The objective of this study was to evaluate the ability of milk infrared spectra to predict cow lameness score (LMS) for use as an indicator of cow health on Australian dairy farms, or as an indicator trait for genetic evaluation purposes. The study involved 3,771 cows from 10 farms in Australia. Milk infrared spectra collected during the monthly herd testing were available in all the farms involved in the study. Lameness score was measured once in each herd, within 72 h from a test day, and merged to the closest spectra records. Lameness score was expressed on a scale from 0 to 3, where 0 is assigned to sound cows and scores 1 to 3 are assigned to cows with increased lameness severity. Partial least squares discriminant analysis was used to develop prediction models for classifying sound (score 0) and not-sound cows (i.e., cows walking unevenly, score greater than 0). Discriminant models were tested in a 10-fold random cross-validation process. Milk infrared spectra correctly classified only 57% of the cows walking unevenly and only 59% of the sound cows. When additional predictors (parity, age at calving, days in milk, and milk yield) were included in the prediction model, the model correctly classified 57% of the cows walking unevenly and 62% of the sound cows. The same model applied only to the cows in the first third of lactation correctly classified 66% of the cows walking unevenly and 57% of the sound cows. When the prediction model was used to identify lame cows (scores 2 and 3), only 49% of them were classified as such. These results are considered to be too poor to envisage a practical application of these models in the near future as on-farm tools to provide an indication of LMS. To investigate whether, at this stage, predictions of the LMS could be useful as large-scale phenotypes for animal breeding purposes, we estimated (co)variance components for actual and predicted LMS using 2,670 and 24,560 records, respectively. As the genetic correlation between actual and predicted LMS was not significantly different from zero, predictions of lameness from milk spectra and additional on-farm variables cannot be used, at this stage, as an indicator trait for actual LMS. More research is needed to find better strategies to predict lameness.

中文翻译：

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牛奶中红外光谱法可用于预测奶牛的la行评分。

这项研究的目的是评估牛奶红外光谱预测奶牛la足得分（LMS）的能力，以用作澳大利亚奶牛场奶牛健康的指标，或用作遗传评估目的的指标性状。该研究涉及来自澳大利亚10个农场的3,771头奶牛。在该研究中涉及的所有农场都可以在月度牛群测试期间收集到牛奶红外光谱。在测试日后的72小时内，每只羊群测量一次me足得分，并合并到最近的光谱记录中。me行评分以0到3的量表表示，其中0表示健全的母牛，而1-3评分则表示la行严重程度增加的母牛。使用偏最小二乘判别分析来开发预测模型，以对声音（得分0）和不健全的母牛（即，行走不均匀的母牛，分数大于0）。判别模型在10倍随机交叉验证过程中进行了测试。牛奶红外光谱正确地将只有57％的奶牛行走不均匀和只有59％的有声奶牛正确分类。当在预测模型中包括其他预测因子（胎次，产犊年龄，产奶天数和产奶量）时，该模型正确地将57％行走不均匀的母牛和62％健全的母牛正确分类。相同的模型仅适用于泌乳期前三分之一的母牛，正确地将66％的母牛行走不均匀和57％的健康母牛分类。当使用预测模型来识别la牛（得分2和3）时，只有49％被归类为la牛。这些结果被认为太差，无法在不久的将来设想这些模型作为提供LMS指示的农场工具的实际应用。为了调查在这个阶段，LMS的预测是否可以用作动物育种的大规模表型，我们分别使用2670和24560记录来估计实际和预测LMS的（协）方差分量。由于实际LMS与预测LMS之间的遗传相关性没有显着差异，因此在这一阶段，不能使用从牛奶光谱和其他农场变量得出的me行预测作为实际LMS的指标特征。需要更多的研究来找到更好的预测la行的策略。LMS的预测可以用作动物育种的大规模表型，我们分别使用2,670和24,560个记录估算了实际LMS和预测LMS的（协）方差分量。由于实际LMS与预测LMS之间的遗传相关性没有显着差异，因此在这一阶段，不能使用从牛奶光谱和其他农场变量得出的me行预测作为实际LMS的指标特征。需要更多的研究来找到更好的预测la行的策略。LMS的预测可以用作动物育种的大规模表型，我们分别使用2,670和24,560个记录估算了实际LMS和预测LMS的（协）方差分量。由于实际LMS与预测LMS之间的遗传相关性没有显着差异，因此在这一阶段，不能将牛奶光谱和其他农场变量中的me行预测用作实际LMS的指标特征。需要更多的研究来找到更好的预测la行的策略。作为实际LMS的指标特征。需要更多的研究来找到更好的预测la行的策略。作为实际LMS的指标特征。需要更多的研究来找到更好的预测la行的策略。