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As overfertilization leads to environmental concerns and the cost of N fertilizer increases, the issue of how to select crop cultivars that can produce high yields on N-deficient soils has become crucially important. However, little information is known about the genetic mechanisms by which crops respond to environmental changes induced by N signaling. Here, we dissected the genetic architecture of N-induced phenotypic plasticity in bread wheat (Triticum aestivum L.) by integrating functional mapping and semiautomatic high-throughput phenotyping data of yield-related canopy architecture. We identified a set of quantitative trait loci (QTLs) that determined the pattern and magnitude of how wheat cultivars responded to low N stress from normal N supply throughout the wheat life cycle. This analysis highlighted the phenological landscape of genetic effects exerted by individual QTLs, as well as their interactions with N-induced signals and with canopy measurement angles. This information may shed light on our mechanistic understanding of plant adaptation and provide valuable information for the breeding of N-deficiency tolerant wheat varieties.

Starting from grain yield, quality and resistance against multiple diseases, the characterization of the cultivar’s behavior increased in recent decades. Needs in quantitative assessments of a larger range of criteria has greatly evolved towards yield stability in a large range of fluctuating environments. Using a large dataset crossing cultivars and environments, we thus explored the relationships between yield and Healthy Area Duration (HAD), as affected by genotype, environment and septoria caused by Zygmoseptoria tritici. A set of indexes was then proposed to properly profile cultivar’s behavior. A curvilinear relationship relating HAD to potential yield was first parameterized. It allows quantifying HAD efficiency. Susceptibility (HAD loss) was differentiated from total tolerance (the ratio between yield loss and HAD loss). Finally the specific tolerance, i.e. not due to HAD level, was quantified. Correlations between indexes pointed out that no trade-off was shown between total tolerance and actual or potential yield as well as disease susceptibility. These correlations partially depended on the nitrogen status of crops, underlining other G×E interactions indexes may trap. Finally, as HAD efficiency appeared more highly linked to actual yield than potential yield we proposed an alternative set on indexes based on Healthy Area Absorption (HAA) that accounted for meteorological variability. Interestingly, these last indexes were insensitive to nitrogen nutrition as well as to cultivar susceptibility to Z. tritici. The developed indexes allowed profiling the cultivars’ behavior under a common range of environments. HAA-based indexes open the way to a useful global characterization of cultivars by breeders. Moreover, HAA can be assessed using high-throughput phenotyping tools. A thorough evaluation of this last point needs to be done.

Leaf rust is responsible for significant wheat yield losses. Its occurrence and severity have increased in recent years, partly because of warmer climate. It is therefore critical to understand and anticipate the effects of climate change on leaf rust. Direct climate effects and indirect effects via host plants that provide a biophysical environment for disease development were both considered. The coupled STICS-MILA model simulates both crop and pathogen dynamics in a mechanistic way and their interaction is managed by two sub-models: one calculating the microclimate within the canopy and the other converting numbers of spores and lesions to affected surfaces. In this study, STICS-MILA was first calibrated and evaluated using leaf rust severity observed at various sites in France for multiple years. STICS-MILA was then run on three contrasting French sites under 2.6, 4.5 and 8.5 RCP future climate scenarios. Results focused firstly on changes in disease earliness and intensity, secondly on disease dynamics, particularly the synchronism between plant and disease developments, and finally on elementary epidemic processes. The calibration and evaluation of STICS-MILA revealed a high sensitivity to the initial amount of primary inoculum (a forcing variable in STICS-MILA) and thus the need to properly simulate the summering and overwintering pathogen survival. The simulations in the context of future climate showed a significant change in host-pathogen synchronism: in the far future, according to RCP 4.5 and 8.5 scenarios, disease onset is expected to occur not only with an advance of around one month but also at an earlier developmental stage of wheat crops. This positive effect results from rising temperatures, nevertheless partly counter-balanced during spring by lower wetness frequency. The crop growth accelerates during juvenile stages, providing a greater support for disease development. The resulting microclimate shortens latency periods and increases infection and sporulation efficiencies, thus causing more infectious cycles. An increase of final disease severity is thus forecasted with climate change.

International audience; Predicting wheat growth stages using ecophysiological models is of particular interest as it allows anticipating important agricultural managements. Numerous ecophysiological models exist but they need cultivar-specific parameterization, which is often costly and time consuming. The work presented here proposes a simple approach to predict wheat growth stages using the allelic composition of wheat cultivars. It relies on using the prediction of a modified version of the ARCWHEAT model for a well parameterized reference cultivar (Soissons) and the marker-based predicted deviation in days to the reference cultivar. First, the deviations to the reference cultivar Soissons for the beginning of stem elongation (87.30) and heading date (delta Z55) were calculated for a large panel of cultivars. Analysis of variance showed prominent genetic effects for delta Z30 and delta Z55 and possible genotype x environment interactions (G x E) for delta Z30. Genotypic means 6230 and delta Z55 were used in association genetics revealing 90 and 83 genetic markers associated to these traits, respectively. Multiple linear regression models predicting delta Z30 using 11 genetic markers (R-2= 76%) or delta Z55 using 17 markers (R-2 =85%) were obtained by a stepwise procedure. Marker PPD-D1 had the largest impact in both models. Finally, marker-based deviations added to the prediction for the reference cultivar Soissons allowed predicting Z30 or Z55 for a large independent validation dataset. The root mean square error of prediction for Z30 and Z55 using the approach proposed in this paper (6.8 and 4.7 days, respectively) was comparable to the one obtained using the conventional approach with cultivar-specific parameters values (6.5 and 4.1, respectively). The models proposed in this paper appeared sufficient in order to predict growth stages of cultivars which cannot be parameterized such as new cultivars coming out on the market. Moreover, genetic markers involved in the multiple linear regression models predicting delta Z30 and delta Z55 may provide interesting candidates to unravel new genes determining earliness in winter wheat.

A model for predicting brown rust severity in France was developed using the systematic screening of climatic variables of the Window Pane approach and data from 400 field trials spanning 30 years. The model was built using novel methods to manage the variable selection problem posed by the very large number of predictor variables generated by Window Pane, namely the elastic-net, and a systematic cross-validation to determine the most frequently retained variables. The model predicts the final severity of brown rust with an RMSEP (root mean square error of prediction) of 22.4%. The model’s ability to predict treatment decisions was tested and exhibited a good performance as shown by an area under the receiver operator curve of 0.85. We also evaluated the suitability of our model for use in France by confronting the range of the climate variables in our dataset against the climatological range of these same variables in France. The final model also gives important insights into the key factors behind variations in brown rust disease pressure.

We calculate the impact of climate change on the effective severity of Septoria tritici blotch (STB) of winter wheat (Triticum aestivum L.) at three representative locations in France. The calculation uses climate models for climate prediction, and a disease model to link disease severity to weather. Four impact criteria are considered: the change in average (over years) severity, the change in interannual variance of severity, the change in number of years with particularly high severity and the change in the number of years with particularly low severity. We also calculate the uncertainty associated with those impact criteria. Three different uncertainty sources are considered: uncertainty in predicting climate, uncertainty in the values of the disease model parameters and uncertainty due to residual error of the disease model. Uncertainty in climate is considered by using different global climate models and downscaling methodologies to produce five different climate series for greenhouse gas emission scenario A1B, for a baseline period comprising harvest years 1971–1999 and a future period spanning 2071–2099. A Bayesian approach, using a Metropolis Hastings within Gibbs algorithm, is used for parameter estimation. This gives a posterior distribution both for the 17 model parameters that were considered and for the variance of residual error. Climate change is predicted to reduce the average severity of STB by 2–6%, depending on location, and to result in more low severity years and fewer high severity years. There is appreciable uncertainty. For example, the probability that average severity will increase rather than decrease is 40%, 18% and 45% for the three locations. We calculated first order sensitivity indices for climate model, parameter vector and residual error considered as three factors. The climate model factor has by far the largest sensitivity index. However, interactions between factors also make a major contribution to overall variance.

There is much evidence that increasing temperatures due to climate change are having negative effects on yields of key staple crops, including wheat. In France particularly, a link has been shown between the stagnating wheat yields and an increase in heat stress occurrence during grain filling. We studied the occurrence of heat stress during grain filling of wheat under climate change by coupling downscaled weather scenarios from the ARPEGE climate model with a modified version of the ARCWHEAT phenology model. We also explored the effects of different agronomic solutions: earlier sowing, use of earlier cultivars and improved genetic tolerance to heat stress. Results show that in the near future (2020–2049) a small to null increase in heat stress may occur. In the far future (2070–2099), the frequency of heat stress during grain filling should increase significantly. Adaptation through earlier sowing dates proves to be the least efficient. Use of earlier heading cultivars is somewhat efficient, and should be sufficient for the near future. Tolerance to heat stress appears to be the most promising adaptation strategy. We discuss the importance of placing earliness and heat tolerance high on the agenda of wheat research and breeding, and the potential use of modelling in evaluating such strategies.

Introduction Simulations using crop models can assist in designing ideotypes for current and future agricultural conditions. This approach has been often in recent years to identify avenues for adapting wheat to climate change. However, this approach has rarely been used to guide commercial breeding programs. We hypothesize that the lack of link between models and the available tools for breeding, i.e. available genetic variability and selection methods. Materials and methods - We use a modified ARCWHEAT2 phenology model and future climate data from the ARPEGE global circulation model to identify targets for future phenologies-We genotyped over 400 French cultivars for known phenology genes and confronted the genetic make-up of these varieties to their success in France over the past 25 years- We developed a methodology to link model parameters to underlying marker data. We tested the performance of the methodology against circa 60 varieties Results Earlier phenology may be an avenue for stress avoidance in the future. Current photoperiod sensitivity of early cultivars already poses problems in terms of adaptation, as exemplified by the interaction between Ppd-D1 and Vrn-A3 We show that a gene-based model can be used to predict wheat phenology without a significant loss in predictive performance. Discussion Analyzing current phenology genes of existing cultivars and their adaptation allowed us to identify a limit to past breeding efforts in obtaining early cultivars. This requires that a more knowledge based approach be taken. Gene-based modelling of phenology is possible on a collection of elite, adapted varieties and provides the tools for constructing genotypes with specific allelic combinations leading to more appropriate constructions of earliness.

Introduction Drought is projected to be an increasing problem for wheat in France. We provide some key figures on current and projected drought stress in France. Evaluating drought tolerance is a complex task. Climate variability can lead to very different drought stress conditions in field experiments. The importance of genotype by environment interactions under drought also requires that trial environments be related to the types of drought prevalent in each target population of environments. We present the framework developed at Arvalis to deal with these complex interactions. Materials and methods - Two dedicated platforms have been developed to carry out genotype evaluations for association genetics panels. A field platform has been in operation for 5 years. Tools developed on the platform include a microplot scale soil characterization and the PhenoMobile automated phenotyping system. The second, under construction, is PhenoField, including automated rain-out shelters and phenotyping systems. - A network of field trials is run on a subset of varieties to identify trait x environment interactions for drought response. - Climatological analysis using a water balance model is carried out across France. Results - The diversity of drought stress intensities over 5 years in the field platform is presented and compared to the climatological analysis of drought in France. - The correlation of traits, for example Carbon Isotope Discrimination, to perform in diverse drought environments is assessed throughout the field network. - The use of microplot scale soil characterization significantly improves precision and heritability on our panel evaluation. - An update on the development of PhenoMobile and PhenoField systems is given. Discussion - Drought tolerance evaluation requires an integration of multiple tools. We combine well characterized sites with high throughput capacities to trial networks and climatological analysis to extrapolate results.