|AIKEN, ROBERT - Kansas State University|
Submitted to: Journal of Environmental Quality
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/14/2013
Publication Date: 5/1/2013
Citation: Todd, R.W., Cole, N.A., Aiken, R.M., Waldrip, H. 2013. Arrhenius equation for modeling feedyard ammonia emissions using temperature and diet crude protein. Journal of Environmental Quality. 42(3):666-671. doi:10.2134/jeq2012.0371.
Interpretive Summary: Temperature and cattle diet crude protein largely control ammonia loss from feedyards. We developed equations to predict feedyard ammonia loss that use either temperature or temperature together with crude protein. When we tested how useful the equations were, we found that they predicted ammonia loss well. In one test, both equations predicted annual ammonia loss within 9% of the measured ammonia loss. In another test, the temperature-only equation didn’t work as well; predicted ammonia loss was within 13-39% of the measured ammonia loss. The temperature-crude protein equation did much better, with predicted ammonia loss within 23%. We concluded that the temperature-crude protein equation predicts ammonia loss best. We recommend the temperature-crude protein equation to predict ammonia emissions until better prediction tools are ready. Ammonia loss from southern High Plains feedyards can be estimated with the equations to report losses or to help develop inventories. We encourage researchers in other regions to test the equations under their conditions.
Technical Abstract: Temperature controls many processes of ammonia volatilization. For example, urea hydrolysis is an enzymatically catalyzed reaction described by the Arrhenius equation. Diet crude protein (CP) controls ammonia emission by affecting N excretion. Objectives were to use the Arrhenius equation to model ammonia emissions from beef feedyards and test predictions against observed emissions. Per capita ammonia emission rate (PCER), air temperature (T), and diet CP were measured for two years at two Texas Panhandle feedyards. Data was fitted to analogs of the Arrhenius equation; PCER=f(T) and PCER=f(T,CP). The models were applied at a third feedyard to predict ammonia emissions and compare predicted to measured emissions. Predicted mean ammonia emissions were within -9% and +2% of observed emission for the f(T) and f(T,CP) models, respectively. Annual emission factors calculated from models underestimated annual ammonia emission by 11% (f(T) model) or overestimated emission by 8% (f(T,CP) model). When T from a regional weather station and three classes of CP drove the models, the f(T) model overpredicted annual ammonia emission of the low CP class by 14%, and underpredicted emissions of the optimum and high CP classes by 1% and 39%, respectively. The f(T,CP) model underpredicted ammonia emissions by 15%, 4%, and 23% for low, optimum and high CP classes, respectively. Ammonia emission were successfully modeled using T only, but including CP improved predictions. Until reliable process models are available, empirical f(T) and f(T,CP) models can model ammonia emissions in the Texas Panhandle, with caution against extending them to other regions without further research.