Abstract：
Early detection of intramammary infection (IMI) can improve animal health and welfare in dairy herds. The implementation of sensors and automatic milking systems (AMS) in dairy production inherently increases the amount of available data and hence also the potential for new approaches to mastitis management. To utilize the full potential of data from AMS and auxiliary sensors, a better understanding of physiological and pathological changes in milking traits associated with different udder pathogens may be imperative. This observational study aimed to investigate pathogen-specific patterns in milking traits recorded in AMS. The milking traits included; online somatic cell count (OCC), electrical conductivity (EC), milk yield (MY), and average milk flow rate (AMF). Data were collected for a study period of 2 years and included 101 492 milkings from 237 lactations in 169 cows from one farm. Measurements of OCC were recorded at cow-level and data on EC, MY, and AMF were obtained at quarter-level. In addition to the data obtained from the AMS, altogether 5756 quarter milk samples (QMS) were collected. Milk samples were obtained monthly for bacteriological culturing. We included findings of 13 known mastitis pathogens to study pathogen-specific patterns in milking traits. These patterns were compared with those in a baseline group consisting of cows that did not have any positive milk culture results throughout the lactation period. Patterns of the milking traits are described for all positive samples both across 305 d in milk (DIM), and in the 15-d period before a positive bacteriological sample. The association between a positive sample and the milking traits (ln(OCC), EC-IQR; the ratio between the quarter with the highest and the quarter with the lowest level of EC, and MY) for the 15 d before the detection of a pathogen was assessed using mixed effects linear regression models. All pathogens were associated with alterations in the level and variability of ln(OCC) relative to lactations with no positive bacteriological samples. A positive sample for Staph. aureus was associated with increased values for MY during the 15 d before a positive diagnosis. It is biologically plausible to interpret changes in OCC and EC-IQR as consequences of an intramammary infection (IMI), while higher MY in bacteriologically-positive cows is most likely linked to the increased risk of infection in high-yielding cows. In this study, the most notable changes in the traits (OCC and EC-IQR) were observed for Staph. aureus and Strep. dysgalactiae, followed by Strep. simulans, Strep. uberis, and Lactococcus lactis. Even if we did not detect significant associations between positive bacteriology and EC-IQR, visual assessment and descriptive statistics indicated that there might be differences suggesting that it could be an informative trait for detecting infection when combined with OCC and possibly other relevant traits using machine learning algorithms.
摘要：
早期发现乳房内感染（IMI）可以改善奶牛群的动物健康和福利。传感器和自动挤奶系统(AMS)在乳制品生产中的实施固有地增加了可用数据的量,并且因此也增加了乳腺炎管理的新方法的潜力。为了充分利用AMS和辅助传感器的数据潜力，更好地了解与不同乳房病原体相关的挤奶性状的生理和病理变化可能是必要的。这项观察性研究旨在研究AMS中记录的挤奶性状中的病原体特异性模式。挤奶性状包括；在线体细胞计数(OCC)，电导率(EC)，产奶量（MY），和平均牛奶流量（AMF）。收集了为期2年的研究数据，其中包括来自一个农场的169头奶牛的237次泌乳中的101492次挤奶。OCC的测量记录在牛水平和EC的数据，我的,AMF是在季度水平获得的。除了从AMS获得的数据之外，共收集了5756份季度牛奶样品(QMS)。每月获取牛奶样品进行细菌学培养。我们纳入了13种已知乳腺炎病原体的发现，以研究挤奶性状中的病原体特异性模式。将这些模式与由在整个泌乳期间没有任何阳性乳培养结果的奶牛组成的基线组中的模式进行比较。描述了在牛奶中305天（DIM）的所有阳性样品的挤奶性状模式，在细菌样本阳性之前的15天。阳性样本与挤奶性状之间的关联(ln(OCC)，EC-IQR；EC最高的季度和最低水平的季度之间的比率，和MY）使用混合效应线性回归模型评估病原体检测前15d。相对于没有阳性细菌学样品的泌乳，所有病原体都与ln（OCC）的水平和变异性的变化有关。葡萄球菌阳性样本。金黄色葡萄球菌与阳性诊断前15d的MY值增加相关。将OCC和EC-IQR的变化解释为乳房内感染（IMI）的后果在生物学上是合理的，而细菌学阳性母牛的MY较高很可能与高产母牛的感染风险增加有关。在这项研究中,在葡萄球菌的性状（OCC和EC-IQR）中观察到最显着的变化。金黄色葡萄球菌和链球菌。中毒，其次是Strep。模拟器,Strep.uberis,和乳酸乳球菌.即使我们没有检测到阳性细菌学和EC-IQR之间的显著关联，视觉评估和描述性统计表明,可能存在差异,这表明当与OCC以及可能使用机器学习算法的其他相关特征结合时,它可能是检测感染的信息特征.
