Agriculture Victoria Research, AgriBio, Centre for AgriBioscience, Bundoora, Victoria 3083, Australia; School of Applied Systems Biology, La Trobe University, Bundoora, Victoria 3083, Australia.
Agriculture Victoria Research, AgriBio, Centre for AgriBioscience, Bundoora, Victoria 3083, Australia.
J Dairy Sci. 2021 Jan;104(1):539-549. doi: 10.3168/jds.2020-18565. Epub 2020 Oct 31.
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 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 correlations 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.
甲烷是乳制品行业高度关注的温室气体,澳大利亚 57%的乳制品排放归因于肠道甲烷。肠道甲烷排放也构成了摄入能量的约 6.5%的损失。遗传选择为减少奶牛的甲烷排放提供了一种独特的缓解策略,同时提高了它们的能量效率。培育目标应侧重于通过减少甲烷排放来提高奶牛的整体可持续性,而不影响重要的经济性状。甲烷产量、甲烷产量和甲烷强度的常见定义得到广泛接受,但对于计算剩余甲烷产量最合适的方法尚未达成共识,因为不同的方法尚未进行比较。在这项研究中,我们检查了 9 种剩余甲烷产量的定义。从 5 年 12 批的 379 头奶牛中获得了个体奶牛甲烷、干物质摄入量 (DMI) 和能量校正奶 (ECM) 的记录,并使用 SF 示踪剂方法和电子饲料记录系统分别在 5 天内进行了测量。计算剩余甲烷的 9 种方法涉及通过遗传和表型回归,将甲烷产量与 DMI 和 ECM 相结合,以校正泌乳天数、胎次和实验批次,使用表型或直接基因组值。由于目前在澳大利亚尚未常规评估 DMI 的直接基因组值 (DGV),因此使用了源自剩余采食量 DGV 和体重估计育种值的 FeedSaved 的 DGV。使用单变量模型计算了遗传力估计值,并使用双变量模型估计了相关性,该模型校正了年批、泌乳天数和泌乳次数的固定效应,并使用基因组关系矩阵进行拟合。剩余甲烷生产候选性状的遗传力较低(0.10±0.09 至 0.21±0.10),其中 ECM 校正后的剩余甲烷生产最高。所有剩余甲烷的定义在表型上(>0.87)和遗传上(>0.79)都高度相关,与其他甲烷候选性状(>0.59)中度至高度相关,具有较高的标准误差。结果表明,直接选择剩余甲烷生产性状将导致所有其他甲烷性状的间接、有利改善。高标准误差突出了通过测量更多动物的甲烷排放和 DMI 来扩大数据集的重要性,或者通过探索替代性状并通过国际合作结合数据。