1a. Objectives (from AD-416):
1. To develop a model based upon physical, biological, and chemical principles that predicts ammonia emission from a feedlot surface. 2. To evaluate the model by comparing predicted ammonia emissions to those measured on two feedlots in Texas over a two year period.
1b. Approach (from AD-416):
Scientific literature related to the processes controlling ammonia emissions from a manure covered feedlot surface will be reviewed. Information will be gathered from this review to formulate relationships for the controlling processes. Computer code will be developed to represent these processes in a simulation through time. This model will be incorporated in the Integrated Farm System Model (IFSM) where it will be used to simulate ammonia emissions from Texas feedlots. Simulated and measured emissions will be compared to determine the accuracy of the model.
3. Progress Report:
This short term project was completed in December. The services of Sasha Hafner of Hafner Consulting were employed to provide scientific understanding in the development of a process-based model to predict ammonia emissions from open lots. Through a literature review and an understanding of the physical, biological, and chemical principles driving ammonia formation and emission, a model was developed to predict ammonia emissions from open lot surfaces. The model was incorporated in our farm simulation model (Integrated Farm System Model) along with a new farm option for simulating beef cattle feed yards. The new ammonia emission model was found to work well in representing ammonia emissions measured on two beef feed yards in Texas. Average daily emissions predicted for these feed yards over two years were within 6% of measured values. The correlation between predicted and measured daily values was 0.52 and 0.66 for the two lots. When used to predict the emissions from an open lot on a dairy farm in Idaho, the model over predicted emissions during the dry summer months, which implies that further work is needed to improve predictions under very dry conditions.