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 reducing 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 (MEF) was officially implemented in April 2023 for the Canadian Holstein breed, aiming to reduce CH₄ emissions without affecting 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 (CH4_MIR), along with milk yield (MY), fat yield (FY), and protein yield (PY), as correlated traits. Traits were expressed in kilograms per day (MY, FY, and PY) or grams per day (CH4_MIR). The MiX99 software was used to fit the single-step, 4-trait animal model. Genomic breeding values for CH4_MIR were then obtained by re-parameterization, using recursive genetic linear regression coefficients on MY, FY, and PY, giving a measure of MEF that is genetically independent of the production traits. The estimated breeding values were expressed as relative breeding values 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 affecting production traits.