Simulation of ammonia emissions for diversified fattening pig units and external storage with contrasting climatic scenarios

Modeling is an essential tool for better understanding ammonia emissions for pig units in relations to practices and climate. It helps to identify the way to transform livestock and to guide the farmers toward a mitigation of ammonia emissions and a better adaptation to climate change. The goal of this study was to simulate ammonia emissions for diversified fattening pig units and external storage, with contrasting clima_c scenarios. A dynamic mechanistic model developed by Padioleau et al. (2024) has been used to simulate ammonia emissions of fattening pig unit with slurry and the pit external storage.
Different scenarios were simulated over one year, combining, two contrasted genetics, several Best Available Prac_ces (BAT: feeding strategies, manure removal frequencies from building, covered external pit), and current (2020) and forecast climatic scenarios (2030 and 2050). For a same genetic and climate, the results of simulated ammonia emissions differ from 60% between the most and the least emitting itineraries thanks to the use of best practices. The emissions are increased in 2050 compared to 2020 due to the increased external temperature. With the Iberian genetics the ammonia emissions are higher than the conven_onal gene_cs, even with the use of BAT. This is due to the longer life period of the pigs. In all the simulations, the most important step is the building stage, even if the storage is a step with a higher increase of ammonia emissions with the climate 2050 compared to the one of 2020, especially when the storage pit stayed uncovered. The model gives a global vision of ammonia emissions in pig farming from excre_on to exit from storage. It also considers numerous variation factors, managed by the breeder through their choice of system and practices, or imposed such as climate change. The use of the model makes it possible to identify the sensitivity of emissions to these variation factors and to better guide breeders towards low-emitting and resilient systems.