Genetic parameters for methane emission traits in Australian dairy cows

Abstract

Methane is a greenhouse gas of high interest to the dairy industry, with 57% of Australia's dairy emissions attributed to enteric methane. Enteric methane emissions also constitute a loss of approximately 6.5% of ingested energy. Genetic selection offers a unique mitigation strategy to decrease the methane emissions of dairy cattle, while simultaneously improving their energy efficiency. Breeding objectives should focus on improving the overall sustainability of dairy cattle by reducing methane emissions without negatively affecting important economic traits. Common definitions for methane production, methane yield, and methane intensity are widely accepted, but there is not yet consensus for the most appropriate method to calculate residual methane production, as the different methods have not been compared. In this study, we examined 9 definitions of residual methane production. Records of individual cow methane, dry matter intake (DMI), and energy corrected milk (ECM) were obtained from 379 animals and measured over a 5-d period from 12 batches across 5 yr using the SF 6 tracer method and an electronic feed recording system, respectively. The 9 methods of calculating residual methane involved genetic and phenotypic regression of methane production on a combination of DMI and ECM corrected for days in milk, parity, and experimental batch using phenotypes or direct genomic values. As direct genomic values (DGV) for DMI are not routinely evaluated in Australia at this time, DGV for FeedSaved, which is derived from DGV for residual feed intake and estimated breeding value for bodyweight, were used. Heritability estimates were calculated using univariate models, and correla-tions were estimated using bivariate models corrected for the fixed effects of year-batch, days in milk, and lactation number, and fitted using a genomic relationship matrix. Residual methane production candidate traits had low to moderate heritability (0.10 ± 0.09 to 0.21 ± 0.10), with residual methane production corrected for ECM being the highest. All definitions of residual methane were highly correlated phenotypically (>0.87) and genetically (>0.79) with one another and moderately to highly with other methane candidate traits (>0.59), with high standard errors. The results suggest that direct selection for a residual methane production trait would result in indirect, favorable improvement in all other methane traits. The high standard errors highlight the importance of expanding data sets by measuring more animals for their methane emissions and DMI, or through exploration of proxy traits and combining data via international collaboration.

Breeding for reduced methane emission and feed-efficient Holstein cows: An international response

“…This was expected, as the only difference was that RMet3 takes into account the 3 traits at the same time (MBW, DMI, and ECM) instead of 2 at a time (RMet1 and RMet2). Likewise, Richardson et al (2021) reported strong genetic (>0.71) and phenotypic (>0.87) correlations between 9 definitions of residual CH 4 involving DMI, ECM, and FeedSaved (amount of feed saved per year through assumed improvements in lifetime metabolic efficiency and reduced maintenance requirements) values in Australian Holstein cattle. Furthermore, Richardson et al (2021) reported genetic correlations between 0.52 to 0.97 between the different residual CH 4 traits and MeP, MeY, and MeI.…”

“…Likewise, Richardson et al (2021) reported strong genetic (>0.71) and phenotypic (>0.87) correlations between 9 definitions of residual CH 4 involving DMI, ECM, and FeedSaved (amount of feed saved per year through assumed improvements in lifetime metabolic efficiency and reduced maintenance requirements) values in Australian Holstein cattle. Furthermore, Richardson et al (2021) reported genetic correlations between 0.52 to 0.97 between the different residual CH 4 traits and MeP, MeY, and MeI. In addition, Donoghue et al (2016) reported lower to moderate genetic correlations (0.32-0.63) and strong phenotypic correlations (0.60-0.76) between MeP and 4 residual CH 4 traits.…”

“…Similar results were reported by van Engelen (2018) between MeY and MY (−0.39). Recently, Richardson et al (2021) presented an even stronger negative genetic correlation (−0.73) between ECM and MeI in Australian Holstein cows. Despite MeI being a trait that is simple to explain to farmers, CH 4 produced per unit of milk, the strong negative correlation between MeI and ECM means it is not advisable to select for this trait.…”

“…Posteriorly, this scale was converted to the 5-point scale and multiplied by 100 to merge with the data sets from the other countries. Daily milk records were available throughout the recording period, with fat and protein percentages obtained through mid-infrared spectroscopy (Richardson et al, 2021).…”

“…Animals were between 68 and 187 DIM at the start of the measurement, and across parities (1 to 9). A detailed description of the data recording has been described previously Richardson et al (2021). The SF 6 tracer technique (Deighton et al, 2014) measures the enteric MeP using SF 6 as a tracer gas, allowing CH 4 emissions to be measured from confined, free range, and grazing animals.…”