Predicting methane emissions from dairy and beef cattle

This thesis is an investigation into the prediction of methane (CH4) emissions from cattle using empirical and mechanistic approaches. Methane is a greenhouse gas implicated in global warming and is being targeted for reduction. In cattle, CH4 is produced during enteric fermentation and can range from 2 -- 12% of GE intake, which represents approximately 36% of the total greenhouse gas production from the agriculture sector in Canada. Thus, it has become the focus of recent research in animal science to develop mitigation strategies for reduction of CH4 production by cattle, and to use models to estimate emissions on various dietary strategies. The first half of this thesis deals with development and application of empirical CH4 prediction equations. Multiple regression equations specific for both beef and dairy cattle, both linear and nonlinear, are developed. Given limited input data, these equations provide a method to estimate CH 4 production based on diet chemical composition data and/or DMI. Extant CH4 prediction equations are also evaluated, specifically those currently used in whole farm models. Most developed and extant empirical equations were able to describe the mean CH4 emission level relatively well, but had difficulty describing high levels of variation in observed data. As an alternative, mechanistic modelling has the potential to describe more of the variation in CH4 emissions. Therefore, the second half of this thesis dealt with beef cattle, a dose dependent monensin adjustment was developed for volatile fatty acid (VFA) stoichiometry in order to improve CH4 predictions for monensin-fed beef cattle. Various VFA stoichiometries were then evaluated, including the monensin adjustment. Results showed improvements but a trend for over-prediction of CH4 emission, and that VFA stoichiometry is an area where further work is required. To illustrate an application of mechanistic modelling, the feeding of high sugar grasses to dairy cattle was assessed within a mechanistic model in terms of potential CH4 emissions, and results suggested an increase in CH4. Overall this thesis provides a series of tools, advancements and explanations that will improve understanding and estimation of CH4 emissions from cattle.