266-8 Temperature Dependence of Feedyard Ammonia Emissions: The Arrhenius Equation.

Poster Number 530

See more from this Division: A03 Agroclimatology & Agronomic Modeling
See more from this Session: Agroclimatology & Agronomic Modeling: II
Wednesday, November 3, 2010
Long Beach Convention Center, Exhibit Hall BC, Lower Level
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Richard Todd1, N. Andy Cole1 and Robert M. Aiken2, (1)USDA-ARS, Bushland, TX
(2)Kansas State University Northwest Research Center, Colby, KS
Ammonia emissions from beef cattle feedyards exhibit an annual pattern similar to that of temperature. This suggests that ammonia emissions may obey the Arrhenius temperature relationship. Our objective was to determine the Arrhenius relationship between mean monthly ammonia emissions from cattle feedyards and mean monthly air temperature and test its predictive power. Two years of per capita ammonia emission rate (A, g/head/d) and temperature (T) data from two commercial feedyards were used to parameterize the Arrhenius equation. The relationship was then used to predict ammonia emissions at a third feedyard. All four regressions of ln(A) against 1/T were linear, with r2 ranging from 0.55 to 0.88. At one feedyard, slopes of the two years were nearly identical, but intercepts were different. This difference was attributed to a change in crude protein content of the diets from 14.4% to 16.5%, the only major management change between years. Combining the four years of data yielded the predictive equation ln(A)=15.62-(3190/T), r2=0.52. This equation was applied to a data set from a third feedyard that included 42 days of ammonia emission and temperature data aggregated into six months. The equation tended to underestimate actual emissions, with monthly deviations ranging from +4% to -45%. Annual mean per capita emission rate was 122 g/head/d, compared with the predicted annual mean per capita emission rate of 101 g/head/d. Ammonia emissions exhibited a temperature dependence that could be described reasonably well by the Arrhenius equation. Crude protein content was another important driver of ammonia emissions that needs to be included to improve the predictive power of the temperature relationship.
See more from this Division: A03 Agroclimatology & Agronomic Modeling
See more from this Session: Agroclimatology & Agronomic Modeling: II