Symposium Review: Development of genomic evaluation for methane efficiency in Canadian Holsteins

Abstract

Reducing methane (CH4) emissions from agriculture, among other sectors, is a key step to reduce global warming. There are many strategies to reduce CH4 emissions in ruminant animals, including genetic selection, which yields cumulative and permanent genetic gains over generations. A single-step genomic evaluation for Methane Efficiency (ME) was officially implemented in April 2023 for the Canadian Holstein breed, aiming to reduce CH4 emissions without impacting production levels. This evaluation was achieved by using milk mid-infrared (MIR) spectral data to predict individual cow CH4 production. The genetic evaluation model included milk MIR predicted CH4 (CH4MIR), along with milk yield (MY), fat yield (FY), and protein yield (PY), as correlated traits. Traits were expressed in kg/day (MY, FY, and PY) or g/day (CH4MIR). The MiX99 software was used to fit the single-step, 4-trait animal model. Genomic breeding values for CH4MIR were then obtained by re-parameterization, using recursive genetic linear regression coefficients on MY, FY, and PY, giving a measure of ME that is genetically independent of the production traits. The estimated breeding values were expressed as Relative Breeding Values (RBV) with a mean of 100 and standard deviation of 5 for the genetic base population, where a higher value indicates the animal produces lower predicted CH4. This national genomic evaluation is another tool that will lower the dairy industry's carbon footprint by reducing CH4 emissions without impacting production traits.