Exploring the Cattle Rumen Microbiome and How it Effects Feed Efficiency and Methane Emissions
Project Title
Systematic Characterization of the Bovine Rumen Microbiome and Determination of its Causal Roles in Cattle Feed Efficiency and Methane Emission Using Machine Learning
Researchers
Leluo Guan (University of Alberta) lguan@ualberta.ca
Tim McAllister (Co-Investistigator; AAFC Lethbridge), Edward Bork (University of Alberta), Angela Canovas (University of Guelph) and Katie Wood (University of Guelph)
| Status | Project Code |
|---|---|
| In progress. Results expected in March, 2028 | FDE.18.21C |
Background
The rumen microbiome is extremely complex and contributes to digestion, animal performance, animal health, and methane production. Diet and environment are known to affect the rumen microbiome but there is also large animal to animal variation from animals in the same pen. This suggests that the host animal genetics also plays a role in the microbiome. These researchers want to better understand the rumen microbiome and how it affects animal performance.
Objectives
- To establish a rumen microbial catalog representing functional rumen microbial populations specifically influencing cattle feed efficiency and methane emissions.
- To develop and utilize machine learning algorithms that determine the contribution of rumen microbiome components (microbes, microbial functions) and metabolites (volatile fatty acids, VFAs) to feed efficiency/methane emission in feedlot steers and grazing cows.
- To determine the contribution of the host genotypes to rumen microbiome using genome wide association study and the contribution of heritable microbes to feed efficiency and methane emission.
- To determine the predictive rumen microbes for the integrated and targeted management strategies that improve feed efficiency/methane emission in feedlot and cow-calf beef cattle.
What they will do
In the first year researchers will generate a catalogue of what makes up the rumen microbiome using 5000 rumen samples from cattle raised under different management factors with feed efficiency and methane emission data collected with them. They will then combine this with data previously collected data and use it to help predict what might be missing from the above catalogue.
Researchers will then use machine learning algorithms to explore to what extent the diet, host genetics, and environment play a role in the microbiome makeup,
Finally, this team will test the model using data from different previous collected tumen samples that have known methane and feed efficacy to evaluate if there is an ability to predict animal feed efficiency and methane emissions based on factors that influence the rumen microbiome.
Implications
This project will give us a better understanding of how the rumen microbiome works and if there are factors that can be influenced within it to result in more efficient cattle.