Reducing dairy cattle methane emissions through genetic improvement

Project Overview

To sustain its competitiveness both nationally and internationally, the Canadian dairy industry must enhance efficiency while reducing its environmental footprint. Genomic evaluation facilitates the selection of animals with reduced environmental impact, while allowing for improved health, productivity, and feed utilization. A key goal for genetic selection within the dairy sector is the reduction of greenhouse gas emissions, especially enteric methane. There is a need for more extensive and continued phenotyping for methane emissions to enhance the efficacy of genomic evaluation and monitoring tools for dairy cattle.

The overall objectives of this project are to improve the accuracy and efficiency of genomic evaluation and enhance tools for monitoring methane emissions from individual dairy cows. This research aims at improving genomic selection tools for animals with lower environmental impact.

What Will the Research Team Do?

The research team will; (i) spearhead the setup of a sustainable data collection system on commercial and research farms; (ii) collect and evaluate cow genotypes and use large datasets to evaluate genetic correlations between methane emissions and other desirable traits; (iii) evaluate the ability of models to predict methane emissions; (iv) conduct a multiple-trait analysis to evaluate the accuracy of genomic predictions on methane emissions; (v) use simulation methods to assess the impact of new methane phenotypes on the accuracy of genomic breeding value predictions; (vi) collaborate with industry partners to create monitoring and benchmark statistics for methane emissions; and (vii) assess different strategies for deleting obsolete data from genetic evaluations to minimize computational demand, biases, and unexpected dispersion of results.

The objectives of this project are to:

1. Collect individual cow genotypes and methane (CH₄) emissions using the sniffer system on 4 commercial farms and 2 research institutions. 
2. Estimate genetic parameters, including genetic correlations between CH₄ emission and CH₄ efficiency with all other relevant traits.
3. Determine genome-wide association for CH₄ emission, CH₄ efficiency, and feed efficiency traits.
4. Assess the prediction of CH₄ emissions by milk mid infrared (MIR) spectrometry data and other commonly available predictors.
5. Evaluate the accuracy of large-scale genomic predictions based on MIR predicted CH₄.
6. Determine the number of new CH₄ emission phenotypes needed to maintain or increase the accuracy of breeding value predictions for MIC predicted CH₄ efficiency.
7. Develop and implement a CH₄ monitoring and benchmark herd tool.
8. Evaluate truncation of old data for genomic evaluation of CH₄ efficiency and determination of best truncation point.