Electrical conductivity (EC) of milk has been introduced as an indicator trait for mastitis over the last decade, and it may be considered as a potential trait in a breeding program where selection for improved udder health is included. In this study, various EC traits were investigated for their association with udder health. In total, 322 cows with 549 lactations were included in the study. Cows were classified as healthy or clinically or subclinically infected, and EC was measured repeatedly during milking on each quarter. Four EC traits were defined; the inter-quarter ratio (IQR) between the highest and lowest quarter EC values, the maximum EC level for a cow, IQR between the highest and lowest quarter EC variation, and the maximum EC variation for a cow. Values for the traits were calculated for every milking throughout the entire lactation. All EC traits increased significantly (P < 0.001) when cows were subclinically or clinically infected. A simple threshold test and discriminant function analysis was used to validate the ability of the EC traits to distinguish between cows in different health groups. Traits reflecting the level rather than variation of EC, and in particular the IQR, performed best to classify cows correctly. By using this trait, 80.6% of clinical and 45.0% of subclinical cases were classified correctly. Of the cows classified as healthy, 74.8% were classified correctly. However, some extra information about udder health status was obtained when a combination of EC traits was used.

No abstract

Current farm sizes do not allow the precise identification and tracking of individual cows and their health and behavioral records. Currently, the application of information technology within intensive dairy farming takes a key role in proper routine management to improve animal welfare and to enhance the comfort of dairy cows. An existing application based on information technology is represented by the GEA CowView system (GEA Farm Technologies, Bönen, Germany). This system is able to detect and monitor animal behavioral activities based on positioning, through the creation of a virtual map of the barn that outlines all the areas where cows have access. The aim of this study was to validate the accuracy, sensitivity, and specificity of data provided by the CowView system. The validation was performed by comparing data automatically obtained from the CowView system with those obtained by a manual labeling procedure performed on video recordings. Data used for the comparisons were represented by the zone-related activities performed by the selected dairy cows and were classified into 2 categories: activity and localization. The duration in seconds of each of the activities/localizations detected both with the manual labeling and with the automated system were used to evaluate the correlation coefficients among data; and subsequently the accuracy, sensitivity, specificity, and positive and negative predictive values of the automated monitoring system were calculated. The results of this validation study showed that the CowView automated monitoring system is able to identify the cow localization/position (alley, trough, cubicles) with high reliability in relation to the zone-related activities performed by dairy cows (accuracy higher than 95%). The results obtained support the CowView system as an innovative potential solution for the easier management of dairy cows.

No abstract

Biological rhythms are an essential regulator of life. There is evidence that circadian rhythm of activity is disrupted under chronic stress in animals and humans, and it may also be less marked during diseases. Here we investigated whether a detectable circadian rhythm of activity exists in dairy cows in commercial settings using a real-time positioning system. We used CowView (GEA Farm Technologies) to regularly record the individual positions of 350 cows in a Danish dairy farm over 5 mo and to infer the cows' activity (resting, feeding, in alley). We ran a factorial correspondence analysis on the cows' activities and used the first component of this analysis to express the variations in activity. On this axis, the activities obtained the following weights: resting = -0.15; in alleys = +0.12; feeding = +0.34. By applying these weights to the proportions of time each cow spent on each of the 3 activities, we were able to chart a circadian rhythm of activity. We found that average level of activity of a cow on a given day and its variations during that day varied with specific states (i.e., estrus, lameness, mastitis). More specifically, circadian variations in activity appeared to be particularly sensitive and to vary 1 to 2 d before the farmer detected a disorder. These findings offer promising avenues for further research to design models to predict physiological or pathological states of cows from real-time positioning data.