Lemal Pauline, Grelet Clément, Dehareng Frédéric, Soyeurt Hélène, Schroyen Martine, Gengler Nicolas
University of Liège, Gembloux Agro-Bio Tech (ULiège-GxABT), 5030 Gembloux, Belgium.
Walloon Agricultural Research Center (CRA-W), 5030 Gembloux, Belgium.
J Dairy Sci. 2025 Feb;108(2):1714-1729. doi: 10.3168/jds.2024-25440. Epub 2024 Dec 16.
Numerous prediction equations have been developed based on mid-infrared (MIR) spectra, and some could be potentially used as biomarkers of heat stress. However, practical experience shows that confusion can easily occur between the effect of heat stress and other effects, such as lactation stage or feeding variation over the year. On this basis, the objective of this study was to identify potential milk components predicted by MIR as biomarkers of heat stress based on a 2-step approach allowing correction for those effects. The first step consisted in the estimation of residuals from test-day random regression models on DIM to remove systematic lactation stage effects. These models also contained, among others, general (i.e., month of production) or specific (i.e., herd × test-day) fixed effects related to feeding and management. During the second step, means and variances of residuals by temperature-humidity index (THI) classes were studied. The models were applied to 611,063 records from 97,042 primiparous Holstein cows from 2015 to 2022 in the south of Belgium. The MIR-predicted milk components with the highest deviations from the mean with increasing THI were protein percentage, casein concentration, magnesium concentration, and (to a lesser extent) PUFA concentration. Concerning residual variances, the highest heteroscedasticity with THI was obtained for milk MIR MUFA, C18:1 cis-9, and citrate concentrations. Conversely, a relative homoscedasticity of variance with increasing THI was observed for several milk MIR components including protein percentage and casein concentration. Based on the criteria of the good biomarkers guidelines, milk protein percentage seems to be the most promising trait of this study, followed by Mg concentration. However, in the context of genetic evaluation, which requires variability, milk MIR MUFA, C18:1 cis-9, or citrate concentration variations, if they are heritable, could be of great interest. Finally, an increase in milk MIR citrate concentration variance could be an early warning for the detection of heat stress in the frame of DHI.
基于中红外（MIR）光谱已经开发出了许多预测方程，其中一些有可能用作热应激的生物标志物。然而，实践经验表明，热应激的影响与其他影响（如泌乳阶段或一年中的饲养变化）之间很容易产生混淆。在此基础上，本研究的目的是基于一种两步法来识别由MIR预测的潜在乳成分作为热应激的生物标志物，该方法能够校正这些影响。第一步包括从基于泌乳天数（DIM）的测试日随机回归模型估计残差，以消除系统性的泌乳阶段影响。这些模型还包含与饲养和管理相关的一般（即生产月份）或特定（即牛群×测试日）固定效应等。在第二步中，研究了按温湿度指数（THI）类别划分的残差的均值和方差。这些模型应用于2015年至2022年比利时南部97,042头初产荷斯坦奶牛的611,063条记录。随着THI升高，与均值偏差最大的MIR预测乳成分是蛋白质百分比、酪蛋白浓度、镁浓度以及（程度稍小的）多不饱和脂肪酸（PUFA）浓度。关于残差方差，乳MIR单不饱和脂肪酸（MUFA）、顺-9 C18:1和柠檬酸盐浓度随THI的异方差性最高。相反，对于包括蛋白质百分比和酪蛋白浓度在内的几种乳MIR成分，观察到随着THI升高方差相对同方差。基于良好生物标志物指南的标准，乳蛋白质百分比似乎是本研究中最有前景的性状，其次是镁浓度。然而，在需要变异性的遗传评估背景下，如果乳MIR MUFA、顺-9 C18:1或柠檬酸盐浓度变化具有遗传性，可能会非常有意义。最后，乳MIR柠檬酸盐浓度方差的增加可能是在奶牛性能测定（DHI）框架内检测热应激的一个早期预警信号。
