Youngmark Emily C, Kraft Jana
Department of Animal and Veterinary Sciences, The University of Vermont, Burlington, VT 05405, USA.
Department of Medicine, Division of Endocrinology, Metabolism and Diabetes, The University of Vermont, Colchester, VT 05446, USA.
Animals (Basel). 2025 Jul 28;15(15):2212. doi: 10.3390/ani15152212.
Measuring methane (CH) emissions from dairy systems is crucial for advancing sustainable agricultural practices aimed at mitigating climate change. However, current CH measurement techniques are primarily designed for controlled research settings and are not readily scalable to diverse production environments. Thus, there is a need to develop accessible, production-level methods for estimating CH emissions. This review examines the relationship between enteric CH emissions and milk fatty acid (FA) composition, highlights key FA groups with potential as biomarkers for indirect CH estimation, and outlines critical factors of predictive model development. Several milk FAs exhibit strong and consistent correlations to CH emissions, supporting their utility as predictive biomarkers. Saturated and branched-chain FAs are generally positively associated with CH emissions, while unsaturated FAs, including linolenic acid, conjugated linoleic acids, and odd-chain FAs, are typically negatively associated. Variability in the strength and direction of correlations across studies is often attributable to differences in diet or lactation stage. Similarly, differences in experimental design, data processing, and model development contribute to much of the variation observed in predictive equations across studies. Future research should aim to (1) identify milk FAs that consistently correlate with CH emissions regardless of diet, (2) develop robust and standardized prediction models, and (3) prioritize the external validation of prediction models across herds and production systems.
测量奶牛养殖系统中的甲烷（CH）排放对于推进旨在缓解气候变化的可持续农业实践至关重要。然而，当前的CH测量技术主要是为受控研究环境设计的，不易扩展到不同的生产环境。因此，需要开发可获取的、生产水平的方法来估算CH排放。本综述探讨了肠道CH排放与乳脂肪酸（FA）组成之间的关系，强调了具有作为间接CH估算生物标志物潜力的关键FA组，并概述了预测模型开发的关键因素。几种乳FA与CH排放呈现出强烈且一致的相关性，支持它们作为预测生物标志物的效用。饱和脂肪酸和支链脂肪酸通常与CH排放呈正相关，而不饱和脂肪酸，包括亚麻酸、共轭亚油酸和奇数链脂肪酸，通常呈负相关。不同研究中相关性强度和方向的差异通常归因于饮食或泌乳阶段的不同。同样，实验设计、数据处理和模型开发的差异也导致了不同研究中预测方程所观察到的大部分变化。未来的研究应旨在：（1）识别无论饮食如何都与CH排放始终相关的乳FA；（2）开发强大且标准化的预测模型；（3）优先对不同牛群和生产系统的预测模型进行外部验证。
