Un modèle dynamique de croissance et rendement basé sur la phénomique pour simuler la variabilité de centaines d’hybrides de maïs dans la diversité des environnements Européens

Under soil water deficit, plants limit transpiration by decreasing leaf area to save water for the end of the crop cycle. A large genetic diversity has been observed in maize for the processes involved in this response. Because of the trade-off between transpiration and photosynthesis, a high plasticity is not always beneficial because it also reduces biomass accumulation and grain yield. The genotype that maximises production in one dry environment therefore does not always perform the best in another dry environment. The aim of this thesis was to predict which combination of trait values related to leaf growth would be beneficial in the diversity of European environments. For this purpose, (i) I have shown that genetic and environmental controls differ between leaf elongation and widening, and established/tested the equations that describe these controls. (ii) I have developed a model of leaf development and expansion, with a particular attention to the parsimony for parameter number and to the possibility of measuring parameter values in phenotyping platforms. (iii) I have developed a simulation framework including 36 years of environmental conditions and management practices of 59 European fields, together with the parameterisation of 254 maize hybrids maximising the maize genetic diversity. (iv) This framework has been used to simulate the optimum crop cycle duration for each site and management practice in current and future conditions. (v) The simulation framework and the adapted cycle duration were then used to determine ideotypes of leaf growth adapted to the different environmental scenarios. Results indicate that sensitive hybrids perform better in southern Europe under rainfed conditions while less-sensitive genotypes perform better in northern Europe or in irrigated fields. However, the best combinations of parameters determined in an unconstrained phenotypic space were not available in the observed genetic diversity. Overall, this study provides elements on where and when a combination of trait values can give a comparative advantage on yield, together with the boundary of possibilities within the current genetic diversity.Sous contrainte hydrique, les plantes limitent leur transpiration en diminuant leur croissance foliaire, économisant ainsi l’eau pour la fin du cycle de culture. Une forte variabilité génétique a été observée chez le maïs pour les processus impliqués dans cette réponse. Le compromis entre transpiration et photosynthèse implique qu’une forte plasticité n’est pas toujours avantageuse car elle diminue aussi l’accumulation de biomasse et le rendement. Un génotype qui maximise la production dans un environnement sec n’est donc pas le meilleur dans un autre environnement sec. Le but de cette thèse était de prédire quelles combinaisons de traits reliés à la croissance foliaire aboutissent aux meilleurs rendements dans différents environnements européens. Pour cela, (i) j’ai montré que les contrôles environnementaux et génétiques diffèrent entre l’élongation et l’élargissement foliaires, et établi/testé les équations décrivant ces contrôles. (ii) J’ai développé un modèle de croissance foliaire, en restant parcimonieux en paramètres et en veillant à ce que les paramètres soient mesurables en plateformes de phénotypage. (iii) J’ai développé un cadre de simulation qui inclut 36 ans de conditions climatiques et les pratiques agricoles dans 59 sites de culture du maïs en Europe, ainsi que la paramétré 254 hybrides de maïs qui maximisent la diversité génétique. (iv) Ce cadre d’analyse a été utilisé pour prédire la durée de cycle optimale dans chacun des environnements étudiés, sous les conditions climatiques présentes et futures. (v) J’ai utilisé le cadre de simulation et ces durées de cycle adaptées pour déterminer les meilleurs idéotypes de croissance foliaire adaptés aux différent scenarios environnementaux. Les résultats montrent que les variétés sensibles sont adaptées à l’Europe du sud en condition non-irriguées alors que l’opposé est adapté au nord ou en condition irriguée. Cependant, les meilleures combinaisons de paramètres déterminées dans un espace phénotypique non contraint n’étaient pas disponible dans la diversité génétique observée. Cette thèse fournit aux sélectionneurs des éléments sur les combinaisons de traits qui fournissent un avantage comparatif dans chaque environnement ainsi que le contour des possibles dans la diversité génétique observée

A phenomics-based dynamic model of growth and yield to simulate hundreds of maize hybrids in the diversity of European environments

Sous contrainte hydrique, les plantes limitent leur transpiration en diminuant leur croissance foliaire, économisant ainsi l’eau pour la fin du cycle de culture. Une forte variabilité génétique a été observée chez le maïs pour les processus impliqués dans cette réponse. Le compromis entre transpiration et photosynthèse implique qu’une forte plasticité n’est pas toujours avantageuse car elle diminue aussi l’accumulation de biomasse et le rendement. Un génotype qui maximise la production dans un environnement sec n’est donc pas le meilleur dans un autre environnement sec. Le but de cette thèse était de prédire quelles combinaisons de traits reliés à la croissance foliaire aboutissent aux meilleurs rendements dans différents environnements européens. Pour cela, (i) j’ai montré que les contrôles environnementaux et génétiques diffèrent entre l’élongation et l’élargissement foliaires, et établi/testé les équations décrivant ces contrôles. (ii) J’ai développé un modèle de croissance foliaire, en restant parcimonieux en paramètres et en veillant à ce que les paramètres soient mesurables en plateformes de phénotypage. (iii) J’ai développé un cadre de simulation qui inclut 36 ans de conditions climatiques et les pratiques agricoles dans 59 sites de culture du maïs en Europe, ainsi que la paramétré 254 hybrides de maïs qui maximisent la diversité génétique. (iv) Ce cadre d’analyse a été utilisé pour prédire la durée de cycle optimale dans chacun des environnements étudiés, sous les conditions climatiques présentes et futures. (v) J’ai utilisé le cadre de simulation et ces durées de cycle adaptées pour déterminer les meilleurs idéotypes de croissance foliaire adaptés aux différent scenarios environnementaux. Les résultats montrent que les variétés sensibles sont adaptées à l’Europe du sud en condition non-irriguées alors que l’opposé est adapté au nord ou en condition irriguée. Cependant, les meilleures combinaisons de paramètres déterminées dans un espace phénotypique non contraint n’étaient pas disponible dans la diversité génétique observée. Cette thèse fournit aux sélectionneurs des éléments sur les combinaisons de traits qui fournissent un avantage comparatif dans chaque environnement ainsi que le contour des possibles dans la diversité génétique observée.Under soil water deficit, plants limit transpiration by decreasing leaf area to save water for the end of the crop cycle. A large genetic diversity has been observed in maize for the processes involved in this response. Because of the trade-off between transpiration and photosynthesis, a high plasticity is not always beneficial because it also reduces biomass accumulation and grain yield. The genotype that maximises production in one dry environment therefore does not always perform the best in another dry environment. The aim of this thesis was to predict which combination of trait values related to leaf growth would be beneficial in the diversity of European environments. For this purpose, (i) I have shown that genetic and environmental controls differ between leaf elongation and widening, and established/tested the equations that describe these controls. (ii) I have developed a model of leaf development and expansion, with a particular attention to the parsimony for parameter number and to the possibility of measuring parameter values in phenotyping platforms. (iii) I have developed a simulation framework including 36 years of environmental conditions and management practices of 59 European fields, together with the parameterisation of 254 maize hybrids maximising the maize genetic diversity. (iv) This framework has been used to simulate the optimum crop cycle duration for each site and management practice in current and future conditions. (v) The simulation framework and the adapted cycle duration were then used to determine ideotypes of leaf growth adapted to the different environmental scenarios. Results indicate that sensitive hybrids perform better in southern Europe under rainfed conditions while less-sensitive genotypes perform better in northern Europe or in irrigated fields. However, the best combinations of parameters determined in an unconstrained phenotypic space were not available in the observed genetic diversity. Overall, this study provides elements on where and when a combination of trait values can give a comparative advantage on yield, together with the boundary of possibilities within the current genetic diversity

Un modèle dynamique de croissance et rendement basé sur la phonémique pour simuler la variabilité de centaines d'hybrides de maïs dans la diversité des environnements Européens

Under soil water deficit, plants limit transpiration by decreasing leaf area to save water for the end of the crop cycle. A large genetic diversity has been observed in maize for the processes involved in this response. Because of the trade-off between transpiration and photosynthesis, a high plasticity is not always beneficial because it also reduces biomass accumulation and grain yield. The genotype that maximises production in one dry environment therefore does not always perform the best in another dry environment. The aim of this thesis was to predict which combination of trait values related to leaf growth would be beneficial in the diversity of European environments. For this purpose, (i) I have shown that genetic and environmental controls differ between leaf elongation and widening, and established/tested the equations that describe these controls. (ii) I have developed a model of leaf development and expansion, with a particular attention to the parsimony for parameter number and to the possibility of measuring parameter values in phenotyping platforms. (iii) I have developed a simulation framework including 36 years of environmental conditions and management practices of 59 European fields, together with the parameterisation of 254 maize hybrids maximising the maize genetic diversity. (iv) This framework has been used to simulate the optimum crop cycle duration for each site and management practice in current and future conditions. (v) The simulation framework and the adapted cycle duration were then used to determine ideotypes of leaf growth adapted to the different environmental scenarios. Results indicate that sensitive hybrids perform better in southern Europe under rainfed conditions while less-sensitive genotypes perform better in northern Europe or in irrigated fields. However, the best combinations of parameters determined in an unconstrained phenotypic space were not available in the observed genetic diversity. Overall, this study provides elements on where and when a combination of trait values can give a comparative advantage on yield, together with the boundary of possibilities within the current genetic diversity.Sous contrainte hydrique, les plantes limitent leur transpiration en diminuant leur croissance foliaire, économisant ainsi l’eau pour la fin du cycle de culture. Une forte variabilité génétique a été observée chez le maïs pour les processus impliqués dans cette réponse. Le compromis entre transpiration et photosynthèse implique qu’une forte plasticité n’est pas toujours avantageuse car elle diminue aussi l’accumulation de biomasse et le rendement. Un génotype qui maximise la production dans un environnement sec n’est donc pas le meilleur dans un autre environnement sec. Le but de cette thèse était de prédire quelles combinaisons de traits reliés à la croissance foliaire aboutissent aux meilleurs rendements dans différents environnements européens. Pour cela, (i) j’ai montré que les contrôles environnementaux et génétiques diffèrent entre l’élongation et l’élargissement foliaires, et établi/testé les équations décrivant ces contrôles. (ii) J’ai développé un modèle de croissance foliaire, en restant parcimonieux en paramètres et en veillant à ce que les paramètres soient mesurables en plateformes de phénotypage. (iii) J’ai développé un cadre de simulation qui inclut 36 ans de conditions climatiques et les pratiques agricoles dans 59 sites de culture du maïs en Europe, ainsi que la paramétré 254 hybrides de maïs qui maximisent la diversité génétique. (iv) Ce cadre d’analyse a été utilisé pour prédire la durée de cycle optimale dans chacun des environnements étudiés, sous les conditions climatiques présentes et futures. (v) J’ai utilisé le cadre de simulation et ces durées de cycle adaptées pour déterminer les meilleurs idéotypes de croissance foliaire adaptés aux différent scenarios environnementaux. Les résultats montrent que les variétés sensibles sont adaptées à l’Europe du sud en condition non-irriguées alors que l’opposé est adapté au nord ou en condition irriguée. Cependant, les meilleures combinaisons de paramètres déterminées dans un espace phénotypique non contraint n’étaient pas disponible dans la diversité génétique observée. Cette thèse fournit aux sélectionneurs des éléments sur les combinaisons de traits qui fournissent un avantage comparatif dans chaque environnement ainsi que le contour des possibles dans la diversité génétique observée

OïO-Tech Project: Developing new phenotyping tools adapted for plant research

The OÏO project comes from the observation that agronomics research lack of tools to precisely characterise plant morphology and development, in contrast with the increasing possibilities offered by the rapid development of new technologies (smaller sensors and controllers, wireless acquisition systems, more powerful processors for analysis …). Moreover, while automatons are getting more and more complex, researchers are given less freedom to adapt data acquisition to their specific needs. For that purpose, OÏO-Tech projects aims at developing new phenotyping systems to measure variables for both plant environment, and its morphological and physiological characteristics, while leaving intact the ability of researchers to transform and adapt both hardware and software depending on their requirements. For development, OÏO-Tech is working in collaboration with the LEPSE (INRA-MONTPELLIER) to develop, test and adapt new prototypes, benefitting from the expertise and structures of a renowned research unit. Our first product has been developed in an effort to precisely measure grain abortion linked to water stress on maize plants. After harvest, maize ears are placed into our “MaGeek Box” where the automaton automatically takes photos of the ear and analyses them to determine the number of grain and the characteristics of those grains (surface, volume, type : aborted or not). Simultaneously, OïO-Tech is developing a planimeter for destructive leaf area measurements. This prototype is designed to be highly adaptable to a big diversity of plants and includes software to treat the data and precisely calculate green leaf area. While prototyping, OïO-Tech makes it an important issue to develop both hardware and software based on open-source systems such as Linux or Rasperry Pi so that users still have the possibility to change and adapt codes and data analysis to their needs

A phenomics-based dynamic model of growth and yield to simulate hundreds of maize hybrids in the diversity of European environments

Under soil water deficit, plants limit transpiration by decreasing leaf area to save water for the end of the crop cycle. A large genetic diversity is observed in maize for the processes involved in this response. We aimed to predict which combination of trait values related to leaf growth would be beneficial in the diversity of European environments. For this purpose, we have first analysed the genetic and environmental controls of leaf elongation and widening. A series of experiments revealed that leaf elongation is related to plant water status whereas leaf widening is related to the carbon available to plant. A GWAS analysis also revealed that elongation and widening depend on different alleles. This analysis resulted in a model that allows simulating leaf area in a large variety of environmental scenarios. This model resulted in estimated leaf area and yield that were close to those observed in 15 fields over Europe. The model was then used to determine ideotypes of leaf growth adapted to the different environmental scenarios. Results indicate that sensitive hybrids perform better in southern Europe under rainfed conditions while less-sensitive genotypes perform better in northern Europe or in irrigated fields. However, the best combinations of parameters determined in an unconstrained phenotypic space were not available in the observed genetic diversity. Overall, this study provides elements on where and when a combination of trait values can give a comparative advantage on yield, together with the boundary of possibilities within the current genetic diversity

Distinct controls of leaf widening and elongation by light and evaporative demand in maize

International audienceLeaf expansion depends on both carbon and water availabilities. In cereals, most of experimental effort has focused on leaf elongation, with essentially hydraulic effects. We have tested if evaporative demand and light could have distinct effects on leaf elongation and widening, and if short term effects could translate into final leaf dimensions. For that, we have monitored leaf widening and elongation in a field experiment with temporary shading, and in a platform experiment with 15-min temporal resolution and contrasting evaporative demands. Leaf widening showed a strong (positive) sensitivity to whole-plant intercepted light and no response to evaporative demand. Leaf elongation was (negatively) sensitive to evaporative demand, without effect of intercepted light per se. We have successfully tested resulting equations to predict leaf length and width in an external dataset of 15 field and 6 platform experiments. These effects also applied to a panel of 251 maize hybrids. Leaf length and width presented quantitative trait loci (QTLs) whose allelic effects largely differed between both dimensions but were consistent in the field and the platform, with high QTLxEnvironment interaction. It is therefore worthwhile to identify the genetic and environmental controls of leaf width and leaf length for prediction of plant leaf area

Analyse des risques psychosociaux et de la qualité de vie au travail. Diagnostic et préconisations (UMR LEPSE)

il s'agit d'un type de produit dont les métadonnées ne correspondent pas aux métadonnées attendues dans les autres types de produit : ACTIVITY_REPORTAnalyse des risques psychosociaux et de la qualité de vie au travail. Diagnostic et préconisations (UMR LEPSE

Measuring security development in information technologies: A scientometric framework using arXiv e-prints

We study security-development patterns in computer-science technologies through (i) the security attention among technologies, (ii) the relation between technological change and security development, and (iii) the effect of opinion on security development. We perform a scientometric analysis on arXiv e-prints (n=340,569) related to 20 computer-science technology categories. Our contribution is threefold. First, we characterize both processes of technological change and security development: while most technologies follow a logistic-growth process, the security development follows an AR(1) process or a random walk with positive drift. Moreover, over the lifetime of computer-science technologies, the security development surges at a late stage. Second, we document no relation between the technological change and the security development. Third, we identify an inverse relation between security attention and experts’ opinion. Along with these results, we introduce new methods for modeling security-development patterns for broader sets of technologies.ISSN:0040-162

Drought tolerance: which mechanisms, traits and alleles for which drought scenarios?

Plants are subjected every day to rapid variation of evaporative demand and soil water availability, resulting in rapid changes in stomatal conductance, expansive growth and metabolism over minutes. Because yield involves several months, the connection between physiological mechanisms and response of yield to drought scenarios faces a massive problem of time scales. Furthermore, yield results from optimization between traits and alleles that lead to either minimize the risk of crop failure or to increase crop production. Evolution has tended to favour conservative processes (short crop cycle, low transpiration and leaf area, large root systems) which are favourable under severe stresses, whereas yield in milder water deficits is associated with the opposite traits. Hence, one aims at identifying which traits and alleles are favourable in which drought scenarios, rather than at a generic ‘drought tolerance’. We deal with these methodological difficulties by combining phenomics, modelling, genetic analysis and genomic prediction. A first strategy explores the genetic variability of key processes, which are translated into parameters of a crop model. This requires detailed analyses in phenotyping platforms with a capacity of thousands of plants, with the relevant time scales. These parameters are analysed by GWAS and simulated via genomic prediction. The model can then simulate yield in hundreds of fields for hundreds of genotypes, from genetic parameters of each genotype and environmental conditions in each field. A second strategy directly explores the responses of yield to environmental conditions in contrasting environmental scenarios, e.g. in 40 fields. This results in a mixed model whose parameters are analysed genetically and can be estimated by genomic prediction, thereby allowing one to predict yields in new genotypes and fields. As a whole, the combination of field and platform data allows identification of combinatio