Methods used for estimating sleep in dairy cattle

Sleep serves several essential functions in all mammals including dairy cattle. Researchers are beginning to estimate sleep in dairy cattle using a combination of physiological measurements (e.g., polysomnography) as well as changes in behavior (e.g., different resting postures). Sleep may provide unique insight into how cows and calves respond to, and cope with, their environments, as a complement to other common measurements such as lying time. Although each of the methods to assess sleep in cattle has its advantages, there remain several challenges with each approach. The objective of this narrative mini-review is to describe current methods for estimating sleep in dairy cattle, including some of the advantages and limitations with each method. We will start with describing the research to date on adult cows, followed by preweaning dairy calves. We end the review with recommendations for researchers interested in assessing sleep in dairy cattle and ideas for future areas of research.

Application of Precision Technologies to Characterize Animal Behavior: A Review

This study aims to evaluate the state of precision livestock farming (PLF)'s spread, utilization, effectiveness, and evolution over the years. PLF includes a plethora of tools, which can aid in a number of laborious and complex tasks. These tools are often used in the monitoring of different animals, with the objective to increase production and improve animal welfare. The most frequently monitored attributes tend to be behavior, welfare, and social interaction. This study focused on the application of three types of technology: wearable sensors, video observation, and smartphones. For the wearable devices, the focus was on accelerometers and global positioning systems. For the video observation, the study addressed drones and cameras. The animals monitored by these tools were the most common ruminants, which are cattle, sheep, and goats. This review involved 108 articles that were believed to be pertinent. Most of the studied papers were very accurate, for most tools, when utilized appropriate; some showed great benefits and potential.

Reindeer in the Arctic reduce sleep need during rumination

Timing and quantity of sleep depend on a circadian (∼24-h) rhythm and a specific sleep requirement.1 Sleep curtailment results in a homeostatic rebound of more and deeper sleep, the latter reflected in increased electroencephalographic (EEG) slow-wave activity (SWA) during non-rapid eye movement (NREM) sleep.2 Circadian rhythms are synchronized by the light-dark cycle but persist under constant conditions.3,4,5 Strikingly, arctic reindeer behavior is arrhythmic during the solstices.6 Moreover, the Arctic's extreme seasonal environmental changes cause large variations in overall activity and food intake.7 We hypothesized that the maintenance of optimal functioning under these extremely fluctuating conditions would require adaptations not only in daily activity patterns but also in the homeostatic regulation of sleep. We studied sleep using non-invasive EEG in four Eurasian tundra reindeer (Rangifer tarandus tarandus) in Tromsø, Norway (69°N) during the fall equinox and both solstices. As expected, sleep-wake rhythms paralleled daily activity distribution, and sleep deprivation resulted in a homeostatic rebound in all seasons. Yet, these sleep rebounds were smaller in summer and fall than in winter. Surprisingly, SWA decreased not only during NREM sleep but also during rumination. Quantitative modeling revealed that sleep pressure decayed at similar rates during the two behavioral states. Finally, reindeer spent less time in NREM sleep the more they ruminated. These results suggest that they can sleep during rumination. The ability to reduce sleep need during rumination-undisturbed phases for both sleep recovery and digestion-might allow for near-constant feeding in the arctic summer.

A review of the diet of the common vampire bat (Desmodus rotundus) in the context of anthropogenic change

The common vampire bat (Desmodus rotundus) maintains a diverse, sanguivorous diet, utilizing a broad range of prey taxa. As anthropogenic change alters the distribution of this species, shifts in predator-prey interactions are expected. Understanding prey richness and patterns of prey selection is, thus, increasingly informative from ecological, epidemiological, and economic perspectives. We reviewed D. rotundus diet and assessed the geographical, taxonomical, and behavioral features to find 63 vertebrate species within 21 orders and 45 families constitute prey, including suitable host species in regions of invasion outside D. rotundus' range. Rodentia contained the largest number of species utilized by D. rotundus, though cattle were the most commonly reported prey source, likely linked to the high availability of livestock and visibility of bite wounds compared to wildlife. Additionally, there was tendency to predate upon species with diurnal activity and social behavior, potentially facilitating convenient and nocturnal predation. Our review highlights the dietary heterogeneity of D. rotundus across its distribution. We define D. rotundus as a generalist predator, or parasite, depending on the ecological definition of its symbiont roles in an ecosystem (i.e., lethal vs. non-lethal blood consumption). In view of the eminent role of D. rotundus in rabies virus transmission and its range expansion, an understanding of its ecology would benefit public health, wildlife management, and agriculture.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42991-023-00358-3.

Testing the Accuracy of Wearable Technology to Assess Sleep Behaviour in Domestic Dogs: A Prospective Tool for Animal Welfare Assessment in Kennels

Sleep is a physiological process that has been shown to impact both physical and psychological heath of individuals when compromised; hence, it has the potential to be used as an indicator of animal welfare. Nonetheless, evaluating sleep in non-human species normally involves manipulation of the subjects (i.e., placement of electrodes on the cranium), and most studies are conducted in a laboratory setting, which limits the generalisability of information obtained, and the species investigated. In this study, we evaluated an alternative method of assessing sleep behaviour in domestic dogs, using a wearable sensor, and compared the measurements obtained to behavioural observations to evaluate accuracy. Differences between methods ranged from 0.13% to 59.3% for diurnal observations and 0.1% to 95.9% for nocturnal observations for point-by-point observations. Comparisons between methods showed significant differences in certain behaviours, such as inactivity and activity for diurnal recordings. However, total activity and total sleep recorded did not differ statistically between methods. Overall, the wearable technology tested was found to be a useful, and a less-time consuming, tool in comparison to direct behavioural observations for the evaluation of behaviours and their indication of wellbeing in dogs. The agreement between the wearable technology and directly observed data ranged from 75% to 99% for recorded behaviours, and these results are similar to previous findings in the literature.

High Precision Classification of Resting and Eating Behaviors of Cattle by Using a Collar-Fitted Triaxial Accelerometer Sensor

Cattle are less active than humans. Hence, it was hypothesized in this study that transmitting acceleration signals at a 1 min sampling interval to reduce storage load has the potential to improve the performance of motion sensors without affecting the precision of behavior classification. The behavior classification performance in terms of precision, sensitivity, and the F1-score of the 1 min serial datasets segmented in 3, 4, and 5 min window sizes based on nine algorithms were determined. The collar-fitted triaxial accelerometer sensor was attached on the right side of the neck of the two fattening Korean steers (age: 20 months) and the steers were observed for 6 h on day one, 10 h on day two, and 7 h on day three. The acceleration signals and visual observations were time synchronized and analyzed based on the objectives. The resting behavior was most correctly classified using the combination of a 4 min window size and the long short-term memory (LSTM) algorithm which resulted in 89% high precision, 81% high sensitivity, and 85% high F1-score. High classification performance (79% precision, 88% sensitivity, and 83% F1-score) was also obtained in classifying the eating behavior using the same classification method (4 min window size and an LSTM algorithm). The most poorly classified behavior was the active behavior. This study showed that the collar-fitted triaxial sensor measuring 1 min serial signals could be used as a tool for detecting the resting and eating behaviors of cattle in high precision by segmenting the acceleration signals in a 4 min window size and by using the LSTM classification algorithm.

Machine learning prediction of sleep stages in dairy cows from heart rate and muscle activity measures

Sleep is important for cow health and shows promise as a tool for assessing welfare, but methods to accurately distinguish between important sleep stages are difficult and impractical to use with cattle in typical farm environments. The objective of this study was to determine if data from more easily applied non-invasive devices assessing neck muscle activity and heart rate (HR) alone could be used to differentiate between sleep stages. We developed, trained, and compared two machine learning models using neural networks and random forest algorithms to predict sleep stages from 15 variables (features) of the muscle activity and HR data collected from 12 cows in two environments. Using k-fold cross validation we compared the success of the models to the gold standard, Polysomnography (PSG). Overall, both models learned from the data and were able to accurately predict sleep stages from HR and muscle activity alone with classification accuracy in the range of similar human models. Further research is required to validate the models with a larger sample size, but the proposed methodology appears to give an accurate representation of sleep stages in cattle and could consequentially enable future sleep research into conditions affecting cow sleep and welfare.

Accelerometer systems as tools for health and welfare assessment in cattle and pigs - A review

Welfare assessment has traditionally been performed by direct observation by humans, providing information at only selected points in time. Recently, this assessment method has been questioned, as 'Precision Livestock Farming' technologies may be able to deliver more valid, reliable and feasible real-time data at the individual level and serve as early monitoring systems for animal welfare. The aim of this paper is to describe how accelerometers can be used for welfare assessment based on the principles of the Welfare Quality assessment protocol. Algorithm development is based mainly on the detection of behavioural traits. So far, high accuracies have been found for movement and resting behaviours in cows and pigs, while algorithm development for feeding and drinking behaviours in pigs lag behind progress in cows where valid algorithms are already available. Welfare studies have used accelerometer technology to address the effects on behaviour of diet, daily cycle, enrichment, housing, social mixing, oestrus, lameness and disease. Additional aspects to consider before a decision is made upon its use in research and in practical applications include battery life and sensor location. While accelerometer systems for cows are already being used by farmers, application in pigs has mainly remained at the research level.