Circadian, feeding, and locomotor activities of artificially reared lambs measured by actigraphy

Eight artificially reared lambs were used to study locomotory and feeding activities in the first three weeks of life by actigraphy. Lambs were fitted with a Bluetooth-enabled (BT) accelerometer and data were downloaded as activity counts at 1-min intervals (Vector Magnitude, VM). Sensors were programmed to act as beacons, and two sensors programmed as receivers were installed next to the rubber nipples of the milk feeder and recorded the serial numbers and labels of other nearby beacons through the BT signals. Mean (±SE) VM was 140 ± 3 counts/min, and time of day and week had significant (P < 0.001) effects. Overall activity did not differ significantly between sexes (males: 139 ± 5; females: 142 ± 5). The proportion of lambs that exhibited a 24-h circadian rhythm decreased with age (week 1 = 75%, week 2 = 63%, week 3 = 50%). Mean number of suckling sessions/day was 3.7 ± 0.2, the mean number of minutes suckling/day was 12.5 ± 0.9, and mean number of minutes/meal was 3.5 ± 0.2. Males dedicated more time/meal than females (males: 4.1 ± 0.4; females: 3.0 ± 0.2 min; P < 0.05). In conclusion, actigraphy is a useful tool for investigating the locomotor and feeding behaviour of artificially reared lambs, which detected a reduction in the circadian rhythmicity and the number of suckling sessions with age

Classifying the posture and activity of ewes and lambs using accelerometers and machine learning on a commercial flock

This is the final version. Available on open access from Elsevier via the DOI in this recordData Availability: A censored version of the data is available upon request.Early decision making in commercial livestock systems is key to maximising animal welfare and production. Detailed information on an animal’s phenotype is needed to facilitate this, but can be difficult to obtain in a commercial setting. Research into the use of bio-logging on sheep to continuously monitor individual behaviour and indirectly inform health and production has seen rapid growth in recent years. Much of this research, however, has been conducted on small numbers of animals in an experimental setting and over limited time periods. Previous studies have also focused on ewes and there has been little research on the potential of bio-logging for collecting behavioural data on lambs, despite clear potential relevance for production. The present study aimed to test the feasibility of deploying accelerometers on a commercial sheep flock at a key point in the annual production cycle (lambing), to validate the viability of automated monitoring of sheep behaviour in a commercial setting. Also, we aimed to develop robust machine learning algorithms that can classify both the posture and physical activity of adult sheep and lambs. We used a Random Forest machine learning algorithm to predict: two mutually exclusive postures in ewes and lambs (standing and lying), achieving average accuracies of 83.7% and 85.9% respectively; four mutually exclusive activities in ewes (grazing, ruminating, inactive and walking), achieving an average accuracy of 70.9%; and three mutually exclusive activities in lambs (inactive, suckling, walking), achieving an average accuracy of 80.8%. These performance accuracies on large numbers of individuals afford the opportunity to provide a detailed understanding of the daily activity budget of ewes and lambs. Monitoring changes in daily patterns across the annual production cycle while capturing changes in environmental conditions such as weather, day length, terrain and management could reveal key indicator metrics that may inform production and health and provide early warning systems for key issues in commercial flocks.Biotechnology & Biological Sciences Research Council (BBSRC

Rumination Detection in Sheep: A Systematic Review of Sensor-Based Approaches

The use of sensors to analyze behavior in sheep has gained increasing attention in scientific research. This systematic review aims to provide an overview of the sensors developed and used to detect rumination behavior in sheep in scientific research. Moreover, this overview provides details of the sensors that are currently commercially available and describes their suitability for sheep based on the information provided in the literature found. Furthermore, this overview lists the best sensor performances in terms of achieved accuracy, sensitivity, precision, and specificity in rumination detection, detailing, when applicable, the sensor position and epoch settings that were used to achieve the best results. Challenges and areas for future research and development are also identified. A search strategy was implemented in the databases PubMed, Web of Science, and Livivo, yielding a total of 935 articles. After reviewing the summaries of 57 articles remaining following filtration (exclusion) of repeated and unsuitable articles, 17 articles fully met the pre-established criteria (peer-reviewed; published between 2012 and 2023 in English or German; with a particular focus on sensors detecting rumination in sheep) and were included in this review. The guidelines outlined in the PRISMA 2020 methodology were followed. The results indicate that sensor-based systems have been utilized to monitor and analyze rumination behavior, among other behaviors. Notably, none of the sensors identified in this review were specifically designed for sheep. In order to meet the specific needs of sheep, a customized sensor solution is necessary. Additionally, further investigation of the optimal sensor position and epoch settings is necessary. Implications: The utilization of such sensors has significant implications for improving sheep welfare and enhancing our knowledge of their behavior in various contexts

Farm Animals’ Behaviors and Welfare Analysis with AI Algorithms: A Review

peer reviewedNumerous bibliographic reviews related to the use of AI for the behavioral detection of farm animals exist, but they only focus on a particular type of animal. We believe that some techniques were used for some animals that could also be used for other types of animals. The application and comparison of these techniques between animal species are rarely done. In this paper, we propose a review of machine learning approaches used for the detection of farm animals’ behaviors such as lameness, grazing, rumination, and so on. The originality of this paper is matched classification in the midst of sensors and algorithms used for each animal category. First, we highlight the most implemented approaches for different categories of animals (cows, sheep, goats, pigs, horses, and chickens) to inspire researchers interested to conduct investigation and employ the methods we have evaluated and the results we have obtained in this study. Second, we describe the current trends in terms of technological development and new paradigms that will impact the AI research. Finally, we critically analyze what is done and we draw new pathways of research to advance our understanding of animal’s behaviors

Sensing solutions for improving the performance, health and wellbeing of small ruminants

Diversity of production systems and specific socio-economic barriers are key reasons explaining why the implementation of new technologies in small ruminants, despite being needed and beneficial for farmers, is harder than in other livestock species. There are, however, helpful peculiarities where small ruminants are concerned: the compulsory use of electronic identification created a unique scenario in Europe in which all small ruminant breeding stock became searchable by appropriate sensing solutions, and the largest small ruminant population in the world is located in Asia, close to the areas producing new technologies. Notwithstanding, only a few research initiatives and literature reviews have addressed the development of new technologies in small ruminants. This Research Reflection focuses on small ruminants (with emphasis on dairy goats and sheep) and reviews in a non-exhaustive way the basic concepts, the currently available sensor solutions and the structure and elements needed for the implementation of sensor-based husbandry decision support. Finally, some examples of results obtained using several sensor solutions adapted from large animals or newly developed for small ruminants are discussed. Significant room for improvement is recognized and a large number of multiple-sensor solutions are expected to be developed in the relatively near future

Sensor-based approaches to monitoring the behaviour, health, and welfare of dairy calves

Currently, the detection of ill health in UK farmed calves is based on intermittent visual observation which is subjective and poorly accurate. Sensor-based monitoring may offer an improved alternative. For example, sensors could be used to monitor behaviour and detect signs of ill health in calves. However, substantial individual variation exists for many behaviours, the extent of which is poorly understood in calves. Here, within- and between- individual variation in calf feeding behaviours are quantified using data from computerised milk feeders. Results show that substantial, temporally stable individual differences exist. In addition, the average behavioural expression of two distinct feeding behaviours were positively and significantly correlated and the between-individual differences observed were shown to be consistent over time and context, and to be associated with weight gain. This improves our understanding of normal variation in calf feeding behaviour, which could be helpful in detecting potential behavioural changes indicative of ill health. Machine learning models were trained and tested using feeding data from computerised milk feeders to detect ill health. In a separate study, a similar methodology was used to detect ill health using reticulo-rumen temperature boluses. Results indicate low and moderate predictive performance, respectively. Study limitations and areas for future research are discussed. Finally, the development of novel technologies to enable a more holistic approach to behavioural monitoring in calves is explored. Results show that signals from a single collar-based sensor can be used to accurately detect nine different behaviours as well as to quantify rarely occurring behaviours, such as locomotor play. Quantifying play behaviour could provide a useful indicator of positive welfare in calves. It is also shown that these behaviours can be detected using computer vision, but that further work is needed to enable generalisation to new camera angles and scenes. Overall, this thesis highlights the potential of sensor-based technologies to improve our understanding of behavioural variation in calves, as well as to monitor a greatly more diverse range of behaviours than previously attempted. It is hoped that this work will contribute towards the improvement of health and welfare in calves

Utilization of information and communication technologies to monitor grazing behaviour in sheep

This thesis is a contribution on the study of feeding behaviour of grazing sheep and its general goal was to evaluate the effectiveness of a tri-axial accelerometer based sensor in the discrimination of the main activities of sheep at pasture, the quantification of the number of bites and the estimation of intake per bite. Based on the literature, it has been observed that feed intake at pasture is a difficult parameter to measure with direct observation, for this reason automated systems for monitoring the activities of free-ranging animals have became increasingly important and common. Among these systems, tri-axial accelerometers showed a good precision and accuracy in the classification of behavioural activities of herbivores, but they do not yet seem able to discriminate jaw movements, which are of great importance for evaluating animal grazing strategies in different pastures and for estimating the daily herbage intake. Thus, the main objective of this research was to develop and test a tri-axial accelerometer based sensor (BEHARUM) for the study of the feeding behaviour of sheep and for the estimation of the bite rate (number of bites per min of grazing) on the basis of acceleration variables. The thesis is organized in 4 main chapters. Chapter 1. This introduction section reports a literature review on the importance of studying the feeding behaviour of ruminants and on the measuring techniques developed over the years for its detection, with specific emphasis on accelerometer based sensors, which showed a good precision and accuracy in the classification of behavioural activities of herbivores. Chapter 2. This chapter describes the results of short tests performed in grazing conditions to discriminate three behavioural activities of sheep (grazing, ruminating and resting) on the base of acceleration data collected with the BEHARUM device. The multivariate statistical analysis correctly assigned 93.0% of minutes to behavioural activities. Chapter 3. This part evaluates the effectiveness of the BEHARUM in discriminating between the main behaviours (grazing, ruminating and other activities) of sheep at pasture and to identify the epoch setting (5, 10, 30, 60, 120, 180 and 300 s) with the best performance. Results show that a discriminant analysis can accurately classify important behaviours such as grazing, ruminating and other activities in sheep at pasture, with a better performance in classifying grazing behaviour than ruminating and other activities for all epochs; the most accurate classification in terms of accuracy and Coehn’s k coefficient was achieved with the 30 s epoch length. Chapter 4. This section illustrates the results of a study that aimed to derive a model to predict sheep behavioural variables like number of bites, bite mass, intake and intake rate, on the basis of variables calculated from acceleration data recorded by the BEHARUM. The experiment was carried out using micro-swards of Italian ryegrass (Lolium multiflorum L.), alfalfa (Medicago sativa L.), oat (Avena sativa L.), chicory (Cichorium intibus L.) and a mixture (Italian ryegrass and alfalfa). The sheep were allowed to graze the micro-swards for 6 minutes and the results show that the BEHARUM can accurately estimate with high to moderate precision (r2=0.86 and RMSEP=3%) the number of bites and the herbage intake of sheep short term grazing Mediterranean forages. Finally, the dissertation ends with a summary of the main implications and findings, and a general discussion and conclusions

Towards the improvement of sheep welfare: Exploring the use of qualitative behavioural assessment (QBA) for the monitoring and assessment of sheep

Challenges faced by sheep in Australia in terms of disease, injury and management may compromise not only health and productivity but also welfare. These challenges represent a growing concern for both producers and the public. Hence there is an obvious need for the development of measures to allow producers, who may have limited access to stock or are constrained by time and/or resource availability, to monitor their sheep. There is a clear benefit to producers being able to readily identify animals whose welfare might be compromised and thus are in need of further care. However, the assessment of animal welfare is challenging under commercial conditions and to date, few measures are available to help producers recognise animals in compromised welfare states. Qualitative behavioural assessment (QBA) is an approach that captures the expressive behaviour of an animal, through the integration and summary of details of behavioural events, posture, and movement. In this way, QBA represents a valuable tool that offers insight into the physical and physiological aspects of animal welfare, and when used in conjunction with other key measures helps to provide a more complete and comprehensive picture of an animal’s welfare state. Furthermore, QBA should be used together with other welfare measures, where it has been proposed to guide the interpretation of welfare data. As a welfare tool, QBA has been applied to assess the behavioural expression in numerous livestock species including pigs and cattle, however, this methodology is less well studied in sheep and more work is needed to validate QBA for practical application. The aim of the research described in this thesis was to investigate whether the QBA methodology could be applied to assess the welfare of sheep subject to various welfare issues relevant to the Australian sheep industry. To this end, over four experimental chapters, QBA was applied to video footage captured of sheep in various states of compromised welfare, including those suffering from common injury and diseases; lameness, inappetence, flystrike, and gastro-intestinal parasitism, and those experiencing pain caused by routine husbandry procedures (ear tagging, castration, mulesing, and tail docking). Moreover, in two experimental chapters (Chapters 4 & 6), video footage was captured of sheep in positive welfare states (reduced gastro-intestinal parasite burden, and habituation to human presence). This video footage was also analysed quantitatively and other welfare measures including those of health/disease status, physical condition and locomotive activity were collected for validation purposes in each study. Over four experimental chapters, it was demonstrated that observers, blind to experimental procedures and treatments, can reach a significant consensus in their interpretation and assessment of the behavioural expression of sheep, and that these assessments can relate meaningfully to the welfare state of the animal. In Chapter 3, observers were able to distinguish between flystruck and non-flystruck sheep using the QBA methodology, and the behavioural expression scores given to each sheep corresponded to the severity of strike and the condition of the wool. In Chapter 4, observers identified differences in the behavioural expression of sheep that related to the severity of gastro-intestinal parasitism (subclinical v. clinical). Moreover, it was discovered that the treatment of sheep to lessen gastro-intestinal parasite burden altered the behavioural expression of parasitised sheep. A significant consensus was also reached amongst observers in the assessment of lambs subject to routine husbandry procedures (ear tagging, castration, mulesing, and tail docking) in Chapter 5. Observers were able to distinguish lambs that were subject to these painful husbandry procedures and were administered either a placebo or analgesics (Tri-Solfen® and meloxicam), from the control lambs which were only restrained. Hence suggesting that the pain caused by these husbandry procedures alters the behavioural patterns and demeanour of lambs in a way that is identifiable to observers using the QBA methodology. Lastly, when observers viewed video footage of sheep traversing a walk-over-weigh (WoW) apparatus in Chapter 6, they were able to distinguish sheep that were either lame or habituated to the test apparatus and human presence, from the control animals. However, in this Chapter, observers were not able to distinguish between all treatment groups evaluated based on their behavioural expression, specifically differences in the demeanour of inappetent and control sheep was not evident, nor were observers able to distinguish between lame and habituated sheep. In summary, the research presented in this thesis indicates that assessments of behavioural expression can be used under most of those conditions investigated to distinguish sheep in poor welfare states due to injury or disease, from those that are healthy. Furthermore, it appears that observers can reliably identify differences in behavioural expression related to positive welfare states. This work has detailed the behavioural expression of sheep as perceived by observers and has led to a greater understanding of the behavioural expression of sheep in different welfare states. It appears that through the assessment of demeanour or body language, QBA offers both relevant and valid assessments which may help producers gain an insight into the welfare state of their sheep. It is suggested that when used in conjunction with other select behavioural measures, QBA may represent a valuable tool for producers to improve the welfare of sheep in their care

Predicting livestock behaviour using accelerometers: A systematic review of processing techniques for ruminant behaviour prediction from raw accelerometer data

peer-reviewedPrecision Technologies are emerging in the context of livestock farming to improve management practices and the health and welfare of livestock through monitoring individual animal behaviour. Continuously collecting information about livestock behaviour is a promising way to address several of these target areas. Wearable accelerometer sensors are currently the most promising system to capture livestock behaviour. Accelerometer data should be analysed properly to obtain reliable information on livestock behaviour. Many studies are emerging on this subject, but none to date has highlighted which techniques to recommend or avoid. In this paper, we systematically review the literature on the prediction of livestock behaviour from raw accelerometer data, with a specific focus on livestock ruminants. Our review is based on 66 surveyed articles, providing reliable evidence of a 3-step methodology common to all studies, namely (1) Data Collection, (2) Data Pre-Processing and (3) Model Development, with different techniques used at each of the 3 steps. The aim of this review is thus to (i) summarise the predictive performance of models and point out the main limitations of the 3-step methodology, (ii) make recommendations on a methodological blueprint for future studies and (iii) propose lines to explore in order to address the limitations outlined. This review shows that the 3-step methodology ensures that several major ruminant behaviours can be reliably predicted, such as grazing/eating, ruminating, moving, lying or standing. However, the areas faces two main limitations: (i) Most models are less accurate on rarely observed or transitional behaviours, behaviours may be important for assessing health, welfare and environmental issues and (ii) many models exhibit poor generalisation, that can compromise their commercial use. To overcome these limitations we recommend maximising variability in the data collected, selecting pre-processing methods that are appropriate to target behaviours being studied, and using classifiers that avoid over-fitting to improve generalisability. This review presents the current situation involving the use of sensors as valuable tools in the field of behaviour recording and contributes to the improvement of existing tools for automatically monitoring ruminant behaviour in order to address some of the issues faced by livestock farming

Sensor-based approaches to monitoring the behaviour, health, and welfare of dairy calves

Currently, the detection of ill health in UK farmed calves is based on intermittent visual observation which is subjective and poorly accurate. Sensor-based monitoring may offer an improved alternative. For example, sensors could be used to monitor behaviour and detect signs of ill health in calves. However, substantial individual variation exists for many behaviours, the extent of which is poorly understood in calves. Here, within- and between- individual variation in calf feeding behaviours are quantified using data from computerised milk feeders. Results show that substantial, temporally stable individual differences exist. In addition, the average behavioural expression of two distinct feeding behaviours were positively and significantly correlated and the between-individual differences observed were shown to be consistent over time and context, and to be associated with weight gain. This improves our understanding of normal variation in calf feeding behaviour, which could be helpful in detecting potential behavioural changes indicative of ill health. Machine learning models were trained and tested using feeding data from computerised milk feeders to detect ill health. In a separate study, a similar methodology was used to detect ill health using reticulo-rumen temperature boluses. Results indicate low and moderate predictive performance, respectively. Study limitations and areas for future research are discussed. Finally, the development of novel technologies to enable a more holistic approach to behavioural monitoring in calves is explored. Results show that signals from a single collar-based sensor can be used to accurately detect nine different behaviours as well as to quantify rarely occurring behaviours, such as locomotor play. Quantifying play behaviour could provide a useful indicator of positive welfare in calves. It is also shown that these behaviours can be detected using computer vision, but that further work is needed to enable generalisation to new camera angles and scenes. Overall, this thesis highlights the potential of sensor-based technologies to improve our understanding of behavioural variation in calves, as well as to monitor a greatly more diverse range of behaviours than previously attempted. It is hoped that this work will contribute towards the improvement of health and welfare in calves