title: Monitoring individual cow udder health in automated milking systems using online somatic cell counts.
abstract: This study presents and validates a detection and monitoring model for mastitis based on automated frequent sampling of online cell count (OCC). Initially, data were filtered and adjusted for sensor drift and skewed distribution using ln-transformation. Acceptable data were passed on to a time-series model using double exponential smoothing to estimate level and trends at cow level. The OCC levels and trends were converted to a continuous (0-1) scale, termed elevated mastitis risk (EMR), where values close to zero indicate healthy cow status and values close to 1 indicate high risk of mastitis. Finally, a feedback loop was included to dynamically request a time to next sample, based on latest EMR values or errors in the raw data stream. The estimated EMR values were used to issue 2 types of alerts, new and (on-going) intramammary infection (IMI) alerts. The new alerts were issued when the EMR values exceeded a threshold, and the IMI alerts were issued for subsequent alerts. New alerts were only issued after the EMR had been below the threshold for at least 8d. The detection model was evaluated using time-window analysis and commercial herd data (6 herds, 595,927 milkings) at different sampling intensities. Recorded treatments of mastitis were used as gold standard. Significantly higher EMR values were detected in treated than in contemporary untreated cows. The proportion of detected mastitis cases using new alerts was between 28.0 and 43.1% and highest for a fixed sampling scheme aiming at 24h between measurements. This was higher for IMI alerts, between 54.6 and 89.0%, and highest when all available measurements were used. The lowest false alert rate of 6.5 per 1,000 milkings was observed when all measurements were used. The results showed that a dynamic sampling scheme with a default value of 24h between measurements gave only a small reduction in proportion of detected mastitis treatments and remained at 88.5%. It was concluded that filtering of raw data combined with a time-series model was effective in detecting and monitoring mastitis status in dairy cows when based on IMI alerts, and by using a dynamically adjusting sampling scheme almost full performance was still obtainable. However, results were less desirable when based on new alerts most likely because of the used gold standard for mastitis, which may not necessarily reflect the onset of and IMI case in contrast to a new alert.
authors.affiliation: Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, DK-8830 Tjele, Denmark. Electronic address: LarsPeter.Sorensen@agrsci.dk.
authors.forename: L P
authors.initials: LP
authors.lastname: Sørensen
authors.affiliation: Department of Animal Science, Research Centre Foulum, Aarhus University, DK-8830 Tjele, Denmark.
authors.forename: M
authors.initials: M
authors.lastname: Bjerring
authors.affiliation: Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, DK-8830 Tjele, Denmark.
authors.forename: P
authors.initials: P
authors.lastname: Løvendahl
mesh_terms: D000818:Animals; D002417:Cattle; D002452:Cell Count / Q000662:veterinary; D003612:Dairying / Q000379:methods*; D005260:Female; D008321:Mammary Glands, Animal / Q000166:cytology; D008414:Mastitis, Bovine / Q000175:diagnosis*; D008892:Milk / Q000166:cytology*
keywords: automated milking; dairy cattle; mastitis detection; online somatic cell count