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Levionnois S, Pradal C, Fournier C, Sanner J, Robert C. Modeling the Impact of Proportion, Sowing Date, and Architectural Traits of a Companion Crop on Foliar Fungal Pathogens of Wheat in Crop Mixtures. Phytopathology 2023; 113:1876-1889. [PMID: 37097642 DOI: 10.1094/phyto-06-22-0197-r] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
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-versus-companion proportion, companion sowing date and growth, and architectural traits. For both pathogens, the companion proportion had the strongest effect, with 25% of companion reducing disease severity by 50%. However, changing companion growth and architectural traits also significantly improved the protective effect. The effect of companion characteristics was consistent across different weather conditions. After decomposing the dilution and barrier effects, the model suggested that the barrier effect is maximized for an intermediate proportion of companion crop. Our study thus supports crop mixtures as a promising strategy to improve disease management. Future studies should identify real species and determine the combination of host and companion traits to maximize the protective effect of the mixture. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.
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Affiliation(s)
- Sébastien Levionnois
- UMR EcoSys, INRAE, AgroParisTech, Campus Agro Paris-Saclay, 91120 Palaiseau, France
- UMR AGAP Institut, Univ. Montpellier, CIRAD, INRAE, Institut Agro, 34398 Montpellier, France
| | - Christophe Pradal
- CIRAD, UMR AGAP Institut, 34398 Montpellier, France
- INRIA & LIRMM, Univ. Montpellier, CNRS, 34090 Montpellier, France
| | - Christian Fournier
- UMR LEPSE, Université de Montpellier, INRAE, Montpellier SupAgro, 34000 Montpellier, France
| | - Jonathan Sanner
- UMR EcoSys, INRAE, AgroParisTech, Campus Agro Paris-Saclay, 91120 Palaiseau, France
| | - Corinne Robert
- UMR EcoSys, INRAE, AgroParisTech, Campus Agro Paris-Saclay, 91120 Palaiseau, France
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2
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Labadie M, Guy K, Demené MN, Caraglio Y, Heidsieck G, Gaston A, Rothan C, Guédon Y, Pradal C, Denoyes B. Spatio-temporal analysis of strawberry architecture: insights into the control of branching and inflorescence complexity. J Exp Bot 2023:7143673. [PMID: 37133320 DOI: 10.1093/jxb/erad097] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 04/25/2023] [Indexed: 05/04/2023]
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.
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Affiliation(s)
- Marc Labadie
- Univ. Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, F-33140, France
- CIRAD, UMR AGAP Institut, F-34398 Montpellier, France
| | - Karine Guy
- INVENIO, MIN de Brienne, 110 quai de Paludate, 33800 Bordeaux, France
| | | | - Yves Caraglio
- CIRAD, UMR AMAP and Université de Montpellier, 34398 Montpellier, France
| | - Gaetan Heidsieck
- Univ. Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, F-33140, France
- CIRAD, UMR AGAP Institut, F-34398 Montpellier, France
| | - Amelia Gaston
- Univ. Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, F-33140, France
| | - Christophe Rothan
- Univ. Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, F-33140, France
| | - Yann Guédon
- CIRAD, UMR AGAP Institut, F-34398 Montpellier, France
| | - Christophe Pradal
- CIRAD, UMR AGAP Institut, F-34398 Montpellier, France
- Inria and LIRMM, Univ Montpellier, CNRS, Montpellier, France
| | - Béatrice Denoyes
- Univ. Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, F-33140, France
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Bauget F, Protto V, Pradal C, Boursiac Y, Maurel C. A root functional-structural model allows assessment of the effects of water deficit on water and solute transport parameters. J Exp Bot 2023; 74:1594-1608. [PMID: 36515073 PMCID: PMC10010609 DOI: 10.1093/jxb/erac471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 12/13/2022] [Indexed: 06/17/2023]
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.
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Affiliation(s)
- Fabrice Bauget
- Institute for Plant Sciences of Montpellier (IPSiM), Univ Montpellier, CNRS, INRAE, Institut Agro, Montpellier, France
| | - Virginia Protto
- Institute for Plant Sciences of Montpellier (IPSiM), Univ Montpellier, CNRS, INRAE, Institut Agro, Montpellier, France
| | - Christophe Pradal
- CIRAD, UMR AGAP Institute, Montpellier, France
- Inria & LIRMM, Univ Montpellier, CNRS, Montpellier, France
| | - Yann Boursiac
- Institute for Plant Sciences of Montpellier (IPSiM), Univ Montpellier, CNRS, INRAE, Institut Agro, Montpellier, France
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Daviet B, Fernandez R, Cabrera-Bosquet L, Pradal C, Fournier C. PhenoTrack3D: an automatic high-throughput phenotyping pipeline to track maize organs over time. Plant Methods 2022; 18:130. [PMID: 36482291 PMCID: PMC9730636 DOI: 10.1186/s13007-022-00961-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 11/22/2022] [Indexed: 06/17/2023]
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 < 2.1 cm). 97.7% and 85.3% of ligulated and growing leaves respectively were assigned to the correct rank after tracking, on 30 plants × 43 dates. The pipeline allowed to extract various development and architecture traits at organ level, with good correlation to manual observations overall, on random subsets of 10-355 plants. CONCLUSIONS We developed a novel phenotyping method based on sequence alignment and deep-learning. It allows to characterise the development of maize architecture at organ level, automatically and at a high-throughput. It has been validated on hundreds of plants during the entire development cycle, showing its applicability on GxE analyses of large maize datasets.
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Affiliation(s)
- Benoit Daviet
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, France
| | - Romain Fernandez
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France
- CIRAD, INRAE, UMR AGAP Institut, Univ Montpellier, Institut Agro, 34398, Montpellier, France
| | | | - Christophe Pradal
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France.
- CIRAD, INRAE, UMR AGAP Institut, Univ Montpellier, Institut Agro, 34398, Montpellier, France.
- Inria & LIRMM, CNRS, Univ Montpellier, Montpellier, France.
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Fernandez R, Crabos A, Maillard M, Nacry P, Pradal C. High-throughput and automatic structural and developmental root phenotyping on Arabidopsis seedlings. Plant Methods 2022; 18:127. [PMID: 36457133 PMCID: PMC9714072 DOI: 10.1186/s13007-022-00960-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 11/18/2022] [Indexed: 06/17/2023]
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 ([Formula: see text] and [Formula: see text] 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 ([Formula: see text] 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.
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Affiliation(s)
- Romain Fernandez
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Amandine Crabos
- Institute for Plant Sciences of Montpellier (IPSiM), Univ Montpellier, CNRS, INRAE, Institut Agro, Montpellier, France
| | - Morgan Maillard
- Institute for Plant Sciences of Montpellier (IPSiM), Univ Montpellier, CNRS, INRAE, Institut Agro, Montpellier, France
| | - Philippe Nacry
- Institute for Plant Sciences of Montpellier (IPSiM), Univ Montpellier, CNRS, INRAE, Institut Agro, Montpellier, France.
| | - Christophe Pradal
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France.
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France.
- Inria & LIRMM, Univ Montpellier, CNRS, Montpellier, France.
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Boursiac Y, Pradal C, Bauget F, Lucas M, Delivorias S, Godin C, Maurel C. Phenotyping and modeling of root hydraulic architecture reveal critical determinants of axial water transport. Plant Physiol 2022; 190:1289-1306. [PMID: 35708646 PMCID: PMC9516777 DOI: 10.1093/plphys/kiac281] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 05/15/2022] [Indexed: 05/26/2023]
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.
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Affiliation(s)
| | | | | | | | - Stathis Delivorias
- Institute for Plant Sciences of Montpellier (IPSiM), Univ Montpellier, CNRS, INRAE, Institut Agro, Montpellier 34060, France
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Senger E, Osorio S, Olbricht K, Shaw P, Denoyes B, Davik J, Predieri S, Karhu S, Raubach S, Lippi N, Höfer M, Cockerton H, Pradal C, Kafkas E, Litthauer S, Amaya I, Usadel B, Mezzetti B. Towards smart and sustainable development of modern berry cultivars in Europe. Plant J 2022; 111:1238-1251. [PMID: 35751152 DOI: 10.1111/tpj.15876] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 06/15/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
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.
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Affiliation(s)
- Elisa Senger
- Institute of Bio- and Geosciences, IBG-4 Bioinformatics, BioSC, CEPLAS, Forschungszentrum Jülich, Jülich, Germany
| | - Sonia Osorio
- Departamento de Biología Molecular y Bioquímica, Instituto de Hortofruticultura Subtropical y Mediterránea 'La Mayora', Universidad de Málaga-Consejo Superior de Investigaciones Científicas, Campus de Teatinos, Málaga, Spain
| | | | - Paul Shaw
- Department of Information and Computational Sciences, The James Hutton Institute, Invergowrie, Scotland, UK
| | - Béatrice Denoyes
- Université de Bordeaux, UMR BFP, INRAE, Villenave d'Ornon, France
| | - Jahn Davik
- Department of Molecular Plant Biology, Norwegian Institute of Bioeconomy Research (NIBIO), Ås, Norway
| | - Stefano Predieri
- Bio-Agrofood Department, Institute for Bioeconomy, IBE-CNR, Italian National Research Council, Bologna, Italy
| | - Saila Karhu
- Natural Resources Institute Finland (Luke), Turku, Finland
| | - Sebastian Raubach
- Department of Information and Computational Sciences, The James Hutton Institute, Invergowrie, Scotland, UK
| | - Nico Lippi
- Bio-Agrofood Department, Institute for Bioeconomy, IBE-CNR, Italian National Research Council, Bologna, Italy
| | - Monika Höfer
- Institute of Breeding Research on Fruit Crops, Federal Research Centre for Cultivated Plants (JKI), Dresden, Germany
| | - Helen Cockerton
- Genetics, Genomics and Breeding Department, NIAB, East Malling, UK
| | - Christophe Pradal
- CIRAD and UMR AGAP Institute, Montpellier, France
- INRIA and LIRMM, University Montpellier, CNRS, Montpellier, France
| | - Ebru Kafkas
- Department of Horticulture, Faculty of Agriculture, Çukurova University, Balcalı, Adana, Turkey
| | | | - Iraida Amaya
- Unidad Asociada deI + D + i IFAPA-CSIC Biotecnología y Mejora en Fresa, Málaga, Spain
- Laboratorio de Genómica y Biotecnología, Centro IFAPA de Málaga, Instituto Andaluz de Investigación y Formación Agraria y Pesquera, Málaga, Spain
| | - Björn Usadel
- Institute of Bio- and Geosciences, IBG-4 Bioinformatics, BioSC, CEPLAS, Forschungszentrum Jülich, Jülich, Germany
- Institute for Biological Data Science, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Bruno Mezzetti
- Department of Agricultural, Food and Environmental Sciences, Università Politecnica delle Marche, Ancona, Italy
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Blanc E, Barbillon P, Fournier C, Lecarpentier C, Pradal C, Enjalbert J. Functional-Structural Plant Modeling Highlights How Diversity in Leaf Dimensions and Tillering Capability Could Promote the Efficiency of Wheat Cultivar Mixtures. Front Plant Sci 2021; 12:734056. [PMID: 34659301 PMCID: PMC8511389 DOI: 10.3389/fpls.2021.734056] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 08/30/2021] [Indexed: 06/02/2023]
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.
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Affiliation(s)
- Emmanuelle Blanc
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE—Le Moulon, Gif-sur-Yvette, France
| | - Pierre Barbillon
- Université Paris-Saclay, AgroParisTech, INRAE, UMR MIA-Paris, 75005, Paris, France
| | | | | | - Christophe Pradal
- CIRAD, UMR AGAP Institut, Montpellier, France
- INRIA and LIRMM, Univ Montpellier, CNRS, Montpellier, France
| | - Jérôme Enjalbert
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE—Le Moulon, Gif-sur-Yvette, France
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9
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Pitchers B, Do FC, Pradal C, Dufour L, Lauri PÉ. Apple tree adaptation to shade in agroforestry: an architectural approach. Am J Bot 2021; 108:732-743. [PMID: 33934329 DOI: 10.1002/ajb2.1652] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Accepted: 12/22/2020] [Indexed: 06/12/2023]
Abstract
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.
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Affiliation(s)
- Benjamin Pitchers
- ABSys, Univ Montpellier, CIHEAM-IAMM, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Frédéric C Do
- Eco&Sols, Univ Montpellier, CIRAD, INRAE, Institut Agro, IRD, Montpellier, France
| | - Christophe Pradal
- CIRAD, UMR AGAP, F-34398, Montpellier, France
- Inria & LIRMM, Univ Montpellier, CNRS, Montpellier, France
| | - Lydie Dufour
- ABSys, Univ Montpellier, CIHEAM-IAMM, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Pierre-Éric Lauri
- ABSys, Univ Montpellier, CIHEAM-IAMM, CIRAD, INRAE, Institut Agro, Montpellier, France
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10
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Takahashi H, Pradal C. Root phenotyping: important and minimum information required for root modeling in crop plants. Breed Sci 2021; 71:109-116. [PMID: 33762880 PMCID: PMC7973500 DOI: 10.1270/jsbbs.20126] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 12/08/2020] [Indexed: 05/10/2023]
Abstract
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.
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Affiliation(s)
- Hirokazu Takahashi
- Graduate School of Bioagricultural Sciences, Nagoya University, Furo-cho, Chikusa, Nagoya, Aichi 464-8601, Japan
| | - Christophe Pradal
- UMR AGAP, CIRAD, F-34398 Montpellier, France
- Inria & LIRMM, University of Montpellier, CNRS, Montpellier, France
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11
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Takahashi H, Pradal C. Root phenotyping: important and minimum information required for root modeling in crop plants. Breed Sci 2021; 71:109-116. [PMID: 33762880 DOI: 10.1071/bt06118] [Citation(s) in RCA: 390] [Impact Index Per Article: 130.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 12/08/2020] [Indexed: 05/24/2023]
Abstract
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.
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Affiliation(s)
- Hirokazu Takahashi
- Graduate School of Bioagricultural Sciences, Nagoya University, Furo-cho, Chikusa, Nagoya, Aichi 464-8601, Japan
| | - Christophe Pradal
- UMR AGAP, CIRAD, F-34398 Montpellier, France
- Inria & LIRMM, University of Montpellier, CNRS, Montpellier, France
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12
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Gauthier M, Barillot R, Schneider A, Chambon C, Fournier C, Pradal C, Robert C, Andrieu B. A functional structural model of grass development based on metabolic regulation and coordination rules. J Exp Bot 2020; 71:5454-5468. [PMID: 32497176 DOI: 10.1093/jxb/eraa276] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 05/27/2020] [Indexed: 05/28/2023]
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.
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Affiliation(s)
- Marion Gauthier
- Université Paris-Saclay, INRAE, AgroParisTech, UMR ECOSYS, Thiverval-Grignon, France
- ITK, Clapiers, France
| | | | - Anne Schneider
- Université d'Angers, INRAE, Agrocampus-Ouest, Angers, France
| | - Camille Chambon
- Université Paris-Saclay, INRAE, AgroParisTech, UMR ECOSYS, Thiverval-Grignon, France
| | - Christian Fournier
- Université de Montpellier, INRAE, Montpellier SupAgro, UMR LEPSE, Montpellier, France
| | - Christophe Pradal
- CIRAD, UMR AGAP, and Inria, Montpellier, France
- AGAP, Univ Montpellier, CIRAD, INRAE, Montpellier SupAgro, Montpellier, France
| | - Corinne Robert
- Université Paris-Saclay, INRAE, AgroParisTech, UMR ECOSYS, Thiverval-Grignon, France
| | - Bruno Andrieu
- Université Paris-Saclay, INRAE, AgroParisTech, UMR ECOSYS, Thiverval-Grignon, France
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13
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Braghiere RK, Gérard F, Evers JB, Pradal C, Pagès L. Simulating the effects of water limitation on plant biomass using a 3D functional-structural plant model of shoot and root driven by soil hydraulics. Ann Bot 2020; 126:713-728. [PMID: 32249296 PMCID: PMC7489072 DOI: 10.1093/aob/mcaa059] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 04/02/2020] [Indexed: 05/28/2023]
Abstract
BACKGROUND AND AIMS Improved 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. METHODS In 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 RESULTS The 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. CONCLUSIONS An 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.
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Affiliation(s)
- Renato K Braghiere
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
- Joint Institute for Regional Earth System Science and Engineering, University of California at Los Angeles, Los Angeles, CA, USA
- Eco&Sols, Univ. Montpellier, CIRAD, INRAE, IRD, SupAgro, Montpellier, France
| | - Frédéric Gérard
- Eco&Sols, Univ. Montpellier, CIRAD, INRAE, IRD, SupAgro, Montpellier, France
| | - Jochem B Evers
- Centre for Crop Systems Analysis (CSA), Wageningen University, Wageningen, The Netherlands
| | - Christophe Pradal
- CIRAD, UMR AGAP, Montpellier, France
- AGAP, Univ. Montpellier, CIRAD, INRAE, SupAgro, Montpellier, France
- INRIA, Univ. Montpellier, France
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14
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Reyes F, Pallas B, Pradal C, Vaggi F, Zanotelli D, Tagliavini M, Gianelle D, Costes E. MuSCA: a multi-scale source-sink carbon allocation model to explore carbon allocation in plants. An application to static apple tree structures. Ann Bot 2020; 126:571-585. [PMID: 31642506 PMCID: PMC7489079 DOI: 10.1093/aob/mcz122] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 08/03/2019] [Indexed: 05/13/2023]
Abstract
BACKGROUND AND AIMS Carbon allocation in plants is usually represented at a topological scale, specific to each model. This makes the results obtained with different models, and the impact of their scales of representation, difficult to compare. In this study, we developed a multi-scale carbon allocation model (MuSCA) that allows the use of different, user-defined, topological scales of a plant, and assessment of the impact of each spatial scale on simulated results and computation time. METHODS Model multi-scale consistency and behaviour were tested on three realistic apple tree structures. Carbon allocation was computed at five scales, spanning from the metamer (the finest scale, used as a reference) up to first-order branches, and for different values of a sap friction coefficient. Fruit dry mass increments were compared across spatial scales and with field data. KEY RESULTS The model was able to represent effects of competition for carbon assimilates on fruit growth. Intermediate friction parameter values provided results that best fitted field data. Fruit growth simulated at the metamer scale differed of ~1 % in respect to results obtained at growth unit scale and up to 60 % in respect to first order branch and fruiting unit scales. Generally, the coarser the spatial scale the more predicted fruit growth diverged from the reference. Coherence in fruit growth across scales was also differentially impacted, depending on the tree structure considered. Decreasing the topological resolution reduced computation time by up to four orders of magnitude. CONCLUSIONS MuSCA revealed that the topological scale has a major influence on the simulation of carbon allocation. This suggests that the scale should be a factor that is carefully evaluated when using a carbon allocation model, or when comparing results produced by different models. Finally, with MuSCA, trade-off between computation time and prediction accuracy can be evaluated by changing topological scales.
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Affiliation(s)
- F Reyes
- DAFNE, University of Tuscia, Viterbo, Italy
- DASB, CRI, Fondazione E. Mach, San Michele all’Adige, Italy
- Faculty of Science and Technology, Free University of Bozen-Bolzano, Bolzano, Italy
- For correspondence. E-mail
| | - B Pallas
- AGAP, University of Montpellier, CIRAD, INRA, SupAgro, Montpellier, France
| | - C Pradal
- AGAP, University of Montpellier, CIRAD, INRA, SupAgro, Montpellier, France
- CIRAD, UMR AGAP and Inria Zenith, Montpellier, France
| | | | - D Zanotelli
- Faculty of Science and Technology, Free University of Bozen-Bolzano, Bolzano, Italy
| | - M Tagliavini
- Faculty of Science and Technology, Free University of Bozen-Bolzano, Bolzano, Italy
| | - D Gianelle
- DASB, CRI, Fondazione E. Mach, San Michele all’Adige, Italy
| | - E Costes
- AGAP, University of Montpellier, CIRAD, INRA, SupAgro, Montpellier, France
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15
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Belhassine F, Fumey D, Chopard J, Pradal C, Martinez S, Costes E, Pallas B. Modelling transport of inhibiting and activating signals and their combined effects on floral induction: application to apple tree. Sci Rep 2020; 10:13085. [PMID: 32753623 PMCID: PMC7403595 DOI: 10.1038/s41598-020-69861-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 06/30/2020] [Indexed: 11/17/2022] Open
Abstract
Floral induction (FI) in shoot apical meristems (SAM) is assumed to be triggered by antagonistic endogenous signals. In fruit trees, FI occurs in some SAM only and is determined by activating and inhibiting signals originating from leaves and fruit, respectively. We developed a model (SigFlow) to quantify on 3D structures the combined impact of such signals and distances at which they act on SAM. Signal transport was simulated considering a signal 'attenuation' parameter, whereas SAM fate was determined by probability functions depending on signal quantities. Model behaviour was assessed on simple structures before being calibrated and validated on a unique experimental dataset of 3D digitized apple trees with contrasted crop loads and subjected to leaf and fruit removal at different scales of tree organization. Model parameter estimations and comparisons of two signal combination functions led us to formulate new assumptions on the mechanisms involved: (i) the activating signal could be transported at shorter distances than the inhibiting one (roughly 50 cm vs 1 m) (ii) both signals jointly act to determine FI with SAM being more sensitive to inhibiting signal than activating one. Finally, the genericity of the model is promising to further understand the physiological and architectural determinisms of FI in plants.
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Affiliation(s)
- Fares Belhassine
- AGAP, Univ Montpellier, INRAE, CIRAD, Montpellier SupAgro, Montpellier, France
- ITK, Clapiers, France
| | | | | | - Christophe Pradal
- AGAP, Univ Montpellier, INRAE, CIRAD, Montpellier SupAgro, Montpellier, France
- CIRAD, UMR AGAP, Montpellier, France
| | - Sébastien Martinez
- AGAP, Univ Montpellier, INRAE, CIRAD, Montpellier SupAgro, Montpellier, France
| | - Evelyne Costes
- AGAP, Univ Montpellier, INRAE, CIRAD, Montpellier SupAgro, Montpellier, France
| | - Benoît Pallas
- AGAP, Univ Montpellier, INRAE, CIRAD, Montpellier SupAgro, Montpellier, France.
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Pradal C, Cohen-Boulakia S, Valduriez P, Shasha D. VersionClimber: Version Upgrades Without Tears. Comput Sci Eng 2019. [DOI: 10.1109/mcse.2019.2921898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
| | | | | | - Dennis Shasha
- Courant InstituteNew York University and Inria, (International Chair) and LIRMM, University of Montpellier
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17
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Perez RPA, Fournier C, Cabrera-Bosquet L, Artzet S, Pradal C, Brichet N, Chen TW, Chapuis R, Welcker C, Tardieu F. Changes in the vertical distribution of leaf area enhanced light interception efficiency in maize over generations of selection. Plant Cell Environ 2019; 42:2105-2119. [PMID: 30801738 DOI: 10.1111/pce.13539] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 02/14/2019] [Accepted: 02/14/2019] [Indexed: 06/09/2023]
Abstract
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.
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Affiliation(s)
- Raphaël P A Perez
- Université de Montpellier, INRA, Montpellier SupAgro, UMR LEPSE, Montpellier, France
- Université de Montpellier, CIRAD, INRA, Montpellier SupAgro, UMR AGAP, Montpellier, France
| | - Christian Fournier
- Université de Montpellier, INRA, Montpellier SupAgro, UMR LEPSE, Montpellier, France
| | | | - Simon Artzet
- Université de Montpellier, INRA, Montpellier SupAgro, UMR LEPSE, Montpellier, France
| | - Christophe Pradal
- Université de Montpellier, CIRAD, INRA, Montpellier SupAgro, UMR AGAP, Montpellier, France
| | - Nicolas Brichet
- Université de Montpellier, INRA, Montpellier SupAgro, UMR LEPSE, Montpellier, France
| | - Tsu-Wei Chen
- Université de Montpellier, INRA, Montpellier SupAgro, UMR LEPSE, Montpellier, France
- Institute of Horticultural Production Systems, Leibniz Universität Hannover, Hannover, Germany
| | - Romain Chapuis
- Université de Montpellier, INRA, Montpellier SupAgro, UE DIASCOPE, Montpellier, France
| | - Claude Welcker
- Université de Montpellier, INRA, Montpellier SupAgro, UMR LEPSE, Montpellier, France
| | - François Tardieu
- Université de Montpellier, INRA, Montpellier SupAgro, UMR LEPSE, Montpellier, France
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18
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Chen TW, Cabrera-Bosquet L, Alvarez Prado S, Perez R, Artzet S, Pradal C, Coupel-Ledru A, Fournier C, Tardieu F. Genetic and environmental dissection of biomass accumulation in multi-genotype maize canopies. J Exp Bot 2019; 70:2523-2534. [PMID: 30137451 PMCID: PMC6487589 DOI: 10.1093/jxb/ery309] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Accepted: 08/14/2018] [Indexed: 05/22/2023]
Abstract
Multi-genotype canopies are frequent in phenotyping experiments and are of increasing interest in agriculture. Radiation interception efficiency (RIE) and radiation use efficiency (RUE) have low heritabilities in such canopies. We propose a revised Monteith equation that identifies environmental and genetic components of RIE and RUE. An environmental term, a component of RIE, characterizes the effect of the presence or absence of neighbours on light interception. The ability of a given plant to compete with its neighbours is then identified, which accounts for the genetic variability of RIE of plants having similar leaf areas. This method was used in three experiments in a phenotyping platform with 765 plants of 255 maize hybrids. As expected, the heritability of the environmental term was near zero, whereas that of the competitiveness term increased with phenological stage, resulting in the identification of quantitative trait loci. In the same way, RUE was dissected as an effect of intercepted light and a genetic term. This approach was used for predicting the behaviour of individual genotypes in virtual multi-genotype canopies. A large effect of competitiveness was observed in multi-genotype but not in single-genotype canopies, resulting in a bias for genotype comparisons in breeding fields.
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Affiliation(s)
- Tsu-Wei Chen
- Université de Montpellier, INRA, LEPSE, Montpellier, France
| | | | | | - Raphaël Perez
- Université de Montpellier, INRA, LEPSE, Montpellier, France
| | - Simon Artzet
- Université de Montpellier, INRA, LEPSE, Montpellier, France
| | | | - Aude Coupel-Ledru
- Université de Montpellier, INRA, LEPSE, Montpellier, France
- CIRAD, UMR AGAP, Montpellier, France
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19
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Gaudio N, Escobar-Gutiérrez AJ, Casadebaig P, Evers JB, Gérard F, Louarn G, Colbach N, Munz S, Launay M, Marrou H, Barillot R, Hinsinger P, Bergez JE, Combes D, Durand JL, Frak E, Pagès L, Pradal C, Saint-Jean S, Van Der Werf W, Justes E. Current knowledge and future research opportunities for modeling annual crop mixtures. A review. Agron Sustain Dev 2019; 39:20. [PMID: 0 DOI: 10.1007/s13593-019-0562-6] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/04/2019] [Indexed: 05/27/2023]
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20
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Barillot R, Chambon C, Fournier C, Combes D, Pradal C, Andrieu B. 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. Ann Bot 2019; 123:727-742. [PMID: 30535066 PMCID: PMC6417479 DOI: 10.1093/aob/mcy208] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Accepted: 10/29/2018] [Indexed: 05/13/2023]
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.
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Affiliation(s)
- Romain Barillot
- UR P3F, INRA, Lusignan, France
- UMR ECOSYS, INRA, AgroParisTech, Université Paris-Saclay, Thiverval-Grignon, France
| | - Camille Chambon
- UMR ECOSYS, INRA, AgroParisTech, Université Paris-Saclay, Thiverval-Grignon, France
| | - Christian Fournier
- UMR LEPSE, INRA, Montpellier SupAgro, Université de Montpellier, Montpellier, France
| | | | - Christophe Pradal
- CIRAD, UMR AGAP, Montpellier, France
- AGAP, Univ Montpellier, CIRAD, INRA, Inria, Montpellier SupAgro, Montpellier, France
- Inria, Zenith, Montpellier, France
| | - Bruno Andrieu
- UMR ECOSYS, INRA, AgroParisTech, Université Paris-Saclay, Thiverval-Grignon, France
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21
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Robert C, Garin G, Abichou M, Houlès V, Pradal C, Fournier C. Plant architecture and foliar senescence impact the race between wheat growth and Zymoseptoria tritici epidemics. Ann Bot 2018; 121:975-989. [PMID: 29373663 PMCID: PMC5906930 DOI: 10.1093/aob/mcx192] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2017] [Accepted: 12/22/2017] [Indexed: 05/12/2023]
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.
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Affiliation(s)
- Corinne Robert
- INRA, UMR 1402 ECOSYS, F-78850 Thiverval-Grignon, France
| | | | - Mariem Abichou
- INRA, UMR 1402 ECOSYS, F-78850 Thiverval-Grignon, France
| | | | - Christophe Pradal
- CIRAD, UMR AGAP and Inria, Virtual Plants, Montpellier, France
- Institut de Biologie Computationnelle, Montpellier, France
| | - Christian Fournier
- CIRAD, UMR AGAP and Inria, Virtual Plants, Montpellier, France
- INRA, UMR 759 LEPSE, Montpellier, France
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22
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Garin G, Pradal C, Fournier C, Claessen D, Houlès V, Robert C. Modelling interaction dynamics between two foliar pathogens in wheat: a multi-scale approach. Ann Bot 2018; 121:927-940. [PMID: 29300857 PMCID: PMC5906911 DOI: 10.1093/aob/mcx186] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2017] [Accepted: 12/13/2017] [Indexed: 05/12/2023]
Abstract
Background and Aims Disease models can improve our understanding of dynamic interactions in pathosystems and thus support the design of innovative and sustainable strategies of crop protections. However, most epidemiological models focus on a single type of pathogen, ignoring the interactions between different parasites competing on the same host and how they are impacted by properties of the canopy. This study presents a new model of a disease complex coupling two wheat fungal diseases, caused by Zymoseptoria tritici (septoria) and Puccinia triticina (brown rust), respectively, combined with a functional-structural plant model of wheat. Methods At the leaf scale, our model is a combination of two sub-models of the infection cycles for the two fungal pathogens with a sub-model of competition between lesions. We assume that the leaf area is the resource available for both fungi. Due to the necrotic period of septoria, it has a competitive advantage on biotrophic lesions of rust. Assumptions on lesion competition are first tested developing a geometrically explicit model on a simplified rectangular shape, representing a leaf on which lesions grow and interact according to a set of rules derived from the literature. Then a descriptive statistical model at the leaf scale was designed by upscaling the previous mechanistic model, and both models were compared. Finally, the simplified statistical model has been used in a 3-D epidemiological canopy growth model to simulate the diseases dynamics and the interactions at the canopy scale. Key Results At the leaf scale, the statistical model was a satisfactory metamodel of the complex geometrical model. At the canopy scale, the disease dynamics for each fungus alone and together were explored in different weather scenarios. Rust and septoria epidemics showed different behaviours. Simulated epidemics of brown rust were greatly affected by the presence of septoria for almost all the tested scenarios, but the reverse was not the case. However, shortening the rust latent period or advancing the rust inoculum shifted the competition more in favour of rust, and epidemics became more balanced. Conclusions This study is a first step towards the integration of several diseases within virtual plant models and should prompt new research to understand the interactions between canopy properties and competing pathogens.
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Affiliation(s)
| | - Christophe Pradal
- CIRAD, UMR AGAP and Inria, VirtualPlants, Montpellier, France
- AGAP, Université de Montpellier, CIRAD, INRA, Inria, Montpellier SupAgro, Montpellier, France
| | - Christian Fournier
- CIRAD, UMR AGAP and Inria, VirtualPlants, Montpellier, France
- INRA, UMR 759 LEPSE, Montpellier, France
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23
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Brichet N, Fournier C, Turc O, Strauss O, Artzet S, Pradal C, Welcker C, Tardieu F, Cabrera-Bosquet L. A robot-assisted imaging pipeline for tracking the growths of maize ear and silks in a high-throughput phenotyping platform. Plant Methods 2017; 13:96. [PMID: 29176999 PMCID: PMC5688816 DOI: 10.1186/s13007-017-0246-7] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2017] [Accepted: 10/25/2017] [Indexed: 05/25/2023]
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.
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Affiliation(s)
- Nicolas Brichet
- LEPSE, INRA, Montpellier SupAgro, Univ Montpellier, Montpellier, France
| | - Christian Fournier
- LEPSE, INRA, Montpellier SupAgro, Univ Montpellier, Montpellier, France
- Inria, Virtual Plants, Montpellier, France
| | - Olivier Turc
- LEPSE, INRA, Montpellier SupAgro, Univ Montpellier, Montpellier, France
| | - Olivier Strauss
- LIRMM, Department of Robotics, Univ Montpellier, 34392 Montpellier, France
| | - Simon Artzet
- LEPSE, INRA, Montpellier SupAgro, Univ Montpellier, Montpellier, France
- Inria, Virtual Plants, Montpellier, France
| | - Christophe Pradal
- Inria, Virtual Plants, Montpellier, France
- CIRAD, UMR AGAP, 34398 Montpellier, France
- AGAP, Univ Montpellier, CIRAD, INRA, Inria, Montpellier SupAgro, Montpellier, France
| | - Claude Welcker
- LEPSE, INRA, Montpellier SupAgro, Univ Montpellier, Montpellier, France
| | - François Tardieu
- LEPSE, INRA, Montpellier SupAgro, Univ Montpellier, Montpellier, France
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Ndour A, Vadez V, Pradal C, Lucas M. Virtual Plants Need Water Too: Functional-Structural Root System Models in the Context of Drought Tolerance Breeding. Front Plant Sci 2017; 8:1577. [PMID: 29018456 PMCID: PMC5622977 DOI: 10.3389/fpls.2017.01577] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2016] [Accepted: 08/29/2017] [Indexed: 05/04/2023]
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.
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Affiliation(s)
- Adama Ndour
- Laboratoire Mixte International Adaptation des Plantes et Microorganismes Associés Aux Stress Environnementaux (LAPSE), Dakar, Senegal
- Laboratoire Commun de Microbiologie (IRD-ISRA-UCAD), Dakar, Senegal
- CERES, IRD, Université de Montpellier, UMR DIADE, Montpellier, France
- Département Maths/Informatique, Faculté des Sciences et Techniques, Université Cheikh Anta Diop, Dakar, Senegal
| | - Vincent Vadez
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India
| | - Christophe Pradal
- UMR AGAP, Univiversité de Montpellier, CIRAD, INRA, Inria, Montpellier SupAgro, Montpellier, France
| | - Mikaël Lucas
- Laboratoire Mixte International Adaptation des Plantes et Microorganismes Associés Aux Stress Environnementaux (LAPSE), Dakar, Senegal
- Laboratoire Commun de Microbiologie (IRD-ISRA-UCAD), Dakar, Senegal
- CERES, IRD, Université de Montpellier, UMR DIADE, Montpellier, France
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25
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Bucksch A, Atta-Boateng A, Azihou AF, Battogtokh D, Baumgartner A, Binder BM, Braybrook SA, Chang C, Coneva V, DeWitt TJ, Fletcher AG, Gehan MA, Diaz-Martinez DH, Hong L, Iyer-Pascuzzi AS, Klein LL, Leiboff S, Li M, Lynch JP, Maizel A, Maloof JN, Markelz RJC, Martinez CC, Miller LA, Mio W, Palubicki W, Poorter H, Pradal C, Price CA, Puttonen E, Reese JB, Rellán-Álvarez R, Spalding EP, Sparks EE, Topp CN, Williams JH, Chitwood DH. Morphological Plant Modeling: Unleashing Geometric and Topological Potential within the Plant Sciences. Front Plant Sci 2017; 8:900. [PMID: 28659934 PMCID: PMC5465304 DOI: 10.3389/fpls.2017.00900] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Accepted: 05/12/2017] [Indexed: 05/21/2023]
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.
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Affiliation(s)
- Alexander Bucksch
- Department of Plant Biology, University of Georgia, AthensGA, United States
- Warnell School of Forestry and Natural Resources, University of Georgia, AthensGA, United States
- Institute of Bioinformatics, University of Georgia, AthensGA, United States
| | | | - Akomian F. Azihou
- Laboratory of Applied Ecology, Faculty of Agronomic Sciences, University of Abomey-CalaviCotonou, Benin
| | - Dorjsuren Battogtokh
- Department of Biological Sciences, Virginia Polytechnic Institute and State University, BlacksburgVA, United States
| | - Aly Baumgartner
- Department of Geosciences, Baylor University, WacoTX, United States
| | - Brad M. Binder
- Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, KnoxvilleTN, United States
| | | | - Cynthia Chang
- Division of Biology, University of Washington, BothellWA, United States
| | - Viktoirya Coneva
- Donald Danforth Plant Science Center, St. LouisMO, United States
| | - Thomas J. DeWitt
- Department of Wildlife and Fisheries Sciences–Department of Plant Pathology and Microbiology, Texas A&M University, College StationTX, United States
| | - Alexander G. Fletcher
- School of Mathematics and Statistics and Bateson Centre, University of SheffieldSheffield, United Kingdom
| | - Malia A. Gehan
- Donald Danforth Plant Science Center, St. LouisMO, United States
| | | | - Lilan Hong
- Weill Institute for Cell and Molecular Biology and Section of Plant Biology, School of Integrative Plant Sciences, Cornell University, IthacaNY, United States
| | - Anjali S. Iyer-Pascuzzi
- Department of Botany and Plant Pathology, Purdue University, West LafayetteIN, United States
| | - Laura L. Klein
- Department of Biology, Saint Louis University, St. LouisMO, United States
| | - Samuel Leiboff
- School of Integrative Plant Science, Cornell University, IthacaNY, United States
| | - Mao Li
- Department of Mathematics, Florida State University, TallahasseeFL, United States
| | - Jonathan P. Lynch
- Department of Plant Science, The Pennsylvania State University, University ParkPA, United States
| | - Alexis Maizel
- Center for Organismal Studies, Heidelberg UniversityHeidelberg, Germany
| | - Julin N. Maloof
- Department of Plant Biology, University of California, Davis, DavisCA, United States
| | - R. J. Cody Markelz
- Department of Plant Biology, University of California, Davis, DavisCA, United States
| | - Ciera C. Martinez
- Department of Molecular and Cell Biology, University of California, Berkeley, BerkeleyCA, United States
| | - Laura A. Miller
- Program in Bioinformatics and Computational Biology, The University of North Carolina, Chapel HillNC, United States
| | - Washington Mio
- Department of Mathematics, Florida State University, TallahasseeFL, United States
| | - Wojtek Palubicki
- The Sainsbury Laboratory, University of CambridgeCambridge, United Kingdom
| | - Hendrik Poorter
- Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, JülichGermany
| | | | - Charles A. Price
- National Institute for Mathematical and Biological Synthesis, University of Tennessee, Knoxville, KnoxvilleTN, United States
| | - Eetu Puttonen
- Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, National Land Survey of FinlandMasala, Finland
- Centre of Excellence in Laser Scanning Research, National Land Survey of FinlandMasala, Finland
| | - John B. Reese
- Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, KnoxvilleTN, United States
| | - Rubén Rellán-Álvarez
- Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad, Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV)Irapuato, Mexico
| | - Edgar P. Spalding
- Department of Botany, University of Wisconsin–Madison, MadisonWI, United States
| | - Erin E. Sparks
- Department of Plant and Soil Sciences and Delaware Biotechnology Institute, University of Delaware, NewarkDE, United States
| | | | - Joseph H. Williams
- Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, KnoxvilleTN, United States
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26
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Reyes F, Gianelle D, Pallas B, Costes E, Pradal C, Tagliavini M, Zanotelli D. A multi-scale model to explore carbon allocation in plants. ACTA ACUST UNITED AC 2017. [DOI: 10.17660/actahortic.2017.1160.41] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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27
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Balduzzi M, Binder BM, Bucksch A, Chang C, Hong L, Iyer-Pascuzzi AS, Pradal C, Sparks EE. Reshaping Plant Biology: Qualitative and Quantitative Descriptors for Plant Morphology. Front Plant Sci 2017; 8:117. [PMID: 28217137 PMCID: PMC5289971 DOI: 10.3389/fpls.2017.00117] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2016] [Accepted: 01/19/2017] [Indexed: 05/04/2023]
Abstract
An emerging challenge in plant biology is to develop qualitative and quantitative measures to describe the appearance of plants through the integration of mathematics and biology. A major hurdle in developing these metrics is finding common terminology across fields. In this review, we define approaches for analyzing plant geometry, topology, and shape, and provide examples for how these terms have been and can be applied to plants. In leaf morphological quantifications both geometry and shape have been used to gain insight into leaf function and evolution. For the analysis of cell growth and expansion, we highlight the utility of geometric descriptors for understanding sepal and hypocotyl development. For branched structures, we describe how topology has been applied to quantify root system architecture to lend insight into root function. Lastly, we discuss the importance of using morphological descriptors in ecology to assess how communities interact, function, and respond within different environments. This review aims to provide a basic description of the mathematical principles underlying morphological quantifications.
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Affiliation(s)
| | - Brad M. Binder
- Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee-KnoxvilleKnoxville, TN, USA
| | - Alexander Bucksch
- Department of Plant Biology, University of GeorgiaAthens, GA, USA
- Warnell School of Forestry and Environmental Resources, University of GeorgiaAthens, GA, USA
- Institute of Bioinformatics, University of GeorgiaAthens, GA, USA
| | - Cynthia Chang
- Division of Biological Sciences, University of Washington-BothellBothell, WA, USA
| | - Lilan Hong
- Weill Institute for Cell and Molecular Biology and Section of Plant Biology, School of Integrative Plant Sciences, Cornell UniversityIthaca, NY, USA
| | | | - Christophe Pradal
- INRIA, Virtual PlantsMontpellier, France
- CIRAD, UMR AGAPMontpellier, France
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28
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Bucksch A, Atta-Boateng A, Azihou AF, Battogtokh D, Baumgartner A, Binder BM, Braybrook SA, Chang C, Coneva V, DeWitt TJ, Fletcher AG, Gehan MA, Diaz-Martinez DH, Hong L, Iyer-Pascuzzi AS, Klein LL, Leiboff S, Li M, Lynch JP, Maizel A, Maloof JN, Markelz RJC, Martinez CC, Miller LA, Mio W, Palubicki W, Poorter H, Pradal C, Price CA, Puttonen E, Reese JB, Rellán-Álvarez R, Spalding EP, Sparks EE, Topp CN, Williams JH, Chitwood DH. Morphological Plant Modeling: Unleashing Geometric and Topological Potential within the Plant Sciences. Front Plant Sci 2017. [PMID: 28659934 DOI: 10.3389/978-2-88945-297-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
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.
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Affiliation(s)
- Alexander Bucksch
- Department of Plant Biology, University of Georgia, AthensGA, United States
- Warnell School of Forestry and Natural Resources, University of Georgia, AthensGA, United States
- Institute of Bioinformatics, University of Georgia, AthensGA, United States
| | | | - Akomian F Azihou
- Laboratory of Applied Ecology, Faculty of Agronomic Sciences, University of Abomey-CalaviCotonou, Benin
| | - Dorjsuren Battogtokh
- Department of Biological Sciences, Virginia Polytechnic Institute and State University, BlacksburgVA, United States
| | - Aly Baumgartner
- Department of Geosciences, Baylor University, WacoTX, United States
| | - Brad M Binder
- Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, KnoxvilleTN, United States
| | | | - Cynthia Chang
- Division of Biology, University of Washington, BothellWA, United States
| | - Viktoirya Coneva
- Donald Danforth Plant Science Center, St. LouisMO, United States
| | - Thomas J DeWitt
- Department of Wildlife and Fisheries Sciences-Department of Plant Pathology and Microbiology, Texas A&M University, College StationTX, United States
| | - Alexander G Fletcher
- School of Mathematics and Statistics and Bateson Centre, University of SheffieldSheffield, United Kingdom
| | - Malia A Gehan
- Donald Danforth Plant Science Center, St. LouisMO, United States
| | | | - Lilan Hong
- Weill Institute for Cell and Molecular Biology and Section of Plant Biology, School of Integrative Plant Sciences, Cornell University, IthacaNY, United States
| | - Anjali S Iyer-Pascuzzi
- Department of Botany and Plant Pathology, Purdue University, West LafayetteIN, United States
| | - Laura L Klein
- Department of Biology, Saint Louis University, St. LouisMO, United States
| | - Samuel Leiboff
- School of Integrative Plant Science, Cornell University, IthacaNY, United States
| | - Mao Li
- Department of Mathematics, Florida State University, TallahasseeFL, United States
| | - Jonathan P Lynch
- Department of Plant Science, The Pennsylvania State University, University ParkPA, United States
| | - Alexis Maizel
- Center for Organismal Studies, Heidelberg UniversityHeidelberg, Germany
| | - Julin N Maloof
- Department of Plant Biology, University of California, Davis, DavisCA, United States
| | - R J Cody Markelz
- Department of Plant Biology, University of California, Davis, DavisCA, United States
| | - Ciera C Martinez
- Department of Molecular and Cell Biology, University of California, Berkeley, BerkeleyCA, United States
| | - Laura A Miller
- Program in Bioinformatics and Computational Biology, The University of North Carolina, Chapel HillNC, United States
| | - Washington Mio
- Department of Mathematics, Florida State University, TallahasseeFL, United States
| | - Wojtek Palubicki
- The Sainsbury Laboratory, University of CambridgeCambridge, United Kingdom
| | - Hendrik Poorter
- Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, JülichGermany
| | | | - Charles A Price
- National Institute for Mathematical and Biological Synthesis, University of Tennessee, Knoxville, KnoxvilleTN, United States
| | - Eetu Puttonen
- Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, National Land Survey of FinlandMasala, Finland
- Centre of Excellence in Laser Scanning Research, National Land Survey of FinlandMasala, Finland
| | - John B Reese
- Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, KnoxvilleTN, United States
| | - Rubén Rellán-Álvarez
- Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad, Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV)Irapuato, Mexico
| | - Edgar P Spalding
- Department of Botany, University of Wisconsin-Madison, MadisonWI, United States
| | - Erin E Sparks
- Department of Plant and Soil Sciences and Delaware Biotechnology Institute, University of Delaware, NewarkDE, United States
| | | | - Joseph H Williams
- Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, KnoxvilleTN, United States
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Lobet G, Pound MP, Diener J, Pradal C, Draye X, Godin C, Javaux M, Leitner D, Meunier F, Nacry P, Pridmore TP, Schnepf A. Root system markup language: toward a unified root architecture description language. Plant Physiol 2015; 167:617-27. [PMID: 25614065 PMCID: PMC4348768 DOI: 10.1104/pp.114.253625] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2014] [Accepted: 01/21/2015] [Indexed: 05/03/2023]
Abstract
The number of image analysis tools supporting the extraction of architectural features of root systems has increased in recent years. These tools offer a handy set of complementary facilities, yet it is widely accepted that none of these software tools is able to extract in an efficient way the growing array of static and dynamic features for different types of images and species. We describe the Root System Markup Language (RSML), which has been designed to overcome two major challenges: (1) to enable portability of root architecture data between different software tools in an easy and interoperable manner, allowing seamless collaborative work; and (2) to provide a standard format upon which to base central repositories that will soon arise following the expanding worldwide root phenotyping effort. RSML follows the XML standard to store two- or three-dimensional image metadata, plant and root properties and geometries, continuous functions along individual root paths, and a suite of annotations at the image, plant, or root scale at one or several time points. Plant ontologies are used to describe botanical entities that are relevant at the scale of root system architecture. An XML schema describes the features and constraints of RSML, and open-source packages have been developed in several languages (R, Excel, Java, Python, and C#) to enable researchers to integrate RSML files into popular research workflow.
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Affiliation(s)
- Guillaume Lobet
- PhytoSYSTEMS, Université de Liège, 4000 Liege, Belgium (G.L.);Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, United Kingdom (M.P.P.);Virtual Plants, Inria, Cirad, Institut National de la Recherche Agronomique, 34095 Montpellier, France (J.D., C.P., C.G.);Institut de Biologie Computationnelle, F-34095 Montpellier, France (C.P.);Earth and Life Institute, Université Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium (X.D., M.J., F.M.);Institut für Bio- und Geowissenschaften: Agrosphäre, Forschungszentrum Jülich, D-52425 Julich, Germany (M.J., A.S.);Computational Science Center, University of Vienna, 1090 Vienna, Austria (D.L.);Biochemistry and Plant Molecular Physiology, Unité Mixte de Recherche 5004 Centre National de la Recherche Scientifique/Institut National de la Recherche Agronomique/SupAgro-M/UM2, Institut de Biologie Intégrative des Plantes, 34060 Montpellier cedex 1, France (P.N.); andSchool of Computer Science, University of Nottingham, Nottingham NG8 1BB, United Kingdom (T.P.P.)
| | - Michael P Pound
- PhytoSYSTEMS, Université de Liège, 4000 Liege, Belgium (G.L.);Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, United Kingdom (M.P.P.);Virtual Plants, Inria, Cirad, Institut National de la Recherche Agronomique, 34095 Montpellier, France (J.D., C.P., C.G.);Institut de Biologie Computationnelle, F-34095 Montpellier, France (C.P.);Earth and Life Institute, Université Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium (X.D., M.J., F.M.);Institut für Bio- und Geowissenschaften: Agrosphäre, Forschungszentrum Jülich, D-52425 Julich, Germany (M.J., A.S.);Computational Science Center, University of Vienna, 1090 Vienna, Austria (D.L.);Biochemistry and Plant Molecular Physiology, Unité Mixte de Recherche 5004 Centre National de la Recherche Scientifique/Institut National de la Recherche Agronomique/SupAgro-M/UM2, Institut de Biologie Intégrative des Plantes, 34060 Montpellier cedex 1, France (P.N.); andSchool of Computer Science, University of Nottingham, Nottingham NG8 1BB, United Kingdom (T.P.P.)
| | - Julien Diener
- PhytoSYSTEMS, Université de Liège, 4000 Liege, Belgium (G.L.);Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, United Kingdom (M.P.P.);Virtual Plants, Inria, Cirad, Institut National de la Recherche Agronomique, 34095 Montpellier, France (J.D., C.P., C.G.);Institut de Biologie Computationnelle, F-34095 Montpellier, France (C.P.);Earth and Life Institute, Université Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium (X.D., M.J., F.M.);Institut für Bio- und Geowissenschaften: Agrosphäre, Forschungszentrum Jülich, D-52425 Julich, Germany (M.J., A.S.);Computational Science Center, University of Vienna, 1090 Vienna, Austria (D.L.);Biochemistry and Plant Molecular Physiology, Unité Mixte de Recherche 5004 Centre National de la Recherche Scientifique/Institut National de la Recherche Agronomique/SupAgro-M/UM2, Institut de Biologie Intégrative des Plantes, 34060 Montpellier cedex 1, France (P.N.); andSchool of Computer Science, University of Nottingham, Nottingham NG8 1BB, United Kingdom (T.P.P.)
| | - Christophe Pradal
- PhytoSYSTEMS, Université de Liège, 4000 Liege, Belgium (G.L.);Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, United Kingdom (M.P.P.);Virtual Plants, Inria, Cirad, Institut National de la Recherche Agronomique, 34095 Montpellier, France (J.D., C.P., C.G.);Institut de Biologie Computationnelle, F-34095 Montpellier, France (C.P.);Earth and Life Institute, Université Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium (X.D., M.J., F.M.);Institut für Bio- und Geowissenschaften: Agrosphäre, Forschungszentrum Jülich, D-52425 Julich, Germany (M.J., A.S.);Computational Science Center, University of Vienna, 1090 Vienna, Austria (D.L.);Biochemistry and Plant Molecular Physiology, Unité Mixte de Recherche 5004 Centre National de la Recherche Scientifique/Institut National de la Recherche Agronomique/SupAgro-M/UM2, Institut de Biologie Intégrative des Plantes, 34060 Montpellier cedex 1, France (P.N.); andSchool of Computer Science, University of Nottingham, Nottingham NG8 1BB, United Kingdom (T.P.P.)
| | - Xavier Draye
- PhytoSYSTEMS, Université de Liège, 4000 Liege, Belgium (G.L.);Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, United Kingdom (M.P.P.);Virtual Plants, Inria, Cirad, Institut National de la Recherche Agronomique, 34095 Montpellier, France (J.D., C.P., C.G.);Institut de Biologie Computationnelle, F-34095 Montpellier, France (C.P.);Earth and Life Institute, Université Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium (X.D., M.J., F.M.);Institut für Bio- und Geowissenschaften: Agrosphäre, Forschungszentrum Jülich, D-52425 Julich, Germany (M.J., A.S.);Computational Science Center, University of Vienna, 1090 Vienna, Austria (D.L.);Biochemistry and Plant Molecular Physiology, Unité Mixte de Recherche 5004 Centre National de la Recherche Scientifique/Institut National de la Recherche Agronomique/SupAgro-M/UM2, Institut de Biologie Intégrative des Plantes, 34060 Montpellier cedex 1, France (P.N.); andSchool of Computer Science, University of Nottingham, Nottingham NG8 1BB, United Kingdom (T.P.P.)
| | - Christophe Godin
- PhytoSYSTEMS, Université de Liège, 4000 Liege, Belgium (G.L.);Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, United Kingdom (M.P.P.);Virtual Plants, Inria, Cirad, Institut National de la Recherche Agronomique, 34095 Montpellier, France (J.D., C.P., C.G.);Institut de Biologie Computationnelle, F-34095 Montpellier, France (C.P.);Earth and Life Institute, Université Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium (X.D., M.J., F.M.);Institut für Bio- und Geowissenschaften: Agrosphäre, Forschungszentrum Jülich, D-52425 Julich, Germany (M.J., A.S.);Computational Science Center, University of Vienna, 1090 Vienna, Austria (D.L.);Biochemistry and Plant Molecular Physiology, Unité Mixte de Recherche 5004 Centre National de la Recherche Scientifique/Institut National de la Recherche Agronomique/SupAgro-M/UM2, Institut de Biologie Intégrative des Plantes, 34060 Montpellier cedex 1, France (P.N.); andSchool of Computer Science, University of Nottingham, Nottingham NG8 1BB, United Kingdom (T.P.P.)
| | - Mathieu Javaux
- PhytoSYSTEMS, Université de Liège, 4000 Liege, Belgium (G.L.);Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, United Kingdom (M.P.P.);Virtual Plants, Inria, Cirad, Institut National de la Recherche Agronomique, 34095 Montpellier, France (J.D., C.P., C.G.);Institut de Biologie Computationnelle, F-34095 Montpellier, France (C.P.);Earth and Life Institute, Université Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium (X.D., M.J., F.M.);Institut für Bio- und Geowissenschaften: Agrosphäre, Forschungszentrum Jülich, D-52425 Julich, Germany (M.J., A.S.);Computational Science Center, University of Vienna, 1090 Vienna, Austria (D.L.);Biochemistry and Plant Molecular Physiology, Unité Mixte de Recherche 5004 Centre National de la Recherche Scientifique/Institut National de la Recherche Agronomique/SupAgro-M/UM2, Institut de Biologie Intégrative des Plantes, 34060 Montpellier cedex 1, France (P.N.); andSchool of Computer Science, University of Nottingham, Nottingham NG8 1BB, United Kingdom (T.P.P.)
| | - Daniel Leitner
- PhytoSYSTEMS, Université de Liège, 4000 Liege, Belgium (G.L.);Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, United Kingdom (M.P.P.);Virtual Plants, Inria, Cirad, Institut National de la Recherche Agronomique, 34095 Montpellier, France (J.D., C.P., C.G.);Institut de Biologie Computationnelle, F-34095 Montpellier, France (C.P.);Earth and Life Institute, Université Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium (X.D., M.J., F.M.);Institut für Bio- und Geowissenschaften: Agrosphäre, Forschungszentrum Jülich, D-52425 Julich, Germany (M.J., A.S.);Computational Science Center, University of Vienna, 1090 Vienna, Austria (D.L.);Biochemistry and Plant Molecular Physiology, Unité Mixte de Recherche 5004 Centre National de la Recherche Scientifique/Institut National de la Recherche Agronomique/SupAgro-M/UM2, Institut de Biologie Intégrative des Plantes, 34060 Montpellier cedex 1, France (P.N.); andSchool of Computer Science, University of Nottingham, Nottingham NG8 1BB, United Kingdom (T.P.P.)
| | - Félicien Meunier
- PhytoSYSTEMS, Université de Liège, 4000 Liege, Belgium (G.L.);Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, United Kingdom (M.P.P.);Virtual Plants, Inria, Cirad, Institut National de la Recherche Agronomique, 34095 Montpellier, France (J.D., C.P., C.G.);Institut de Biologie Computationnelle, F-34095 Montpellier, France (C.P.);Earth and Life Institute, Université Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium (X.D., M.J., F.M.);Institut für Bio- und Geowissenschaften: Agrosphäre, Forschungszentrum Jülich, D-52425 Julich, Germany (M.J., A.S.);Computational Science Center, University of Vienna, 1090 Vienna, Austria (D.L.);Biochemistry and Plant Molecular Physiology, Unité Mixte de Recherche 5004 Centre National de la Recherche Scientifique/Institut National de la Recherche Agronomique/SupAgro-M/UM2, Institut de Biologie Intégrative des Plantes, 34060 Montpellier cedex 1, France (P.N.); andSchool of Computer Science, University of Nottingham, Nottingham NG8 1BB, United Kingdom (T.P.P.)
| | - Philippe Nacry
- PhytoSYSTEMS, Université de Liège, 4000 Liege, Belgium (G.L.);Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, United Kingdom (M.P.P.);Virtual Plants, Inria, Cirad, Institut National de la Recherche Agronomique, 34095 Montpellier, France (J.D., C.P., C.G.);Institut de Biologie Computationnelle, F-34095 Montpellier, France (C.P.);Earth and Life Institute, Université Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium (X.D., M.J., F.M.);Institut für Bio- und Geowissenschaften: Agrosphäre, Forschungszentrum Jülich, D-52425 Julich, Germany (M.J., A.S.);Computational Science Center, University of Vienna, 1090 Vienna, Austria (D.L.);Biochemistry and Plant Molecular Physiology, Unité Mixte de Recherche 5004 Centre National de la Recherche Scientifique/Institut National de la Recherche Agronomique/SupAgro-M/UM2, Institut de Biologie Intégrative des Plantes, 34060 Montpellier cedex 1, France (P.N.); andSchool of Computer Science, University of Nottingham, Nottingham NG8 1BB, United Kingdom (T.P.P.)
| | - Tony P Pridmore
- PhytoSYSTEMS, Université de Liège, 4000 Liege, Belgium (G.L.);Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, United Kingdom (M.P.P.);Virtual Plants, Inria, Cirad, Institut National de la Recherche Agronomique, 34095 Montpellier, France (J.D., C.P., C.G.);Institut de Biologie Computationnelle, F-34095 Montpellier, France (C.P.);Earth and Life Institute, Université Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium (X.D., M.J., F.M.);Institut für Bio- und Geowissenschaften: Agrosphäre, Forschungszentrum Jülich, D-52425 Julich, Germany (M.J., A.S.);Computational Science Center, University of Vienna, 1090 Vienna, Austria (D.L.);Biochemistry and Plant Molecular Physiology, Unité Mixte de Recherche 5004 Centre National de la Recherche Scientifique/Institut National de la Recherche Agronomique/SupAgro-M/UM2, Institut de Biologie Intégrative des Plantes, 34060 Montpellier cedex 1, France (P.N.); andSchool of Computer Science, University of Nottingham, Nottingham NG8 1BB, United Kingdom (T.P.P.)
| | - Andrea Schnepf
- PhytoSYSTEMS, Université de Liège, 4000 Liege, Belgium (G.L.);Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, United Kingdom (M.P.P.);Virtual Plants, Inria, Cirad, Institut National de la Recherche Agronomique, 34095 Montpellier, France (J.D., C.P., C.G.);Institut de Biologie Computationnelle, F-34095 Montpellier, France (C.P.);Earth and Life Institute, Université Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium (X.D., M.J., F.M.);Institut für Bio- und Geowissenschaften: Agrosphäre, Forschungszentrum Jülich, D-52425 Julich, Germany (M.J., A.S.);Computational Science Center, University of Vienna, 1090 Vienna, Austria (D.L.);Biochemistry and Plant Molecular Physiology, Unité Mixte de Recherche 5004 Centre National de la Recherche Scientifique/Institut National de la Recherche Agronomique/SupAgro-M/UM2, Institut de Biologie Intégrative des Plantes, 34060 Montpellier cedex 1, France (P.N.); andSchool of Computer Science, University of Nottingham, Nottingham NG8 1BB, United Kingdom (T.P.P.)
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Garin G, Fournier C, Andrieu B, Houlès V, Robert C, Pradal C. A modelling framework to simulate foliar fungal epidemics using functional-structural plant models. Ann Bot 2014; 114:795-812. [PMID: 24925323 PMCID: PMC4217683 DOI: 10.1093/aob/mcu101] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2013] [Accepted: 04/28/2014] [Indexed: 05/04/2023]
Abstract
BACKGROUND AND AIMS Sustainable agriculture requires the identification of new, environmentally responsible strategies of crop protection. Modelling of pathosystems can allow a better understanding of the major interactions inside these dynamic systems and may lead to innovative protection strategies. In particular, functional-structural plant models (FSPMs) have been identified as a means to optimize the use of architecture-related traits. A current limitation lies in the inherent complexity of this type of modelling, and thus the purpose of this paper is to provide a framework to both extend and simplify the modelling of pathosystems using FSPMs. METHODS Different entities and interactions occurring in pathosystems were formalized in a conceptual model. A framework based on these concepts was then implemented within the open-source OpenAlea modelling platform, using the platform's general strategy of modelling plant-environment interactions and extending it to handle plant interactions with pathogens. New developments include a generic data structure for representing lesions and dispersal units, and a series of generic protocols to communicate with objects representing the canopy and its microenvironment in the OpenAlea platform. Another development is the addition of a library of elementary models involved in pathosystem modelling. Several plant and physical models are already available in OpenAlea and can be combined in models of pathosystems using this framework approach. KEY RESULTS Two contrasting pathosystems are implemented using the framework and illustrate its generic utility. Simulations demonstrate the framework's ability to simulate multiscaled interactions within pathosystems, and also show that models are modular components within the framework and can be extended. This is illustrated by testing the impact of canopy architectural traits on fungal dispersal. CONCLUSIONS This study provides a framework for modelling a large number of pathosystems using FSPMs. This structure can accommodate both previously developed models for individual aspects of pathosystems and new ones. Complex models are deconstructed into separate 'knowledge sources' originating from different specialist areas of expertise and these can be shared and reassembled into multidisciplinary models. The framework thus provides a beneficial tool for a potential diverse and dynamic research community.
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Affiliation(s)
- Guillaume Garin
- ITK, avenue de l'Europe, F-34830 Clapiers, France
- INRA, UMR 1091 EGC, F-78850 Thiverval-Grignon, France
| | | | - Bruno Andrieu
- INRA, UMR 1091 EGC, F-78850 Thiverval-Grignon, France
| | | | | | - Christophe Pradal
- CIRAD, UMR AGAP and INRIA, Virtual Plants, F-34398 Montpellier, France
- Institut de Biologie Computationnelle, F-34095 Montpellier, France
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Boudon F, Pradal C, Cokelaer T, Prusinkiewicz P, Godin C. L-py: an L-system simulation framework for modeling plant architecture development based on a dynamic language. Front Plant Sci 2012; 3:76. [PMID: 22670147 PMCID: PMC3362793 DOI: 10.3389/fpls.2012.00076] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2011] [Accepted: 04/04/2012] [Indexed: 05/04/2023]
Abstract
The study of plant development requires increasingly powerful modeling tools to help understand and simulate the growth and functioning of plants. In the last decade, the formalism of L-systems has emerged as a major paradigm for modeling plant development. Previous implementations of this formalism were made based on static languages, i.e., languages that require explicit definition of variable types before using them. These languages are often efficient but involve quite a lot of syntactic overhead, thus restricting the flexibility of use for modelers. In this work, we present an adaptation of L-systems to the Python language, a popular and powerful open-license dynamic language. We show that the use of dynamic language properties makes it possible to enhance the development of plant growth models: (i) by keeping a simple syntax while allowing for high-level programming constructs, (ii) by making code execution easy and avoiding compilation overhead, (iii) by allowing a high-level of model reusability and the building of complex modular models, and (iv) by providing powerful solutions to integrate MTG data-structures (that are a common way to represent plants at several scales) into L-systems and thus enabling to use a wide spectrum of computer tools based on MTGs developed for plant architecture. We then illustrate the use of L-Py in real applications to build complex models or to teach plant modeling in the classroom.
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Fournier C, Pradal C, Chelle M, Boudon F, Louarn G, Robert C, Combes D, Cokelaer T, Bertheloot J, Ma K, Saint-Jean S, Verdenal A, Escobar-Gutièrrez A, Andrieu B, Godin C. Sharing efforts for modelling plant systems: from publications to reusable software components. Comp Biochem Physiol A Mol Integr Physiol 2009. [DOI: 10.1016/j.cbpa.2009.04.549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Pradal C, Dufour-Kowalski S, Boudon F, Fournier C, Godin C. OpenAlea: a visual programming and component-based software platform for plant modelling. Funct Plant Biol 2008; 35:751-760. [PMID: 32688829 DOI: 10.1071/fp08084] [Citation(s) in RCA: 74] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2008] [Accepted: 07/29/2008] [Indexed: 05/20/2023]
Abstract
The development of functional-structural plant models requires an increasing amount of computer modelling. All these models are developed by different teams in various contexts and with different goals. Efficient and flexible computational frameworks are required to augment the interaction between these models, their reusability, and the possibility to compare them on identical datasets. In this paper, we present an open-source platform, OpenAlea, that provides a user-friendly environment for modellers, and advanced deployment methods. OpenAlea allows researchers to build models using a visual programming interface and provides a set of tools and models dedicated to plant modelling. Models and algorithms are embedded in OpenAlea 'components' with well defined input and output interfaces that can be easily interconnected to form more complex models and define more macroscopic components. The system architecture is based on the use of a general purpose, high-level, object-oriented script language, Python, widely used in other scientific areas. We present a brief rationale that underlies the architectural design of this system and we illustrate the use of the platform to assemble several heterogeneous model components and to rapidly prototype a complex modelling scenario.
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Affiliation(s)
- Christophe Pradal
- CIRAD, UMR DAP and INRIA, Virtual Plants, TA A-96/02, 34398 Montpellier Cedex 5, France
| | | | - Frédéric Boudon
- CIRAD, UMR DAP and INRIA, Virtual Plants, TA A-96/02, 34398 Montpellier Cedex 5, France
| | | | - Christophe Godin
- INRIA, UMR DAP, Virtual Plants, TA A-96/02, 34398 Montpellier Cedex 5, France
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