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Despite the importance of yam ( Dioscorea spp.) tuber quality traits, and more precisely texture attributes, high-throughput screening methods for varietal selection are still lacking. This study sets out to define the profile of good quality pounded yam and provide screening tools based on predictive models using near infrared reflectance spectroscopy. Seventy-four out of 216 studied samples proved to be moldable, i.e. suitable for pounded yam. While samples with low dry matter (4%) and high protein (>6%) contents, low hardness (0.5) and high cohesiveness (>0.5) grouped mostly non-moldable genotypes, the opposite was not true. This outline definition of a desirable chemotype may allow breeders to choose screening thresholds to support their choice. Moreover, traditional near infrared reflectance spectroscopy quantitative prediction models provided good prediction for chemical aspects (R2 > 0.85 for dry matter, starch, protein and sugar content), but not for texture attributes (R2

The integrative capacity of crop models is of great value to identify in-silico optimal combinations of traits (ideotypes) and traits x cultu ral practices in targeted agro-environments. This approach becomes even more challenging when considering the multiple environ mental factors constituting future agro-climatic scenarios : increasing stress frequency and severity (e.g., heat, drought, wind, flooding); enhanced atmospheric C02 concentration (e-COzl, and also adoption of more sustainable and resilient cultural practices (agroecology) combining of productivity with ecosystem services. Several studies reported the weaknesses of crop models in predicting crop performance in response to climate change. While these limitations were until now mainly explained by poor simulation by crop models of physiological responses to heat and drought, crop model shortcomings of the representation of Carbon (C) source-sink relations and interactions, involving phenotypic plasticity of both source and sink, should explain this limitations and have received less attention (Chang and Zhu 2017). Recent studies reported a down-regulation of C source capacity (i.e. photosynthesis) in C3 crops under e-C02 by sink lim itation in the afternoon, involving low TPU levels (Triose Phosphate Utilization) (rice: (Fabre et al. 2019). (Fabre, in prep) indicated that high constitutive source-sink ratios increase photosynthesis under e-C02 • Finally, (Kikuchi et al. 2017) demonstrated in a FACE trial that rice plants with high adaptive plasticity of tillering and panicle size respond better to e-C02 • Although particularly relevant to C3 crops that respond strongly to e-C02 , this also applies to C4 crops when they are C-sink limited (Oszvald et al., 2018). Therefore, Carbon source-sink relationships and their physiological and morphological adaptability (feedbacks) are pivotai in predicting crop ideotypes in a climate change context. ln addition, the agro-ecological transition needs better crop models to design solutions for improving (1) crop energy and carbon use, (2) resilience under abiotic stresses, and (3) ecosystem services such as channeling assimilates into the roots/soil (4p1000 initiative: C sequestration and soil improvement). This implies to further model plantplant and/or plant-soil interactions and related impacts on C source-sink relationships and competitions for (light) resources. Crop models should indeed be able to predict trade-offs among several crop performance objectives such as multiple production purposes (e.g., grain and biomass), between potential productivity and adaptation, and between productivity and ecosystem services such as C sequestration into the soil. For this, we will provide examples of analytical and modeling concepts. More quantitative, extrapolatable evidence is needed to understand the importance of C source and sink traits, their adaptive plasticity as they interact during plant development, and their impact on crop performance under anticipated agro-climatic conditions. This requires a dialogue between experimental and modeling research for which we are presenting concepts here. Our laboratory focuses on rice (C3) and sorghum (C4) model cereals using crop models simulating sink- and source driven phenotypic plasticity, namely SAMARA (Kumar et al., 2016) and Ecomeristem (Larue et al., 2019). Sorne (experimental, modelling) preliminary resu lts will be shown but a broader dynamics is needed. Once further improved, the models will be used to (i) estimate in silico the prediction errors caused by ignoring source-sink feedbacks and plasticity; (ii) predict the potential of improved trait combinations and plasticity on the performance of future crops; and (iii) propose how existing, generic crop models should be improved and what type of data will be needed for that.

International audience; This study aimed to understand the physiological basis of rice photosynthetic response to C source-sink imbalances, focusing on the dynamics of the photosynthetic parameter triose phosphate utilization (TPU). Here, rice (Oriza sativa L.) indica cultivar IR64 were grown in controlled environment chambers under current ambient CO2 concentration until heading, and thereafter two CO2 treatments (400 and 800 mu mol mol(-1)) were compared in the presence and absence of a panicle-pruning treatment modifying the C sink. At 2 weeks after heading, photosynthetic parameters derived from CO2 response curves, and non-structural carbohydrate content of flag leaf and internodes were measured three to four times of day. Spikelet number per panicle and flag leaf area on the main culm were recorded. Net C assimilation and TPU decreased progressively after midday in panicle-pruned plants, especially under 800 mu mol mol(-1) CO2. This TPU reduction was explained by sucrose accumulation in the flag leaf resulting from the sink limitation. Taking together, our findings suggest that TPU is involved in the regulation of photosynthesis in rice under elevated CO2 conditions, and that sink limitation effects should be considered in crop models.

Sweet and biomass sorghum are expected to contribute increasingly to bioenergy production. Better understanding the impacts of the genotypic and environmental variabilities on biomass component traits and their properties is essential to optimize energy yields. This study aimed to evaluate whether traits contributing to stem biomass growth and biochemical composition at different biological scales (co)vary with the genotype and the water status in sorghum. Height genotypes were studied over two years in field conditions in southern France under two water treatments (well watered vs. 25 days' dry down during stem elongation). Main stem internode number, size, (non)structural carbohydrate, and lignin contents were measured at the end of the stress period and/or at final harvest, together with biochemical and histological analyses of the youngest expanded internode. The tallest genotypes showed the highest stem dry weights and lignin contents. Stem (structural) biomass density was positively correlated with lignin content, particularly in internode parenchyma. Stem soluble sugar and lignin contents were inversely proportional across genotypes and water conditions. Genotypes contrasted for drought sensitivity and recovery capacity of stem growth and biochemical composition. The length and cell wall deposition of internodes expanding under water deficit were reduced and did not recover, these responses being weakly correlated. Genotypic variability was pointed out in the growth recovery of internodes expanding under re-watered conditions. According to the observed genotypic variability and the absence of antagonistic correlations between the responses of the different traits to water availability, it is suggested that biomass sorghum varieties optimizing their responses to water availability in terms of growth and cell wall deposition can be developed for different bioenergy targets.

Sorghum can significantly contribute to growing needs in ligno-cellulosic biomass for bio-sourced product diversification, particularly in water-limited conditions. However, the genotypic and environmental variability of stem biomass production and quality is still poorly understood, limiting its genetic improvement. This study aimed to identify the morphogenetic, biochemical and histological traits underlying at internode level the genotypic and environmental variability of stem biomass accumulation by sorghum. Three field experiments were organized to compare 8 genotypes under irrigated and drought (applied during stem elongation). The eco-physiological model Ecomeristem was used to explore trait impact for different cropping situations and production targets. Both stem biomass production and quality were affected by water deficit due to the reduction of the number, length and ligno-cellulosic content of expanded internodes, whereas their soluble sugar content was increased and diameter unaffected. Internodes developed after re-watering observed a remarkable recovery whereas those developed under stress did not recover. Genotypic variability for drought sensitivity and recovery was highlighted but no correlation was found between them. The drought response of growth, biochemical and histological traits was slightly correlated, suggesting only partial trade-offs between stem biomass production and quality response to drought, obviously under complex physiological and genetic controls. Once validated on available data, the crop growth model Ecomeristem was used to in silico explore trait impact on biomass production depending on the variation in key cropping criteria for biomass sorghum worldwide: targeted production (structural, nonstructural carbohydrates), planting density and water availability. Different ideotypes were suggested for each simulated situation, mainly defined by the trade-off between tillering propensity and internode sink capacity (related either to size or biomass density). It is suggested that not only internode biomass accumulation but also tillering capacity should be further considered for phenotyping and ideotyping biomass sorghum in its targeted cropping environments.

Deciphering the genetic determinism of the traits of agronomic interest and predicting the genetic values of genotypes are key steps towards the development of efficient breeding strategies. Both steps take advantage of comparative analyses between studies to validate chromosomic regions' effects and reach a better accuracy regarding estimated genetic values. In this context there is a crucial need for researchers to be able to compare their phenotypic traits with other studies in order maximize genetic analyses efficiency.

International audience; Air temperature is one of the most critical climatic factors controlling rice growth, development, and production in current and future climatic scenarii predicting increasingly frequent situations of extreme and/or fluctuating temperatures. With its large spectrum of geographical origins and cropping areas, one can credit tropical japonica rice subspecies of a probable genetic diversity of its response to air temperature, which is of major interest for the breeding of better adapted rice varieties. A panel of 195 rice accessions (175 japonica plus 20 reference cultivars) was studied in controlled environment to estimate cardinal (base, optimum, and maximum) temperatures based on the monitoring of the elongation rate (LERmax) of the sixth leaf on the main stem in response to six fixed thermal treatments ranging from 16 to 35 degrees C. A dedicated statistical framework was elaborated for estimating LERmax, cardinal temperature and related uncertainties. Developed statistical framework enhanced the precision of cardinal temperatures estimated compared to previously reported methods, especially for base temperature. Maximum temperature was trickier to estimate and will require further studies. A significant genotypic variability for base and optimal temperature was pointed out, suggesting tropical japonica subspecies represents a relevant genetic pool to breed for rice genotypes adapted to various thermal situations. These results also suggested that using genotype-dependent cardinal temperature values should enhance the way crop growth models account for genotype x environment interactions hence their predictive value in current and future climatic conditions.

Face aux enjeux de compréhension des écosystèmes marins et de gestion des usagesmarins, les modèles complexes se révèlent des outils pertinents pour tester lesmodifications induites par le changement global, anticiper des évolutions des socioécosystèmesmarins et aider à la sélection de stratégies de gestion. Construire unmodèle numérique et faire des simulations est une chose, mesurer la confiance dessorties du modèle en est une autre. Une étape indispensable dans l’usage des modèlesnumériques est la confrontation des sorties du modèle aux observations du systèmemodélisé pour caler le modèle. La sélection de stratégies de gestion et la calibrationsont deux finalités de l’optimisation.Les problèmes d’optimisation en modélisation halieutique sont le plus souventcomplexes avec des caractéristiques mathématiques diverses. La fonction à optimiserpeut être déterministe ou stochastique, avec ou sans contraintes, à une ou plusieursdimensions. Le nombre de paramètres à optimiser peut varier de l’unité à plusieurscentaines et le coût informatique peut induire de fortes restrictions sur le nombre desimulations réalisables avec le modèle, d’une centaine à quelques milliers pour lesmoins coûteux.Aucun guide pratique n’est disponible dans la littérature pour mettre en oeuvre uneoptimisation rigoureuse avec un modèle complexe. Nous proposons ici une démarched’optimisation articulée en 3 étapes (prétraitement, choix de l’algorithme et posttraitement),basée des outils et méthodes existants et dont la réalisation peut être nonlinéaire. Ce guide inspiré d’une analyse des expériences d’un groupe de modélisateursouvre des pistes de recherche pour pallier aux difficultés, aux autocensures etfrustrations des modélisateurs.

Sorghum is increasingly used as a biomass crop worldwide. Its genetic diversity provides a large range of stem biochemical composition suitable for various end-uses as bioenergy or forage. Its drought tolerance enables it to reasonably sustain biomass production under water limited conditions. However, drought effect on the accumulation of sorghum stem biomass remains poorly understood which limits progress in crop improvement and management. This study aimed at identifying the morphological, biochemical and histological traits underlying biomass accumulation in the sorghum stem and its plasticity in response to water deficit. Two hybrids (G1, G4) different in stem biochemical composition (G4, more lignified, less sweet) were evaluated during 2 years in the field in Southern France, under two water treatments differentiated during stem elongation (irrigated; 1 month dry-down until an average soil water deficit of -8.85 bars). Plant phenology was observed weekly. At the end of the water treatment and at final harvest, plant height, stem and leaf dry-weight and the size, biochemical composition and tissue histology of internodes at 2-4 positions along the stem were measured. Stem biomass accumulation was significantly reduced by drought (in average 42% at the end of the dry-down). This was due to the reduction of the length, but not diameter, of the internodes expanded during water deficit. These internodes had more soluble sugar but lower lignin and cellulose contents. This was associated with a decrease of the areal proportion of lignified cell wall in internode outer zone whereas the areal proportion of this zone was not affected. All internodes for a given genotype and environment followed a common histochemical dynamics. Hemicellulose content and the areal proportion of inner vs. outer internode tissues were set up early during internode growth and were not drought responsive. G4 exhibited a higher drought sensitivity than G1 for plant height only. At final harvest, the stem dry weight was only 18% lower in water deficit (re-watered) compared to well-watered treatment and internodes growing during re-watering were similar to those on the well-watered plants. These results are being valorized to refine the phenotyping of sorghum diversity panels and breeding populations.

Atmospheric CO2 is expected to reach near 800 ppm in 2100, accompanied by a rise of temperature. This will considerably impact crop performance due to a direct impact on leaf C assimilation, and finally on yield components' elaboration (tillering, leaf area, panicle number, grain filling). Making crop models more predictive in future climate scenario is essential and implies firstly to better simulate the C gain generated by photosynthesis response to CO2. Crop models commonly compute biomass production using light interception (εi) and use (εb) efficiencies (Monteith's approach). Few of them consider for so key crop architectural traits, leaf photosynthesis and stomatal conductance. EcoMeristem is a functional-structural crop model, simulating cereals' plant growth and phenotypic plasticity at the organ level in response to plant C and water status. It is thus relevant to capture yield components' regulation by climate parameters, particularly CO2. However it was initially developed using εi and εb. Also, a light interception model accounting for key crop architectural parameters and leaf photosynthesis model inspired from FvCB model accounting for key climate change and leaf parameters, were recently implemented and confronted to experimental data on rice. This study aims to compare the original and the novel version of EcoMeristem in the way they simulate the regulation of yield elaboration for a few morphologically contrasted rice genotypes in response to radiation, temperature and CO2. Sensitivity analyses and simulation results will be presented and discussed with respect to the challenge of using crop modelling to support breeding in climate change context.

Studies of genotype x environment x management (GXEXM) interactions commonly use Crop Simulation Models (CSM). The minimum datasets required for a successful model implementation are multi-scale, multi-species and multi-disciplinary. We observed that although they are organized differently, CSM input files and field experiment datasets shared the same measurements (yield, leaf area index, biomass, etc.) and a few similar tables corresponding to the minimum dataset (weather, soil, crop, and management data). Based on this analysis, we have designed the schema of DAPHNE. We used the relevant technology of metadata. Thus, in DAPHNE, all variable labels are stored in a metadata table including the units and methods of measurements and the observed and experimental units. The main advantage of this technology is that the addition of any variable does not imply to reconsider the structure of the database. Database query performance is also improved. DAPHNE already has a wide application in GXEXM experiments on sorghum and sugarcane. The genericness of the schema of DAPHNE can allow intercomparison of CSM that require the same datasets with no common data structure.

Sorghum genetic diversity offers the opportunity to develop multipurpose genotypes combining high grain, ligno-cellulosic biomass and/or sweet juice productions. It provides a large spectrum of adaptive traits, particularly to drought, essential for ensuring production stability in an increasingly fluctuating climatic context. Sorghum is thus a crucial crop to breed for and contribute to meet future expectations regarding food, feed and bioenergy productions, while minimizing resource and land use competition. Accordingly, ideotypes must be developed combining appropriate developmental, morphogenetic and biochemical traits depending on targeted cropping contexts (eg. dedicated vs. intermediate crop, wateravailability) and end-uses (eg. energy, feed). Trait impact on plant phenotype construction must be first quantified, considering possible linkages and trade-offs among them that can appreciably modulate their respective benefits depending on the genotype and the environment. With this respect, ecophysiological modelling plays an original role since, by formalizing experimental knowledge on individual processes, it helps analyzing their trade-offs and impacts on resulting plant phenotypic variability, across genotypes and environments. Supported by appropriate mathematical tools, it can help quantifying elemental traits within a range of genetic diversity, in away not accessible experimentally, toward their genetic study and the in silico optimization of their combination for a given context. This approach is currently set-up in two companion projects aiming to develop multipurpose sorghum for both Mediterranean and semi-arid, drought prone environments. Field trials were carried out to identify traits controlling vegetative biomass production, composition and expressing variability across genotypes and water situations, at tissue, organ and plant level. Accordingly, the plant growth model Ecomeristem is progressively adapted to capture key processes controlling such phenotypic variability. Recent progresses and future developments will be presented, highlighting how modelling enables to analyze trait compensations and impacts on grain and biomass production and finally define ideotypes depending on targeted contexts. (Texte intégral)