Recumbency as an Equine Welfare Indicator in Geriatric Horses and Horses with Chronic Orthopaedic Disease

Introducing the Mellorater-The Five Domains Model in a Welfare Monitoring App for Animal Guardians

When monitoring an animal's welfare, it helps to have comprehensive and day-to-day information about the animal's life. The goal is to ensure that animal guardians (carers, keepers, and owners) use such information to act in the animals' best interests. This article introduces the Mellorater, an animal welfare monitoring app based on the 2020 Five Domains Model. This framework provides a means of capturing comprehensive information about the world in which individual animals exist. The Mellorater asks animal guardians to rate their agreement with 18 statements covering any focal animal's nutrition, environment, health, and behavioural interactions using a five-point Likert scale. No specialist training is required other than following straightforward instructions on using the app, which are provided. The Mellorater is not proposed as a validated welfare auditing tool because it relies on reflective self-reporting and, thus, is vulnerable to the user's subjectivity. If users' subjectivity is stable over time, then the longitudinal data may be considered useful proxies for trends in quality of life. That said, it has the potential to be used by trained auditors if scientifically validated, species-specific indicators are applied. The Mellorater collects anonymous data and has been approved for a study to explore how the use of such scales may differ among guardians of different species and in different contexts. In this paper, we conduct the following: (1) summarise the app's purposes; (2) clarify its capabilities and limitations; and (3) invite animal welfare scholars, veterinarians, health and welfare professionals, and animal guardians to use it.

From facial expressions to algorithms: a narrative review of animal pain recognition technologies

Facial expressions are essential for communication and emotional expression across species. Despite the improvements brought by tools like the Horse Grimace Scale (HGS) in pain recognition in horses, their reliance on human identification of characteristic traits presents drawbacks such as subjectivity, training requirements, costs, and potential bias. Despite these challenges, the development of facial expression pain scales for animals has been making strides. To address these limitations, Automated Pain Recognition (APR) powered by Artificial Intelligence (AI) offers a promising advancement. Notably, computer vision and machine learning have revolutionized our approach to identifying and addressing pain in non-verbal patients, including animals, with profound implications for both veterinary medicine and animal welfare. By leveraging the capabilities of AI algorithms, we can construct sophisticated models capable of analyzing diverse data inputs, encompassing not only facial expressions but also body language, vocalizations, and physiological signals, to provide precise and objective evaluations of an animal's pain levels. While the advancement of APR holds great promise for improving animal welfare by enabling better pain management, it also brings forth the need to overcome data limitations, ensure ethical practices, and develop robust ground truth measures. This narrative review aimed to provide a comprehensive overview, tracing the journey from the initial application of facial expression recognition for the development of pain scales in animals to the recent application, evolution, and limitations of APR, thereby contributing to understanding this rapidly evolving field.

The behavior of horses stabled in a large group at essential resources (watering point and lying halls)

Since there is a practical need, this study focused on the behavior of 51 horses kept in one large group in order to make management suggestions regarding the animal:watering point ratio and the design of lying halls. Drinking events and interactions between pairs of horses were analyzed for 18 days as well as the occupancy time of the watering point. The time period, had significant effects on drinking events with visible interactions (P < 0.05). The highest least square means (LSM) and standard errors (SE) of drinking events with and without visible interaction were observed between 12 noon-2 p.m. (8.6 ± 1.6 and 13.6 ± 2.2 drinking events, respectively) which was also the time period of main occupancy of the watering point (41.9 ± 2.7 min/2h). About 57% of drinking events were classed as with visible interaction, of which over 90% were mild (i.e. laying back of ears, tightening of face). Horses' behavior (resting while standing/others) and lying positions (sternal/lateral) in different lying halls were analyzed over 36 days by generalized mixed models. The sternal position was preferred (12.4 ± 1.4 - 39.2 ± 3.0%) compared to the lateral one (1.4 ± 0.4% - 8.3 ± 1.6%). Older horses spent significantly less time in the sternal position (13.8 ± 1.6%) and rested more standing (45.4 ± 3.3%) compared to younger horses (26.4 ± 4.5% and 29.1 ± 4.9%, respectively) (P < 0.05). Overall, this study provides first insights into drinking and lying behavior of horses kept in a large group.