|Rotz, Clarence - Al|
Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract only
Publication Acceptance Date: 4/26/2011
Publication Date: 10/16/2011
Citation: Rotz, C.A., Li, C. 2011. Estimating emissions and evaluating mitigation strategies with process-based models[abstract]. ASA-CSSA-SSSA Annual Meeting Abstracts. Paper No. 64155. Interpretive Summary: An interpretive summary is not required.
Technical Abstract: Measuring gaseous emissions from farms is expensive and requires multiple years of measurement to obtain average or typical emission levels. Considering the many sources and sinks on a farm, a comprehensive measurement of all emissions is essentially impossible. The need to quantify emissions has led to reliance upon process-based models to predict emissions as influenced by the local climate and management strategies used on the farm. This type of model simulates the formation and release of compounds to the atmosphere using scientifically derived relationships. Empirical data is used to evaluate these models, and they are sometimes used to establish model parameters. When the important processes and their interactions are appropriately modeled, a flexible tool is created that predicts emissions over a wide range of conditions. In agricultural emissions work, process-based models have been most widely applied to ammonia where important processes include urea hydrolysis, dissociation, diffusion, aqueous-gas partitioning, and mass transport to the atmosphere. Similar process models are now being applied to hydrogen sulfide and volatile organic compounds. Greenhouse gas emissions are often controlled by formation rather than emission processes. Enteric fermentation, nitrification, denitrification, and other microbial processes are modeled to predict the formation and release of methane and nitrous oxide. Process-based models are being refined and made available in software tools for estimating emissions and evaluating mitigation strategies in animal production. Tools such as DeNitrifcation-DeComposition (DNDC) and Dairy Gas Emission Model (DairyGEM) focus on the emission of important compounds. The Integrated Farm System Model (IFSM) provides a more comprehensive model for estimating emissions along with leaching and runoff losses and farm economics. As these software tools develop, process-based modeling provides effective research and educational aids for guiding us toward more sustainable animal production systems.