation into many programming languages and simulation platforms

Author

Ioannis N. Athanasiadis, Christophe Pradal, Cyrille Ahmed Midingoyi, Frédérick Garcia, Cheryl Porter, Peter J. Thorburn, Pierre Martre, Helene Raynal, Gerrit Hoogenboom, Marcello Donatelli, Davide Fumagalli, Dean Holzworth, Andreas Enders, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Wageningen University and Research [Wageningen] (WUR), Consiglio per la Ricerca in Agricoltura e l’analisi dell’economia agraria (CREA), Institut für Nutzpflanzenwissenschaften und Ressourcenschutz (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, European Commission - Joint Research Centre [Ispra] (JRC), JRC Institute for Environment and Sustainability (IES), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), University of Florida [Gainesville] (UF), AGroécologie, Innovations, teRritoires (AGIR), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), ANR-16-CONV-0004,DIGITAG,Institut Convergences en Agriculture Numérique(2016), European Project: 817617,H2020,IPM Decisions(2019), European Project: 810775,DRAGON, Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Consiglio per la Ricerca in Agricoltura e l’analisi dell’economia agraria = Council for Agricultural Research and Economics (CREA), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRAE), and Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Source code, 010504 meteorology & atmospheric sciences, Java, Computer science, media_common.quotation_subject, Plant Science, Reuse, computer.software_genre, 01 natural sciences, Biochemistry, Genetics and Molecular Biology (miscellaneous), Laboratory of Geo-information Science and Remote Sensing, F01 - Culture des plantes, Laboratorium voor Geo-informatiekunde en Remote Sensing, Crop model, 0105 earth and related environmental sciences, media_common, Reusability, computer.programming_language, business.industry, Programming language, U10 - Informatique, mathématiques et statistiques, Modélisation des cultures, model reuse, source transformation, Modèle de simulation, 04 agricultural and veterinary sciences, Modular design, Python (programming language), PE&RC, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, software reuse, transpiler, Modeling and Simulation, 040103 agronomy & agriculture, Programming paradigm, 0401 agriculture, forestry, and fisheries, Software design, business, Agronomy and Crop Science, computer
Abstract

The diversity of plant and crop process-based modelling platforms in terms of implementation language, software design and architectural constraints limits the reusability of the model components outside the platform in which they were originally developed, making model reuse a persistent issue. To facilitate the intercomparison and improvement of process-based models and the exchange of model components, several groups in the field joined to create the Agricultural Model Exchange Initiative (AMEI). Agricultural Model Exchange Initiative proposes a centralized framework for exchanging and reusing model components. It provides a modular and declarative approach to describe the specification of unit models and their composition. A model algorithm is associated with each model specification, which implements its mathematical behaviour. This paper focuses on the expression of the model algorithm independently of the platform specificities, and how the model algorithm can be seamlessly integrated into different platforms. We define CyML, a Cython-derived language with minimum specifications to implement model component algorithms. We also propose CyMLT, an extensible source-to-source transformation system that transforms CyML source code into different target languages such as Fortran, C#, C++, Java and Python, and into different programming paradigms. CyMLT is also able to generate model components to target modelling platforms such as DSSAT, BioMA, Record, SIMPLACE and OpenAlea. We demonstrate our reuse approach with a simple unit model and the capacity to extend CyMLT with other languages and platforms. The approach we present here will help to improve the reproducibility, exchange and reuse of process-based models.

Published
2020

33. iCROPM 2020: Crop Modeling for the Future

Author

Christophe Pradal, Pierre Martre, Eric Justes, Franck Ewert, Marie Launay, Gerrit Hoogenboom, Senthold Asseng, University of Florida [Gainesville] (UF), Département Performances des systèmes de production et de transformation tropicaux (Cirad-PERSYST), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Agroclim (AGROCLIM), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), University of Bonn, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Technical University of Munich (TUM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Universität Bonn = University of Bonn, and Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, 010504 meteorology & atmospheric sciences, Computer science, 01 natural sciences, Crop, F01 - Culture des plantes, Genetics, Climate change, Model development, Environmental impact assessment, Cropping system, Modélisation environnementale, 0105 earth and related environmental sciences, U10 - Informatique, mathématiques et statistiques, Modélisation des cultures, business.industry, Environmental resource management, 04 agricultural and veterinary sciences, food security, Private sector, simulation, sustainability, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Outreach, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Animal Science and Zoology, business, Agronomy and Crop Science
Abstract

During the past decade, the interest in using crop models for research, education, extension, outreach and in the private sector has rapidly increased. The iCROPM 2020 Symposium entitled ‘Crop Modeling for the Future’, held in February 2020, therefore, provided a great opportunity for over 400 scientists from 50 different countries to exchange information on crop model development, evaluation with experimental data and implementation. A key outcome was the understanding that crop models simulate the dynamics of the soil-plant-atmosphere continuum. Thus, the models can be used for a quantitative and multi-criteria assessment of cropping system functioning, agronomic performance and environmental impact. This should result in more practical applications that can provide actionable information for stakeholders.

Published
2020

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

Author

Christophe Pradal, Romain Barillot, Camille Chambon, Anne Schneider, Bruno Andrieu, Christian Fournier, Marion Gauthier, Corinne Robert, Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), ITK [Clapiers], Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Université d'Angers (UA), Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), CIFRE convention between ITK and ANRT, Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM)

Subjects
0106 biological sciences, Physiology, F62 - Physiologie végétale - Croissance et développement, Plant Science, 01 natural sciences, nitrogen, Interactions biologiques, Shoot morphogenesis, 2. Zero hunger, Component (thermodynamics), U10 - Informatique, mathématiques et statistiques, Shoot, leaf growth, [SDE]Environmental Sciences, Biological system, Modèle mathématique, plant architecture, Scale (ratio), grass, Triticum aestivum, morphogenesis, Biology, Poaceae, Models, Biological, 010603 evolutionary biology, Métabolisme, Time schedule, Morphogénèse, Computer Simulation, Croissance, blé d'hiver, Plant morphogenesis, Modèle de simulation, 15. Life on land, Carbon, Models, Structural, Plant Leaves, Metabolic regulation, plasticity, functional–structural plant modelling, metabolism, 010606 plant biology & botany
Abstract

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.

Published
2020

35. A 3D architectural model of grass shoot morphogenesis and plasticity, driven by organ metabolite concentrations and coordination rules

Author

Marion Gauthier, Romain Barillot, Anne Schneider, Camille Chambon, Christian Fournier, Christophe Pradal, Bruno Andrieu, Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), ITK [Clapiers], Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut de Recherche en Horticulture et Semences (IRHS), Université d'Angers (UA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-INSTITUT AGRO Agrocampus Ouest, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Hochschule Geisenheim University, University of Hannover, Université d'Angers (UA)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM)

Subjects
carbon, plasticity, leaf growth, fungi, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, food and beverages, metabolism, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, nitrogen
Abstract

Introduction - Phenotypic plasticity - the ability of one genotype to produce different phenotypes depending on growth conditions - is a core aspect of the interactions between plants and their environment. For instance, leaf traits define the ability of plants to capture light, as well as their exposition and responses to various signals and stresses. In turn, leaf traits such as dimensions, composition and mass are highly regulated by growth conditions. The explicit description of shoot architecture in functional-structural models (FSPM) open new possibilities to express these feedback loops, which regulate plant fitness and productivity. However, formalizing into models the processes that build the plastic responses of traits to growth conditions is a major bottleneck to date. Most FSPMs that address the coupling between resources availability and growth consider only carbon (C) and drive resource allocation by sink priorities defined from empirical relations. Besides, the determinism of traits such as the areal density, which links mass growth with dimension growth, are poorly understood, so that these traits are frequently approximated as constant, while they have been shown to vary widely with growth conditions. Finally, the lack of process-based formalisms of existing models impairs our ability to simulate morphogenesis under contrasting growth conditions. As a step toward more mechanistic approaches to simulate shoot morphogenesis, we propose a plantscale FSPM of C and nitrogen (N) economy of the growing grass in which the morphogenesis is fully integrated with the plant metabolism. Model description - The model represents the plant as a collection of tillers made of several growing and mature shoot phytomers (identifying lamina, sheath and internode mature tissues and growth zones), a single roots compartment and a shared pool mimicking the phloem. Each compartment has a structural mass and concentrations in mobile and storage metabolites. The plant is seen as a self-regulated system which relies on two processes: i) local C and N concentrations in the growth zones drive organ elongation rate, specific structural mass and width, ii) coordination rules link the timing of extension of the organs between successive phytomers. These processes were implemented into CN-Wheat (Barillot et al., 2016ab), a detailed FSPM of C and N metabolism previously developed for a culm with a static architecture. The new version of CN-Wheat presented here is the coupling of (i) a model of leaf, internode and root growth, (ii) a dynamic representation of the 3D geometry of plants which builds on the ADEL-Wheat model (Fournier et al., 2003), (iii) a model of light distribution (Chelle and Andrieu, 1998), and finally (iv) a model simulating photosynthesis, N acquisition, synthesis and allocation of C and N metabolites, and senescence at the organ level, which extends the initial version of CN-Wheat. All above mentionned processes are regulated, at organ scale, by the environmental conditions and the concentrations in C and N metabolites. Results and Discussion - The model was calibrated for wheat during the vegetative stages (from seedling emergence to the beginning of stem extension). First, the model was evaluated in field conditions representative of North-Western Europe, which resulted in realistic patterns of leaf dimensions, extension and senescence dynamics, organ mass and composition. A key result was the ability of the model to simulate, as emerging properties, key plant and agronomic traits e.g. the phyllochron, shoot/roots ratio dynamic, N dilution and radiation use efficiency. Then, we evaluated the model's ability to simulate plant plasticity under different scenarios of plant density, incoming light and soil nitrate concentration. The model simulated realistic responses of leaf traits such as dimensions, specific leaf area and specific leaf N; the figure illustrates the simulated variations of lamina dimensions and N vertical gradient with N resources. Conclusion - Our model is innovative by the high level of explicitation of the processes underlying shoot morphogenesis, which provides new possibilities to link the phenotypic plasticity of plants to their C and N metabolic status at the place and time where traits are built. The approach could be extended to include other factors involved in plasticity, such as hormones or direct responses to the environment perceived. The genericity of the modelling frame makes it also applicable to explore shoot morphogenesis in other grass species.

Published
2020

36. Efficient execution of scientific workflows in the cloud through adaptive caching

Author

Christophe Pradal, Esther Pacitti, Patrick Valduriez, François Tardieu, Daniel de Oliveira, and Gaëtan Heidsieck

Subjects
0106 biological sciences, Exploit, Computer science, Distributed computing, Cloud computing, Application des ordinateurs, 02 engineering and technology, Reuse, 01 natural sciences, information, Projet de recherche, 020204 information systems, Factor (programming language), 0202 electrical engineering, electronic engineering, information engineering, Architecture, computer.programming_language, Adaptabilité, business.industry, Task (computing), Workflow, Diffusion de l'information, Cache, U30 - Méthodes de recherche, business, computer, données ouvertes, 010606 plant biology & botany
Abstract

The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing (e.g., computing resources, services, metadata, data sources) across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. This, the 44th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains six fully revised and extended papers selected from the 35th conference on Data Management – Principles, Technologies and Applications, BDA 2019. The topics covered include big data, graph data streams, workflow execution in the cloud, privacy in crowdsourcing, secure distributed computing, machine learning, and data mining for recommendation systems.

Published
2020

37. Scientific workflows for computational reproducibility in the life sciences: Status, challenges and opportunities

Author

Olivier Collin, Sarah Cohen-Boulakia, Konrad Hinsen, Alban Gaignard, Khalid Belhajjame, Pierre Larmande, Fabien Mareuil, Christine Froidevaux, Frédéric Lemoine, Christophe Blanchet, Jérôme Chopard, Christophe Pradal, Hervé Ménager, Yvan Le Bras, Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision (LAMSADE), Université Paris Dauphine-PSL, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Plateforme bioinformatique GenOuest [Rennes], Université de Rennes (UR)-Plateforme Génomique Santé Biogenouest®-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Mathématiques, Informatique et STatistique pour l'Environnement et l'Agronomie (MISTEA), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Centre hospitalier universitaire de Nantes (CHU Nantes), Synchrotron SOLEIL (SSOLEIL), Centre National de la Recherche Scientifique (CNRS), Centre de biophysique moléculaire (CBM), Université d'Orléans (UO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Diversité, adaptation, développement des plantes (UMR DIADE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Centre d'Informatique pour la Biologie, Institut Pasteur [Paris] (IP), Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Modeling plant morphogenesis at different scales, from genes to phenotype (VIRTUAL PLANTS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA)-Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut Français de Bioinformatique - UMS CNRS 3601 (IFB-CORE), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), The authors acknowledge the support of GDR CNRS MaDICS, pro-gramme CPER Region Bretagne 'CeSGO', and programme Region Paysde la Loire 'Connect Talent' (SyMeTRIC). We acknowledge funding by thecall Infrastructures in Biology and Health' in the framework of the FrenchInvestments for the Future' (ANR-11-INBS-0012 and ANR-11-INBS-0013).This work was conducted in part at the IBC (Institute of Computational Bi-ology) in Montpellier, France., ANR-11-INBS-0013,IFB (ex Renabi-IFB),Institut français de bioinformatique(2011), ANR-11-INBS-0011,NeurATRIS,Infrastructure de Recherche Translationnelle pour les Biothérapies en Neurosciences(2011), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), CentraleSupélec-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Plateforme Génomique Santé Biogenouest®-Inria Rennes – Bretagne Atlantique, Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA), Université d'Orléans (UO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC), Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Institut Pasteur [Paris], Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Université Paris Dauphine-PSL-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Inria Sophia Antipolis - Méditerranée (CRISAM), Université d'Orléans (UO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA), ANR-11-INBS-0013/11-INBS-0013,ReNaBi-IFB,Institut français de bioinformatique(2011), ANR-11-INBS-0011/11-INBS-0011,NeurATRIS,Infrastructure de Recherche Translationnelle pour les Biothérapies en Neurosciences(2011), Cohen-Boulakia, Sarah, Infrastructures - Institut français de bioinformatique - - IFB (ex Renabi-IFB)2011 - ANR-11-INBS-0013 - INBS - VALID, and Infrastructures - Infrastructure de Recherche Translationnelle pour les Biothérapies en Neurosciences - - NeurATRIS2011 - ANR-11-INBS-0011 - INBS - VALID

Subjects
0301 basic medicine, Scientific Workflows, Computer Networks and Communications, Computer science, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], [INFO.INFO-DS] Computer Science [cs]/Data Structures and Algorithms [cs.DS], Context (language use), 02 engineering and technology, Domain (software engineering), 03 medical and health sciences, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB], Packaging environments, Use case, Set (psychology), environments, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], Reproducibility, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Data science, 030104 developmental biology, Workflow, C30 - Documentation et information, Packaging, 13. Climate action, Hardware and Architecture, Scientific workflows, Provenance, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], 020201 artificial intelligence & image processing, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], U30 - Méthodes de recherche, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Software
Abstract

International audience; With the development of new experimental technologies, biologists are faced with an avalanche of data to be computationally analyzed for scientific advancements and discoveries to emerge. Faced with the complexity of analysis pipelines, the large number of computational tools, and the enormous amount of data to manage, there is compelling evidence that many if not most scientific discoveries will not stand the test of time: increasing the reproducibility of computed results is of paramount importance. The objective we set out in this paper is to place scientific workflows in the context of reproducibility. To do so, we define several kinds of repro-ducibility that can be reached when scientific workflows are used to perform experiments. We characterize and define the criteria that need to be catered for by reproducibility-friendly scientific workflow systems, and use such criteria to place several representative and widely used workflow systems and companion tools within such a framework. We also discuss the remaining challenges posed by reproducible scientific workflows in the life sciences. Our study was guided by three use cases from the life science domain involving in silico experiments.

Published
2017

38. InfraPhenoGrid: A scientific workflow infrastructure for plant phenomics on the Grid

Author

Simon Artzet, Christian Fournier, Vincent Negre, Michael Mielewczik, Christophe Pradal, Patrick Valduriez, Jérôme Chopard, Sarah Cohen-Boulakia, Pascal Neveu, Dimitri Dupuis, Didier Parigot, Modeling plant morphogenesis at different scales, from genes to phenotype (VIRTUAL PLANTS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA)-Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Mathématiques, Informatique et STatistique pour l'Environnement et l'Agronomie (MISTEA), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), École nationale supérieure d'architecture de Paris-La Villette (ENSAPLV), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), The authors acknowledge the support of France Grilles for providing computing resources on the French National Grid Infrastructure, ANR-11-INBS-0012,PHENOME,Centre français de phénomique végétale(2011), European Project: 267196,EC:FP7:PEOPLE,FP7-PEOPLE-2010-COFUND,AGREENSKILLS(2012), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), ANR-11-INBS-0012/11-INBS-0012,PHENOME,Centre français de phénomique végétale(2011), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)

Subjects
0106 biological sciences, 0301 basic medicine, Computer Networks and Communications, Computer science, F60 - Physiologie et biochimie végétale, Distributed computing, acquisition de données, Context (language use), Phenome, 01 natural sciences, F30 - Génétique et amélioration des plantes, 03 medical and health sciences, Phenomics, phénotypage, caractère physiologique, condition environnementale, base de données, 2. Zero hunger, Vegetal Biology, Food security, SIMPLE (military communications protocol), Point (typography), U10 - Informatique, mathématiques et statistiques, Scientic Work ows, Grid, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Data science, plateforme de phénotypage, Scientific workflows, Provenance, Grid computing, 030104 developmental biology, Workflow, C30 - Documentation et information, OpenAlea, 13. Climate action, Hardware and Architecture, dispositif de recherche, Biologie végétale, Software, 010606 plant biology & botany
Abstract

International audience; 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.

Published
2017

39. Adaptive Caching for Data-Intensive Scientific Workflows in the Cloud

Author

Esther Pacitti, François Tardieu, Gaëtan Heidsieck, Christophe Pradal, Patrick Valduriez, Daniel de Oliveira, Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Instituto de Computação [Niteroi-Rio de Janeiro] (IC-UFF), Universidade Federal Fluminense [Rio de Janeiro] (UFF), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), ANR-11-INBS-0013,IFB (ex Renabi-IFB),Institut français de bioinformatique(2011), ANR-11-INBS-0012,PHENOME,Centre français de phénomique végétale(2011), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), ANR-11-INBS-0013/11-INBS-0013,ReNaBi-IFB,Institut français de bioinformatique(2011), and ANR-11-INBS-0012/11-INBS-0012,PHENOME,Centre français de phénomique végétale(2011)

Subjects
050101 languages & linguistics, Computer science, Données, Distributed computing, Phénotype, Cloud computing, Recherche, 02 engineering and technology, Reuse, 0202 electrical engineering, electronic engineering, information engineering, 0501 psychology and cognitive sciences, Architecture, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], business.industry, U10 - Informatique, mathématiques et statistiques, 05 social sciences, Scientific Workflow, F70 - Taxonomie végétale et phytogéographie, Informatique, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Task (computing), Workflow, C30 - Documentation et information, Workflow Execution, Système d'information, Adaptive Caching, 020201 artificial intelligence & image processing, Cache, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], [SDE.BE]Environmental Sciences/Biodiversity and Ecology, business, Cloud
Abstract

International audience; Many scientific experiments are now carried on using scien-tific workflows, which are becoming more and more data-intensive and complex. We consider the efficient execution of such workflows in the cloud. Since it is common for workflow users to reuse other workflows or data generated by other workflows, a promising approach for efficient workflow execution is to cache intermediate data and exploit it to avoid task re-execution. In this paper, we propose an adaptive caching solution for data-intensive workflows in the cloud. Our solution is based on a new scientific workflow management architecture that automatically manages the storage and reuse of intermediate data and adapts to the variations in task execution times and output data size. We evaluated our solution by implementing it in the OpenAlea system and performing extensive experiments on real data with a data-intensive application inplant phenotyping. The results show that adaptive caching can yield major performance gains,e.g., up to 120.16% with 6 workflow re-executions.

Published
2019

40. Modelling wheat shoot morphogenesis at plant scale from organ-level metabolites

Author

Marion Gauthier, Romain Barillot, Anne Schneider, Camille Chambon, Christian Fournier, Christophe Pradal, Amélie Pinet, Bruno Andrieu, Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de la Recherche Agronomique (INRA), Institut de Recherche en Horticulture et Semences (IRHS), Université d'Angers (UA)-Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), ITK, and Society of Experimental Biology (SEB). GBR.

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2019

41. HydroShoot: a functional-structural plant model for simulating hydraulic structure, gas and energy exchange dynamics of complex plant canopies under water deficit - application to grapevine (Vitisvinifera L.)

Author

Eric Lebon, Michaël Chelle, Thierry Simonneau, Christophe Pradal, Gaëtan Louarn, Rami Albasha, Christian Fournier, Jorge Prieto, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), ITK [Clapiers], Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Instituto Nacional de Tecnología Agropecuaria [Mendoza] (INTA), Instituto Nacional de Tecnología Agropecuaria (INTA), Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de la Recherche Agronomique (INRA), European Project: 311775,EC:FP7:KBBE,FP7-KBBE-2012-6-singlestage,INNOVINE(2013), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), ITK, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Modeling plant morphogenesis at different scales, from genes to phenotype (VIRTUAL PLANTS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA)-Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)

Subjects
0106 biological sciences, Canopy, Hydraulic structures, Cubierta de Copas, Mécanique des fluides, Energy balance, Irradiance, Plant Science, 01 natural sciences, Functional-structural plant model (FSPM), Intercambio de Gases, Underwater, Canopia, Déficit Hídrico, Transpiration, Functional-structural plant modelling FSPM, 2. Zero hunger, 0303 health sciences, Simulation Models, Estrés de Sequia, 04 agricultural and veterinary sciences, Energy budget, Hydraulic structure, Modeling and Simulation, Shoot, Drought Stress, Interception, Échange gazeux, Grapevine (Vitis vinifera L.), F60 - Physiologie et biochimie végétale, Soil science, Biochemistry, Genetics and Molecular Biology (miscellaneous), Sécheresse, 03 medical and health sciences, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Grapevine Vitis vinifera L, Eau du sol, Consommation d'énergie, Water deficit, Gas-exchange, 030304 developmental biology, Simulation modeling, Modèle de simulation, 15. Life on land, Vid, Gas Exchange, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Grapevines, Vitis Vinifera, Vitis vinifera, Soil water, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, U30 - Méthodes de recherche, Agronomy and Crop Science, Modelos de Simulación, 010606 plant biology & botany
Abstract

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

Published
2019

42. Changes in the vertical distribution of leaf area enhanced light interception efficiency in maize over generations of selection

Author

François Tardieu, Claude Welcker, Tsu-Wei Chen, Christian Fournier, Christophe Pradal, Simon Artzet, Llorenç Cabrera-Bosquet, Nicolas Brichet, Raphaël Perez, Romain Chapuis, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Leibniz Universität Hannover [Hannover] (LUH), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), 'Infrastructure Biologie Santé' Phenome funded by the National Research Agency (ANR-11-INBS-0012), Agence Nationale de la Recherche project ANR-10-BTBR-01 (Amaizing), ANR-11-INBS-0012,PHENOME,Centre français de phénomique végétale(2011), and ANR-10-BTBR-0001,AMAIZING,Développer de nouvelles variétés de maïs pour une agriculture durable: une approche intégrée de la génomique à la sélection(2010)

Subjects
0106 biological sciences, 0301 basic medicine, Canopy, Light, Physiology, Range (biology), Sélection, Distribution (economics), Plant Science, Breeding, 01 natural sciences, F50 - Anatomie et morphologie des plantes, F30 - Génétique et amélioration des plantes, maquette 3d, Photosynthèse, 2. Zero hunger, Vegetal Biology, Plant density, Feuille, food and beverages, Architectural traits, plateforme de phénotypage, Phenotype, High-throughput phenotyping, écophysiologie végétale, Interception, Génotype, architecture de la plante, Genotype, maïs, F60 - Physiologie et biochimie végétale, Biology, surface foliaire, Zea mays, 03 medical and health sciences, Selection (genetic algorithm), Hybrid, Morphologie végétale, Light interception, business.industry, fungi, Maize (Zea mays L), interception de la lumière, 15. Life on land, Heritability, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Carbon, Plant Leaves, Plant Breeding, 030104 developmental biology, Agronomy, Maize (Zea mays L.), business, Biologie végétale, 010606 plant biology & botany
Abstract

International audience; Breeders select for yield, thereby indirectly selecting for traits that contribute to it. We tested if breeding has affected a range of traits involved in plant architecture and light interception, via the analysis of a panel of 60 maize hybrids released from 1950 to 2015. This was based on novel traits calculated from reconstructions derived from a phenotyping platform. The contribution of these traits to light interception was assessed in virtual field canopies composed of 3D plant reconstructions, with a model tested in a real field. Two categories of traits had different contributions to genetic progress. (a) The vertical distribution of leaf area had a high heritability and showed a marked trend over generations of selection. Leaf area tended to be located at lower positions in the canopy, thereby improving light penetration and distribution in the canopy. This potentially increased the carbon availability to ears, via the amount of light absorbed by the intermediate canopy layer. (b) Neither the horizontal distribution of leaves in the relation to plant rows nor the response of light interception to plant density showed appreciable trends with generations. Hence, among many architectural traits, the vertical distribution of leaf area was the main indirect target of selection.

Published
2019

43. Investigation of complex canopies with a functional–structural plant model as exemplified by leaf inclination effect on the functioning of pure and mixed stands of wheat during grain filling

Author

Didier Combes, Romain Barillot, Christian Fournier, Christophe Pradal, Camille Chambon, Bruno Andrieu, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de la Recherche Agronomique (INRA), Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Université Paris-Saclay, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), ANR-13-AGRO-0008,WHEATAMIX,Augmenter la diversité génétique au sein des parcelles de blé pour renforcer la multifonctionnalité et la durabilité de la production dans le Bassin Parisien(2013), and ANR-10-BTBR-0003,BREEDWHEAT,Développer de nouvelles variétés de blé pour une agriculture durable(2010)

Subjects
0106 biological sciences, light model, [SDV]Life Sciences [q-bio], Triticum aestivum, F62 - Physiologie végétale - Croissance et développement, Plant Science, functional–structural plant model, Environment, Biology, Grain filling, leaf inclination, F50 - Anatomie et morphologie des plantes, Models, Biological, 010603 evolutionary biology, 01 natural sciences, planophile, nitrogen, Resource Acquisition Is Initialization, Plant traits, light competition, Triticum, wheat, Life span, U10 - Informatique, mathématiques et statistiques, Plant density, Plant models, Original Articles, erectophile, 15. Life on land, plant interactions, Carbon, Plant Leaves, mixture, Linear relationship, Edible Grain, Biological system, 010606 plant biology & botany
Abstract

Background and Aims Because functional–structural plant models (FSPMs) take plant architecture explicitly into consideration, they constitute a promising approach for unravelling plant–plant interactions in complex canopies. However, existing FSPMs mainly address competition for light. The aim of the present work was to develop a comprehensive FSPM accounting for the interactions between plant architecture, environmental factors and the metabolism of carbon (C) and nitrogen (N). • Methods We developed an original FSPM by coupling models of (1) 3-D wheat architecture, (2) light distribution within canopies and (3) C and N metabolism. Model behaviour was evaluated by simulating the functioning of theoretical canopies consisting of wheat plants of contrasting leaf inclination, arranged in pure and mixed stands and considering four culm densities and three sky conditions. • Key Results As an emergent property of the detailed description of metabolism, the model predicted a linear relationship between absorbed light and C assimilation, and a curvilinear relationship between grain mass and C assimilation, applying to both pure stands and each component of mixtures. Over the whole post-anthesis period, planophile plants tended to absorb more light than erectophile plants, resulting in a slightly higher grain mass. This difference was enhanced at low plant density and in mixtures, where the erectophile behaviour resulted in a loss of competitiveness. • Conclusion The present work demonstrates that FSPMs provide a framework allowing the analysis of complex canopies such as studying the impact of particular plant traits, which would hardly be feasible experimentally. The present FSPM can help in interpreting complex interactions by providing access to critical variables such as resource acquisition and allocation, internal metabolic concentrations, leaf life span and grain filling. Simulations were based on canopies identically initialized at flowering; extending the model to the whole cycle is thus required so that all consequences of a trait can be evaluated.

Published
2019

44. Current knowledge and future research opportunities for modeling annual crop mixtures. A review

Author

Jochem B. Evers, Wopke van der Werf, Noémie Gaudio, Jean-Louis Durand, Gaëtan Louarn, Philippe Hinsinger, Eric Justes, Abraham J. Escobar-Gutiérrez, Hélène Marrou, Sébastien Saint-Jean, Jacques Eric Bergez, Sebastian Munz, Christophe Pradal, Pierre Casadebaig, Ela Frak, Marie Launay, Loïc Pagès, Frédéric Gérard, Romain Barillot, Didier Combes, Nathalie Colbach, AGroécologie, Innovations, teRritoires (AGIR), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA), Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de la Recherche Agronomique (INRA), Wageningen University and Research Centre [Wageningen] (WUR), Ecologie fonctionnelle et biogéochimie des sols et des agro-écosystèmes (UMR Eco&Sols), Institut National de la Recherche Agronomique (INRA)-Institut de Recherche pour le Développement (IRD)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Agroécologie [Dijon], Institut National de la Recherche Agronomique (INRA)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Université Bourgogne Franche-Comté [COMUE] (UBFC), University of Hohenheim, UE Agroclim (UE AGROCLIM), Fonctionnement et conduite des systèmes de culture tropicaux et méditerranéens (UMR SYSTEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Unité de recherche Plantes et Systèmes de Culture Horticoles (PSH), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT), Wageningen University and Research [Wageningen] (WUR), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Agroclim (AGROCLIM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), European Union, INRA Environment and Agronomy Division through the project IDEA, European Project: 727217,ReMIX(2017), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA), Université de Bourgogne (UB)-Institut National de la Recherche Agronomique (INRA)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and URP3F, INRAE, Lusignan, France

Subjects
0106 biological sciences, Niche complementarity, F08 - Systèmes et modes de culture, Agricultural engineering, Large range, Quantitative Biology - Quantitative Methods, 01 natural sciences, Individual-based models, modèle de cultures, ComputingMilieux_MISCELLANEOUS, Quantitative Methods (q-bio.QM), 2. Zero hunger, annual crop mixtures, intercrops, genotypes mixtures, crop models, individual-based models, functional-structural plant models, model users, 04 agricultural and veterinary sciences, Research opportunities, PE&RC, Genotypes mixtures, Agricultural sciences, Crop simulation model, Crop and Weed Ecology, Modèle mathématique, Environmental Engineering, culture en melange, Model users, systeme de culture, Annual crop mixtures, Crop, Culture sous couvert végétal, Culture intercalaire, Intercrops, culture annuelle, Plante de couverture, Plante annuelle, Quantitative Biology - Populations and Evolution, business.industry, Mélange de semences, Populations and Evolution (q-bio.PE), Sowing, 15. Life on land, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Functional–structural plant models, Agriculture, Modélisation, FOS: Biological sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Crop models, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, U30 - Méthodes de recherche, business, Agronomy and Crop Science, Cropping, Sciences agricoles, 010606 plant biology & botany
Abstract

Growing mixtures of annual arable crop species or genotypes is a promising way to improve crop production without increasing agricultural inputs. To design optimal crop mixtures, choices of species, genotypes, sowing proportion, plant arrangement, and sowing date need to be made but field experiments alone are not sufficient to explore such a large range of factors. Crop modeling allows to study, understand and ultimately design cropping systems and is an established method for sole crops. Recently, modeling started to be applied to annual crop mixtures as well. Here, we review to what extent crop simulation models and individual-based models are suitable to capture and predict the specificities of annual crop mixtures. We argued that: 1) The crop mixture spatio-temporal heterogeneity (influencing the occurrence of ecological processes) determines the choice of the modeling approach (plant or crop centered). 2) Only few crop models (adapted from sole crop models) and individual-based models currently exist to simulate annual crop mixtures. 3) Crop models are mainly used to address issues related to crop mixtures management and to the integration of crop mixtures into larger scales such as the rotation, whereas individual-based models are mainly used to identify plant traits involved in crop mixture performance and to quantify the relative contribution of the different ecological processes (niche complementarity, facilitation, competition, plasticity) to crop mixture functioning. This review highlights that modeling of annual crop mixtures is in its infancy and gives to model users some important keys to choose the model based on the questions they want to answer, with awareness of the strengths and weaknesses of each of the modeling approaches., Comment: 42 pages, 5 figures

Published
2019

45. Phenomenal: An automatic open source library for 3D shoot architecture reconstruction and analysis for image-based plant phenotyping

Author

Jérôme Chopard, Tsu-Wei Chen, Sarah Cohen-Boulakia, Simon Artzet, Nicolas Brichet, François Tardieu, Christophe Pradal, Christian Fournier, Michael Mielewczik, Llorenç Cabrera-Bosquet, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Leibniz University Hannover, Mathématiques, Informatique et STatistique pour l'Environnement et l'Agronomie (MISTEA), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Modeling plant morphogenesis at different scales, from genes to phenotype (VIRTUAL PLANTS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA)-Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Rothamsted Research, Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Leibniz Universität Hannover=Leibniz University Hannover, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Biotechnology and Biological Sciences Research Council (BBSRC), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM)

Subjects
0106 biological sciences, Computer science, Quantitative trait locus, computer.software_genre, 01 natural sciences, Synthetic data, Image analysis, 03 medical and health sciences, Software, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, 3D reconstruction, Architecture, 030304 developmental biology, 0303 health sciences, business.industry, Scientific workflow, Open source, Automatic pipeline, Plant phenotyping, Variety (cybernetics), Range (mathematics), Workflow, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Data mining, business, computer, 010606 plant biology & botany
Abstract

In the era of high-throughput visual plant phenotyping, it is crucial to design fully automated and flexible workflows able to derive quantitative traits from plant images. Over the last years, several software supports the extraction of architectural features of shoot systems. Yet currently no end-to-end systems are able to extract both 3D shoot topology and geometry of plants automatically from images on large datasets and a large range of species. In particular, these software essentially deal with dicotyledons, whose architecture is comparatively easier to analyze than monocotyledons. To tackle these challenges, we designed the Phenomenal software featured with: (i) a completely automatic workflow system including data import, reconstruction of 3D plant architecture for a range of species and quantitative measurements on the reconstructed plants; (ii) an open source library for the development and comparison of new algorithms to perform 3D shoot reconstruction and (iii) an integration framework to couple workflow outputs with existing models towards model-assisted phenotyping. Phenomenal analyzes a large variety of data sets and species from images of high-throughput phenotyping platform experiments to published data obtained in different conditions and provided in a different format. Phenomenal has been validated both on manual measurements and synthetic data simulated by 3D models. It has been also tested on other published datasets to reproduce a published semi-automatic reconstruction workflow in an automatic way. Phenomenal is available as an open-source software on a public repository.

Published
2019

46. Towards a model of wheat leaf morphogenesis at plant scale driven by organ-level metabolites

Author

Anne Schneider, Buno Andrieu, Marion Gauthier, Christian Fournier, Christophe Pradal, Amelie Pinet, Romain Barillot, Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Université Paris Saclay (COmUE), Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de la Recherche Agronomique (INRA), Institut de Recherche en Horticulture et Semences (IRHS), Université d'Angers (UA)-Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Modeling plant morphogenesis at different scales, from genes to phenotype (VIRTUAL PLANTS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA)-Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), ITK, and Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)

Subjects
0106 biological sciences, Plant growth, plant architecture, Scale (ratio), [SDV]Life Sciences [q-bio], morphogenesis, 01 natural sciences, nitrogen, Functional-Structural Plant Modeling, Phyllochron, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Nitrogen cycle, Management practices, Mathematics, 2. Zero hunger, Specific mass, carbon, 04 agricultural and veterinary sciences, plasticity, Shoot, leaf growth, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Leaf morphogenesis, Biological system, metabolism, 010606 plant biology & botany
Abstract

International audience; Leaf dimensions, specific mass and composition are traits of interest, as leaves constitute the main exchange surface with the aboveground environment. These variables arise from the interplay between many processes, and vary with growth conditions. Models of plant growth are useful tools to explore a wide range of climatic scenarios, management practices and genotypes. However, most models lacks process-based formalisms allowing simulating shoot architecture plasticity. We propose a functional-structural wheat model that couples carbon and nitrogen metabolism with leaf morphogenesis during the vegetative stage. The originality of our model relies on the interaction between leaf growth and the metabolism of carbon and nitrogen in the growing zone, which is possible thanks to an explicit and detailed formalism of the processes at organ level. The model simulates the appearance of successive leaves using coordination rules instead of a constant phyllochron as a driving mechanism. As a first step, main modules were evaluated separately: the coordination model and the metabolism model of a single growing leaf. The model shows interesting emergent properties: phyllochron stability, pattern of mature leaf length along the culm and realistic kinetics of length, dry mass and concentrations in both growing and mature zones. A qualitative evaluation strategy of the completely integrated model at plant scale is then proposed. As a conclusion, the model appears to be a useful concept, which could be transposed to other grasses.

Published
2018

47. AutoWIG: automatic generation of python bindings for C++ libraries

Author

Christophe Pradal, Pierre Fernique, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Modeling plant morphogenesis at different scales, from genes to phenotype (VIRTUAL PLANTS), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Mathématiques, Informatique et STatistique pour l'Environnement et l'Agronomie (MISTEA), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), ANR-11-INBS-0012/11-INBS-0012,PHENOME,Centre français de phénomique végétale(2011), ANR-11-BINF-0002,IBC,Institut de Biologie Computationnelle de Montpellier(2011), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA)-Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), ANR-11-INBS-0012, ANR-11-INBS-0012,PHENOME,Centre français de phénomique végétale(2011), ANR-11-BINF-0002,IBC,Institut de biologie Computationnelle(2011), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Godin, Christophe

Subjects
FOS: Computer and information sciences, Speedup, Automatic bindings generation, Computer science, Reuse, computer.software_genre, 01 natural sciences, lcsh:QA75.5-76.95, 010305 fluids & plasmas, Computer Science - Software Engineering, 010104 statistics & probability, Software, Programmation informatique, Engineering, Scientific Computing and Simulation, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Python language, bioinformatique, computer.programming_language, Programming language, U10 - Informatique, mathématiques et statistiques, Transpiler, Template, C30 - Documentation et information, Bio-informatique, automatisation, bibliothèque numérique, General Computer Science, Bioinformatics, [INFO.INFO-SE] Computer Science [cs]/Software Engineering [cs.SE], [INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE], C++ language, C++ language, scientific computing, Python language, 0103 physical sciences, 0101 mathematics, Automatique, C++, business.industry, Windows Runtime, Analyse de données, Automatic Control Engineering, Python (programming language), [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Subjects Data Science, Software Engineering (cs.SE), système d'information, scientific computing, C++/CX, Python, Programming Languages, lcsh:Electronic computers. Computer science, business, computer
Abstract

The Python programming language is one of the most popular language in scientific computing. However, most scientific Python packages incorporates C and C++ libraries. While several semi-automatic solutions and tools exist to wrap C++ libraries (Cython, Boost.Python and SWIG), the process of wrapping a large C++ library is cumbersome and time consuming. Some solutions have been developed in the past (e.g. Py++) but require to write complex code to automate the process, and rely on technologies that are not maintained. AutoWIG relies on the LLVM/Clang technology for parsing C/C++ code and the Mako templating engine for generating Boost.Python wrappers. Rather than having to write parsers in Python such as in Py++, the approach is similar to XDress, but for Boost.Python wrappers instead of Cython wrappers. We illustrate the usage of AutoWIG on a complex collection of C++ libraries for statistical analysis.

Published
2018

48. An Architecture for the Integration of Different Functional and Structural Plant Models

Author

Christophe Pradal, Qinqin Long, Winfried Kurth, Benoît Pallas, Vincent Migault, Department Ecoinformatics, Biometrics and Forest Growth, Georg-August-Universität Göttingen, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de la Recherche Agronomique (INRA), Georg-August-University [Göttingen], Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Department Ecoinformatics, Biometrics and Forest Growth [Göttingen Univ], Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)

Subjects
Multiscale, FOS: Computer and information sciences, Computer science, multi-scale, Distributed computing, GroIMP, 02 engineering and technology, modèle de simulation, modèle structure fonction, Computer Science - Computers and Society, Software, 020204 information systems, Computers and Society (cs.CY), 0202 electrical engineering, electronic engineering, information engineering, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Functional and structural, Architecture, Multiscale tree graph, FSPM, Vegetal Biology, architecture de l'arbre, business.industry, Usability, Plant models, simulation, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Range (mathematics), MTG, OpenAlea, Simulation, 020201 artificial intelligence & image processing, business, Functional structural plant model, Biologie végétale, architecture de la plante
Abstract

International audience; Plant scientists use Functional Structural Plant Models (FSPMs) to model plant systems within a limited space-time range. To allow FSPMs to abstract complex plant systems beyond a single model's limitation, an integration that compounds different FSPMs could be a possible solution. However, the integration involves many technical dimensions and a generic software infrastructure for all integration cases is not possible. In this paper, we analyze the requirements of the integration with all the technical dimensions. Instead of an infrastructure, we propose a generic architecture with specific process-related components as a logical level solution by combining an ETL (Extract, Transform and Load) based sub architecture and a C/S (Client/Server) based sub architecture. This allows the integration of different FSP models hosted on the same and different FSP modeling platforms in a flexible way. We demonstrate the usability of the architecture by the implementation of a full infrastructure for the integration of two specific FSPMs, and we illustrate the effectiveness of the infrastructure by several integrative tests. CCS Concepts • Software and its engineering➝Integration frameworks, Software and its engineering➝Cooperating communicating processes, Software and its engineering➝Data flow architectures Keywords Functional structural plant model; simulation; multiscale; Multiscale tree graph; OpenAlea and GroIMP platform.

Published
2018

49. The Agricultural Model Exchange Initiative

Author

Martre, Pierre, Donatelli, Marcello, Christophe Pradal, Enders, Andreas, Ahmed Midingoyi, Cyrille, Athanasiadis, Ioannis, Fumagalli, Davide, Holzworth, Dean, Hoogenboom, Gerrit, Porter, C. H., Raynal, Hélène, Rizzoli, Andrea E., and Thorburn, P.

Abstract

Process-based crop simulation models are increasingly popular tools to analysis and predict the response of agricultural systems to climatic, agronomic and, more recently genetic, factors. The diversity of models in use illustrates the success of crop modeling. For instance, in it is current work the AgMIP Wheat team uses 42 different wheat models. Several groups in AgMIP have reported large uncertainty in climate impact studies, which is calling for more systematic model intercomparisons and improvements at the process level. However, the limited possibilities of model components (algorithms) and code exchange between platforms/models hinders such work. Collaborative efforts between crop physiologists, crop modelers, and software engineers are urgently needed to ease the integration in crop models of new knowledge in plant and soil sciences. To this end, several leading groups in the field have recently liaised to form the Agricultural Model Exchange Initiative (AMEI). AMEI is an open initiative that aims to address different challenges for exchanging model units at different granularities (from individual processes to whole plant) between modeling frameworks by (1) defining standards to describe model units and composition exchange format based on a declarative representation; (2) including unit tests with invariants and shared standard parametrizations; and (3) developing a web-platform to publish, document, and exchange the model units. We will provide a conceptual and technical overview of the state of the work and give practical examples of successful component exchange between different frameworks.

Published
2018

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

Author

Guillaume Garin, Corinne Robert, Christian Fournier, Christophe Pradal, Vianney Houlès, Mariem Abichou, Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, ITK [Clapiers], Modeling plant morphogenesis at different scales, from genes to phenotype (VIRTUAL PLANTS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA)-Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)

Subjects
0106 biological sciences, 0301 basic medicine, Canopy, architecture, leaf senescence, [SDV]Life Sciences [q-bio], F62 - Physiologie végétale - Croissance et développement, Plant Science, F50 - Anatomie et morphologie des plantes, 01 natural sciences, epidemics, Crop, 03 medical and health sciences, Race (biology), Septoria, Ascomycota, wheat, virtual plant model, canopy traits, Colonization, Triticum, H20 - Maladies des plantes, Plant Diseases, 2. Zero hunger, FSPM, biology, Host (biology), fungi, food and beverages, Original Articles, Spores, Fungal, 15. Life on land, biology.organism_classification, Plant disease, Plant Leaves, 030104 developmental biology, Agronomy, 13. Climate action, Host-Pathogen Interactions, Zymoseptoria tritici, Septoria tritici blotch, Epidemic model, 010606 plant biology & botany
Abstract

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. (Resume d'auteur)

Published
2018

Catalog

Books, media, physical & digital resources
See catalog results