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  • Title: Pathogen-specific patterns of milking traits in automatic milking systems.
    Author: Olofsson C, Toftaker I, Rachah A, Reksen O, Kielland C.
    Journal: J Dairy Sci; 2024 Aug; 107(8):6035-6051. PubMed ID: 38554822.
    Abstract:
    Early detection of 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 use 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 SCC (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 5,756 quarter milk samples 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 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 Staphylococcus 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 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 Streptococcus dysgalactiae, followed by Streptococcus simulans, Streptococcus 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.
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