Detection of changes in the circadian rhythm of cattle in relation to disease, stress, and reproductive events.

Disease and stress can disrupt the circadian rhythm of activity in animals. Sensor technologies can automatically detect variations in daily activity, but it remains difficult to detect exactly when the circadian rhythm disruption starts. Here we report a mathematical Fourier-Based Approximation with Thresholding (FBAT) method designed to detect changes in the circadian activity rhythm of cows whatever the cause of change (typically disease, stress, oestrus). We used data from an indoor positioning system that provides the time per hour spent by each cow resting, in alleys, or eating. We calculated the hourly activity level of each cow by attributing a weight to each activity. We considered 36-h time series and used Fourier transform to model the variations in activity during the first and last 24 h of these 36-h series. We then compared the Euclidian distance between the two models against a given threshold above which we considered that rhythm had changed. We tested the method on four datasets (giving a cumulative total of ~120000 cow*days) that included disease episodes (acidosis, lameness, mastitis or other infectious diseases), reproductive events (oestrus or calving) and external stimuli that can stress animals (e.g. relocation). The method obtained over 80% recall of normal days and detected 95% of abnormal rhythms due to health or reproductive events. FBAT could be implemented in precision livestock farming system monitoring tools to alert caretakers to individual animals needing specific care. The FBAT method also has the potential to detect anomalies in humans to guide healthcare intervention or in wild animals to detect disturbances. We anticipate that chronobiological studies could apply FBAT to help relate circadian rhythm anomalies to specific events.

Technical note: Validation and comparison of 2 commercially available activity loggers.

To validate the accuracy of 2 commercially available activity loggers in determining lying, standing, walking, and number of steps in dairy cows, 30 cows were fitted with the CowScout Leg (GEA Farm Technologies, Bönen, Germany) system and the IceTag (IceRobotics Ltd., Edinburgh, Scotland) system. The CowScout Leg logger reports standing and lying in 15-min periods, whereas the IceTag logger reports standing and lying every second. To make data comparable, the IceTag data were therefore also summarized over 15-min periods corresponding to the paired CowScout Leg sensor. These data from the 2 systems were then analyzed (more than 1,000 cow days in total). Video recordings of a total of 29.5 h were used for labeling the behaviors of the selected cows (n = 10) and these labels were used as a gold standard to determine the accuracy with which these 2 loggers can record behavioral states lying, standing, walking, and the behavioral event number of steps. A concordance correlation coefficient analysis showed that both the standing and lying durations obtained with the 2 systems were almost perfectly correlated with the video labeling (IceTag: ρc = 0.999 and 0.999, respectively; CowScout Leg: ρc = 0.995 and 0.996, respectively). However, both loggers performed poorly regarding number of steps (classified as an event; IceTag: ρc = 0.629; CowScout Leg: ρc = 0.678) and CowScout Leg did not detect walking (classified as a state) very accurately (ρc = 0.860). The IceTag system does not measure walking behavior. When comparing the 2 loggers, the correlation between them for standing and lying was substantial (ρc = 0.953 and ρc = 0.953, respectively). The number of steps poorly correlated between the 2 loggers (ρc = 0.593), which might be due to the CowScout Leg logger being attached to the front leg and the IceTag logger being attached to the hind leg. We conclude that both the IceTag and the CowScout Leg logger are able to record standing and lying almost perfectly, but the step counting by both loggers and the walking recording by the CowScout Leg logger are not very accurate.

Short communication: Early modification of the circadian organization of cow activity in relation to disease or estrus.

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.