Animal welfare with Chinese characteristics: Chinese poultry producers’ perceptions of, and attitudes towards, animal welfare

Copyright: \ua9 2024 Yang et al.China’s poultry industry faces challenges in adopting and sustaining cage-free systems for poultry production. Effective interventions are crucial to support producers transitioning from cages to alternative systems or maintaining cage-free systems to improve animal welfare. However, little is known about how Chinese poultry producers perceive animal welfare in relation to cage-free systems and the importance of animal welfare in poultry production. Through a qualitative interview study with 30 Chinese farm owners, managers and senior managers from large-scale egg and broiler farms using cages and non-cage systems (collectively referred to as “producers”), this paper explores Chinese poultry producers’ attitudes and perceptions regarding animal welfare and welfare in different poultry housing systems. Template analysis was used to analyse the data from semi-structured interviews, which generated themes related to the participants’ awareness and understanding of the concept of animal welfare, the factors that impacted their choices of different housing systems, and the perceived priorities in poultry production. The responses revealed that the participating producers had a strong awareness and knowledge of animal welfare. However, the participants’ understanding of the term is heterogeneous: generally, egg producers emphasised natural behaviours, whereas broiler producers prioritised health and productivity. Nevertheless, profitability, leadership, and organisational policies primarily influenced housing system choices rather than animal welfare values. Economic motives drove egg producers towards cage-free systems, prompted by consumers’ and companies’ demand for cage-free eggs committed to transitioning away from cages by 2025. In conclusion, tailored interventions for different poultry sectors within China are necessary. While animal welfare values matter, economic incentives seem more promising for steering the shift towards and maintaining cage-free poultry production

Heart Rate and Heart Rate Variability Change with Sleep Stage in Dairy Cows

Changes to the amount and patterns of sleep stages could be a useful tool to assess the effects of stress or changes to the environment in animal welfare research. However, the gold standard method, polysomnography PSG, is difficult to use with large animals such as dairy cows. Heart rate (HR) and heart rate variability (HRV) can be used to predict sleep stages in humans and could be useful as an easier method to identify sleep stages in cows. We compared the mean HR and HRV and lying posture of dairy cows at pasture and when housed, with sleep stages identified through PSG. HR and HRV were higher when cows were moving their heads or when lying flat on their side. Overall, mean HR decreased with depth of sleep. There was more variability in time between successive heart beats during REM sleep, and more variability in time between heart beats when cows were awake and in REM sleep. These shifts in HR measures between sleep stages followed similar patterns despite differences in mean HR between the groups. Our results show that HR and HRV measures could be a promising alternative method to PSG for assessing sleep in dairy cows.fals

Lying posture does not accurately indicate sleep stage in dairy cows

\ua9 2021 Elsevier B.V. Quality sleep is important for physical health and welfare in animals. However, we know little about dairy cow sleep, and how much they need. Practical techniques are needed to monitor sleep in cows to determine how different management practices affect their sleep and their welfare. It is impractical to use ‘gold standard’ electrophysiological - polysomnography (PSG) to identify sleep in cows. Previous work suggests lying postures are useful to identify sleep stages in calves, but the reliability of lying behaviour to identify these sleep stages in adult cows is uncertain. We compared the lying postures of adult dairy cows (deep bedded on straw or in a pasture) with PSG, to determine if lying postures could be used to accurately identify rapid eye movement (REM) and the different stages of non-REM (NREM) sleep. Lying in the typical “sleep” posture with the head turned and resting on the flank identified approximately 70 % of REM sleep in outdoor managed cows but was less accurate in indoor housed cows that showed REM sleep in numerous postures. Lying with the head still and low did not identify stages of NREM sleep in either group. Using the tucked ‘sleep posture’ to estimate total sleep would be an over estimation of REM sleep, but also an underestimation of total sleep as this posture would omit most NREM sleep. Lying postures are not useful indicators of sleep stages in dairy cows and additional research is required to identify efficacious alternative techniques

Machine learning prediction of sleep stages in dairy cows from heart rate and muscle activity measures.

Sleep is important for cow health and shows promise as a tool for assessing welfare, but methods to accurately distinguish between important sleep stages are difficult and impractical to use with cattle in typical farm environments. The objective of this study was to determine if data from more easily applied non-invasive devices assessing neck muscle activity and heart rate (HR) alone could be used to differentiate between sleep stages. We developed, trained, and compared two machine learning models using neural networks and random forest algorithms to predict sleep stages from 15 variables (features) of the muscle activity and HR data collected from 12 cows in two environments. Using k-fold cross validation we compared the success of the models to the gold standard, Polysomnography (PSG). Overall, both models learned from the data and were able to accurately predict sleep stages from HR and muscle activity alone with classification accuracy in the range of similar human models. Further research is required to validate the models with a larger sample size, but the proposed methodology appears to give an accurate representation of sleep stages in cattle and could consequentially enable future sleep research into conditions affecting cow sleep and welfare.fals

Heart rate and heart rate variability change with sleep stage in dairy cows

\ua9 2021 by the authors. Licensee MDPI, Basel, Switzerland.Changes to the amount and patterns of sleep stages could be a useful tool to assess the effects of stress or changes to the environment in animal welfare research. However, the gold standard method, polysomnography PSG, is difficult to use with large animals such as dairy cows. Heart rate (HR) and heart rate variability (HRV) can be used to predict sleep stages in humans and could be useful as an easier method to identify sleep stages in cows. We compared the mean HR and HRV and lying posture of dairy cows at pasture and when housed, with sleep stages identified through PSG. HR and HRV were higher when cows were moving their heads or when lying flat on their side. Overall, mean HR decreased with depth of sleep. There was more variability in time between successive heart beats during REM sleep, and more variability in time between heart beats when cows were awake and in REM sleep. These shifts in HR measures between sleep stages followed similar patterns despite differences in mean HR between the groups. Our results show that HR and HRV measures could be a promising alternative method to PSG for assessing sleep in dairy cows

What type of loafing areas do housed dairy cattle prefer?

\ua9 2021. Providing continuously-housed dairy cows with a loafing area may allow them space to express behaviours that are affected by the housing environment. The aim of this study was to investigate whether dairy cows had a preference for loafing area type and if loafing area type affected behaviour performed within it. Three groups of 12 and one group of 11 lactating cows (n = 47) were housed in a cubicle shed with two nearby loafing areas 1) a concrete-floored roofed area and 2) a grassed paddock fenced to the same size as the concrete area. After 3d baseline period without access, cows were trained to access the loafing areas over 2 days. A 5d preference test followed, where cows had free access to the cubicle shed and both loafing areas from 08:45 until 12:45 and 15:30–18:30. Behaviour was observed via live observations (scan sampling) in the mornings and afternoons and activity sensors (IceTags) continuously recorded lying bouts 24 h/d until the end of the experiment. Results of the live observations showed that the cows were in the paddock area for more of the scans than the concrete area (P < 0.01). Descriptive statistics showed cows behaving differently in the two areas, lying down more when in the paddock area and standing more when in the concrete area (lying behaviour: paddock area = 69%; concrete area = 0%). Active behaviours (loafing behaviours) such as social interactions were recorded in both loafing areas (active standing behaviour: paddock area = 8%; concrete area = 23%). The weather and ground conditions affected behaviour. In dry conditions, cows lay down in the paddock area. When the ground was saturated, the cows lay down in the cubicle shed. There was no statistical evidence of overall differences in behaviours (P = 0.35) recorded during the baseline and preference testing periods. However, based on sensor data, cows had longer lying bouts over 24 h on the days when they had access to the loafing areas compared to the days when they did not (P = 0.028). This suggests that cows prefer paddock loafing areas to concrete areas when lying opportunities are presented, but proportionately, more active standing or ‘loafing’ behaviours are performed in the concrete area

Machine learning prediction of sleep stages in dairy cows from heart rate and muscle activity measures

\ua9 2021, The Author(s). Sleep is important for cow health and shows promise as a tool for assessing welfare, but methods to accurately distinguish between important sleep stages are difficult and impractical to use with cattle in typical farm environments. The objective of this study was to determine if data from more easily applied non-invasive devices assessing neck muscle activity and heart rate (HR) alone could be used to differentiate between sleep stages. We developed, trained, and compared two machine learning models using neural networks and random forest algorithms to predict sleep stages from 15 variables (features) of the muscle activity and HR data collected from 12 cows in two environments. Using k-fold cross validation we compared the success of the models to the gold standard, Polysomnography (PSG). Overall, both models learned from the data and were able to accurately predict sleep stages from HR and muscle activity alone with classification accuracy in the range of similar human models. Further research is required to validate the models with a larger sample size, but the proposed methodology appears to give an accurate representation of sleep stages in cattle and could consequentially enable future sleep research into conditions affecting cow sleep and welfare

Can sleep and resting behaviours be used as indicators of welfare in shelter dogs (Canis lupusfamiliaris)?

Previous research on humans and animals suggests that the analysis of sleep patterns may reliably inform us about welfare status, but little research of this kind has been carried out for non-human animals in an applied context. This study explored the use of sleep and resting behaviour as indicators of welfare by describing the activity patterns of dogs (Canis lupus familiaris) housed in rescue shelters, and comparing their sleep patterns to other behavioural and cognitive measures of welfare. Sleep and activity patterns were observed over five non-consecutive days in a population of 15 dogs. Subsequently, the characteristics of sleep and resting behaviour were described and the impact of activity on patterns of sleep and resting behaviour analysed. Shelter dogs slept for 2.8% of the day, 14.3% less than previously reported and experienced less sleep fragmentation at night (32 sleep bouts). There were no statistically significant relationships between behaviours exhibited during the day and sleep behaviour. A higher proportion of daytime resting behaviour was significantly associated with a positive judgement bias, less repetitive behaviour and increased time spent coded as ‘relaxed’ across days by shelter staff. These results suggest that, in the context of a busy shelter environment, the ability to rest more during the day could be a sign of improved welfare. Considering the non-linear relationship between sleep and welfare in humans, the relationship between sleep and behavioural indicators of welfare, including judgement bias, in shelter dogs may be more complex than this study could detect

A new metric for quantifying the relative impact of risk factors on loss of working life illustrated in a population of working dogs

In a resource-limited world, organisations attempting to reduce the impact of health or behaviour issues need to choose carefully how to allocate resources for the highest overall impact. However, such choices may not always be obvious. Which has the biggest impact? A large change to a small number of individuals, or a small change to a large number of individuals? The challenge is identifying the issues that have the greatest impact on the population so potential interventions can be prioritised. We addressed this by developing a score to quantify the impact of health conditions and behaviour problems in a population of working guide dogs using data from Guide Dogs, UK. The cumulative incidence of different issues was combined with information about their impact, in terms of reduction in working life, to create a work score. The work score was created at population-level to illustrate issues with the greatest impact on the population and to understand contributions of breeds or crossbreeds to the workforce. An individual work deficit score was also created and means of this score used to illustrate the impact on working life within a subgroup of the population such as a breed, or crossbreed generation. The work deficit scores showed that those removed for behavioural issues had a greater impact on the overall workforce than those removed for health reasons. Additionally trends over time illustrated the positive influence of interventions Guide Dogs have made to improve their workforce. Information highlighted by these scores is pertinent to the effort of Guide Dogs to ensure partnerships are lasting. Recognising that the scores developed here could be transferable to a wide variety of contexts and species, most notably human work force decisions; we discuss possible uses and adaptations such as reduction in lifespan, quality of life and yield in production animals

A "Candidate-Interactome" Aggregate Analysis of Genome-Wide Association Data in Multiple Sclerosis

Though difficult, the study of gene-environment interactions in multifactorial diseases is crucial for interpreting the relevance of non-heritable factors and prevents from overlooking genetic associations with small but measurable effects. We propose a “candidate interactome” (i.e. a group of genes whose products are known to physically interact with environmental factors that may be relevant for disease pathogenesis) analysis of genome-wide association data in multiple sclerosis. We looked for statistical enrichment of associations among interactomes that, at the current state of knowledge, may be representative of gene-environment interactions of potential, uncertain or unlikely relevance for multiple sclerosis pathogenesis: Epstein-Barr virus, human immunodeficiency virus, hepatitis B virus, hepatitis C virus, cytomegalovirus, HHV8-Kaposi sarcoma, H1N1-influenza, JC virus, human innate immunity interactome for type I interferon, autoimmune regulator, vitamin D receptor, aryl hydrocarbon receptor and a panel of proteins targeted by 70 innate immune-modulating viral open reading frames from 30 viral species. Interactomes were either obtained from the literature or were manually curated. The P values of all single nucleotide polymorphism mapping to a given interactome were obtained from the last genome-wide association study of the International Multiple Sclerosis Genetics Consortium & the Wellcome Trust Case Control Consortium, 2. The interaction between genotype and Epstein Barr virus emerges as relevant for multiple sclerosis etiology. However, in line with recent data on the coexistence of common and unique strategies used by viruses to perturb the human molecular system, also other viruses have a similar potential, though probably less relevant in epidemiological terms