Modelling greenhouse gas emissions at farm level across Switzerland

For a better understanding of the environmental impacts of the agricultural sector and based on federal regulation, the monitoring of the agri-environmental system of Switzerland (MAUS) is tracking the development of different environmental indicators, among them regional GHG emissions. For this purpose, we developed a GHG emission model to calculate farm-scale, yearly, management-influenced emissions.The considered categories of greenhouse gas emissions largely follow the approach of Switzerland's national greenhouse gas inventory under the UNFCCC (FOEN, 2023), while adaptations in the calculation of emissions from manure management were implemented. Among them, the ALFAM2 (Hafner et al., 2019) methodology was used for slurry application emission estimation and slurry storage emission factors were revised based on the publication from Kupper et al. 2020.The manure management part of the model depicts nitrogen flows along the manure cascade. At each step (1. barn, pasture, yard; 2. storage; 3. application), a fraction of total ammoniacal nitrogen is being lost as N2O, NH3, NOx or N2. CH4 emissions from manure management are calculated in parallel to the nitrogen containing emissions, following the methods of Soliva et al., 2006. NH3, N2O and CO2 emissions originating from mineral fertilizer, organic products and harvest residues are calculated by multiplying nitrogen or carbonate inputs with respective emission factors. Further, CH4 from enteric fermentation is implemented according to the 2019 IPCC guidelines for greenhouse gas inventories, taking into account gross energy intake. As exact and exhaustive data is not available for every single Swiss farm, data from various sources were combined and averaged on different levels if necessary.Farm-based calculations allow to monitor the effects of management changes on GHG emissions and to summarize the results at different geographical resolutions depending on the goals of the according study. To analyze regional differences for MAUS, the emissions were summarized per municipality and set in relation to utilized agricultural area. Monte-Carlo-like simulations were run to examine sensitivities of individual input variables and uncertainties, which showed generally a large influence of animal numbers and milk urea concentrations on total farm GHG emissions.Within MAUS, it is planned to calculate emissions annually to detect potential trends. Further, newly available data sources, like farm specific mineral fertilizer applications, will be considered to make more detailed calculations.FOEN, 2023: Switzerland’s Greehouse Gas Inventory 1990-2021: National Inventory Document. Submission of April 2023 under the United Nations Framework Convention on Climate Change. Federal Office for the Environment, Bern. URL: https://www.bafu.admin.ch/bafu/en/home/topics/climate/state/data/climate-reporting/ghg-inventories/latest.html (20.12.2023).Hafner, S.D., Pacholski, A., Bittman, S., Carozzi, M., Chantigny, M., Génermont, S., Häni, C., Hansen, M.N., Huijsmans, J., Kupper, T., Misselbrook, T., Neftel, A., Nyord, T., Sommer, S.G., 2019. A flexible semi-empirical model for estimating ammonia volatilization from field-applied slurry. Atmospheric Environment 199, 474-484.Kupper, T., Häni, C., Neftel, A., Kincaid, C., Bühler, M., Amon, B., VanderZaag, A., 2020. Ammonia and greenhouse gas emissions from slurry storage - A review. Agriculture, Ecosystems and Environment 300Soliva, C.R., 2007. Dokumentation der Berechnungsgrundlage von Methan aus der Verdauung und dem Hofdünger landwirtschaftlicher Nutztiere. Federal Office for the Environment, Bern.