Methods to Improve Our Understanding of the Health and Welfare Status of Sheep (Ovis Aries) and the Influences of their Immediate Environment

Studies into the effective use of accelerometers in the automated assessment of sheep behaviour to improve welfare has increased exponentially with promising preliminary results. Previous research has focused primarily on explicit behaviour classification, for example, parturition and urination events, with a view to create a commercial tool that will provide health warnings for farmers. Yet the majority of trials have not been conducted in a farm environment. This study aims to provide essential primary research investigating environmental variables that may influence the behavioural patterns of a commercial flock. This vital information has been largely overlooked and crucial when considering tools that provide health warnings, due to the many factors that influence sheep behaviour such as weather, vegetation, soil type, land typography and breed (Hinch, 2017). The primary aim of this study was to assess the most appropriate model to predict the behaviours of commercial ewes. This was achieved by deploying accelerometers on a commercial flock and simultaneously collecting manual observations and video recordings of flock’s individual activity. The raw acceleration data was processed to create 6 variables. Behaviour classification was also evaluated using three ethograms, each with two mutually exclusive behavioural/postural states: 1. Head Position (head up/down), 2. Posture (standing/lying), 3. Activity (resting/grazing). Three Window setting (3, 5 and 7 seconds) and five machine learning algorithms 4 (Linear Discriminate Analysis (LDA), Classification and Regression Trees (CART), K Nearest Neighbour (KNN), Support Vector Machines (SVM) and Random Forest (RF)) were evaluated. Results indicated a RF with a 7 second window the optimal model across all ethograms. (Accuracy by ethogram; 1) 91.5%, 2) 91.0% and 3) 99.3%). The secondary aim of this study was to use a Linear Mixed Model (LMM) to investigate the influence of temperature and rainfall on grazing and resting behaviours. This was accomplished by using the initially developed model (RF) on data collected from an unsupervised commercial flock, recorded in a second trial. Results indicated that there was a significant positive relationship between grazing durations and rainfall (p.001), this finding conflicts with previous research observations and is yet unpublished. In addition, prior sheep behaviour research has suggested ‘foraging’ as the dominant activity, results from this trial indicate the dominant daily activity was resting (67% of daily activity). In conclusion this study highlights the difficultly of defining what ‘normal’ sheep behaviour is and that it is not viable to implement a ‘one-size fits all’ approach. Further research is required in the behavioural assessment for this particularly malleable species

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

Feature Extraction and Random Forest to Identify Sheep Behavior from Accelerometer Data

Sensor technologies play an essential part in the agricultural community and many other scientific and commercial communities. Accelerometer signals and Machine Learning techniques can be used to identify and observe behaviours of animals without the need for an exhaustive human observation which is labour intensive and time consuming. This study employed random forest algorithm to identify grazing, walking, scratching, and inactivity (standing, resting) of 8 Hebridean ewes located in Cheshire, Shotwick in the UK. We gathered accelerometer data from a sensor device which was fitted on the collar of the animals. The selection of the algorithm was based on previous research by which random forest achieved the best results among other benchmark techniques. Therefore, in this study, more focus was given to feature engineering to improve prediction performance. Seventeen features from time and frequency domain were calculated from the accelerometer measurements and the magnitude of the acceleration. Feature elimination was utilised in which highly correlated ones were removed, and only nine out of seventeen features were selected. The algorithm achieved an overall accuracy of 99.43% and a kappa value of 98.66%. The accuracy for grazing, walking, scratching, and inactive was 99.08%, 99.13%, 99.90%, and 99.85%, respectively. The overall results showed that there is a significant improvement over previous methods and studies for all mutually exclusive behaviours. Those results are promising, and the technique could be further tested for future real-time activity recognition

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

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

Precision livestock farming, automats and new technologies: possible applications in extensive dairy sheep farming

Precision livestock farming (PLF) technologies are becoming increasingly common in modern agriculture. They are frequently integrated with other new technologies in order to improve human–livestock interactions, productivity and economical sustainability of modern farms. New systems are constantly being developed for concentrated farming operations as well as for extensive and pasture-based farming systems. The development of technologies for grazing animals is of particular interest for the Mediterranean extensive sheep farming sector. Dairy sheep farming is a typical production system of the area linked to its historical and cultural traditions. The area provides roughly 40% of the world sheep milk, having 27% of the milk-producing ewes. Developed countries of the area (France, Italy, Greece and Spain – FIGS) have highly specialized production systems improved through animal selection, feeding techniques and intensification of production. However, extensive systems are still practiced alongside intensive ones due to their lower input costs and better resilience to market fluctuations. In the current article, we evaluate possible PLF systems and their suitability to be incorporated in extensive dairy sheep farming as practiced in the FIGS countries. Available products include: electronic identification systems (now mandatory in the EU) such as ear tags, ruminal boluses and sub-cutaneous radio-frequency identification; on-animal sensors such as accelerometers, global positioning system) and social activity loggers; and stationary management systems such as walk-over-weights, automatic drafter (AD), virtual fencing and milking parlour-related technologies. The systems were considered according to their suitability for the management and business model common in dairy sheep farming. However, adoption of new technologies does not take place immediately in small and medium scale extensive farmer. As sheep farmers usually belong to more conservative technology consumers, characterized by average age of 60 and a very transparent community, dynamics which does not favour financial risk taking involved with new technologies. Financial barriers linked to production volumes and resource management of extensive farming are also a barrier for innovation. However, future prospective could increase the importance of technology and promote its wider adoption. Trends such as global sheep milk economics, global warming, awareness to animal welfare, antibiotics resistance and European agricultural policies could influence the farming practices and stimulate wider adoption of PLF systems in the near future

Behavioral fingerprinting: Acceleration sensors for identifying changes in livestock health

During disease or toxin challenges, the behavioral activities of grazing animals alter in response to adverse situations, potentially providing an indicator of their welfare status. Behavioral changes such as feeding behavior, rumination and physical behavior as well as expressive behavior, can serve as indicators of animal health and welfare. Sometimes behavioral changes are subtle and occur gradually, often missed by infrequent visual monitoring until the condition becomes acute. There is growing popularity in the use of sensors for monitoring animal health. Acceleration sensors have been designed to attach to ears, jaws, noses, collars and legs to detect the behavioral changes of cattle and sheep. So far, some automated acceleration sensors with high accuracies have been found to have the capacity to remotely monitor the behavioral patterns of cattle and sheep. These acceleration sensors have the potential to identify behavioral patterns of farm animals for monitoring changes in behavior which can indicate a deterioration in health. Here, we review the current automated accelerometer systems and the evidence they can detect behavioral patterns of animals for the application of potential directions and future solutions for automatically monitoring and the early detection of health concerns in grazing animals