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1. Using numerical plant models and phenotypic correlation space to design achievable ideotypes

Author

Picheny, Victor, Casadebaig, Pierre, Trepos, Ronan, Faivre, Robert, Da Silva, David, Vincourt, Patrick, Costes, Evelyne, UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Laboratoire des interactions plantes micro-organismes (LIPM), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Recherche Agronomique (INRA), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Institut National de la Recherche Agronomique (INRA)-Centre National de la Recherche Scientifique (CNRS), Architecture et Fonctionnement des Espèces Fruitières [AGAP] (AFEF), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)

Subjects
plant architecture, performance de production, production functions, sunflower, [SDV]Life Sciences [q-bio], pommier, Quantitative Biology - Quantitative Methods, Models, Biological, modèle structure fonction, apple tree, Quantitative Biology - Populations and Evolution, Quantitative Methods (q-bio.QM), méthode d'optimisation, Populations and Evolution (q-bio.PE), simulation numérique, Numerical Analysis, Computer-Assisted, tournesol, Phenotype, corrélation phénotypique, FOS: Biological sciences, numerical simulation, Malus, Feasibility Studies, Helianthus, modèle de production, architecture de la plante
Abstract

Numerical plant models can predict the outcome of plant traits modifications resulting from genetic variations, on plant performance, by simulating physiological processes and their interaction with the environment. Optimization methods complement those models to design ideotypes, i.e. ideal values of a set of plant traits resulting in optimal adaptation for given combinations of environment and management, mainly through the maximization of a performance criteria (e.g. yield, light interception). As use of simulation models gains momentum in plant breeding, numerical experiments must be carefully engineered to provide accurate and attainable results, rooting them in biological reality. Here, we propose a multi-objective optimization formulation that includes a metric of performance, returned by the numerical model, and a metric of feasibility, accounting for correlations between traits based on field observations. We applied this approach to two contrasting models: a process-based crop model of sunflower and a functional-structural plant model of apple trees. In both cases, the method successfully characterized key plant traits and identified a continuum of optimal solutions, ranging from the most feasible to the most efficient. The present study thus provides successful proof of concept for this enhanced modeling approach, which identified paths for desirable trait modification, including direction and intensity., Comment: 25 pages, 5 figures, 2017, Plant, Cell and Environment

Published
2017
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2. Mesurer le niveau de résistance quantitative des variétés de tournesol face au mildiou dans les processus de sélection et d’évaluation variétale : un enjeu fort pour la gestion durable du risque

Author

Mestries, Emmannelle, Auclert, B., Poisson-Bammé, B., Penaud, Annette, Grimault, Valerie, Perrot, S., Robert, A., Roche, Sylvie, Serre, Frédéric, Tourvieille de Labrouhe, Denis, Vear, Felicity, Boniface, Marie-Claude, Bordat, Amandine, Pouilly, Nicolas, Vincourt, Patrick, Munos, Stephane, Terres Inovia, Station Nationale d’Essais de Semences (SNES), UE 1375 Phénotypage Au Champ des Céréales, Institut National de la Recherche Agronomique (INRA)-Santé des plantes et environnement (S.P.E.)-Biologie et Amélioration des Plantes (BAP)-Phénotypage Au Champ des Céréales (PHACC), Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Laboratoire des interactions plantes micro-organismes (LIPM), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Recherche Agronomique (INRA), Biologie du fruit et pathologie (BFP), Université Bordeaux Segalen - Bordeaux 2-Institut National de la Recherche Agronomique (INRA)-Université Sciences et Technologies - Bordeaux 1, Institut National de la Recherche Agronomique (INRA)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA), and Université Bordeaux Segalen - Bordeaux 2-Institut National de la Recherche Agronomique (INRA)-Université Sciences et Technologies - Bordeaux 1 (UB)

Subjects
phenotyping, quantitative resistance, résistance quantitative, conditions contrôlées, phénotypage, [SDV]Life Sciences [q-bio], Plasmopara halstedii, controlled conditions
Abstract

La résistance quantitative du tournesol (Helianthus annuus) au mildiou (Plasmopara halstedii), mise en évidence récemment, est difficile à évaluer. Un réseau de onze expérimentations associant la GEVESSNES, deux laboratoires de l’INRA et Terres Inovia a été construit afin de proposer une méthodologie permettant de mieux caractériser et quantifier cette résistance. En plus de lignées témoins, 17 lignées recombinantes obtenues par l’INRA et portant entre 0 et 3 QTL (quantitative trait loci) impliqués dans la résistance quantitative du tournesol au mildiou ont été testées face à 5 pathotypes de mildiou : 710, 704, 714, 304 et 334. Bien que des différences aient été observées entre laboratoires, les résultats ont permis de poser les bases d’un futur protocole utilisable en routine pour valoriser ce type de résistance dès la création variétale jusqu’à l’inscription puis la post-inscription des variétés., A quantitative resistance of sunflower (Helianthus annuus) to downy mildew (Plasmopara halstedii) has been identified a few years ago. An experimental network involving several labs (GEVES-SNES, INRA and Terres Inovia) over several years aimed at describing and quantifying such a quantitative response. It was hoped to propose a methodology useable to improve sustainable resistance. In addition to check varieties and breeding lines, 17 recombinant inbred lines carrying 0 to 3 quantitative trait loci alleles for quantitative resistance were tested with five downy mildew pathotypes (710, 704, 714, 304, 334). While the experimental results revealed some discrepancies between experiments, this study provided a first experimental protocol to evaluate quantitative resistance both for breeding and for variety registration and development

Published
2016
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3. Increased genetic diversity improves crop yield stability under climate variability: a computational study on sunflower

Author

Casadebaig, Pierre, Trepos, Ronan, Picheny, Victor, Langlade, Nicolas, Vincourt, Patrick, Debaeke, Philippe, Agrosystèmes Cultivés et Herbagers (ARCHE), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Unité de Biométrie et Intelligence Artificielle (UBIA), Institut National de la Recherche Agronomique (INRA), Laboratoire des interactions plantes micro-organismes (LIPM), Institut National de la Recherche Agronomique (INRA)-Centre National de la Recherche Scientifique (CNRS), ANR SUNRISE ANR-11-BTBR-0005, ANR-11-BTBR-0005,SUNRISE,Ressources génétiques de tournesol pour l'amélioration de la stabilité de production d'huile sous c(2011), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure Agronomique de Toulouse-Institut National Polytechnique (Toulouse) (Toulouse INP), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Recherche Agronomique (INRA), and ANR-11-BTBR-0005/11-BTBR-0005,SUNRISE,Ressources génétiques de tournesol pour l'amélioration de la stabilité de production d'huile sous c(2011)

Subjects
FOS: Biological sciences, [SDV]Life Sciences [q-bio], Populations and Evolution (q-bio.PE), modeling, Quantitative Biology - Populations and Evolution, GxE interaction, simulation, phenotypic plasticity, gene to phenotype, sunflower crop, ideotype
Abstract

A crop can be represented as a biotechnical system in which components are either chosen (cultivar, management) or given (soil, climate) and whose combination generates highly variable stress patterns and yield responses. Here, we used modeling and simulation to predict the crop phenotypic plasticity resulting from the interaction of plant traits (G), climatic variability (E) and management actions (M). We designed two in silico experiments that compared existing and virtual sunflower cultivars ( Helianthus annuus L. ) in a target population of cropping environments by simulating a range of indicators of crop performance. Optimization methods were then used to search for GEM combinations that matched desired crop specifications. Computational experiments showed that the fit of particular cultivars in specific environments is gradually increasing with the knowledge of pedo-climatic conditions. At the regional scale, tuning the choice of cultivar impacted crop performance the same magnitude as the effect of yearly genetic progress made by breeding. When considering virtual genetic material, designed by recombining plant traits, cultivar choice had a greater positive impact on crop performance and stability. Results suggested that breeding for key traits conferring plant plasticity improved cultivar global adaptation capacity whereas increasing genetic diversity allowed to choose cultivars with distinctive traits that were more adapted to specific conditions. Consequently, breeding genetic material that is both plastic and diverse may improve yield stability of agricultural systems exposed to climatic variability. We argue that process-based modeling could help enhancing spatial management of cultivated genetic diversity and could be integrated in functional breeding approaches

Published
2014

4. A biomarker based on gene expression indicates plant water status in controlled and natural environments

Author

MARCHAND, Gwenaëlle, Mayjonade, Baptiste, Vares, Didier, Blanchet, Nicolas, Boniface, Marie-Claude, Maury, Pierre, Andrianasolo, Fety, Burger, Philippe, Debaeke, Philippe, Casadebaig, Pierre, Vincourt, Patrick, Langlade, Nicolas, Unité mixte de recherche interactions plantes-microorganismes, Centre National de la Recherche Scientifique (CNRS)-Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Centre National de la Recherche Scientifique (CNRS), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), and Centre Technique Interprofessionnel des Oléagineux Métropolitains (CETIOM)

Subjects
sunflower, indicator, [SDV]Life Sciences [q-bio], fungi, food and beverages, soil water content, drought, transcriptomic, Quantitative Biology - Quantitative Methods, leaf water potential, [SHS]Humanities and Social Sciences, FOS: Biological sciences, [SDE]Environmental Sciences, helianthus annuus L, soil water deficit, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, ftsw, Quantitative Methods (q-bio.QM)
Abstract

Plant or soil water status are required in many scientific fields to understand plant responses to drought. Because the transcriptomic response to abiotic conditions, such as water deficit, reflects plant water status, genomic tools could be used to develop a new type of molecular biomarker. Using the sunflower (Helianthus annuus L.) as a model species to study the transcriptomic response to water deficit both in greenhouse and field conditions, we specifically identified three genes that showed an expression pattern highly correlated to plant water status as estimated by the pre-dawn leaf water potential, fraction of transpirable soil water, soil water content or fraction of total soil water in controlled conditions. We developed a generalized linear model to estimate these classical water status indicators from the expression levels of the three selected genes under controlled conditions. This estimation was independent of the four tested genotypes and the stage (pre- or post-flowering) of the plant. We further validated this gene expression biomarker under field conditions for four genotypes in three different trials, over a large range of water status, and we were able to correct their expression values for a large diurnal sampling period., Plant, Cell & Environment, 2013

Published
2013
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5. Effect of recurrent selection on agronomic traits for several sunflower gene pools

Author

Griveau, Yves, Kaan, F., Serieys, Herve, Tersac, Michel, VINCOURT, Patrick, Varès, D., Bervillé, A., ProdInra, Migration, Unité de recherches de génétique et amélioration des plantes, and Institut National de la Recherche Agronomique (INRA)

Subjects
[SDV] Life Sciences [q-bio], [SDV]Life Sciences [q-bio], POOL DE GENES, GENETIQUE, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
1999

6. Influence du genotype et du milieu sur le nombre d'ovules presents a la floraison chez le mais

Author

Derieux, M., Bonhomme, Raymond, Duburcq, J.B., Ruget, Francoise, Vincourt, Patrick, Unité de recherche Génétique et amélioration des plantes (GAP), Institut National de la Recherche Agronomique (INRA), Laboratoire de bioclimatologie, Unité de recherche Génétique et Amélioration des Plantes Fourragères (UGAPF), and ProdInra, Migration

Subjects
[SDV] Life Sciences [q-bio], [SDV]Life Sciences [q-bio], ECOPHYSIOLOGIE
Published
1983

10. La culture d'embryons isoles in vitro dans un programme d'amelioration du tournesol. (Note de Assad Alissa, Robert Jonard, Herve Serieys et Patrick Vincourt, presentee par Roger Gautheret.)

Author

Alissa, A., Jonard, R., Serieys, Herve, VINCOURT, Patrick, Domaine expérimental de Melgueil (MONTP MELGUEIL UE), Institut National de la Recherche Agronomique (INRA), and ProdInra, Migration

Subjects
[SDV] Life Sciences [q-bio], [SDV]Life Sciences [q-bio], AMELIORATION DES PLANTES, ComputingMilieux_MISCELLANEOUS
Abstract

National audience

Published
1986

12. Relations entre production de matiere seche aerienne et energie interceptee chez differents genotypes de mais

Author

BONHOMME, Raymond, Ruget, Francoise, DERIEUX, M., VINCOURT, Patrick, Laboratoire de bioclimatologie, Institut National de la Recherche Agronomique (INRA), Unité de recherche Génétique et amélioration des plantes (GAP), Unité de recherche Génétique et Amélioration des Plantes Fourragères (UGAPF), and ProdInra, Migration

Subjects
[SDV] Life Sciences [q-bio], ASSIMILATION DU GAZ CARBONIQUE, [SDV]Life Sciences [q-bio], ECOPHYSIOLOGIE, ComputingMilieux_MISCELLANEOUS
Abstract

National audience

Published
1982

16. Distances genetiques entre des populations de tournesol

Author

Bazan, O., Blanchard, P., Brunel, Dominique, VINCOURT, Patrick, ProdInra, Migration, Station d'amélioration des plantes, and Institut National de la Recherche Agronomique (INRA)

Subjects
[SDV] Life Sciences [q-bio], [SDV]Life Sciences [q-bio], GENETIQUE
Published
1988

18. Analyse des essais multilocaux par la methode des index

Author

VINCOURT, Patrick, DERIEUX, M., GALLAIS, A., ProdInra, Migration, Unité de recherche Génétique et Amélioration des Plantes Fourragères (UGAPF), Institut National de la Recherche Agronomique (INRA), and Unité de recherche Génétique et amélioration des plantes (GAP)

Subjects
[SDV] Life Sciences [q-bio], [SDV]Life Sciences [q-bio]
Published
1982

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