Ammonia emission prediction for dairy cattle housing from reaction kinetic modeling to the barn scale

• Main Authors: Amon, B. (Author), Amon, T. (Author), Bannink, A. (Author), Doumbia, E.M (Author), Hempel, S. (Author), Janke, D. (Author), Ouatahar, L. (Author), Willink, D. (Author)
• Format: Article
• Language: English
• Published: Elsevier B.V. 2022
• Subjects: Ammonia; Ammonia emissions; Ammonia volatilization; Boundary conditions; Dairy cattle; Dairy cattle housing; Dairy cattles; Emissions prediction; Housing; Housing systems; Mechanistic models; Modeling approach; Process modeling; Process-models; Reaction kinetics; Uncertainty analysis; Urea; Ventilation; Volatilization models
• Online Access: View Fulltext in Publisher

 One way to estimate ammonia emission rates from naturally ventilated housing systems is to scale-up mechanistic modeling results. However, obtaining the relevant data to set initial and boundary conditions adequately is usually very challenging and for a whole barn barely possible. This study has investigated the potential of coupling different mechanistic modeling approaches towards an overarching barn scale ammonia emission model, which might permit ammonia emission projections for naturally ventilated housing systems with minimal measurement efforts. To this end, we combined an ammonia volatilization model for shallow urine or slurry puddles with a dynamic mechanistic model of digestion and excretion of nitrogen, an empirical model to estimate urination volumes, semi-empirical models for pH and temperature dynamics of the puddles and a mechanistic air flow model. The ammonia volatilization model was integrated with a time step of one second over a period of twenty-four hours, while the relevant boundary conditions were updated on an hourly base (determined by the other mentioned submodels). Projections and uncertainties of the approach were investigated for a farm case with about ten months of on-farm measurements in a naturally ventilated dairy cattle building with scraped solid floor in Northern Germany. The results showed that the nested model was in general capable to reproduce the long-term emission trend and variability, while the short-term variability was damped compared with the emission measurements. A sensitivity study indicated that particularly a refinement of the submodules for urine puddle alkalizing, urination volume and urea concentration distributions as well as for local near-surface wind speeds have a great potential to further improve the overall model accuracy. The cleaning efficiency of the scraper has turned out to be a crucial and sensitive parameter in the modeling, which so far has been described insufficiently by measurements or modeling approaches. © 2022 The Authors