2019, Groenestein, C.M., Hutchings, N.J., Haenel, H.D., Amon, B., Menzi, H., Mikkelsen, M.H., Misselbrook, T.H., van Bruggen, C., Kupper, T., Webb, J.

The increasing global demand for food and the environmental effects of reactive nitrogen losses in the food production chain, increase the need for efficient use of nitrogen (N). Of N harvested in agricultural plant products, 80% is used to feed livestock. Because the largest atmospheric loss of reactive nitrogen from livestock production systems is ammonia (NH3), the focus of this paper is on N lost as NH3 during the production of animal protein. The focus of this paper is to understand the key factors explaining differences in Nitrogen Use Efficiency (NUE) of animal production among various European countries. Therefore we developed a conceptual framework to describe the NUE defined as the amount of animal-protein N per N in feed and NH3–N losses in the production of milk, beef, pork, chicken meat and eggs in The Netherlands, Switzerland, United Kingdom, Germany, Austria and Denmark. The framework describes how manure management and animal-related parameters (feed, metabolism) relate to NH3 emissions and NUE. The results showed that the animal product with the lowest NUE had the largest NH3 emissions and vice versa, which agrees with the reciprocal relationship between NUE and NH3 within the conceptual framework. Across animal products for the countries considered, about 20% of the N in feed is lost as NH3. The significant smallest proportion (12%) of NH3–N per unit of Nfeed is from chicken production. The proportions for other products are 17%, 19%, 20% and 22% for milk, pork, eggs and beef respectively. These differences were not significantly different due to the differences among countries. For all countries, NUE was lowest for beef and highest for chicken. The production of 1 kg N in beef required about 5 kg N in feed, of which 1 kg N was lost as NH3–N. For the production of 1 kg N in chicken meat, 2 kg N in feed was required and 0.2 kg was lost as NH3. The production of 1 kg N in milk required 4 kg N in feed with 0.6 kg NH3–N loss, the same as pork and eggs, but those needed 3 and 3.5 kg N in feed per kg N in product respectively. Except for beef, the differences among these European countries were mainly caused by differences in manure management practices and their emission factors, rather than by animal-related factors including feed and digestibility influencing the excreted amount of ammoniacal N (TAN). For beef, both aspects caused important differences. Based on the results, we encourage the expression of N losses as per N in feed or per N in product, in addition to per animal place, when comparing production efficiency and NUE. We consider that disaggregating emission factors into a diet/animal effect and a manure management effect would improve the basis for comparing national NH3 emission inventories. © 2018 The Authors

2019, Kipling, R.P., Topp, C.F.E., Bannink, A., Bartley, D.J., Blanco-Penedo, I., Cortignani, R., del Prado, A., Dono, G., Faverdin, P., Graux, A.-I., Hutchings, N.J., Lauwers, L., Özkan Gülzari, Ş., Reidsma, P., Rolinski, S., Ruiz-Ramos, M., Sandars, D.L., Sándor, R., Schönhart, M., Seddaiu, G., van Middelkoop, J., Shrestha, S., Weindl, I., Schönhart, M., Seddaiu, G., van Middelkoop, J., Shrestha, S., Weindl, I., Eory, V.

Modelling is key to adapting agriculture to climate change (CC), facilitating evaluation of the impacts and efficacy of adaptation measures, and the design of optimal strategies. Although there are many challenges to modelling agricultural CC adaptation, it is unclear whether these are novel or, whether adaptation merely adds new motivations to old challenges. Here, qualitative analysis of modellers’ views revealed three categories of challenge: Content, Use, and Capacity. Triangulation of findings with reviews of agricultural modelling and Climate Change Risk Assessment was then used to highlight challenges specific to modelling adaptation. These were refined through literature review, focussing attention on how the progressive nature of CC affects the role and impact of modelling. Specific challenges identified were: Scope of adaptations modelled, Information on future adaptation, Collaboration to tackle novel challenges, Optimisation under progressive change with thresholds, and Responsibility given the sensitivity of future outcomes to initial choices under progressive change. © 2019 The Authors