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dc.contributor.supervisor Kebreab, Ermias (Animal Science) Ominski, Kim (Animal Science) en_US
dc.contributor.author Alemu, Aklilu W
dc.date.accessioned 2013-01-16T14:20:31Z
dc.date.available 2013-01-16T14:20:31Z
dc.date.issued 2012 en_US
dc.date.issued 2011 en_US
dc.date.issued 2011
dc.identifier.citation Alemu, A. W., Dijkstra, J., Bannink, A., France, J. and Kebreab, E. 2011. Rumen stoichiometric models and their contribution and challenges in predicting enteric methane production. Anim. Feed Sci. Technol. 166-167: 761-778. en_US
dc.identifier.citation 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. en_US
dc.identifier.uri http://hdl.handle.net/1993/14668
dc.description.abstract Mathematical modeling in animal agriculture can be applied at various levels including at the tissue, organ, animal, farm, regional and global levels. The purposes of this research were i) to evaluate models used to estimate volatile fatty acid (VFA) and methane (CH4) production and assess their impact on regional enteric CH4 inventory, and ii) to develop a process-based, whole-farm model to estimate net farm GHG emissions. In the first study, four VFA stoichiometric models were evaluated for their prediction accuracy of rumen VFA and enteric CH4 production. Comparison of measured and model predicted values demonstrated that predictive capacity of the VFA models varied with respect to the type of VFA in rumen fluid which impacted estimated enteric CH4 production. Moving to a larger scale assessment, we examined the enteric CH4 inventory from Manitoba beef cattle (from 1990 to 2008) using two mechanistic rumen models that incorporate VFA stoichiometric models: COWPOLL and MOLLY, and two empirical models: Intergovernmental Panel on Climate Change (IPCC) Tier 2 and a nonlinear equation (Ellis). The estimated absolute enteric CH4 production varied among models (7 to 63%) indicating that estimates of GHG inventory depend on model selection. This is an important consideration if the values are to be used for management and/or policy-related decisions. Development of models at the individual farm component level (animal, soil, crop) does not accurately reflect net GHG emissions generated from the whole production system. We developed a process-based, whole-farm model (Integrated Components Model, ICM), using the existing farm component models COWPOLL, manure-DNDC and some aspects of IPCC to integrate farm components and their associated GHG emissions. Estimates of total farm GHG emissions and their relative contribution using the ICM were comparable to estimates using two other whole-farm models (Integrated Farm System Model and Holos model). Variation was observed among models both in estimating whole-farm GHG emissions and the relative contribution of the different sources in the production system. Overall, whole-farm models are required to explore management options that will mitigate GHG emissions and promote best management practices. However, for full assessment of the production system, other benefits of the system (e.g., carbon sequestration, ecosystem services), which are not part of current whole-farm models, must be considered. en_US
dc.publisher Elsevier B.V. (Animal Feed Science and Technology) en_US
dc.publisher Canadian Journal of Animal Science. en_US
dc.subject Greenhouse gas emission en_US
dc.subject Stoichiometric model en_US
dc.subject Volatile fatty acid en_US
dc.subject Beef production system en_US
dc.subject Whole-farm model en_US
dc.subject Enteric methane emissions en_US
dc.subject Manitoba en_US
dc.subject Model en_US
dc.title Modelling greenhouse gas emissions in cattle: From rumen to the whole-farm en_US
dc.degree.discipline Animal Science en_US
dc.contributor.examiningcommittee Plaizier, Kees (JC) (Animal Science) Tenuta, Mario (Soil Science) Tedeschi, Luis (Texas A & M University) en_US
dc.degree.level Doctor of Philosophy (Ph.D.) en_US
dc.description.note February 2013 en_US


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