BACKGROUND A robust proxy for estimating methane (CH4 ) emissions of individual dairy cows would be valuable especially for selective breeding. This study aimed to improve the robustness and accuracy of prediction models that estimate daily CH4 emissions from milk Fourier-transform mid-infrared (FT-MIR) spectra by 1) increasing the reference dataset and 2) adjusting for routinely recorded phenotypic information. Prediction equations for CH4 were developed using a combined dataset including daily CH4 measurements (n = 1,089; g/day) collected using the SF6 tracer technique (n = 513) and measurements using the respiration chambers (RC, n = 576). Furthermore, in addition to the milk FT-MIR spectra, the variables of milk yield (MY) on the test day, parity (P) and breed (B) of cows were included in the regression analysis as explanatory variables. RESULTS Models developed based on a combined RC and SF6 dataset predicted the expected pattern in CH4 values (in g/day) during a lactation cycle, namely an increase during the first weeks after calving followed by a gradual decrease until the end of lactation. The model including MY, P and B information provided the best prediction results (cross-validation statistics: R2 = 0.68 and standard error = 57 g CH4 /day). CONCLUSIONS The models developed accounted for more of the observed variability in CH4 emissions than previously developed models and thus were considered more robust. This approach is suitable for large-scale studies (e.g. animal genetic evaluation) where robustness is paramount for accurate predictions across a range of animal conditions. This article is protected by copyright. All rights reserved.

中文翻译：

---

使用牛奶中红外光谱提高预测奶牛每日甲烷排放量的稳健性和准确性

背景 用于估计个体奶牛的甲烷 (CH4) 排放量的可靠代理将是有价值的，尤其是对于选择性育种。本研究旨在通过 1) 增加参考数据集和 2) 调整常规记录的表型信息来提高预测模型的稳健性和准确性，该模型估计牛奶傅立叶变换中红外 (FT-MIR) 光谱的每日 CH4 排放量。CH4 的预测方程是使用组合数据集开发的，其中包括使用 SF6 示踪技术（n = 513）收集的每日 CH4 测量值（n = 1,089；g/天）和使用呼吸室的测量值（RC，n = 576）。此外，除了牛奶 FT-MIR 光谱外，测试日的产奶量 (MY)、胎次 (P) 和品种 (B) 等变量也作为解释变量纳入回归分析。结果 基于组合的 RC 和 SF6 数据集开发的模型预测了泌乳周期中 CH4 值（g/天）的预期模式，即在产犊后的前几周内增加，然后逐渐减少，直到泌乳结束。包含 MY、P 和 B 信息的模型提供了最佳预测结果（交叉验证统计：R2 = 0.68 和标准误差 = 57 g CH4 /天）。结论 与以前开发的模型相比，开发的模型解释了更多观察到的 CH4 排放变异性，因此被认为更可靠。这种方法适用于大规模研究（例如动物遗传评估），其中稳健性对于在一系列动物条件下进行准确预测至关重要。本文受版权保护。版权所有。