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The use of life cycle assessment (LCA) as a comprehensive tool to assess environmental impacts of bioenergies is recommended. Nevertheless, several methodological points remain under debate, particularly regarding the feedstock production step, which is a key stage of bioenergy chains. The present work focuses on field emissions during feedstock production, improving assessment methods by the use of process-based models. To do so, a real bioenergy chain, the local feedstock supply for a boiler located in northern France, was studied. The LCA compares flax shives, (the reference) with four other biomass sources: Miscanthus, cereal straw, linseed straw, and triticale as a whole plant. Six feedstock supply scenarios were also compared. The study aimed to test a new LCA methodology for agricultural chains by integrating local characteristics (such as climate, soil, and crop management data) and using models to estimate field dynamics of pesticide emissions and soil organic carbon (SOC). Results showed that flax shives and linseed straw had the lowest impacts, except for global warming: as a consequence, supply scenarios with the largest share of flax shives had the lowest impacts. For all selected impact categories, transportation and fertilization were the main contributors. SOC dynamics led to high C sequestration level (e.g. with Miscanthus) or to high CO 2 emissions level (e.g. with flax shives), thus significantly influencing the global warming impact. Sensitivity analysis showed a large influence of allocation method (economic or mass-based). This study demonstrated the relevance of integrating simulation models using local data in agricultural LCAs, especially for dynamics of SOC and pesticide from fields. Moreover, this work brought scientific elements to support the choice of flax shives as the main biomass feedstock, and the ranking of the other sources as alternative biomass supplies for the boiler.

International audienceAgricultural soils are a major source of atmospheric nitrous oxide (N2O), a potent greenhouse gas (GHG). Because N2O emissions strongly depend on soil type, climate, and crop management, their inventory requires the combination of biophysical and economic modeling, to simulate farmers' behavior. Here, we coupled a biophysical soil-crop model, CERES-EGS, with an economic farm type supply model, AROPAj, at the regional scale in northern France. Response curves of N2O emissions to fertilizer nitrogen (Nf) inputs were generated with CERES-EGC, and linearized to obtain emission factors. The latter ranged from 0.001 to 0.0225 kg N2O-N kg-1 Nf, depending on soil and crop type, compared to the fixed 0.0125 value of the IPCC guidelines. The modeled emission factors were fed into the economic model AROPAj which relates farm-level GHG emissions to production factors. This resulted in a N2O efflux 20% lower than with the default IPCC method. The costs of abating GHG emissions from agriculture were calculated using a first-best tax on GHG emissions, and a second-best tax on their presumed factors (livestock size and fertilizer inputs). The first-best taxation was relatively efficient, achieving an 8\% reduction with a tax of 11 euro/t-CO2-equivalent, compared to 68 euro/t-CO2eq for the same target with the second-best scheme

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