Single-confinement housing can pose welfare risks to domestic horses. This study investigated horses’ stress responses when confined to single stalls in different stable designs in a tropical savanna region to address a literature gap. In total, 23 horses were assigned to a stable with a central corridor and solid external walls (A) (N = 8), a stable with one side corridor and solid external walls (B) (N = 6) or a stable with a central corridor and no solid external walls (C) (N = 9). Air velocity, relative humidity, air temperature and noxious gases were measured inside the stables, and the heart rate and HRV of the horses were also determined. The relative humidity was lower in stable C than in stable A (p < 0.05), while the air temperature was higher in stable C than in stable B (p < 0.05) during the day. The airflow and ammonia levels were higher in stable C than in stables B and A (p < 0.01–0.0001). Overall, horses’ HRV in stable A was lower than in those in stables B and C (p < 0.05-0.01). Horses in stable A tended to experience more stress than those in other stables.

Although domestic cats are susceptible to infection with SARS-CoV-2, the role of the virus in causing feline disease is less well defined. We conducted a large-scale study to identify SARS-CoV-2 infections in UK pet cats, using active and passive surveillance. Remnant feline respiratory swab samples, submitted for other pathogen testing between May 2021 and February 2023, were screened using RT-qPCR. In addition, we appealed to veterinarians for swab samples from cats suspected of having clinical SARS-CoV-2 infections. Bespoke testing for SARS-CoV-2 neutralising antibodies was also performed, on request, in suspected cases. One RT-qPCR-positive cat was identified from the active surveillance (1/549, 0.18 %), during the Delta wave (1/175, 0.57 %). Passive surveillance detected one cat infected with the Alpha variant, and two of ten cats tested RT-qPCR-positive during the Delta wave. No cats tested RT-qPCR-positive after the emergence of Omicron BA.1 and its descendants although 374 were tested by active and eleven by passive surveillance. We describe four cases of SARS-CoV-2 infection in pet cats, identified by RT-qPCR and/or serology, that presented with a range of clinical signs, as well as their SARS-CoV-2 genome sequences. Our results demonstrate that, although uncommon, clinical infections in cats can and do occur.

Our wellbeing is greatly influenced by our childhood and adolescence, and the relationships that we form during those phases of our development. The human-dog bond started thousands of years ago. The higher prevalence of dog ownership around the world, especially in households including children along with the growing number of people studying dogs most likely explain the growing literature focusing on child-dog interactions. We review the potential effects of child-dog interactions on the physical, mental, and social wellbeing of both species. A scoping search of the SCOPUS database found several hundred documents meeting selection criteria. It allowed us to define the numerous ways in which children and dogs can interact, be it neutral (e.g., sharing a common area), positive (e.g., petting), or negative (e.g., biting). Then, we found evidence for an association between interacting with dogs during childhood and an array of health and mental benefits like stress relief and the development of empathy. Walking a dog and playing with one are perfect physical activity opportunities. Additionally, interacting with a dog can help lower stress and may have a role in the development of empathy. Nonetheless, a number of detrimental outcomes have also been identified in both humans and dogs. Children are the most at-risk population regarding dog bites and dog-borne zoonoses, which may lead to a subsequent fear of dogs or even death. Moreover, pet bereavement is generally inevitable when living with a canine companion and should not be trivialized. In terms of dogs, children sometimes take part in caretaking behaviors toward them which include going on walks. They are opportunities for dogs to relieve themselves outside, but also to exercise and socialize. In contrast, a lack of physical activity can lead to the onset of obesity. Dogs may present greater levels of stress when in the presence of children. Finally, the welfare of assistance, therapy, and free-roaming dogs remains underexplored. Overall, the study of the effects, positive as well as negative, on both species still requires further development. We call for more longitudinal studies and hope for cross-cultural research in the future in order to better understand the impact child-dog interactions might have.

The influence of climate conditions and stable design on horses housed in single stalls can have significant effects, especially in regions with tropical savannah climates. A recent study observed variations in autonomic responses in horses housed in different stable designs during the summer, but there is limited information about their conditions during the monsoon. This study investigated the stable microenvironment and autonomic regulation of horses housed in different stable archi-tectures during the monsoon in a tropical savannah environment. Twenty-two horses were allo-cated to one of three stable architectures, each with varying stable microenvironments. The findings reveal that the heart rate variability (HRV) was lower in horses in a stable with a solid external wall and lower volume-to-horse ratio compared to those in a stable with a solid external wall but a higher volume-to-horse ratio or one without a solid external wall. Additionally, the study found that stable microenvironments and HRV modulation were correlated with stable architecture. Autonomic responses differed among horses in different stable architectures, indicating that stable microen-vironments and, to some extent, volume-to-horse ratio play a role in horses’ autonomic regulation. The findings have implications for improvements in housing and welfare in tropical savannah environments.

The metaverse, a virtual world comprising a collective virtual shared space where users interact with one another through avatars and computer-generated objects, aims to closely mimic our real world by integrating elements of Artificial Intelligence (AI), immersive reality, advanced connectivity, and Web3. As metaverse technologies gain momentum across multiple sectors, including animal farming, their potential for addressing complex challenges such as climate change and sustainability in precision food production systems becomes increasingly apparent. However, it is crucial to consider the ethical implications and the role of sensor data and livestock behavior analysis in developing metaverse technologies for modern animal farming, given the sensitive and controversial nature of animal welfare. Failure to address these ethical considerations and harness the power of sensor data and behavior analysis could lead to a lack of credibility and insensitivity towards adopting metaverse technologies in the animal farming sector. It is essential to ensure that the development of metaverse technologies does not prioritize technology over animal welfare, ethics, socio-economic implications, and the potential for data-driven insights. Addressing diversity and equity in the context of animal farming and the metaverse is crucial to avoid perpetuating existing inequalities during the implementation of metaverse technologies. This groundbreaking paper ventures into unexplored territory, shedding light on the untapped potential of the metaverse for modern animal farming. While research on this topic is still in its infancy, we embark on a journey of visionary speculation, presenting a compelling technology forecast that envisions the extraordinary possibilities awaiting us in the future. By delving into the metaverse's transformative capabilities, we provide a glimpse into a world where animal farming transcends its traditional limitations and embraces a new era of efficiency, sustainability, and ethical practices.

Numerous researchers and institutions have been developing in ovo sexing technologies to improve animal welfare by identifying male embryos in an early embryonic stage and disposing of them before pain perception. This review gives a complete overview of the technological approaches reported in papers and patents by performing a thorough search using Web of Science and Patstat databases for papers and patents, respectively. Based on a total of 45 papers and 100 patent families up until 2021 reported worldwide, 11 technology categories were defined: six non-optical and five optical techniques. Every category was described for its characteristics while assessing its potential for application. Next, the dynamics of the publications of in ovo sexing techniques in both paper and patent fields were described through growth curves, and the interest or actual status was visualized using the number of paper citations and the actual legal status of the patents. When comparing the reported technologies in papers to those in patents, scientific gaps were observed, as some of the patented technologies were not reported in the scientific literature, e.g., ion mobility and mass spectrometry approaches. Generally, more diverse approaches in all categories were found in patents, although they do require more scientific evidence through papers or industrial adoption to prove their robustness. Moreover, although there is a recent trend for non-invasive techniques, invasive methods like analyzing DNA through PCR or hormones through immunosensing are still being reported (and might continue to be) in papers and patents. It was also observed that none of the technologies complies with all the industry requirements. Thus, more research and harmony between consumers, industry, and governments is necessary.

Perinatal mortality is a major issue in laboratory mouse breeding. We compared a counting method using daily checks (DAILY_CHECK) with a method combining daily checks with detailed video analyses to detect cannibalisms (VIDEO_TRACK) for estimating the number of C57BL/6 pups born, died and weaned in 193 litters from trios with (TRIO-OVERLAP) or without (TRIO-NO_OVERLAP) the presence of another litter. Linear mixed models were used at litter level. To understand if cannibalism was associated with active killing (infanticide), we analysed VIDEO_TRACK recordings of 109 litters from TRIO-OVERLAP, TRIO-NO_OVERLAP or SOLO (single dams). We used Kaplan-Meier method and logistic regression at pup level. For DAILY_CHECK, the mean litter size was 35% smaller than for VIDEO_TRACK (P<0.0001) and the number of dead pups was twice lower (P<0.0001). The risk of pup loss was higher for TRIO-OVERLAP than TRIO-NO_OVERLAP (P<0.0001). A high number of pup losses occurred between birth and the first cage checking. Analyses of VIDEO_TRACK data indicated that pups were clearly dead at the start of most of the cannibalism events and infanticide was rare. As most pups die and disappear before the first cage check, many breeding facilities are likely to be unaware of their real rates of mouse pup mortality.

The liver health of Gilthead sea bream and European sea bass fed fishmeal-free diets including various proportions of plant proteins, insect and poultry by-product meals, was investigated through biochemical and histological analyses using a new Liver Index (LI) formula. Four isoproteic (45% DM) and isolipidic (20% DM) diets were compared: a control, plant-based diet (CV) and other three test ones where 40% of a plant protein-rich ingredient mixture was replaced by Hermetia illucens (H40) or poultry by-product (P40) meals alone or in combination (H10P30). The trials lasted 12 and 18 weeks for sea bream and sea bass, respectively. The results so far ob-tained highlighted species-specific differences in the physiological response to dietary changes. In sea bream, the biochemical and histological responses suggest favorable physiological status and liver health, with higher serum cholesterol (CHO) and triglyceride (TAG) levels and moderate hepatocyte lipid accumulation with the H10P30 diet compared to CV (p<0.05). In sea bass, all diets resulted in elevated serum TAG levels and liver lipid accumulation particularly in fish fed the P40 one (p<0.05) which resulted in the highest LI, coupled with higher frequency of severe lipid accumulation, hypertrophy, cord loss, peripheral nuclei displacement and pyknosis. In conclusion sea bream well adapted to the test diets, while sea bass exhibited altered hepatic lipid metabolism leading to incipient liver steatosis, likely due to the high lipid content of the diets including the insect and poultry meals. The LI developed in this study proved to be a reliable tool for assessing the effects of dietary changes on liver health of sea bream and sea bass, consistent with biochemical and histological findings.

In this paper, we critically examine the burgeoning role of advanced computational methodologies in deciphering the complex tapestry of farm animal behaviors and emotions. Leveraging digital imaging and artificial intelligence, we unearth nuanced behavioral patterns and micro-expressions, offering predictive insights into animal emotional states. Sound vocalization analysis, often overlooked, emerges as a pivotal tool, decoding intricate communicative nuances and emotional undertones. Cognitive tests, including mirror and bias assessments, challenge long-standing perceptions, revealing surprising depths of animal self-awareness and cognitive sophistication. However, the paper also underscores the imperative of integrating these tools with a profound understanding of animal psyche, ensuring technology serves as an enhancer, not a replacement, of traditional observational methods. This research not only highlights the transformative potential of cognitive computing in animal welfare but also calls for a judicious application, ensuring technology augments, not undermines, the intrinsic value of human-animal interactions and understanding.

In the following article, we analyzed a variable present in the ISTAT-BES dataset, namely "People You Can Count On"-PYCC for the Italian regions. Following an analysis of the time series aimed at highlighting the trends of the regions, we created a clustering with a k-Means algorithm optimized with the Silhouette coefficient. The data shows the presence of two clusters. We then present an econometric model aimed at estimating the value of PYCC based on a set of socio-economic variables. The results are also discussed in light of the economic policy implications.

This critical review illuminates the transformative potential of Artificial Intelligence (AI) and sensor technologies in the dairy livestock export industry, an area facing mounting pressure for heightened efficiency and sustainability. We rigorously scrutinize the uptake of these novel technologies in identifying 'shy feeders,' automating weight monitoring of individual livestock, and refining cattle enumeration procedures. The investigation unravels their capacity to bolster animal welfare standards, minimize supply chain discrepancies, and amplify operational productivity. Moreover, the research delves into how these innovations may enhance market access and competitiveness in a swiftly shifting global dairy landscape. We further highlight the challenges encountered and future trajectories, providing a strategic framework for technology integration within the livestock export sector. Ultimately, this review underlines the importance of adopting AI and sensor technologies, indicating a shift towards precision digital livestock farming that amalgamates efficiency, animal welfare, and profitability.

The assessment of welfare and disease progression in animal models is critical. Most tools rely on evaluating individual subjects, whereas social behaviors, also sensitive to acute illness, chronic diseases, or mental health, are scarcely monitored because of their complexity, are invasive, and time-consuming. We propose the evaluation of social nesting, a species-typical behavior naturally occurring in standard housing conditions, for such behavioral monitoring. We provide an example of its use to evaluate social deficits and the long-term effects of neonatal sensorial stimulation in male and female adult 3xTg-AD mice for Alzheimer's disease compared to sex- and age-matched NTg counterparts with normal aging. Social nesting was sensitive to genotype (worse in 3xTg-AD mice), sex (worse in males), profile, and treatment (distinct temporal patterns, time to observe the maximum score and incidence of the perfect nest). Since social nesting can be easily included in housing routines, this neuroethological approach can be useful for animal's welfare, monitoring the disease's progress, and evaluating potential risk factors and effects of preventive/therapeutical strategies. Finally, the non-invasive, painless, simple, short time and low-cost features of this home-cage monitoring are advantages that make social nesting feasible to be successfully implemented in most animal department settings.

This paper pioneers a novel exploration of environmental impacts in livestock farming, with a focus on pig farming's intersection with climate change and sustainability. It emphasizes the transformative potential of data-driven Artificial Intelligence (AI) methodologies, specifically the Internet of Things (IoT) and multimodal data analysis, in promoting equitable and sustainable food systems. The study observes five pigs, aged 86 to 108 days, using a tripartite sensor that records heart rate, respiration rate, and accelerometer data. The unique experimental design alternates between periods of isolation during feeding and subsequent pairing, enabling the investigation of stress-induced changes. Key inquiries include discerning patterns in heart rate data during isolation versus paired settings, fluctuations in respiration rates, and behavioral shifts induced by isolation or pairing. The study also explores potential detection of gait abnormalities, correlations between pigs' age and their gait or activity patterns, and the evolution of pigs' walking abilities with age. The paper scrutinizes accelerometer data to detect activity changes when pigs are paired, potentially indicating increased stress or aggression. It also examines the adaptation of pigs to alternating isolation and pairing over time, and how their heart rate, respiration rate, and activity data reflect this process. The study considers other significant variables, such as time of day and isolation duration, affecting the pigs' physiological parameters. Sensor data is further utilized to identify behavioral patterns during periods of feeding, isolation, or pairing. In conclusion, this study harnesses IoT and multimodal data analysis in a groundbreaking approach to pig welfare research. It underscores the compelling potential of technology to inform about overall pig welfare, particularly stress levels and gait quality, and the power of data-driven insights in fostering equitable, healthy, and environmentally conscious livestock production systems.

The integration of Artificial Intelligence (AI) in dairy farm management through biometric facial recognition for cows is a significant stride in livestock management. This review critically evaluates the evolution, applications, and challenges of AI-driven biometric facial recognition in dairy farming. It emphasizes the role of this technology in enhancing individual monitoring of dairy cows, providing accurate health, behavior, and productivity tracking. Originally derived from human facial recognition systems, this approach utilizes distinctive bovine facial features for essential, non-invasive, real-time monitoring in large-scale operations. The progression of AI from elementary pattern recognition to advanced Convolutional Neural Networks (CNNs) and deep learning models marks a shift toward data-driven farming. This study addresses key challenges such as environmental variability, data collection hurdles, ethical concerns, and technological limitations. It also contrasts various AI models, spotlighting their unique strengths and practical utility in dairy farming scenarios. Despite these challenges, facial recognition technology holds promise for improving farm efficiency, animal welfare, and sustainable practices, highlighting the need for continuous research and development. The review concludes by advocating for future research focused on environmental adaptability, ethical AI application, cross-breed compatibility, and integration with other farming technologies. Ultimately, it underscores AI's transformative potential in modernizing dairy farming towards a more data-oriented, responsible agricultural future.

This paper proposes a paradigm shift in AI governance by integrating Indigenous knowledge systems to foster inclusivity and cultural sensitivity. Traditional AI governance frameworks are primarily Western-centric, often overlooking the community-oriented values and ethical considerations essential to Indigenous cultures. This study highlights principles such as Kaitiakitanga from Māori culture, which emphasizes guardianship and environmental stewardship, and Hózhó from Navajo philosophy, which stresses harmony and balance. By engaging Indigenous leaders and knowledge holders throughout the AI lifecycle, this approach ensures technology aligns with community values, addressing critical issues like data sovereignty, ethical technology use, and cultural sensitivity. This paper underscores the necessity of relational accountability in AI governance, which includes prioritizing the health of ecosystems alongside community welfare. This model advocates for an AI framework that not only rectifies historical marginalization but also empowers Indigenous communities to shape the technologies affecting their lives. Furthermore, integrating Indigenous perspectives strengthens the ethical foundation of AI systems, advancing sustainability, social justice, and cultural responsiveness in technology applications. The proposed framework illustrates how Indigenous ethical concepts can transform AI governance by aligning technological progress with values of ecological stewardship and community welfare, creating an AI landscape that respects and reflects diverse cultural contexts. Ultimately, this research demonstrates that Indigenous knowledge systems can significantly enhance AI’s effectiveness and equity, paving the way for more sustainable and culturally attuned technological solutions.