Context Questions Methods Step 1. We used modeling and simulation to predict the crop phenotypic plasticity resulting from the interaction of plant traits, climatic variability and management actions. e SUNFLO crop model [1,2] was parameterized for 35 recent oleic and linoleic sunﬂower cultivars. e values of the genotype-dependent parameters were obtained by measuring 10 phenotypic traits in dedicated ﬁeld platforms (2 locations per year) or controlled conditions [3,4]. Variety assessment is a key component of crop performance improvement (e.g. potential yield, quality, disease resistance, abiotic stress tolerance, environmental beneﬁts) and this process is still largely based on experimental approaches in ﬁeld. Plant phenotyping and crop modeling open the possibility to predict crop performance in agricultural conditions that are not tested in the variety assessment chain, yet which can occur in the target population of environments. Our objective is to present an integrated framework for variety assessment based on crop modeling in order to widen and complete the current informations on varieties provided by national advisory services or extension services for a large range of environmental and agronomic conditions. e potential use of this framework will be illustrated on sunﬂower crop as a proof of concept. Results Figure 1. Design of the MET used for model evaluation. First, we phenotyped newly released hybrids varieties in independent trials in ﬁeld and controlled conditions to set up the genotype-dependant parameterization of the crop model. en, we evaluated SUNFLO prediction capacity on the MET (relative RMSE for oil yield: 15.8%) and its capacity to rank those commercial hybrids (Kendall’s tau = 0.39, p < 0.01) for oil yield. We also tested the model capacity to simulate GxE interactions in the numerical experiment (step 3) by comparing two nested linear mixed models diﬀering by the interaction term (LRT: χ2(482) = 3561, p < 0.001, interaction model was a better ﬁt). Additionally, we showed that the SUNFLO crop model could provides indicators of the crop exposition to abiotic stress, which could be used to explain yield variance (Figure 3). ‣ How to leverage crop modeling to improve the assessment and recommendations for new varieties ? ‣ Can we recommend varieties to reduce phenotype-environment mismatch in the targeted cropping area ? Figure 2. A four step approach to include crop modeling in the variety recommendation process. e representation of the variety assessment chain (upper part in the diagram) is based on the current French system, with two years on trials before variety release (variety evaluation MET), and two year aer (agricultural extension MET). e presented approach leverage existing trials to get informations on tested varieties and to evaluate the crop model (steps 1-2). Aer designing numerical experiments, simulation would then provides recommendation for variety choice, accounting for growing conditions and climatic uncertainty (steps 3-4). e simulation of phenotypic plasticity makes possible a set of applications in variety assessment for breeders and advisers [5]: ‣ e characterization of abiotic stress as perceived by plants in each trial environment, providing that few control genotypes are included. ‣ e benchmarking of performance for material registered in diﬀerent years, i.e. having experienced distinct climatic conditions. ‣ e determination of agronomic suitability of released varieties, and especially their suitability to drought-prone areas and the optimal management for each variety type. Conclusions and perspectives References and contact 1. Casadebaig P, Guilioni L, Lecoeur J, Christophe A, Champolivier L, Debaeke P. SUNFLO, a model to simulate genotype-speciﬁc performance of the sunﬂower crop in contrasting environments. Agricultural and Forest Meteorology. Elsevier; 2011;151: 163–178. doi:10.1016/j.agrformet.2010.09.012 2. Lecoeur J, Poiré-Lassus R, Christophe A, Pallas B,