Marie J. Haskell

Catch-neuter-vaccinate-release (CNVR) programmes for free-roaming dogs (FRD) are humane and effective in controlling dog populations in developing countries. However, each component, from capture to release, can impact an individual animal’s welfare. This study aimed to develop a standardised welfare assessment scoring system for evaluating the welfare of dogs undergoing routine CNVR procedures at a veterinary training facility in Goa, India and to assess the impact of a targeted staff training intervention by comparing welfare assessment scores before and after its implementation. A score-based protocol was designed, incorporating 22 animal-, resource-, and management-based parameters covering six key steps of the CNVR procedure: catching/transport; cage/holding area; pre-operative period; surgery; post-operative period; and release. Eighty-two dogs were assessed initially. Areas for improvement were identified and informed the design of a targeted staff training intervention involving theory-based lectures and interactive sessions. Knowledge was assessed before and after receiving the intervention, with total scores on the assessment improving. The welfare assessment was repeated on another 81 dogs. Total welfare scores for individual dogs improved significantly after the staff intervention compared with before. This study demonstrates that a welfare assessment tool can be used to assess the welfare of individual dogs in a busy CNVR programme; furthermore, it can inform the compilation and delivery of a targeted staff training intervention and demonstrate improvements in dog welfare after such an intervention.

Background: Methods for assessing dairy cattle welfare, such as the gold standard Qualitative Behaviour Assessment (QBA), require training and are time consuming in a sector facing labour constraints and staffing shortages. This research explores whether automated measurements from existing animal-mounted sensors can be used as a basis for objective welfare assessment in dairy cattle, supporting consumer, processor and farmer needs by demonstrating positive welfare on farm. Data (mean standing and lying duration, mean standing and lying bout frequency, mean standing and lying bout duration, mean maximum standing and lying bout duration, mean lying and standing bout frequency, mean step count) were acquired from commercially available ankle-mounted accelerometers intended for oestrus detection and compared to 20 QBA metrics from 107 animals at pasture and during housing on four dairy farms. Results: Our analysis showed that sensor data was significantly correlated with QBA data, suggesting that sensor data can be used to accurately characterise animal behaviour. Although sensor data was influenced by location (housed and pasture), data obtained from ankle-mounted pedometers showed high accuracy (61%) in predicting an animal’s mood as positive or negative. In addition, step count and standing time were strongly correlated with positive behaviour QBA scores, suggesting that increased step count with decreased standing time may be an indicator of positive welfare. QBA results showed that animals at pasture displayed more positive behaviours, with 70.2% of pasture cattle exhibiting QBA scores associated with positive behaviours/mood, in comparison to 34.0% of housed cattle. Results also showed that skewedness of sensor data from cattle at pasture was an accurate indicator of behavioural synchrony (i.e. animals exhibiting the same behaviour at the same time), a known measure of positive welfare, although more granular time data is needed to investigate further. Conclusions: This study demonstrates the potential of using automated animal-mounted sensor data to assess positive welfare in dairy cattle, with sensor-derived behavioural features enabling classification of mood states (positive or negative) in 61% of observations.

The increased uptake of sensor technologies and precision farming tools for the dairy cattle sector is enabling real-time monitoring of animal health, welfare, and productivity. These digital advancements provide high-frequency, objective, and large-scale phenotypic data for breeding purposes. This review explores the potential of sensor-derived data to improve genetic and genomic evaluations in dairy cattle and outlines key challenges, opportunities, and approaches associated with their implementation. While these data streams have great potential for genetic evaluations, their integration into national and international breeding programs remains limited due to fragmentation across sensor brands, lack of standardization, and challenges related to data accessibility, data access and portability rights, business interests, and governance. A crucial aspect of leveraging digital technologies in dairy cattle breeding is data harmonization and integration. We highlight the importance of establishing standardized data collection and data sharing protocols, implementing robust quality control and data cleaning methodologies, as well as defining novel sensor-based traits and estimating their genetic background. In this context, we compiled heritability estimates for novel traits derived from data recorded by sensors and other technologies in dairy cattle populations. The development of phenomics in breeding programs, which involves integrating multisource data-including sensor-based, genomic, and management information-will be key to accelerating genetic progress, especially for traits related to animal welfare, health, resilience, and efficiency. This review presents a roadmap for the effective use of sensor-derived data in genetic evaluations, advocating for centralized data infrastructures, transparent data-sharing agreements, and the role of different stakeholders from academia and industry, including organizations such as the International Committee on Animal Recording (ICAR) in establishing global standards and guidelines. By addressing these challenges, dairy breeding programs can fully harness precision dairy farming technologies to enhance production and environmental efficiency, improve animal health and welfare, and drive sustainable genetic advancements in the dairy cattle sector.

Prolonged cow-calf contact (CCC) is of growing importance to the dairy sector due to increasing societal interest, implementation of CCC on farms, and research efforts. Incorporating CCC into dairy systems may be a polarizing change for academics and farmers. However, by considering the challenges with curiosity, including those mutual to CCC and non-CCC systems, there may be an opportunity to collectively improve the management of dairy animals. The aim of this review was to describe current issues and constraints in CCC, propose opportunities to advance knowledge of CCC, and inspire forward-thinking questions for dairy systems. There are known challenges for CCC implementation, such as research reproducibility (e.g., suitable controls, validity types) and on-farm application (e.g., farmer perspectives, policies, and corporate standards). To facilitate practical solutions for farmers wanting to adopt CCC we need research describing the effects of CCC systems on animal health and behavior. Already researchers have begun to explore cow and calf performance and health, methods for decreasing stress at weaning and separation (e.g., duration of contact, gradual weaning), foster cows, and opportunities for positive animal welfare in CCC systems (e.g., affiliative and play behavior). However, because dairying takes place in a complex system, changes may affect different facets of the system's sustainability. We suggest that the development of CCC systems should happen in dialog with stakeholders. Cow-calf contact is an uncommon practice in dairy systems and exists in different contexts; thus, there are many questions to address before advice can be given to interested dairy stakeholders. Perhaps, these CCC-related questions are an invitation to contemplate how we want dairy systems to look like in 30 years.

This study aimed to identify if sensor technology could be used to detect sickness-type signs (caused by a live vaccine) in laying hens compared to physiological and clinical sign scoring and behaviour observation. The experiment comprised 5 replicate batches (4 hens and 12 days per batch) using previously non-vaccinated hens (n = 20). Hens were moved on day 1 to a large experimental room with various designated zones (e.g., litter, perches, nest box), where they wore two sensors (FitBark, TrackLab). Saline was applied using ocular and nasal drops on day 3 as a control. A live vaccine (Infectious Laryngotracheitis, ILT, vaccine), applied using the same method on day 6, was used to induce mild respiratory and other responses. Physiological and clinical signs, and behaviour from videos were also recorded by a single observer. There were significant changes in body weight (p < 0.001), feed intake (p = 0.031), cloacal temperature (p < 0.001) and three out of five clinical signs (ocular discharge (p < 0.001), conjunctivitis (p < 0.001) and depression (p = 0.009)) over days. A significant decrease (p < 0.001) in activity level (FitBark) and distance travelled (both sensors) were identified over the study days, and activity and distance travelled were highly significantly associated (p < 0.001) with total clinical scores, with hens showing reduced activity and distance travelled with worsening total clinical scores. With behaviour observations from videos, the proportions of sitting, foraging and feeding behaviours (p = 0.044, 0.036 and 0.004, respectively), the proportion of total visit duration to the litter zone (p < 0.001) and perch (p = 0.037) with TrackLab and the proportions of visit counts of hens in the litter zone (p = 0.012) from video scanning changed significantly with days. This study suggests that the vaccine challenge caused associated changes in clinical/physiological signs and activity/distance travelled data from the sensors. Sensors may have a role in detecting changes in activity and movement in individual hens indicative of health or welfare problems.