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Bernard F.,French National Institute for Agricultural Research | Bernard F.,ARVALIS Institute du vegetal | Sache I.,French National Institute for Agricultural Research | Suffert F.,French National Institute for Agricultural Research | Chelle M.,French National Institute for Agricultural Research
New Phytologist | Year: 2013

The thermal performance curve is an ecological concept relating the phenotype of organisms and temperature. It requires characterization of the leaf temperature for foliar fungal pathogens. Epidemiologists, however, use air temperature to assess the impacts of temperature on such pathogens. Leaf temperature can differ greatly from air temperature, either in controlled or field conditions. This leads to a misunderstanding of such impacts. Experiments were carried out in controlled conditions on adult wheat plants to characterize the response of Mycosphaerella graminicola to a wide range of leaf temperatures. Three fungal isolates were used. Lesion development was assessed twice a week, whereas the temperature of each leaf was monitored continuously. Leaf temperature had an impact on disease dynamics. The latent period of M. graminicola was related to leaf temperature by a quadratic relationship. The establishment of thermal performance curves demonstrated differences among isolates as well as among leaf layers. For the first time, the thermal performance curve of a foliar fungal pathogen has been established using leaf temperature. The experimental setup we propose is applicable, and efficient, for other foliar fungal pathogens. Results have shown the necessity of such an approach, when studying the acclimatization of foliar fungal pathogens. © 2013 New Phytologist Trust. Source


Attard E.,CNRS Microbial Ecology | Recous S.,French National Institute for Agricultural Research | Chabbi A.,French National Institute for Agricultural Research | De Berranger C.,French National Institute for Agricultural Research | And 5 more authors.
Global Change Biology | Year: 2011

Land-use practices aiming at increasing agro-ecosystem sustainability, e.g. no-till systems and use of temporary grasslands, have been developed in cropping areas, but their environmental benefits could be counterbalanced by increased N2O emissions produced, in particular during denitrification. Modelling denitrification in this context is thus of major importance. However, to what extent can changes in denitrification be predicted by representing the denitrifying community as a black box, i.e. without an adequate representation of the biological characteristics (abundance and composition) of this community, remains unclear. We analysed the effect of changes in land uses on denitrifiers for two different agricultural systems: (i) crop/grassland conversion and (ii) cessation/application of tillage. We surveyed potential denitrification (PD), the abundance and genetic structure of denitrifiers (nitrite reducers), and soil environmental conditions. N2O emissions were also measured during periods of several days on control plots. Time-integrated N2O emissions and PD were well correlated among all control plots. Changes in PD were partly due to changes in denitrifier abundance but were not related to changes in the structure of the denitrifier community. Using multiple regression analysis, we showed that changes in PD were more related to changes in soil environmental conditions than in denitrifier abundance. Soil organic carbon explained 81% of the variance observed for PD at the crop/temporary grassland site, whereas soil organic carbon, water-filled pore space and nitrate explained 92% of PD variance at the till/no-till site, without any residual effect of denitrifier abundance. Soil environmental conditions influenced PD by modifying the specific activity of denitrifiers, and to a lesser extent by promoting a build-up of denitrifiers. Our results show that an accurate simulation of carbon, oxygen and nitrate availability to denitrifiers is more important than an accurate simulation of denitrifier abundance and community structure to adequately understand and predict changes in PD in response to land-use changes. © 2010 Blackwell Publishing Ltd. Source


Papaix J.,French National Institute for Agricultural Research | Goyeau H.,French National Institute for Agricultural Research | Du Cheyron P.,ARVALIS Institute du vegetal | Monod H.,French National Institute for Agricultural Research | Lannou C.,French National Institute for Agricultural Research
New Phytologist | Year: 2011

In plant pathology, the idea of designing variety management strategies at the scale of cultivated landscapes is gaining more and more attention. This requires the identification of effects that take place at large scales on host and pathogen populations. Here, we show how the landscape varietal composition influences the resistance level (as measured in the field) of the most grown wheat varieties by altering the structure of the pathogen populations. For this purpose, we jointly analysed three large datasets describing the wheat leaf rust pathosystem (Puccinia triticina/Triticum aestivum) at the country scale of France with a Bayesian hierarchical model. We showed that among all compatible pathotypes, some were preferentially associated with a variety, that the pathotype frequencies on a variety were affected by the landscape varietal composition, and that the observed resistance level of a variety was linked to the frequency of the most aggressive pathotypes among all compatible pathotypes. This data exploration establishes a link between the observed resistance level of a variety and landscape composition at the national scale. It illustrates that the quantitative aspects of the host-pathogen relationship have to be considered in addition to the major resistance/virulence factors in landscape epidemiology approaches. © 2011 The Authors. New Phytologist © 2011 New Phytologist Trust. Source


Chardon F.,French National Institute for Agricultural Research | Noel V.,ARVALIS Institute du vegetal | Masclaux-Daubresse C.,French National Institute for Agricultural Research
Journal of Experimental Botany | Year: 2012

There is evidence that crop yields are showing a trend of stagnation in many countries. This review aims to make an inventory of the last decade's crop productions and the associated economic and environmental challenges. Manipulating nitrogen use efficiency in crops appears to be the best way to conciliate global food security, respecting environmental policies, and the need to produce biofuels. In such a context, the specifications of ideal plants for the future are discussed with regards to human needs and taking into account current physiological and genetic knowledge. The approaches undertaken so far to design an ideal crop and to find suitable new germplasms are discussed. The interest in using model plants in agronomic research is illustrated through the recent data provided by studies exploring natural variation in Arabidopsis thaliana. Efficient Arabidopsis ideotypes are proposed and discussed. © 2012 The Author. Source


Gouache D.,ARVALIS Institute du vegetal | Bensadoun A.,ARVALIS Institute du vegetal | Brun F.,British Petroleum | Page C.,European Center for Research and Advanced Training in Scientific Computation | And 2 more authors.
Agricultural and Forest Meteorology | Year: 2013

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. © 2012 Elsevier B.V. Source

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