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.

Use of rumination and activity monitoring for the identification of dairy cows with health disorders: Part II. Mastitis

“…Multiple automated data collection systems based on sensors (e.g., daily milk weights, milk composition, electrical conductivity, somatic cell counts) have been tested and are available to detect mastitis through changes in milk production and its attributes (Kamphuis et al, 2008;Koop et al, 2015;Sørensen et al, 2016). Conversely, data about the use of automated rumination time and physical activity monitoring systems to detect cows with mastitis are scarce, because only a few studies have evaluated rumination time in cows with induced clinical mastitis or challenged with LPS (Siivonen et al, 2011;Fogsgaard et al, 2012;Fitzpatrick et al, 2013), and no studies have assessed a combination of both rumination and activity data to identify cows with mastitis.…”

Identification of Cows Susceptible to Mastitis based on Selected Genotypes by Using Decision Trees and A Generalized Linear Model

“…Still higher detection performance was characteristic of the neural networks constructed by Sun et al (2010) [41], for which Se, Sp and Acc were 0.79 -0.87, 0.91 -0.92 and 0.87 -0.91, respectively, depending on the input variables used and the network structure. Another detection and monitoring model based on the online recording of SCC [42] yielded an Se of 0.28 to 0.43 when reporting new mastitis cases, and Se between 0.55 to 0.89 when indicating on-going intramammary infections. The lowest proportion of false alarms observed in this study was 0.07.…”

Bivariate threshold models for genetic evaluation of susceptibility to and ability to recover from mastitis in Danish Holstein cows

“…Højsgaard and Friggens (2010) introduced the concept of "degree of infection" to define a cow's health status on a continuous scale where low values indicate healthy cows and high values indicate mastitic cows. A more recent concept, elevated mastitis risk, was introduced by Sørensen et al (2016). Elevated mastitis risk (EMR) evaluates a cow for the risk of having contracted mastitis based on online SCC (OCC) recorded with an AMS.…”

“…The objectives were, therefore, (1) to evaluate whether the model developed by Franzén et al (2012) and is identifiable and can be fitted to real data, and (2) to estimate genetic parameters of mastitis susceptibility and recovery ability for Danish Holstein cows using bivariate threshold models. Sørensen et al (2016) to develop a mastitis detection model.…”