Alemu, A. W., Ominski, K. H. and Kebreab, E. 2011. Estimation of enteric methane emissions trends (1990-2008) from Manitoba beef cattle using empirical and mechanistic models. Can. J. Anim. Sci. 91: 305-321. The objective of this study was to estimate and assess trends in enteric methane (CH₄) emissions from the Manitoba beef cattle population from the base year of 1990 to 2008 using mathematical models. Two empirical (statistical) models: Intergovernmental Panel on Climate Change (IPCC) Tier 2 and a nonlinear equation (Ellis), and two dynamic mechanistic models: MOLLY (v3) and COWPOLL were used. Beef cattle in Manitoba were categorized in to 29 distinct subcategories based on management practice, physiological status, gender, age and production environment. Data on animal performance, feeding and management practices and feed composition were collected from the literature as well as from provincial and national sources. Estimates of total enteric CH₄ production from the Manitoba beef cattle population varied between 0.9 and 2.4 Mt CO₂ eq. from 1990 to 2008. Regardless of the type of models used, average CH₄ emissions for 2008 were estimated to be 45.2% higher than 1990 levels. More specifically, CH₄ emissions tended to increase between 1990 and 1996. Emissions were relatively stable between 1996 and 2002, increased between 2003 and 2005, but declined by 13.2% between 2005 and 2008, following the same trend as that observed in the beef cattle population. Models varied in their estimates of CH₄ conversion rate (Y_m, percent gross energy intake), emission factor (kg CH₄ head^-1 yr^-1) and CH₄ production. Total CH₄ production estimates ranged from 1.2 to 2.0 Mt CO₂ eq. for IPCC Tier 2, from 0.9 to 1.5 Mt CO₂ eq. for Ellis, from 1.3 to 2.1 Mt CO₂ eq. for COWPOLL and from 1.5 to 2.4 Mt CO₂ eq. for MOLLY. The results indicate that enteric CH₄ estimates and emission trends in Manitoba were influenced by the type of model and beef cattle population. As such, it is necessary to use appropriate models for reliable estimates for enteric CH₄ inventory. A more robust approach may be to integrate different models by using mechanistic models to estimate regional Y_m values, which may then be used as input for the IPCC Tier 2 model.