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Nitrous oxide (N2O) is a greenhouse gas with a global warming potential approximately 298 times greater than that of CO2. In 2006, the Intergovernmental Panel on Climate Change (IPCC) estimated N2O emission due to synthetic and organic nitrogen (N) fertilization at 1% of applied N. We investigated the uncertainty on this estimated value, by fitting 13 different models to a published dataset including 985 N2O measurements. These models were characterized by (i) the presence or absence of the explanatory variable “applied N”, (ii) the function relating N2O emission to applied N (exponential or linear function), (iii) fixed or random background (i.e. in the absence of N application) N2O emission and (iv) fixed or random applied N effect. We calculated ranges of uncertainty on N2O emissions from a subset of these models, and compared them with the uncertainty ranges currently used in the IPCC-Tier 1 method. The exponential models outperformed the linear models, and models including one or two random effects outperformed those including fixed effects only. The use of an exponential function rather than a linear function has an important practical consequence: the emission factor is not constant and increases as a function of applied N. Emission factors estimated using the exponential function were lower than 1% when the amount of N applied was below 160 kg N ha−1. Our uncertainty analysis shows that the uncertainty range currently used by the IPCC-Tier 1 method could be reduced.

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The diversity of growing conditions and the development of new outlets for agricultural products favour a diversity of crop management systems requiring various cultivars, with specific characteristics. Genotype performance is usually assessed through multi-environment trials comparing a variable number of genotypes grown in a wide range of soils, climatic conditions and cropping systems. Field experiments show empirical evidence for the interactions between genotype, environment and cropping system. However, such interactions are rarely taken into account to design ideotypes or for cultivar assessment, or in the definition of crop management plans adapted to cultivars. Agronomic models, built to simulate the dynamic response of crops to their environment, and thus to techniques which modify it, appear to be appropriate tools to evaluate and predict these interactions. This paper reviews the three main uses of model-based predictions of the interactions between genotype, environment and cropping system: definition of breeding targets, characterisation of the environments in cultivar experiments and support for the choice of the best cultivar to grow in a given situation. Models specifically allow understanding the influence of one or a combination of specific traits on performances and long-term ecological impacts. We show that a diversity of models is required, from physiologically based crop models to agroecology-based cropping system or landscape models, able to account well for farmers' practices. A way of taking cultivars into account in crop models is proposed, based on three main steps: the choice of the model, the identification and estimation of its cultivar parameters, and testing the model for decision support. Finally, the analysis of the limitations for wider use of crop models in variety breeding and assessment addresses some major questions for future research

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