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10 Pág. Centro de Biotecnología y Genómica de Plantas (CBGP), The poverty of disease resistance gene reservoirs limits the breeding of crops for durable resistance against evolutionary dynamic pathogens. Zymoseptoria tritici which causes Septoria tritici blotch (STB), represents one of the most genetically diverse and devastating wheat pathogens worldwide. No fully virulent Z. tritici isolates against synthetic wheats carrying the major resistant gene Stb16q have been identified. Here, we use comparative genomics, mutagenesis and complementation to identify Stb16q, which confers broad-spectrum resistance against Z. tritici. The Stb16q gene encodes a plasma membrane cysteine-rich receptor-like kinase that was recently introduced into cultivated wheat and which considerably slows penetration and intercellular growth of the pathogen., Our works was supported by the “Fond de Soutien à l’Obtention Vegetale” (FSOV), the French National Institute for Agricultural Research (INRA), and the ETH Zurich Career Seed Grant (SEED-58 16-1). We thank Sreedhar Kilaru and Gero Steinberg for providing the strain 3D7 expressing eGFP. Confocal laser scanning microscopy experiments were performed in the Scientific Center for Optical and Electron Microscopy (ScopeM), ETH Zurich and at the SPIBOC imaging facility of the Institut Sophia Agrobiotech. B.F. was supported by INRA and the French Government (National Research Agency, ANR) through the ‘Investments for the Future’ LabEx SIGNALIFE: program reference #ANR-11-LABX-0028-01.

International audience; This study provides the first assessment of the Noah and Noah-GEM (photosynthesis-based Gas exchange Evapotranspiration Model) land surface model using observations from the Avignon, France CarboEurope agricultural site during 2006 and 2007. Noah and Noah-GEM are integrated within a Land Data Assimilation System (LDAS) framework. The LDAS fields of soil moisture, temperature field, and surface and subsurface water and energy budget terms are useful for meteorological model initial conditions , and agricultural applications. The models were integrated using 1 km grid spacing with meteorological forcing from the Japanese global reanalysis (JRA). Consistent with results compiled over the US Southern Great Plains, the Noah and Noah-GEM based model performance was comparable for sor-ghum and wheat cropland. Both models had a relatively better performance during the low LAI plant growth stage however the performance deteriorated during peak green conditions and the bias between the observed and modeled latent heat flux was consistently higher by 100 W m À2. To further diagnose this bias, a series of experiments were undertaken by considering observed biweekly dynamic leaf area index (LAI), vegetation height, roughness length (z 0), and albedo changes. These experiments were conducted using Noah-GEM because of similar results between Noah and Noah-GEM and also because Noah-GEM has an explicit C3 and C4 photosynthesis model. The results were compared with the default model run as well as in situ surface flux and soil moisture/temperature observations. Prescribing onsite characteristics led to modest improvements in the model fields, however the model still could not capture the peak growing heat flux values of sensible heat for both C3 and C4 plants. Additional experiments were undertaken to investigate the inconsistencies in model parameterization. These include experiments with a CO 2-based transpiration and thermal roughness formulation in surface-layer physics ; the surface coupling coefficient through the ''Zilitinkevich constant''; effect of soil texture and model spin-up time. Based on the study results and the experiments, we conclude that a high resolution LDAS/Noah setup can be driven using global reanalysis fields producing reasonably good results when evaluated against point observations. The model performance was enhanced after using dynamic LAI and albedo feedback; however the key feature was the tuning of the model structure through coupling and modifying V max as a function of LAI. These results highlight the need for improvements in the turbulent surface layer and plant physiological modules, and model deficiencies cannot be overcome by onsite biophysical data alone.

International audience; The augmented Noah land surface model described in the first part of the two-part series was evaluated here over global river basins. Across various climate zones, global-scale tests can reveal a model's weaknesses and strengths that a local-scale testing cannot. In addition, global-scale tests are more challenging than local- and catchment-scale tests. Given constant model parameters (e. g., runoff parameters) across global river basins, global-scale tests are more stringent. We assessed model performance against various satellite and ground-based observations over global river basins through six experiments that mimic a transition from the original Noah LSM to the fully augmented version. The model shows transitional improvements in modeling runoff, soil moisture, snow, and skin temperature, despite considerable increase in computational time by the fully augmented Noah-MP version compared to the original Noah LSM. The dynamic vegetation model favorably captures seasonal and spatial variability of leaf area index and green vegetation fraction. We also conducted 36 ensemble experiments with 36 combinations of optional schemes for runoff, leaf dynamics, stomatal resistance, and the β factor. Runoff schemes play a dominant and different role in controlling soil moisture and its relationship with evapotranspiration compared to ecological processes such as the β factor, vegetation dynamics, and stomatal resistance. The 36-member ensemble mean of runoff performs better than any single member over the world's 50 largest river basins, suggesting a great potential of land-based ensemble simulations for climate prediction.