Comparison of models used for national agricultural ammonia emission inventories in Europe: Liquid manure systems

Abstract

Ammonia (NH 3) emissions from agriculture commonly account for >80% of the total NH 3 emissions. Accurate agricultural NH 3 emission inventories are therefore required for reporting within the framework of the Gothenburg Protocol of the UN Convention on Long-range Transboundary Air Pollution. To allow a co-ordinated implementation of the Protocol, different national inventories should be comparable. A core group of emission inventory experts therefore developed a network and joint programme to achieve a detailed overview of the best inventory techniques currently available and compiled and harmonized the available knowledge on emission factors (EFs) for nitrogen (N)-flow emission calculation models and initiated a new generation of emission inventories. As a first step in summarizing the available knowledge, six N-flow models, used to calculate national NH 3 emissions from agriculture in different European countries, were compared using standard datasets. Two scenarios for slurry-based systems were run separately for dairy cattle and for pigs, with three different levels of model standardisation: (a) standardised inputs to all models (FF scenario); (b) standard N excretion, but national values for EFs (FN scenario); (c) national values for N excretion and EFs (NN scenario). Results of the FF scenario showed very good agreement among models, indicating that the underlying N flows of the different models are highly similar. As a result of the different national EFs and N excretion rates, larger differences among the results were observed for the FN and the NN scenarios. Reasons for the differences were primarily attributed to differences in the agricultural practices and climatic factors reflected in the EFs and the N excretion rates. The scientific debate necessary to understand the variation in the results generated awareness and consensus concerning available scientific data and the importance of specific processes not yet included in some models.