No abstract

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

Predicting wheat phenology is important for cultivar selection, for effective crop management and provides a baseline for evaluating the effects of global change. Evaluating how well crop phenology can be predicted is therefore of major interest. Twenty-eight wheat modeling groups participated in this evaluation. Our target population was wheat fields in the major wheat growing regions of Australia under current climatic conditions and with current local management practices. The environments used for calibration and for evaluation were both sampled from this same target population. The calibration and evaluation environments had neither sites nor years in common, so this is a rigorous evaluation of the ability of modeling groups to predict phenology for new sites and weather conditions. Mean absolute error (MAE) for the evaluation environments, averaged over predictions of three phenological stages and over modeling groups, was 9 days, with a range from 6 to 20 days. Predictions using the multimodeling group mean and median had prediction errors nearly as small as the best modeling group. About two thirds of the modeling groups performed better than a simple but relevant benchmark, which predicts phenology by assuming a constant temperature sum for each development stage. The added complexity of crop models beyond just the effect of temperature was thus justified in most cases. There was substantial variability between modeling groups using the same model structure, which implies that model improvement could be achieved not only by improving model structure, but also by improving parameter values, and in particular by improving calibration techniques.

-Sunflower appears as a potentially highly competitive crop, thanks to the diversification of its market and the richness of its oil. However, seed oil concentration (OC) -a commercial criterion for crushing industry -is subjected to genotypic and environmental effects that make it sometimes hardly predictable. It is assumed that more understanding of oil physiology combined with the use of crop models should permit to improve prediction and management of grain quality for various end-users. Main effects of temperature, water, nitrogen, plant density and fungal diseases were reviewed in this paper. Current generic and specific crop models which simulate oil concentration were found to be empirical and to lack of proper evaluation processes. Recently two modeling approaches integrating ecophysiological knowledge were developed by Andrianasolo (2014, Statistical and dynamic modelling of sunflower (Helianthus annuus L.) grain composition as a function of agronomic and environmental factors, Ph.D. Thesis, INP Toulouse): (i) a statistical approach relating OC to a range of explanatory variables (potential OC, temperature, water and nitrogen stress indices, intercepted radiation, plant density) which resulted in prediction quality from 1.9 to 2.5 oil points depending on the nature of the models; (ii) a dynamic approach, based on "source-sink" relationships involving leaves, stems, receptacles (as sources) and hulls, proteins and oil (as sinks) and using priority rules for carbon and nitrogen allocation. The latter model reproduced dynamic patterns of all source and sink components faithfully, but tended to overestimate OC. A better description of photosynthesis and nitrogen uptake, as well as genotypic parameters is expected to improve its performance.Keywords: Seed oil concentration / sunflower / genotype / crop management / crop model Résumé -Analyse et modélisation des facteurs contrôlant la teneur en huile chez le tournesol. Le tournesol apparaît comme une culture potentiellement compétitive grâce à la diversité de ses débouchés et de la richesse en huile de ses graines. Cependant, la teneur en huile de la graine (TH) -critère commercial pour la trituration -dépend d'effets génotypiques et environnementaux ce qui en complexifie parfois la prédiction. Nous faisons l'hypothèse qu'une meilleure compréhension de la physiologie de l'accumulation d'huile combinée à l'utilisation de modèles de culture permettrait d'améliorer la prédiction et la gestion de la qualité du grain pour différents usages. Les principaux effets de la température, de l'eau, de l'azote, de la densité de peuplement et des maladies fongiques sont revus dans cette synthèse. Les modèles de culture génériques et spécifiques apparaissent empiriques pour ce qui concerne TH et manquent d'évaluation pour ce critère. Récemment, deux approches de modélisation intégrant des connaissances écophysiolo-giques ont été développées par Andrianasolo (2014, Modélisation statistique et dynamique de la composition de la graine de tournesol (Helianthus annuus L...

Sunflower (Helianthus annuus L.) raises as a competitive oilseed crop in the current environmentally friendly context. To help targeting adequate management strategies, we explored statistical models as tools to understand and predict sunflower oil concentration. A trials database was built upon experiments carried out on a total of 61 varieties over the 2000-2011 period, grown in different locations in France under contrasting management conditions (nitrogen fertilization, water regime, plant density). 25 literature-based predictors of seed oil concentration were used to build 3 statistical models (multiple linear regression, generalized additive model (GAM), regression tree (RT)) and compared to the reference simple one of Pereyra-Irujo and Aguirrezábal (2007) based on 3 variables. Performance of models was assessed by means of statistical indicators, including root mean squared error of prediction (RMSEP) and model efficiency (EF). GAM-based model performed best (RMSEP = 1.95%; EF = 0.71) while the simple model led to poor results in our database (RMSEP = 3.33%; EF = 0.09). We computed hierarchical contribution of predictors in each model by means of R 2 and concluded to the leading determination of potential oil concentration (OC), followed by post-flowering canopy functioning indicators (LAD2 and MRUE2), plant nitrogen and water status and high temperatures effect. Diagnosis of error in the 4 statistical models and their domains of applicability are discussed. An improved statistical model (GAM-based) was proposed for sunflower oil prediction on a large panel of genotypes grown in contrasting environments.