性能指标能解释多少奶牛之间肠道甲烷的变化?

IF 3.7 1区 农林科学 Q1 AGRICULTURE, DAIRY & ANIMAL SCIENCE Journal of Dairy Science Pub Date : 2024-07-01 DOI:10.3168/jds.2023-24094
Giulio Giagnoni , Nicolas C. Friggens , Marianne Johansen , Morten Maigaard , Wenji Wang , Peter Lund , Martin R. Weisbjerg
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引用次数: 0

摘要

奶牛肠道产生的甲烷是人为温室气体排放的主要来源。最近的研究表明,选择 CH4 排放量较低的奶牛是可能的,但如果使用牧场已记录的性能指标,而不是测量每头奶牛的气体排放量,则会更简单。这些性能指标可用于选择低排放奶牛。本分析的目的是量化奶牛之间的甲烷产量变化有多少可以用性能指标的变化来解释。我们使用了一个包含 3 个实验的数据集(共 149 头重复测量的泌乳奶牛)来估算 GreenFeed 性能和气体测量的奶牛间差异(奶牛估计值之间的差异)。奶牛估算值是通过线性混合模型获得的,该模型将期间内的日粮效应作为固定效应,将实验内的奶牛作为随机效应。首先将奶牛的甲烷产量估计值分别与性能和气体测量值进行回归,然后将性能和二氧化碳产量测量值分为三个子集,进行主成分分析和主成分回归。能解释奶牛间甲烷产量差异的变量是性能指标中的DMI(R2 = 0.44)和气体指标中的二氧化碳产量(R2 = 0.61)。当仅使用性能指标时,对指标进行分组可将 R2 提高到 0.53,而当 CO2 产量被添加到重要的性能指标时,R2 则提高到 0.66。我们发现,这种边际改进不足以证明使用分组措施而非单个措施是合理的,因为后者可以避免过度拟合并简化模型。我们还简要讨论了可用于提高奶牛间 CH4 产量变化解释力的其他指标。最后,可以考虑使用剩余 CH4 作为衡量 CH4 效率的指标,将 DMI 或 CO2 产量作为唯一的预测变量。
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How much can performance measures explain of the between-cow variation in enteric methane?

Enteric CH4 produced from dairy cows contributes to the emission of greenhouse gases from anthropogenic sources. Recent studies have shown that the selection of lower CH4-emitting cows is possible, but doing so would be simpler if performance measures already recorded on farm could be used, instead of measuring gas emissions from individual cows. These performance measures could be used for selection of low emitting cows. The aim of this analysis was to quantify how much of the between-cow variation in CH4 production can be explained by variation in performance measures. A dataset with 3 experiments and a total of 149 lactating dairy cows with repeated measures was used to estimate the between-cow variation (the variation between cow estimates) for performance and gas measures from GreenFeed (C-Lock, Rapid City, SD). The cow estimates were obtained with a linear mixed model with the diet within period effect as a fixed effect and the cow within experiment as a random effect. The cow estimates for CH4 production were first regressed on the performance and gas measures individually, and then performance and CO2 production measures were grouped in 3 subsets for principal component analysis and principal component regression. The variables that explained most of the between-cow variation in CH4 production were DMI (R2 = 0.44), among the performance measures, and CO2 production (R2 = 0.61), among gas measures. Grouping the measures increased the R2 to 0.53 when only performance measures were used, and to 0.66 when CO2 production was added to the significant performance measures. We found the marginal improvement to be insufficient to justify the use of grouped measures rather than an individual measure because the latter simplifies the model and avoids over-fitting. Investigation of other measures that can be explored to increase explanatory power of between-cow variation in CH4 production is briefly discussed. Finally, the use of residual CH4 as a measure for CH4 efficiency could be considered by using either DMI or CO2 production as the sole predicting variables.

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来源期刊
Journal of Dairy Science
Journal of Dairy Science 农林科学-奶制品与动物科学
CiteScore
7.90
自引率
17.10%
发文量
784
审稿时长
4.2 months
期刊介绍: The official journal of the American Dairy Science Association®, Journal of Dairy Science® (JDS) is the leading peer-reviewed general dairy research journal in the world. JDS readers represent education, industry, and government agencies in more than 70 countries with interests in biochemistry, breeding, economics, engineering, environment, food science, genetics, microbiology, nutrition, pathology, physiology, processing, public health, quality assurance, and sanitation.
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