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1. A data-driven simulation platform to predict cultivars’ performances under uncertain weather conditions

Author

Gustavo de los Campos, Paulino Pérez-Rodríguez, Matthieu Bogard, David Gouache, and José Crossa

Subjects
Science
Abstract

Predicting crop performance in environments with limited field testing is challenging. Here the authors combine field experimental, DNA sequence, and weather data to predict genotypes’ future performance. They demonstrate the potential of this approach on a large dataset of wheat grain yield.

Published
2020
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3. Scanner-Based Minirhizotrons Help to Highlight Relations between Deep Roots and Yield in Various Wheat Cultivars under Combined Water and Nitrogen Deficit Conditions

Author

François Postic, Katia Beauchêne, David Gouache, and Claude Doussan

Subjects
wheat, root plasticity, minirhizotron, drought resistance, nitrogen stress, Agriculture
Abstract

Breeding for crops in the context of climate change necessitates phenotyping tools for roots in field conditions. Such in-field phenotyping requires the development of rapid and non-destructive measurement techniques for the screening of relevant root traits under sub-optimal conditions. In this study, we used scanner-based minirhizotrons to measure in situ the root length and surface/volume densities of roots for four wheat varieties, under four different growth conditions: irrigated and rainfed coupled with optimal and sub-optimal N fertilization under a Mediterranean climate. For all the treatments, grain yield correlates with minirhizotron-based root surface density measured at anthesis (r2 = 0.48). Irrigated and rainfed conditions led to contrasted relations between roots and grain yield: no correlation was found in irrigated plots, while under rainfed conditions and sub-optimal fertilization, the higher yields are related to a higher root colonization of the deeper soil layers (r2 = 0.40). Shoot biomass was correlated to grain yield in irrigated conditions, but not in rainfed conditions. However, for the latter, the total root weight, the proportion of which being mainly located in the top soil, is not related to the grain yield. In this way, we show the relationship between these higher grain yields and a stress avoidance mechanism of the root system characterized by a higher root density in the deep soil layers. Thus, unlike shoot biomass measurements, scanner-based minirhizotron allows the direct detection of such a stress-related root development, and therefore opens the door to a better prediction of grain yield.

Published
2019
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4. Bread Wheat (Triticum aestivum L.) Grain Protein Concentration Is Related to Early Post-Flowering Nitrate Uptake under Putative Control of Plant Satiety Level.

Author

François Taulemesse, Jacques Le Gouis, David Gouache, Yves Gibon, and Vincent Allard

Subjects
Medicine, Science
Abstract

The strong negative correlation between grain protein concentration (GPC) and grain yield (GY) in bread wheat complicates the simultaneous improvement of these traits. However, earlier studies have concluded that the deviation from this relationship (grain protein deviation or GPD) has strong genetic basis. Genotypes with positive GPD have an increased ability to uptake nitrogen (N) during the post-flowering period independently of the amount of N taken up before flowering, suggesting that genetic variability for N satiety could enable the breakage of the negative relationship. This study is based on two genotypes markedly contrasted for GPD grown under semi-hydroponic conditions differentiated for nitrate availability both before and after flowering. This allows exploration of the genetic determinants of post-flowering N uptake (PANU) by combining whole plant sampling and targeted gene expression approaches. The results highlights the correlation (r² = 0.81) with GPC of PANU occurring early during grain development (flowering-flowering + 250 degree-days) independently of GY. Early PANU was in turn correlated (r² = 0.80) to the stem-biomass increment after flowering through its effect on N sink activity. Differences in early PANU between genotypes, despite comparable N statuses at flowering, suggest that genetic differences in N satiety could be involved in the establishment of the GPC. Through its strong negative correlation with genes implied in N assimilation, root nitrate concentration appears to be a good marker for evaluating instantaneous plant N demand, and may provide valuable information on the genotypic N satiety level. This trait may help breeders to identify genotypes having high GPC independently of their GY.

Published
2016
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5. Post-flowering nitrate uptake in wheat is controlled by N status at flowering, with a putative major role of root nitrate transporter NRT2.1.

Author

François Taulemesse, Jacques Le Gouis, David Gouache, Yves Gibon, and Vincent Allard

Subjects
Medicine, Science
Abstract

In bread wheat (Triticum aestivum L.), the simultaneous improvement of both yield and grain protein is difficult because of the strong negative relationship between these two traits. However, some genotypes deviate positively from this relationship and this has been linked to their ability to take up nitrogen (N) during the post-flowering period, regardless of their N status at flowering. The physiological and genetic determinants of post-flowering N uptake relating to N satiety are poorly understood. This study uses semi-hydroponic culture of cv. Récital under controlled conditions to explore these controls. The first objective was to record the effects of contrasting N status at flowering on post-flowering nitrate (NO₃⁻) uptake under non-limiting NO₃⁻ conditions, while following the expression of key genes involved in NO₃⁻ uptake and assimilation. We found that post-flowering NO₃⁻ uptake was strongly influenced by plant N status at flowering during the first 300-400 degree-days after flowering, overlapping with a probable regulation of nitrate uptake exerted by N demand for growth. The uptake of NO₃⁻ correlated well with the expression of the gene TaNRT2.1, coding for a root NO₃⁻ transporter, which seems to play a major role in post-flowering NO₃⁻ uptake. These results provide a useful knowledge base for future investigation of genetic variability in post-flowering N uptake and may lead to concomitant gains in both grain yield and grain protein in wheat.

Published
2015
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6. A data-driven simulation platform to predict cultivars’ performances under uncertain weather conditions

Author

José Crossa, David Gouache, Gustavo de los Campos, Paulino Pérez-Rodríguez, and Matthieu Bogard

Subjects
0106 biological sciences, 0301 basic medicine, Agricultural genetics, Computer science, Yield (finance), Science, Monte Carlo method, General Physics and Astronomy, Machine learning, computer.software_genre, 01 natural sciences, General Biochemistry, Genetics and Molecular Biology, Field (computer science), Article, Data-driven, 03 medical and health sciences, lcsh:Science, Multidisciplinary, business.industry, Simulation modeling, food and beverages, General Chemistry, 030104 developmental biology, Data point, Field trial, Grain yield, lcsh:Q, Data integration, Artificial intelligence, business, computer, 010606 plant biology & botany
Abstract

In most crops, genetic and environmental factors interact in complex ways giving rise to substantial genotype-by-environment interactions (G×E). We propose that computer simulations leveraging field trial data, DNA sequences, and historical weather records can be used to tackle the longstanding problem of predicting cultivars’ future performances under largely uncertain weather conditions. We present a computer simulation platform that uses Monte Carlo methods to integrate uncertainty about future weather conditions and model parameters. We use extensive experimental wheat yield data (n = 25,841) to learn G×E patterns and validate, using left-trial-out cross-validation, the predictive performance of the model. Subsequently, we use the fitted model to generate circa 143 million grain yield data points for 28 wheat genotypes in 16 locations in France, over 16 years of historical weather records. The phenotypes generated by the simulation platform have multiple downstream uses; we illustrate this by predicting the distribution of expected yield at 448 cultivar-location combinations and performing means-stability analyses., Predicting crop performance in environments with limited field testing is challenging. Here the authors combine field experimental, DNA sequence, and weather data to predict genotypes’ future performance. They demonstrate the potential of this approach on a large dataset of wheat grain yield.

Published
2020

7. Scanner-Based Minirhizotrons Help to Highlight Relations between Deep Roots and Yield in Various Wheat Cultivars under Combined Water and Nitrogen Deficit Conditions

Author

David Gouache, Katia Beauchene, Claude Doussan, François Postic, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), ARVALIS - Institut du végétal [Paris], Terres Inovia, and FSOV-2012-E

Subjects
0106 biological sciences, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Irrigation, drought resistance, minirhizotron, Context (language use), Root system, nitrogen stress, 01 natural sciences, lcsh:Agriculture, Anthesis, blé, besoin azoté, phénotypage, wheat, stress azote, Cultivar, Mathematics, 2. Zero hunger, Topsoil, changement climatique, déficit hydrique, Crop yield, root plasticity, lcsh:S, 04 agricultural and veterinary sciences, 15. Life on land, Agricultural sciences, résistance à la sécheresse, plasticité racinaire, Agronomy, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Soil horizon, Agronomy and Crop Science, Sciences agricoles, 010606 plant biology & botany, cultivar
Abstract

Breeding for crops in the context of climate change necessitates phenotyping tools for roots in field conditions. Such in-field phenotyping requires the development of rapid and non-destructive measurement techniques for the screening of relevant root traits under sub-optimal conditions. In this study, we used scanner-based minirhizotrons to measure in situ the root length and surface/volume densities of roots for four wheat varieties, under four different growth conditions: irrigated and rainfed coupled with optimal and sub-optimal N fertilization under a Mediterranean climate. For all the treatments, grain yield correlates with minirhizotron-based root surface density measured at anthesis (r2 = 0.48). Irrigated and rainfed conditions led to contrasted relations between roots and grain yield: no correlation was found in irrigated plots, while under rainfed conditions and sub-optimal fertilization, the higher yields are related to a higher root colonization of the deeper soil layers (r2 = 0.40). Shoot biomass was correlated to grain yield in irrigated conditions, but not in rainfed conditions. However, for the latter, the total root weight, the proportion of which being mainly located in the top soil, is not related to the grain yield. In this way, we show the relationship between these higher grain yields and a stress avoidance mechanism of the root system characterized by a higher root density in the deep soil layers. Thus, unlike shoot biomass measurements, scanner-based minirhizotron allows the direct detection of such a stress-related root development, and therefore opens the door to a better prediction of grain yield.

Published
2019
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8. Use of hyperspectral image data outperforms vegetation indices in prediction of maize yield

Author

Fernando Matías Aguate, Lorena González Pérez, Matthieu Bogard, Juan Burgueño, José Crossa, Samuel Trachsel, Mónica Balzarini, David Gouache, and Gustavo de los Campos

Subjects
0106 biological sciences, 0301 basic medicine, NDVI, Hyperspectral imaging, PLS, Biology, purl.org/becyt/ford/4.5 [https], 01 natural sciences, Reflectivity, Normalized Difference Vegetation Index, 03 medical and health sciences, 030104 developmental biology, Agronomy, CIENCIAS AGRÍCOLAS, Yield (wine), medicine, Otras Ciencias Agrícolas, medicine.symptom, Vegetation (pathology), Agronomy and Crop Science, purl.org/becyt/ford/4 [https], 010606 plant biology & botany
Abstract

Hyperspectral cameras can provide reflectance data at hundreds of wavelengths. This information can be used to derive vegetation indices (VIs) that are correlated with agronomic and physiological traits. However, the data generated by hyperspectral cameras are richer than what can be summarized in a VI. Therefore, in this study, we examined whether prediction equations using hyperspectral image data can lead to better predictive performance for grain yield than what can be achieved using VIs. For hyperspectral prediction equations, we considered three estimation methods: ordinary least squares, partial least squares (a dimension reduction method), and a Bayesian shrinkage and variable selection procedure. We also examined the benefits of combining reflectance data collected at different time points. Data were generated by CIMMYT in 11 maize (Zea mays L.) yield trials conducted in 2014 under heat and drought stress. Our results indicate that using data from 62 bands leads to higher prediction accuracy than what can be achieved using individual VIs. Overall, the shrinkage and variable selection method was the best-performing one. Among the models using data from a single time point, the one using reflectance collected at 28 d after flowering gave the highest prediction accuracy. Combining image data collected at multiple time points led to an increase in prediction accuracy compared with using single-time-point data. Fil: Aguate, Fernando Matías. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias. Departamento de Desarrollo Rural. Area de Estadística y Biometría; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina Fil: Trachsel, Samuel. Centro Internacional de Mejoramiento de Maiz y Trigo; México Fil: González Pérez, Lorena. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias. Departamento de Desarrollo Rural. Area de Estadística y Biometría; Argentina. Sustainable Intensification Program; México Fil: Burgueño, Juan. Centro Internacional de Mejoramiento de Maiz y Trigo; México Fil: Crossa, José. Centro Internacional de Mejoramiento de Maiz y Trigo; México Fil: Balzarini, Monica Graciela. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias. Departamento de Desarrollo Rural. Area de Estadística y Biometría; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina Fil: Gouache, David. Arvalis - Institut Du Vegetal; Francia Fil: Bogard, Matthieu. Arvalis - Institut Du Vegetal; Francia Fil: de los Campos, Gustavo. Michigan State University; Estados Unidos

Published
2017

9. Epistatic determinism of durum wheat resistance to the wheat spindle streak mosaic virus

Author

Yan Holtz, Morgane Ardisson, Sylvain Santoni, Nicolas O. Rode, David Gouache, Michel Bonnefoy, Jacques David, Gérard Poux, Véronique Marie-Jeanne, Vincent Ranwez, Pierre Roumet, Véronique Viader, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut National de la Recherche Agronomique (INRA), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Biologie et Génétique des Interactions Plante-Parasite (UMR BGPI), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA), ARVALIS - Institut du végétal [Paris], ARVALIS (TRAM project), Agence National de la Recherche (ANR SEAD), Marie Sklodowska-Curie/AgreenSkills Program, 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), ANR-13-ADAP-0011,SEAD,Comment l'autofécondation affecte-t-elle l'adaptation : Conséquences génétiques et démographiques(2013), European Project: 267196,EC:FP7:PEOPLE,FP7-PEOPLE-2010-COFUND,AGREENSKILLS(2012), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'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)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)

Subjects
Genetic Markers, 0106 biological sciences, 0301 basic medicine, résistance aux maladies, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Genotype, Genetic Linkage, Quantitative Trait Loci, Locus (genetics), Plant disease resistance, Quantitative trait locus, 01 natural sciences, maladie de la mosaïque, 03 medical and health sciences, Gene interaction, Gene mapping, Plant virus, Genetics, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Plant breeding, pathologie végétale, Crosses, Genetic, Triticum, Disease Resistance, Plant Diseases, biology, Chromosome Mapping, food and beverages, Epistasis, Genetic, General Medicine, Potyviridae, biology.organism_classification, resistance to diseases, Phenotype, 030104 developmental biology, Agronomy, blé dur, Wheat spindle streak mosaic virus, maladie virale, hard wheat, [SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology, Original Article, Agronomy and Crop Science, 010606 plant biology & botany, Biotechnology
Abstract

Key message The resistance of durum wheat to the Wheat spindle streak mosaic virus (WSSMV) is controlled by two main QTLs on chromosomes 7A and 7B, with a huge epistatic effect. Abstract Wheat spindle streak mosaic virus (WSSMV) is a major disease of durum wheat in Europe and North America. Breeding WSSMV-resistant cultivars is currently the only way to control the virus since no treatment is available. This paper reports studies of the inheritance of WSSMV resistance using two related durum wheat populations obtained by crossing two elite cultivars with a WSSMV-resistant emmer cultivar. In 2012 and 2015, 354 recombinant inbred lines (RIL) were phenotyped using visual notations, ELISA and qPCR and genotyped using locus targeted capture and sequencing. This allowed us to build a consensus genetic map of 8568 markers and identify three chromosomal regions involved in WSSMV resistance. Two major regions (located on chromosomes 7A and 7B) jointly explain, on the basis of epistatic interactions, up to 43% of the phenotypic variation. Flanking sequences of our genetic markers are provided to facilitate future marker-assisted selection of WSSMV-resistant cultivars. Electronic supplementary material The online version of this article (doi:10.1007/s00122-017-2904-6) contains supplementary material, which is available to authorized users.

Published
2017
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10. Nitrogen nutrition index predicted by a crop model improves the genomic prediction of grain number for a bread wheat core collection

Author

Pierre Martre, Sylvie Huet, Karine Chenu, Arnaud Gauffreteau, David Gouache, Delphine Ly, Jacques Bordes, Renaud Rincent, Gilles Charmet, Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Queensland Alliance for Agriculture and Food Innovation (QAAFI), University of Queensland [Brisbane], Agronomie, Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] (MaIAGE), Institut National de la Recherche Agronomique (INRA), Terres Inovia, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Enterprise Competitiveness Fund Project 'Semences de Demain', INRA metaprogram SELGEN, Auvergne Region, Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), QAAFI, AgroParisTech-Institut National de la Recherche Agronomique (INRA), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Institut National de la Recherche Agronomique (INRA)

Subjects
0106 biological sciences, 0301 basic medicine, Nitrogen stress, prédiction génétique, Soil Science, Agricultural engineering, Biology, 01 natural sciences, Crop, 03 medical and health sciences, blé, wheat, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Plant breeding, Gene–environment interaction, Crop model, Environment characterization, 2. Zero hunger, rendement en grain, Genomic prediction, business.industry, Genotype-by-environment interactions, fungi, Crop growth, Grain number, food and beverages, genotype environment interaction, Biotechnology, interaction génotype environnement, 030104 developmental biology, stress environnemental, business, index de nutrition azotée, Agronomy and Crop Science, Genomic selection, modèle de production, 010606 plant biology & botany
Abstract

In plant breeding, one of the major challenges of genomic selection is to account for genotype-by-environment (G × E) interactions, and more specifically how varieties are adapted to various environments. Crop growth models (CGM) were developed to model the response of plants to environmental conditions. They can be used to characterize eco-physiological stresses in relation to crop growth and developmental stages, and thereby help to dissect G × E interactions. Our study aims at demonstrating how environment characterization using crop models can be integrated to improve both the understanding and the genomic predictions of G × E interactions. We evaluated the usefulness of using CGM to characterize environments by comparing basic and CGM-based stress indicators, to assess how much of the G × E interaction can be explained and whether gains in prediction accuracy can be made. We carried out a case study in wheat (Triticum aestivum) to model nitrogen stress in a CGM in 12 environments defined by year × location × nitrogen treatment. Interactions between 194 varieties of a core collection and these 12 different nitrogen conditions were examined by analyzing grain number. We showed that (i) CGM based indicators captured the G × E interactions better than basic indicators and that (ii) genomic predictions were slightly improved by modeling the genomic interaction with the crop model based characterization of nitrogen stress. A framework was proposed to integrate crop model environment characterization into genomic predictions. We describe how this characterization promises to improve the prediction accuracy of adaptation to environmental stresses.

Published
2017
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11. Applying remote sensing expertise to crop improvement: progress and challenges to scale up high throughput field phenotyping from research to industry

Author

Antoine Fournier, Agathe Mini, Alexis Comar, Frédéric Baret, Benoit de Solan, Katia Beauchene, David Gouache, ARVALIS - Institut du végétal [Paris], Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), and Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
0106 biological sciences, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Engineering, Greenhouse, Technology readiness level, drought, maize, 01 natural sciences, Field (computer science), nitrogen use efficiency, remote sensing, Robustness (computer science), wheat, plant breeding, Throughput (business), technology readiness level, Remote sensing, 2. Zero hunger, business.industry, 04 agricultural and veterinary sciences, Work (electrical), Remote sensing (archaeology), 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, DECIPHER, high-throughput phenotyping, business, 010606 plant biology & botany
Abstract

Digital and image analysis technologies in greenhouses have become commonplace in plant science research and started to move into the plant breeding industry. However, the core of plant breeding work takes place in fields. We will present successive technological developments that have allowed the migration and application of remote sensing approaches at large into the field of crop genetics and physiology research, with a number of projects that have taken place in France. These projects have allowed us to develop combined sensor plus vector systems, from tractor mounted and UAV (unmanned aerial vehicle) mounted spectroradiometry to autonomous vehicle mounted spectroradiometry, RGB (red-green-blue) imagery and Lidar. We have tested these systems for deciphering the genetics of complex plant improvement targets such as the robustness to nitrogen and water deficiency of wheat and maize. Our results from wheat experiments indicate that these systems can be used both to screen genetic diversity for nitrogen stress tolerance and to decipher the genetics behind this diversity. We will present our view on the next critical steps in terms of technology and data analysis that will be required to reach cost effective implementation in industrial plant breeding programs. If this can be achieved, these technologies will largely contribute to resolving the equation of increasing food supply in the resource limited world that lies ahead.

Published
2016
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12. Breeding for increased nitrogen-use efficiency: a review for wheat (T. aestivumL.)

Author

Bertrand Hirel, David Gouache, Jacques Le Gouis, Yvan Moënne-Loccoz, John Foulkes, Fabien Cormier, Biogemma, Div Plant & Crop Sci, Sutton Bonington Campus, University of Nottingham, Institut Jean-Pierre Bourgin ( IJPB ), Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, U 3559, Centre National de la Recherche Scientifique ( CNRS ), ARVALIS - Institut du Végétal, Génétique Diversité et Ecophysiologie des Céréales ( GDEC ), Institut National de la Recherche Agronomique ( INRA ) -Université Blaise Pascal - Clermont-Ferrand 2 ( UBP ), Ecologie microbienne ( EM ), Centre National de la Recherche Scientifique ( CNRS ) -Ecole Nationale Vétérinaire de Lyon ( ENVL ) -Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Institut National de la Recherche Agronomique ( INRA ) -VetAgro Sup ( VAS ), ANR BacterBl e (ANR-14-CE19-0017), Institut Jean-Pierre Bourgin (IJPB), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Centre National de la Recherche Scientifique (CNRS), ARVALIS - Institut du végétal [Paris], Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Laboratoire d'Ecologie Microbienne - UMR 5557 (LEM), Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Ecole Nationale Vétérinaire de Lyon (ENVL)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Ecole Nationale Vétérinaire de Lyon (ENVL)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS), and Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Institut National de la Recherche Agronomique (INRA)

Subjects
0106 biological sciences, Canopy, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, ble tendre, Nitrogen assimilation, bread wheat, chemistry.chemical_element, Context (language use), Plant Science, Biology, Photosynthesis, 01 natural sciences, F30 - Génétique et amélioration des plantes, reproduction, Nutrient, absorption azotée, Genetics, sélection, [ SDV.SA ] Life Sciences [q-bio]/Agricultural sciences, 2. Zero hunger, Molecular breeding, efficience d'utilisation de l'azote, breeding, nitrogen-utilization efficiency, nitrogen uptake efficiency, business.industry, 04 agricultural and veterinary sciences, 15. Life on land, Nitrogen, Agricultural sciences, F61 - Physiologie végétale - Nutrition, chemistry, Agronomy, soft wheat, Agriculture, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, business, Agronomy and Crop Science, Sciences agricoles, F04 - Fertilisation, 010606 plant biology & botany
Abstract

Nitrogen fertilizer is the most used nutrient source in modern agriculture and represents significant environmental and production costs. In the meantime, the demand for grain increases and production per area has to increase as new cultivated areas are scarce. In this context, breeding for an efficient use of nitrogen became a major objective. In wheat, nitrogen is required to maintain a photosynthetically active canopy ensuring grain yield and to produce grain storage proteins that are generally needed to maintain a high end-use quality. This review presents current knowledge of physiological, metabolic and genetic factors influencing nitrogen uptake and utilization in the context of different nitrogen management systems. This includes the role of root system and its interactions with microorganisms, nitrate assimilation and its relationship with photosynthesis as postanthesis remobilization and nitrogen partitioning. Regarding nitrogen-use efficiency complexity, several physiological avenues for increasing it were discussed and their phenotyping methods were reviewed. Phenotypic and molecular breeding strategies were also reviewed and discussed regarding nitrogen regimes and genetic diversity.

Published
2016
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13. Bread Wheat (Triticum aestivum L.) Grain Protein Concentration Is Related to Early Post-Flowering Nitrate Uptake under Putative Control of Plant Satiety Level

Author

Yves Gibon, David Gouache, François Taulemesse, Vincent Allard, Jacques Le Gouis, Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Station Expérimentale, ARVALIS - Institut du végétal [Paris], Biologie du fruit et pathologie (BFP), Université Bordeaux Segalen - Bordeaux 2-Institut National de la Recherche Agronomique (INRA)-Université Sciences et Technologies - Bordeaux 1, French 'Fonds de Soutien a l'Obtention Vegetale' (FSOV) 2010F, ANRT (Association Nationale de la Recherche et de la Technologie) CIFRE 878/2011, and Université Bordeaux Segalen - Bordeaux 2-Institut National de la Recherche Agronomique (INRA)-Université Sciences et Technologies - Bordeaux 1 (UB)

Subjects
0106 biological sciences, [SDV]Life Sciences [q-bio], Plant genetics, Gene Expression, lcsh:Medicine, Plant Science, Plant Genetics, 01 natural sciences, Plant Roots, chemistry.chemical_compound, Nitrate, Gene Expression Regulation, Plant, Genotype, Gene expression, Biomass, lcsh:Science, Flowering Plants, Triticum, Plant Proteins, 2. Zero hunger, Multidisciplinary, Plant physiology, food and beverages, Agriculture, 04 agricultural and veterinary sciences, Bread, Plants, Chemistry, Plant Physiology, [SDE]Environmental Sciences, Physical Sciences, Wheat, corrélation, Research Article, Nitrogen, Crops, Flowers, Biology, Animal science, Extraction techniques, absorption azotée, Genetics, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, analyse génomique, Genetic variability, Grasses, Gene, Evolutionary Biology, blé d'hiver, Nitrates, Population Biology, floraison, lcsh:R, Organisms, Chemical Compounds, Biology and Life Sciences, RNA extraction, Research and analysis methods, Agronomy, chemistry, 040103 agronomy & agriculture, Genetic Polymorphism, 0401 agriculture, forestry, and fisheries, lcsh:Q, Protein concentration, Population Genetics, 010606 plant biology & botany, Crop Science, Cereal Crops
Abstract

International audience; The strong negative correlation between grain protein concentration (GPC) and grain yield (GY) in bread wheat complicates the simultaneous improvement of these traits. However, earlier studies have concluded that the deviation from this relationship (grain protein deviation or GPD) has strong genetic basis. Genotypes with positive GPD have an increased ability to uptake nitrogen (N) during the post-flowering period independently of the amount of N taken up before flowering, suggesting that genetic variability for N satiety could enable the breakage of the negative relationship. This study is based on two genotypes markedly contrasted for GPD grown under semi-hydroponic conditions differentiated for nitrate availability both before and after flowering. This allows exploration of the genetic determinants of post-flowering N uptake (PANU) by combining whole plant sampling and targeted gene expression approaches. The results highlights the correlation (r(2) = 0.81) with GPC of PANU occurring early during grain development (flowering-flowering + 250 degree-days) independently of GY. Early PANU was in turn correlated (r(2) = 0.80) to the stem-biomass increment after flowering through its effect on N sink activity. Differences in early PANU between genotypes, despite comparable N statuses at flowering, suggest that genetic differences in N satiety could be involved in the establishment of the GPC. Through its strong negative correlation with genes implied in N assimilation, root nitrate concentration appears to be a good marker for evaluating instantaneous plant N demand, and may provide valuable information on the genotypic N satiety level. This trait may help breeders to identify genotypes having high GPC independently of their GY.

Published
2016
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14. Parameterizing patterns in wheat architecture to simulate the 3D dynamics of wheat crops

Author

Mariem Abichou, Christian Fournier, Tino Dornbusch, Camille Chambon, David Gouache, Benoit de Solan, Bruno Andrieu, Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), ARVALIS - Institut du végétal [Paris], Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), and Wheat Initiative - International Research Initiative for Wheat Improvement. INT.

Subjects
organ dimension, senescence, triticum, phyllochron, tillering, wheat, modèle 3d, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, three dimensional model, winter wheat
Abstract

Structural 3D plant models aim at mimicking the dynamics of plant and crop structure based on experimental data. Interfaced with physical models, the 3D models can be used as tools to investigate how plant structure modulates the interaction with the environment. An example of such model is Adel wheat (Fournier et al.; 2003) which is used to analyse the consequences of architectural traits for the capture or light, pesticide, spores or the simulation of signals perceived by sensors. The purpose of this work was to revise the parametrisation of plant architecture in Adel-wheat, so as to improve the compromise between flexibility and complexity. We established parametric functions that represent the emergence, ligulation and senescence of lamina, the dynamic of active tiller population, the final dimension of all tillers organ and the final leaf number produced on each tiller. A large data base was used to define and evaluate these parametrisations, covering different densities, sowing dates, and nitrogen fertilizations and with different commercial varieties grown under field conditions. The functions were defined so that parameters are simple to interpret and easy to derive from field measurements. The new parametrisations allow for simulation of wheat crops from plant emergence to full maturity with less experimental effort, and a better agreement between simulated and measured leaf area index, than previously. Beside, this provides a view of general patterns existing in wheat architecture, which could be used in other models. Finally the work represents one step forward in the ability to characterize and represent the architectural traits for a number of genotypes and environments.

Published
2015

15. Re-parametrisation of Adel-wheat allows reducing the experimental effort to simulate the 3D development of winter wheat

Author

Mariem Abichou, Christian Fournier, Tino Dornbusch, Camille Chambon, Rim Baccar, Jessica Bertheloot, Tiphaine Vidal, Corinne Robert, David Gouache, Bruno Andrieu, Environnement et Grandes Cultures (EGC), AgroParisTech-Institut National de la Recherche Agronomique (INRA), Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut de Recherche en Horticulture et Semences (IRHS), Université d'Angers (UA)-Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, ARVALIS - Institut du végétal [Paris], Institut National de la Recherche Agronomique (INRA)-AgroParisTech, 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), Risto Sievänen and Eero Nikinmaa and Christophe Godin and Anna Lintunen and Pekka Nygren, Modeling plant morphogenesis at different scales, from genes to phenotype (VIRTUAL PLANTS), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA)-Université d'Angers (UA), AVALIS Institut du Végétal, Direction Scientifique, Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA)-Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)

Subjects
blé d'hiver, architecture, Tillering, croissance de la feuille, organogenesis, modèle 3d, [SDV]Life Sciences [q-bio], simulation models, Geometry, architecture des plantes, three dimensional model, modèle de simulation, 3D modelling, modèle structure fonction, Parameterisation, Phyllochron, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, organogénèse
Abstract

Eds. Risto Sievänen, Eero Nikinmaa, Christophe Godin, Anna Lintunen & Pekka Nygren; A parameterisation of wheat architecture was developed, having high flexibility to simulate contrasted genotypes and growth conditions with a reasonably low number of parameters. Field measurements at 4-5 dates allowed to simulate crops from emergence to maturity with a good agreement between simulated and measured ground cover and GAI. Dynamics of leaf angles were shown to impact strongly ground cover.

Published
2013

16. Crop architecture and crop tolerance to fungal diseases and insect herbivory. Mechanisms to limit crop losses

Author

David Gouache, Neil Paveley, I.J. Bingham, J. Smith, Marie-Odile Bancal, Pierre Bancal, John Foulkes, Bertrand Ney, Environnement et Grandes Cultures (EGC), AgroParisTech-Institut National de la Recherche Agronomique (INRA), Crop and Soil Systems Research Group, Scotland's Rural College (SCUR), Division of Plant and Crop Sciences, University of Nottingham, UK (UON), Sustainable Crop Management, ADAS UK, Scotland's Rural College (SRUC), French Ministry of Agriculture, Scottish Government's Rural Environment Science & Analytical Services Division (RESAS), and Institut National de la Recherche Agronomique (INRA)-AgroParisTech

Subjects
0106 biological sciences, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, media_common.quotation_subject, Plant Science, Insect, Horticulture, Biology, 01 natural sciences, diseases, Crop, modelling, 03 medical and health sciences, crop architecture, insects, 030304 developmental biology, media_common, 2. Zero hunger, 0303 health sciences, Herbivore, tolerance, Resistance (ecology), Ecology, business.industry, fungi, Plant physiology, food and beverages, 15. Life on land, Biotic stress, Agronomy, Agriculture, PEST analysis, business, Agronomy and Crop Science, 010606 plant biology & botany
Abstract

Plant tolerance to biotic stresses (mostly limited here to fungal pathogens and insects) is the ability of a plant to maintain performance in the presence of expressed disease or insect herbivory. It differs from resistance (the capacity to eliminate or limit pests and pathogens by genetic and molecular mechanisms) and avoidance (the ability to escape infection by epidemics). The ways to tolerance of pests and diseases are multiple and expressed at different scales. The contribution of organs to the capture and use of resources depends on canopy and root architecture, so the respective locations of disease and plant organs will have a strong effect on the crop’s response. Similarly, tolerance is increased when the period of crop sensitivity lies outside the period within which the pest or pathogen is present. The ability of the plant to compensate for the reduced acquisition of resources by the production of new organs or by remobilization of reserves may also mitigate biotic stress effects. Numerous examples exist in the literature and are described in this article. Quantification of tolerance remains difficult because of: (i) the large number of potential mechanisms involved; (ii) different rates of development of plants, pests and pathogens; and (iii) various compensatory mechanisms. Modelling is, therefore, a valuable tool to quantify losses, but also to prioritize the processes involved.

Published
2013
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17. A highly recombinant and multi-parental MAGIC population for genetic mapping in wheat

Author

Stéphanie Thepot, Ian Mackay, Gwendal RESTOUX, Christine Dillmann, David Gouache, Jerome Enjalbert, Isabelle Goldringer, Génétique Quantitative et Evolution - Le Moulon (Génétique Végétale) (GQE-Le Moulon), Centre National de la Recherche Scientifique (CNRS)-AgroParisTech-Université Paris-Sud - Paris 11 (UP11)-Institut National de la Recherche Agronomique (INRA), Norwich Research Park, Génétique Animale et Biologie Intégrative (GABI), and AgroParisTech-Institut National de la Recherche Agronomique (INRA)

Subjects
[SDV.GEN]Life Sciences [q-bio]/Genetics, [SDV.GEN.GA]Life Sciences [q-bio]/Genetics/Animal genetics, [SDV]Life Sciences [q-bio], [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2013

18. The ECHAP project: Reducing fungicide use by associating optimal treatment strategies and canopies promoting disease escape

Author

Corinne Robert, Mariem Abichou, Bruno Andrieu, Marie-Odile Bancal, Enrique Barriuso, Carole Bedos, Pierre Benoit, Valerie Bergheaud, Marc Bidon, Bernard Bonicelli, Camille Chambon, Eric Cotteux, Jessica da Costa, Brigitte Durand, Christian Fournier, Nathalie Gagnaire, Damien Gaudillat, Christophe Gigot, Guillaume Girardin, David Gouache, Josiane Jean-Jacques, Laure Mamy, Bertrand Ney, Neil Paveley, Benjamin Perriot, Samuel Poidevin, Valerie Pot-Genty, Christophe Pradal, Catherine Richard, Sébastien Saint-Jean, Jerome Salse, Carole Sinfort, Smith, Julie A., Alexandra ter Halle, Erik van den Berg, Anne Sophie Walker, Agronomie, AgroParisTech-Institut National de la Recherche Agronomique (INRA), Environnement et Grandes Cultures (EGC), Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Physico-chimie et Ecotoxicologie des Sols d'agrosystèmes contaminés, 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), Génétique Diversité et Ecophysiologie des Céréales (GDEC), Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Institut National de la Recherche Agronomique (INRA), Information – Technologies – Analyse Environnementale – Procédés Agricoles (UMR ITAP), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), BIOlogie et GEstion des Risques en agriculture (BIOGER), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Centre 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 international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), and Physicochimie et Ecotoxicologie des SolS d'Agrosystèmes Contaminés (PESSAC)

Subjects
resistance, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, architecture, evaluation, fungicide, interception, septoria tritici
Abstract

absent

Published
2012

19. Modelling the effect of wheat canopy architecture as affected by sowing density on Septoria tritici epidemics using a coupled epidemic-virtual plant model

Author

Christian Fournier, Rim Baccar, Bruno Andrieu, Tino Dornbusch, David Gouache, Corinne Robert, Environnement et Grandes Cultures (EGC), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Modeling plant morphogenesis at different scales, from genes to phenotype (VIRTUAL PLANTS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA)-Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Service Génétique et Protection des Plante (ARVALIS), ARVALIS - Institut du végétal [Paris], INRA, Department of Environnement et Agronomie, ARVALIS, Institut du vegetal, AgroParisTech-Institut National de la Recherche Agronomique (INRA), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and 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
0106 biological sciences, Canopy, modélisation tridimensionnelle, Time Factors, Field experiment, Population, plant–pathogen interaction, Plant Science, Biology, interaction plante-pathogène, Atmospheric sciences, Models, Biological, 01 natural sciences, modelling, Soil, Septoria, Ascomycota, Canopy architecture, wheat, sowing density, crop architecture, Computer Simulation, triticum aestivum, education, Triticum, Plant Diseases, 2. Zero hunger, education.field_of_study, Temperature, Sowing, food and beverages, 3-D virtual plant model, Virtual plant, Original Articles, 04 agricultural and veterinary sciences, Spores, Fungal, biology.organism_classification, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Plant Leaves, Agronomy, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Epidemic model, 010606 plant biology & botany, septoria tritici
Abstract

International audience; Background and Aims The relationship between Septoria tritici, a splash-dispersed disease, and its host is complex because of the interactions between the dynamic plant architecture and the vertical progress of the disease. The aim of this study was to test the capacity of a coupled virtual wheat-Septoria tritici epidemic model (Septo3D) to simulate disease progress on the different leaf layers for contrasted sowing density treatments.Methods A field experiment was performed with winter wheat 'Soissons' grown at three contrasted densities. Plant architecture was characterized to parameterize the wheat model, and disease dynamic was monitored to compare with simulations. Three simulation scenarios, differing in the degree of detail with which plant variability of development was represented, were defined.Key Results Despite architectural differences between density treatments, few differences were found in disease progress; only the lower-density treatment resulted in a slightly higher rate of lesion development. Model predictions were consistent with field measurements but did not reproduce the higher rate of lesion progress in the low density. The canopy reconstruction scenario in which inter-plant variability was taken into account yielded the best agreement between measured and simulated epidemics. Simulations performed with the canopy represented by a population of the same average plant deviated strongly from the observations.Conclusions It was possible to compare the predicted and measured epidemics on detailed variables, supporting the hypothesis that the approach is able to provide new insights into the processes and plant traits that contribute to the epidemics. On the other hand, the complex and dynamic responses to sowing density made it difficult to test the model precisely and to disentangle the various aspects involved. This could be overcome by comparing more contrasted and/or simpler canopy architectures such as those resulting from quasi-isogenic lines differing by single architectural traits.

Published
2011
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20. Why are wheat yields stagnating in Europe? A comprehensive data analysis for France

Author

Philippe Gate, David Gouache, Francois-Xavier Oury, Frédéric Huard, Nadine Brisson, Gilles Charmet, Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), UE Agroclim (UE AGROCLIM), and Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Institut National de la Recherche Agronomique (INRA)

Subjects
résistance aux maladies, 0106 biological sciences, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, ble tendre, plante céréaliere, Yield (finance), CLIMATE CHANGE, Soil Science, Climate change, rendement, WHEAT YIELD, GENETIC PROGRESS, CROP MANAGEMENT, AGROCLIMATOLOGY, CROP SIMULATION, Grain filling, 01 natural sciences, Climatic data, Temperate climate, triticum aestivum, 2. Zero hunger, changement climatique, Agroforestry, oléagineux, Crop yield, Global warming, food and beverages, 04 agricultural and veterinary sciences, rotation culturale, Agricultural sciences, Heat stress, fertilisation azotée, Geography, Agronomy, 13. Climate action, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Agronomy and Crop Science, Sciences agricoles, qualité du sol, 010606 plant biology & botany
Abstract

The last two decades are witnessing a decline in the growth trend of cereal yields in many European countries. The present study analyses yield trends in France using various sources of data: national and regional statistics, scattered trials, results of agroclimatic models using climatic data. Effects in genetic changes through breeding, agronomy and climate are investigated as possible causes. Our results show that genetic progress has not declined but it was partly counteracted, from 1990 on, by climate change which in general is unfavorable to cereal yields in temperate climates because of heat stress during grain filling and drought during stem elongation. We cannot however, from the decade beginning in 2000, rule out agronomic causes, related to policy and economy, in particular the decline of legumes in the cereal rotations, replaced by oilseed rape and to a lesser extent the decrease in nitrogen fertilization

Published
2010
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21. Does canopy architecture play a role in the effect of plant density and sowing date on epidemics of Septoria tritici in wheat crops?

Author

David Gouache, Corinne Robert, Christian Fournier, Bruno Andrieu, Bertrand Ney, William Lee, Philippe Gate, Environnement et Grandes Cultures (EGC), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Modeling plant morphogenesis at different scales, from genes to phenotype (VIRTUAL PLANTS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA)-Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), ARVALIS - Institut du végétal [Paris], Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), AgroParisTech-Institut National de la Recherche Agronomique (INRA), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)

Subjects
0106 biological sciences, Canopy, Physiology, 030310 physiology, SEPTO3D, Growing season, 010603 evolutionary biology, 01 natural sciences, Biochemistry, Crop, 03 medical and health sciences, Septoria, Phyllochron, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, Leaf area index, Molecular Biology, 2. Zero hunger, 0303 health sciences, biology, business.industry, Sowing, food and beverages, 15. Life on land, biology.organism_classification, Agronomy, Agriculture, business
Abstract

International audience; Septoria tritici is one of the most damageable wheat foliar diseases in Europe. It has been suggested that sowing date and plant density influence its epidemics. The aim of this study is to better understand these effects and to evaluate the hypothesis that they result from changes in canopy architecture. Indeed, several field studies have pointed out that canopy architecture may influence S. tritici development. However because interactions between canopy structure and pathogen are numerous and change constantly during the growing season, field data analyses are complicated. To overcome these difficulties, the approach here combines modelling and experiments. In 2007 and 2008, field trials in three locations were set up in which sowing date and density were varied. Disease assessments on the upper leaves showed that these treatments resulted in a wide range of epidemic's levels. The effect of sowing date and density on epidemics varied with the year and the location with a strongest effect of the sowing date. Canopy architecture also showed variation in organs dimensions, phyllochron and tillering dynamics. We used Septo3D, a wheat architectural model coupled with a S. tritici model to simulate the time course of leaf area index and crop architecture in each treatment. We used these results to evaluate the hypothesis that change in canopy architecture is determinant for the change in epidemics between treatments. Our approach appears of interest for evaluating the role of crop architecture in epidemic development and could thus aid in identifying beneficial agricultural practice combinations

Published
2009
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22. Identifying traits leading to tolerance of wheat to Septoria tritici blotch.

Author

Pierre, Bancal, Marie-Odile, Bancal, François, Collin, and David, Gouache

Subjects
*WHEAT speckled leaf blotch, *GENOTYPES, *WHEAT yields, *AGING in plants, *PLANT fertilization
Abstract

Identifying tolerance traits to diseases in wheat genotypes has an increased interest to minimize pesticide use and to complement resistance and escape. Yield tolerance to Septoria tritici blotch (STB) was studied pooling up three experiments involving 18 genotypes, 5 years and 6 sites in France, amounting to 161 genotype × year × site × management combinations. Each combination involves a crop pair (treated or not against foliar diseases) repeated two to three times. Most crops were grown under high fertilization, and STB was the main disease present in untreated crops. Crop traits (ear density, grain number and weight, area of leaf laminas) were recorded; green area of leaf laminas over time was fitted to a Gompertz equation, producing metrics for senescence traits (time and duration). Over the whole dataset, LAI from 1.1 to 7.5 m 2 m −2 ; yields from 280 to 1122 gDM m −2 and relative yield losses up to 70% were recorded. Fungicide treated crops exhibited slightly larger ear density and leaf lamina area independently of the intensity of epidemics. As an overall trend, yield became more determined by source traits when epidemics occurred. Yield loss was proportional ( r 2 = 0.7) to senescence advance by disease. Decrease in grain number and weight were also correlated ( r 2 = 0.4 and 0.8, respectively) to yield loss. Two epidemic indices were built to compare data across year × site combinations. Then yield in untreated crop was predicted ( r 2 = 0.87) from yield in corresponding treated crop, and interaction of epidemic indices with traits of the treated crops that therefore were pointed out as responsible for tolerance variability. Late senescing crops exhibited a greater tolerance to epidemics. Conversely, grain weight was a major key of intolerance. To minimize the trade-off between yield potential and tolerance it is thus suggested to maximize grain number. This study represents a first step in identifying key traits involved in tolerance to STB in varying agronomic conditions and cultivars. [ABSTRACT FROM AUTHOR]

Published
2015
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