Remote sensing techniques offer a unique solution for mapping stress and monitoring its time-course. This article reviews the main issues to be addressed for quantifying stress level from remote sensing observations, and to mitigate its impact on crop production by managing cultural practices. The case of nitrogen fertilization is used here as a paradigm. The derivation of canopy state variables such as the leaf area index (LAI) and chlorophyll content (C(ab)) is first addressed. It is demonstrated that the inversion of radiative transfer models leads to useful estimates of these variables. However, because of the ill-posed nature of the inverse problem, better accuracy is achieved when using prior information on the distribution of the variables and when multiplying LAI by C(ab) to get canopy level chlorophyll content. This variable, LAIxC(ab) is well suited for quantifying canopy level nitrogen content. It is used for nitrogen stress evaluation by comparison with a reference unstressed situation which is, however, not easy to get in practice. The combination of remote sensing observations with crop models provides an elegant solution for stress quantification through assimilation approaches. It fuses several sources of information within our knowledge of the processes involved and accounts for the environmental budget which can be integrated when making decisions about cultural practices. Conclusions are drawn on the issues related to the retrieval of canopy state variables from remote sensing data, to the link between these observables and crop models, and to the assimilation approaches. Avenues for further research are finally discussed along with the required observation system.

This study provides a framework for modelling a large number of pathosystems using FSPMs. This structure can accommodate both previously developed models for individual aspects of pathosystems and new ones. Complex models are deconstructed into separate 'knowledge sources' originating from different specialist areas of expertise and these can be shared and reassembled into multidisciplinary models. The framework thus provides a beneficial tool for a potential diverse and dynamic research community.

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We introduce the notion of a 'race' for the colonization of emerging healthy host tissue between the growing canopy and the developing epidemics. This race is 2-fold: (1) an upward race at the canopy scale where STB must catch the newly emerging leaves before they grow away from the spore sources; and (2) a local race at the leaf scale where STB must use the resources of its host before it is caught by leaf apical senescence. The results shed new light on the importance of dynamic interactions between host and pathogen.

International audienceThe use of crop models for nitrogen fertiliser management raises several issues. A first problem is to define suitable criteria for optimising nitrogen fertilisation in function of economic, quality and environmental objectives. A second issue is to assess the capacity of the crop model to predict the variables involved in the calculation of the criteria such as grain yield, grain protein content, residual soil mineral nitrogen or nitrogen balance. A third issue is to evaluate the results obtained by applying the decision rules selected by the crop model. The three problems are considered in this paper in the case of winter wheat and the STICS model. Fourteen field experiments with various N fertilisation strategies were used for evaluating the model. STICS predicted grain yield and nitrogen balance more accurately than protein content and soil mineral N at harvest. Among the eight criteria tested for optimising N fertilisation, those using a maximal threshold on nitrogen balance seem to be the most valuable for satisfying agricultural and environmental objectives. Under conditions of environmental constraint, STICS was more efficient than the reference method (AZOBIL) at selecting the optimal nitrogen fertilisation scenario