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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.

Wheat phenology allows escape from seasonal abiotic stresses including frosts and high temperatures, the latter being forecast to increase with climate change. The use of marker-based crop models to identify ideotypes has been proposed to select genotypes adapted to specific weather and management conditions and anticipate climate change. In this study, a marker-based crop model for wheat phenology was calibrated and tested. Climate analysis of 30 years of historical weather data in 72 locations representing the main wheat production areas in France was performed. We carried out marker-based crop model simulations for 1019 wheat cultivars and three sowing dates, which allowed calculation of genotypic stress avoidance frequencies of frost and heat stress and identification of ideotypes. The phenology marker-based crop model allowed prediction of large genotypic variations for the beginning of stem elongation (GS30) and heading date (GS55). Prediction accuracy was assessed using untested genotypes and environments, and showed median genotype prediction errors of 8.5 and 4.2 days for GS30 and GS55, respectively. Climate analysis allowed the definition of a low risk period for each location based on the distribution of the last frost and first heat days. Clustering of locations showed three groups with contrasting levels of frost and heat risks. Marker-based crop model simulations showed the need to optimize the genotype depending on sowing date, particularly in high risk environments. An empirical validation of the approach showed that it holds good promises to improve frost and heat stress avoidance.

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.

International audience; Structural 3D plant models aim at mimicking the dynamics of plant and crop structure based on experimental data. Such models can be interfaced with physical models to investigate plant-environment interactions. This work aimed at defining functions that represent the leaf and tiller development of individual wheat plants, and that could be fitted to the specific traits produced in a broad range of situations.A dataset of the dynamics of wheat plant (Triticum aestivum) architecture was collected for 55 experimental situations, including 11 growing seasons, three sowing densities, three sowing dates, and 13 commercial cultivars. Data were analysed to identify conserved patterns in the dynamics of leaf emergence and of tiller emergence and senescence.The broad range of conditions tested allowed us to evaluate the robustness of relationships proposed in previous studies and to identify novel patterns. Amongst them, we observed: (i) that leaf emergence dynamics may follow either a linear or a bilinear pattern for the same genotype. When a change in phyllochron occurred, it coincided with the initiation of the flag leaf; (ii) the delay between leaf and tiller emergence was not constant, but increased very regularly for successive phytomers; (iii) the number of leaves emerged at tillering cessation decreased with plant density but depended also on the final number of leaves on the main stem (MS) and marked differences existed between cultivars. Finally, we defined functions representing leaf and tiller dynamics with parameters that have a simple botanical interpretation and are easy to derive from field measurements. Assessing plant density, crop leaf stage at 5–6 dates and tiller population at 2 dates during the cycle provide the required data.This study defines a rationale to analyse and represent the dynamics of the architecture of individual wheat plants. The method can be used to determine the dynamics of architecture in 3D models and should be transposable to a wide range of cereal species.

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.

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.

Predicting annual crop yields is of interest for many agricultural applications. We present a prediction scheme at the departmental level, circa 100 km by 100 km, of winter wheat yields in France, applied for 23 departments, using official yield statistics from 1986 to 2010. Each model is a linear combination of 5–7 variables, selected from an initial pool of over 250 candidates. Candidate variables were generated using a phenological model and a crop water balance model, applied to a representative cropping situation for the department. Variable selection was carried out with forward stepwise regression methods. The variable selection process was cross-validated, so as to select robust variables. Model prediction performance was also evaluated by cross-validation. Satisfactory models were created for 20 departments, with root mean square error of prediction ranging from 0.25 t/ha to 0.39 t/ha. During use, whole season weather data is not available: this is complemented by frequential calculation over the past 20 years of historical weather data. We assessed the impact of time of prediction on model error by hindcasting yields for all 25 years of the dataset. We estimate that predictions can start 20 days after heading on average. We analysed predictive performance in an independent dataset and propose recommendations for use of these models outside their training dataset. The models give new insight as to the climatic factors that are key in determining yield in France.

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.

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

Multiparental populations are innovative tools for fine mapping large numbers of loci. Here we explored the application of a wheat Multiparent Advanced Generation Inter-Cross (MAGIC) population for QTL mapping. This population was created by 12 generations of free recombination among 60 founder lines, following modification of the mating system from strict selfing to strict outcrossing using the ms1b nuclear male sterility gene. Available parents and a subset of 380 SSD lines of the resulting MAGIC population were phenotyped for earliness and genotyped with the 9K i-Select SNP array and additional markers in candidate genes controlling heading date. We demonstrated that 12 generations of strict outcrossing rapidly and drastically reduced linkage disequilibrium to very low levels even at short map distances and also greatly reduced the population structure exhibited among the parents. We developed a Bayesian method, based on allelic frequency, to estimate the contribution of each parent in the evolved population. To detect loci under selection and estimate selective pressure, we also developed a new method comparing shifts in allelic frequency between the initial and the evolved populations due to both selection and genetic drift with expectations under drift only. This evolutionary approach allowed us to identify 26 genomic areas under selection. Using association tests between flowering time and polymorphisms, 6 of these genomic areas appeared to carry flowering time QTL, 1 of which corresponds to Ppd-D1, a major gene involved in the photoperiod sensitivity. Frequency shifts at 4 of 6 areas were consistent with earlier flowering of the evolved population relative to the initial population. The use of this new outcrossing wheat population, mixing numerous initial parental lines through multiple generations of panmixia, is discussed in terms of power to detect genes under selection and association mapping. Furthermore we provide new statistical methods for use in future analyses of multiparental populations.

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.

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.

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.

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.

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.

Knowledge acquired over the paste decades on wheat physiology allows for an estimation of the impacts of key climatic factors on wheat physiology, yield components, as well as a quantification of the main climatic risks for the crop. Using historical weather data sets starting in 1955 from a large number of weather stations, we have concluded that a certain number of climatic risks have changed through comparison of two sequences: 1955-1980 and 1981-2005. Despite a significant anticipation of phenological stages, number of grains per m 2 has been significantly affected by an incresase in water stress during stem extension; also, kernel weight is more strongly penalized through the joint effects of increased drought and especially increased heat stress during grain filling. These tendancies vary geographically. Certain regions have been affected early on (mediteranean region), whereas others are almost untouched as yet (western maritime front). Studies using projected future weather data validates this tendancy and allows its extrapolation. We propose adaptation pathways through escape based on varietal earliness and identify new phenological ideotypes and modes of growth better adapted to future risk. As a complement, the interest of selecting for cultivars tolerant to abiotic stresses is put forward, especially cultivars efficient with regards to water use and resistant to high temperatures. For these aspects, simple diagnostic methods and characterization tools are given. Finally, a preliminary analysis of the adaptation of cropping practices (sowing, nitrogen fertilization, lodging control) is initiated.

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.

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.

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.

ECHAP : un projet pour identifier les possibilités de réduction de l’utilisation des fongicides en utilisant l’architecture des couverts. 45e Congrès du Groupe Français des Pesticides Devenir et impact des pesticides : verrous à lever et nouveaux enjeux

The use of multiparental populations for QTL discovery has been recently highlighted by different theoretical and experimental developments. Here, we explored the interest of French populations using heterogeneous genetic stocks of cultivated wheat, maintained in situ over 12 sites since 1984 with an outcrossing mating system. We studied one of these populations (Le Moulon, 48.4°N, 21°E), derived from 12 cycles of random crosses between 60 founders, selected to maximize genetic diversity. Outcrossing was allowed by the integration of a nuclear male sterility allele (ms1b, Probus donor) in the population. We analyzed 1,000 Single Seed Descent lines (SSD) derived from the 12th generation of cultivation. This population was genotyped using the 9 K i-select SNPs (Single Nucleotide Polymorphisms) array, covering the whole genome. Polymorphism and quality checks resulted in the selection of around 6,500 SNPs. First, the evolution of genetic diversity was explored through the comparison of SSD lines and the inferred initial population. The low population structure and the strong decay in linkage disequilibrium between SSD lines and the inferred initial population confirmed the efficiency of the 12 cycles of the random outcrossing in producing a highly diverse and recombined population. Two years of observations of population earliness under different environments were used to show the complementarity of association genetics, which allowed the detection of already known Vrn major genes, and evolutionary approach, which, lead to the discovery of two new minor effect QTLs.

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.

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.

International audience

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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.

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.

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

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

National audience

[Departement_IRSTEA]Territoires [TR1_IRSTEA]SEDYVIN [ADD1_IRSTEA]Adaptation des territoires au changement globalCahier thématique; National audience; Ce deuxième cahier diffuse des connaissances scientifiques (non exhaustives) sur les effets du changement climatique sur l’agriculture et la forêt en Provence-Alpes-Côte d’Azur. Les cultures (vergers, céréales, riz, maraîchage…) et les forêts (feuillus, conifères) bénéficient des bienfaits du climat méditerranéen qui offre des conditions favorables au développement des plantes sous certaines conditions, mais souffrent aussi des événements extrêmes qui le ponctuent à intervalles irréguliers. Le climat méditerranéen qui sévit en région PACA pourrait se résumer par Toulourenc (« tout ou rien » en provençal), du nom du cours d’eau à caractère torrentiel qui coule dans la vallée étroite située au pied du versant nord du mont Ventoux. Avec le changement climatique actuel, les aspects négatifs de notre climat sont appelés à se renforcer et font déjà peser sur les terroirs agricoles et les forêts emblématiques de la région de nouvelles contraintes auxquelles il est nécessaire de faire face pour éviter des conséquences trop néfastes. Mais la pérennité et le développement des systèmes agricoles et forestiers ne dépendent pas seulement de l’évolution du climat. L’urbanisation, l’occupation des sols, les pollutions locales (sol, air, eau), les incendies, mais aussi les pratiques culturales et la gestion forestière, jouent un rôle fondamental. Il convient donc de privilégier une approche transversale. Cette publication souligne les conséquences du changement climatique sur l’agriculture et la forêt en prenant soin d’identifier les enjeux environnementaux, économiques et sociaux, les risques à l’échelle régionale et locale, mais aussi les solutions susceptibles de réduire les impacts (atténuation, adaptation) et les éventuelles opportunités à saisir. Comme dans le précédent cahier, la contribution des chercheurs et experts, exerçant leur métier en région PACA et dans les territoires limitrophes4, sous forme d’articles et de zooms, apporte des éléments de compréhension afin de mieux cerner les problématiques liées au changement climatique.

The choice of sowing dates is a real challenge because it determines which minimize the climatic risks and maximize the needs of the crop which are described by phenoclimatic variables (climatic variables which occur when the plant is sensitive to it). The aim of this study is to think over the methods leading to the choice of winter bread wheat sowing dates. These methods are either unsuitable, either inexistent. We propose an alternative method which deals with climatic change and we built a new methodology which is able to sort sowing dates according to a level a stress for the crop. We still have to improve on the choice of the phenoclimatic variables and to use other variables to define clearly the best sowing dates.; Le choix des dates de semis est un enjeu majeur car il détermine l'exposition de la culture aux aléas climatiques. Arvalis-Institut du végétal cherche à préconiser les dates de semis qui permettent de minimiser des risques ou maximiser la satisfaction des besoins phénoclimatiques i.e. qui interviennent à un stade de sensibilité de la plante. L'objectif de ce mémoire est de repenser la succession de méthodes associées au choix des dates de semis, celles-ci ayant des lacunes ou bien étant inexistantes. Nous proposons donc une méthode alternative pour tenir compte du changement climatique ainsi qu'une méthode qui permet de hiérarchiser les dates de semis en fonction d'un niveau de stress associé. Il serait cependant nécessaire de réaliser une étude préliminaire pour mieux définir les variables phénoclimatiques à utiliser et de prendre en compte des variables autres que physiologiques.