Quantification of the effect of in utero events on lifetime resilience in dairy cows.

Currently, the dairy industry is facing many challenges that could affect its sustainability, including climate change and public perception of the industry. As a result, interest is increasing in the concept of identifying resilient animals, those with a long productive lifespan, as well as good reproductive performance and milk yield. There is much evidence that events in utero, that is, the developmental origins of health and disease hypothesis, alter the life-course health of offspring and we hypothesized that these could alter resilience in calves, where resilience is identified using lifetime data. The aim of this study was to quantify lifetime resilience scores (LRS) using an existing scoring system, based on longevity with secondary corrections for age at first calving and calving interval, and to quantify the effects of in utero events on the LRS using 2 datasets. The first was a large dataset of cattle on 83 farms in Great Britain born from 2006 to 2015 and the second was a smaller, more granular dataset of cattle born between 2003 and 2015 in the Langhill research herd at Scotland's Rural College. Events during dam's pregnancy included health events (lameness, mastitis, use of an antibiotic or anti-inflammatory medication), the effect of heat stress as measured by temperature-humidity index, and perturbations in milk yield and quality (somatic cell count, percentage fat, percentage protein and fat:protein ratio). Daughters born to dams that experienced higher temperature-humidity indexes while they were in utero during the first and third trimesters of pregnancy had lower LRS. Daughter LRS were also lower where milk yields or median fat percentages in the first trimester were low, and when milk yields were high in the third trimester. Dam LRS was positively associated with LRS of their offspring; however, as parity of the dam increased, LRS of their calves decreased. Similarly, in the Langhill herd, dams of a higher parity produced calves with lower LRS. Additionally, dams that recorded a high maximum locomotion score in the third trimester of pregnancy were negatively associated with lower calf LRS in the Langhill herd. Our results suggest that events that occur during pregnancy have lifelong consequences for the calf's lifetime performance. However, experience of higher temperature-humidity indexes, higher dam LRS, and mothers in higher parities explained a relatively small proportion of variation in offspring LRS, which suggests that other factors play a substantial role in determining calf LRS. Although "big data" can contain a considerable amount of noise, similar findings between the 2 datasets indicate it is likely these findings are real.

Modelling livestock infectious disease control policy under differing social perspectives on vaccination behaviour.

The spread of infection amongst livestock depends not only on the traits of the pathogen and the livestock themselves, but also on the veterinary health behaviours of farmers and how this impacts their implementation of disease control measures. Controls that are costly may make it beneficial for individuals to rely on the protection offered by others, though that may be sub-optimal for the population. Failing to account for socio-behavioural properties may produce a substantial layer of bias in infectious disease models. We investigated the role of heterogeneity in vaccine response across a population of farmers on epidemic outbreaks amongst livestock, caused by pathogens with differential speed of spread over spatial landscapes of farms for two counties in England (Cumbria and Devon). Under different compositions of three vaccine behaviour groups (precautionary, reactionary, non-vaccination), we evaluated from population- and individual-level perspectives the optimum threshold distance to premises with notified infection that would trigger responsive vaccination by the reactionary vaccination group. We demonstrate a divergence between population and individual perspectives in the optimal scale of reactive voluntary vaccination response. In general, minimising the population-level perspective cost requires a broader reactive uptake of the intervention, whilst optimising the outcome for the average individual increased the likelihood of larger scale disease outbreaks. When the relative cost of vaccination was low and the majority of premises had undergone precautionary vaccination, then adopting a perspective that optimised the outcome for an individual gave a broader spatial extent of reactive response compared to a perspective wanting to optimise outcomes for everyone in the population. Under our assumed epidemiological context, the findings identify livestock disease intervention receptiveness and cost combinations where one would expect strong disagreement between the intervention stringency that is best from the perspective of a stakeholder responsible for supporting the livestock industry compared to a sole livestock owner. Were such discord anticipated and achieving a consensus view across perspectives desired, the findings may also inform those managing veterinary health policy the requisite reduction in intervention cost and/or the required extent of nurturing beneficial community attitudes towards interventions.

A multistate modeling approach to investigate long-term effects of claw horn disruption lesions and early lesion development in dairy cows.

Claw horn disruption lesions (CHDL) are a leading cause of lameness in dairy cattle, and the development, effect, and pathology of these lesions remains an open area of interest within dairy cattle health. Current literature typically attempts to measure the effect of risk factors on the development of CHDL over a relatively short time period. Further understanding of the interaction of CHDL and the long-term effect of early CHDL in a cow's life remains an important area of research which is so far mostly unexplored. In this study 57,974 cows from 1,332 herds were selected and their regular claw trimming records containing important claw health information were used to model the long-term effect of lesions in a cow's lifetime in a 6-state multistate model. A multistate model predicts the time before transition from any one state to another and the probability of transition to a future state. The 6 lesion states that were modeled were as follows: never had a lesion, first recorded lesion event, no recorded lesion after first lesion event, second or subsequent recorded lesion event, no recorded lesion after second or subsequent lesion event, and culled. The effect of various cow level covariates on the transition probabilities between various states was tested. For the first time, this study shows the importance and effect of the first lesion and other cow level factors on long-term claw health. Model results showed that the timing and severity of the first recorded lesion event significantly influenced the likelihood of a future lesion being present. Cows with CHDL present within the 180 d of first calving had a short-term increased risk and long-term decreased risk of a future lesion, compared with cows that present with CHDL later than 180 d of first lactation. Moreover, presence of a severe first lesion increased a cow's risk of a future lesion being present. The model was used to evaluate the relative difference between high-risk cows (age of first calving ≥793 d, breeding values in the lowest quartile) and low-risk cows (age of first calving ≤718 d, breeding values in the highest quartile). Our results indicated that these low-risk cows present with a lesion on an average 3 mo later than high-risk cows. Furthermore, results from the model evaluation of a simulated herd with cows with breeding values in the higher quartile indicated that cows present with a CHDL on an average 7.5 mo later compared with a herd where cows have breeding values distributed in a lower quartile.

Understanding public preferences for different dairy farming systems using a mixed-methods approach.

Global production of milk has doubled over the past 50 yr, yet dairy farming in high-income countries faces scrutiny over practices perceived to affect animal welfare. One such practice is housing dairy cows year-round without access to pasture, which is the norm across North America and increasing within Europe, despite evidence of significant public support for grazing. Diverging opinion between the farming community and the public about what animal welfare means could be a key factor; however, lack of insight into the understanding and motivations underpinning public preferences for grazing could also hamper resolution. On the basis that more information could increase engagement between parties, 60 members of the public across the United Kingdom were interviewed to understand their perspectives of 3 dairy farming scenarios incorporating different amounts of grazing or housing. Their responses were analyzed using a mixed-methods approach combining reflexive thematic analysis with linguistic analysis. The integrated results indicated participants had a dual vision of the cow, seeing her as both domestic and wild. A scenario with housing in winter and grazing in summer therefore suited her, providing both protection and naturalness, and was most associated with analytic thinking. Interviewees also confessed ignorance about the cow's needs, either deferring to others' judgment-including the cow herself-or using familiarity and anthropomorphism to assess the scenarios. This again resulted in most optimism, confidence, and positivity for housing in winter and grazing in summer, and most negativity for housing cows year-round. Grazing was aspirational, but keeping cows outside in winter was confusing and concerning. These findings offer opportunities for the dairy industry to adapt communication or systems to better meet societal views; for example, incorporating access to pasture or increasing cow choice.

Is it just about grazing? UK citizens have diverse preferences for how dairy cows should be managed.

Conflicting views between the dairy industry and the public about how dairy cows should be managed, together with an increase in the availability of alternatives to dairy foods, challenge future markets for milk producers. Members of the public value animal welfare as well as naturalness and grazing, but neither the relative importance of specific aspects of management nor the diversity of views underlying these preferences have been established. To better understand these issues, 2,054 UK citizens recruited through a research panel took part in an online survey. They were asked to rank 17 attributes relating to dairy cow management and milk production through the novel application of best-worst scaling, a discrete choice methodology that allows a trade-off between items. Hierarchical Bayesian analysis of the results revealed 3 attributes of equal top importance: (1) access to grazing, (2) cow health and welfare, and (3) cow comfort. Alongside this overarching ranking, underlying differences in preferences were identified in 6 approximately equally sized citizen groups within the sample, which were identified through latent class analysis. Each latent class expressed significantly different priorities from the other, and each had different indicative sociodemographic, attitudinal, experiential, and value-oriented characteristics, as established through a multinomial logistic model. If the diversity of preference between the citizen groups found in the sample is reflected within wider populations, there may be opportunities for the dairy industry to improve communication about positive practices, develop new dairy product markets, and consider changes to dairy farming systems to better meet different citizens' needs. Furthermore, the defining characteristics and priorities of each group raise the question of whether grazing in particular, but also other attributes presented within the study, is understood in different ways by different subgroups of citizens.

Machine Learning Algorithms to Classify and Quantify Multiple Behaviours in Dairy Calves Using a Sensor: Moving beyond Classification in Precision Livestock.

Previous research has shown that sensors monitoring lying behaviours and feeding can detect early signs of ill health in calves. There is evidence to suggest that monitoring change in a single behaviour might not be enough for disease prediction. In calves, multiple behaviours such as locomotor play, self-grooming, feeding and activity whilst lying are likely to be informative. However, these behaviours can occur rarely in the real world, which means simply counting behaviours based on the prediction of a classifier can lead to overestimation. Here, we equipped thirteen pre-weaned dairy calves with collar-mounted sensors and monitored their behaviour with video cameras. Behavioural observations were recorded and merged with sensor signals. Features were calculated for 1-10-s windows and an AdaBoost ensemble learning algorithm implemented to classify behaviours. Finally, we developed an adjusted count quantification algorithm to predict the prevalence of locomotor play behaviour on a test dataset with low true prevalence (0.27%). Our algorithm identified locomotor play (99.73% accuracy), self-grooming (98.18% accuracy), ruminating (94.47% accuracy), non-nutritive suckling (94.96% accuracy), nutritive suckling (96.44% accuracy), active lying (90.38% accuracy) and non-active lying (90.38% accuracy). Our results detail recommended sampling frequencies, feature selection and window size. The quantification estimates of locomotor play behaviour were highly correlated with the true prevalence (0.97; p < 0.001) with a total overestimation of 18.97%. This study is the first to implement machine learning approaches for multi-class behaviour identification as well as behaviour quantification in calves. This has potential to contribute towards new insights to evaluate the health and welfare in calves by use of wearable sensors.

Genetic diversity of Koala retrovirus env gene subtypes: insights into northern and southern koala populations.

Koala retrovirus (KoRV) is a recently endogenized retrovirus associated with neoplasia and immunosuppression in koala populations. The virus is known to display sequence variability and to be present at varying prevalence in different populations, with animals in southern Australia displaying lower prevalence and viral loads than northern animals. This study used a PCR and next-generation sequencing strategy to examine the diversity of the KoRV env gene in both proviral DNA and viral RNA forms in two distinct populations representative of the 'northern' and 'southern' koala genotypes. The current study demonstrated that the full range of KoRV subtypes is present across both populations, and in both healthy and sick animals. KoRV-A was the predominant proviral subtype in both populations, but there was marked diversity of DNA and RNA subtypes within individuals. Many of the northern animals displayed a higher RNA viral diversity than evident in their proviral DNA, indicating relatively higher replication efficiency of non-KoRV-A subtypes. The southern animals displayed a lower absolute copy number of KoRV than the northern animals as reported previously and a higher preponderance of KoRV-A in individual animals. These discrepancies in viral replication and diversity remain unexplained but may indicate relative protection of the southern population from KoRV replication due to either viral or host factors and may represent an important protective effect for the host in KoRV's ongoing entry into the koala genome.

Developing and evaluating threshold-based algorithms to detect drinking behavior in dairy cows using reticulorumen temperature.

In this study, we assessed for the first time the use of a reticuloruminal temperature bolus and a thresholding method to detect drinking events and investigated different factors that can affect drinking behavior. First, we validated the detection of drinking events using 16 cows that received a reticuloruminal bolus. For this, we collected continuous drinking behavior data for 4 d using video recordings and ambient and water temperature for the same 4 d. After all the data were synchronized, we performed 2 threshold algorithms: a general-fixed threshold and a cow-day specific threshold algorithm. In the general-fixed threshold, a positive test was considered if the temperature of any cow fell below a fixed threshold; in the cow-day specific threshold, a positive test was considered when the temperature of specific cows fell below the threshold value deviations around the mean temperature of the cow for that day. The former was evaluated using a threshold varying between 35.7 and 39.5°C, and the latter using the formula µ-n10σ, where µ = mean of the temperature of each cow for one day, n = 1, 2, …, 20, and σ = standard deviation of the temperature of each cow on that day. The performance of the validation of detection using each of the threshold types was computed using different metrics, including overall accuracy, precision, recall (also known as sensitivity), F-score, positive predictive value, negative predictive value, false discovery rate, false omission rate, and Cohen's kappa statistic. The findings of the first study showed that the cow-day specific threshold of n = 10 performed better (true positives = 466; false positives = 167; false negatives = 165; true negatives = 8,416) than using a general-fixed threshold of 38.1°C (true positives = 449; false positives = 181; false negatives = 182; true negatives = 8,402). With the information gained in this first study, we investigated the different factors associated with temperature drop characteristics per cow: number of drops, mean amplitude of the drop, and mean recovery time. For this, we used data from 54 cows collected for almost 1 yr to build a mixed-effect multilevel model that included days in milk, parity, average monthly milk production, and ambient temperature as explanatory variables. Cow characteristics and ambient temperature had significant effects on drinking events. Our results provide a platform for automated monitoring of drinking behavior, which has potential value in prediction of health and welfare in dairy cattle.

A Combined Offline and Online Algorithm for Real-Time and Long-Term Classification of Sheep Behaviour: Novel Approach for Precision Livestock Farming.

Real-time and long-term behavioural monitoring systems in precision livestock farming have huge potential to improve welfare and productivity for the better health of farm animals. However, some of the biggest challenges for long-term monitoring systems relate to "concept drift", which occurs when systems are presented with challenging new or changing conditions, and/or in scenarios where training data is not accurately reflective of live sensed data. This study presents a combined offline algorithm and online learning algorithm which deals with concept drift and is deemed by the authors as a useful mechanism for long-term in-the-field monitoring systems. The proposed algorithm classifies three relevant sheep behaviours using information from an embedded edge device that includes tri-axial accelerometer and tri-axial gyroscope sensors. The proposed approach is for the first time reported in precision livestock behavior monitoring and demonstrates improvement in classifying relevant behaviour in sheep, in real-time, under dynamically changing conditions.

Feature Selection and Comparison of Machine Learning Algorithms in Classification of Grazing and Rumination Behaviour in Sheep.

Grazing and ruminating are the most important behaviours for ruminants, as they spend most of their daily time budget performing these. Continuous surveillance of eating behaviour is an important means for monitoring ruminant health, productivity and welfare. However, surveillance performed by human operators is prone to human variance, time-consuming and costly, especially on animals kept at pasture or free-ranging. The use of sensors to automatically acquire data, and software to classify and identify behaviours, offers significant potential in addressing such issues. In this work, data collected from sheep by means of an accelerometer/gyroscope sensor attached to the ear and collar, sampled at 16 Hz, were used to develop classifiers for grazing and ruminating behaviour using various machine learning algorithms: random forest (RF), support vector machine (SVM), k nearest neighbour (kNN) and adaptive boosting (Adaboost). Multiple features extracted from the signals were ranked on their importance for classification. Several performance indicators were considered when comparing classifiers as a function of algorithm used, sensor localisation and number of used features. Random forest yielded the highest overall accuracies: 92% for collar and 91% for ear. Gyroscope-based features were shown to have the greatest relative importance for eating behaviours. The optimum number of feature characteristics to be incorporated into the model was 39, from both ear and collar data. The findings suggest that one can successfully classify eating behaviours in sheep with very high accuracy; this could be used to develop a device for automatic monitoring of feed intake in the sheep sector to monitor health and welfare.

Exploring expert opinion on the practicality and effectiveness of biosecurity measures on dairy farms in the United Kingdom using choice modeling.

Biosecurity, defined as a series of measures aiming to stop disease-causing agents entering or leaving an area where farm animals are present, is very important for the continuing economic viability of the United Kingdom dairy sector, and for animal welfare. This study gathered expert opinion from farmers, veterinarians, consultants, academics, and government and industry representatives on the practicality and effectiveness of different biosecurity measures on dairy farms. The study used best-worst scaling, a technique that allows for greater discrimination between choices and avoids the variability in interpretation associated with other methods, such as Likert scales and ranking methods. Keeping a closed herd was rated as the most effective measure overall, and maintaining regular contact with the veterinarian was the most practical measure. Measures relating to knowledge, planning, and veterinary involvement; buying-in practices; and quarantine and treatment scored highly for effectiveness overall. Measures relating to visitors, equipment, pest control, and hygiene scored much lower for effectiveness. Overall, measures relating to direct animal-to-animal contact scored much higher for effectiveness than measures relating to indirect disease transmission. Some of the most effective measures were also rated as the least practical, such as keeping a closed herd and avoiding nose-to-nose contact between contiguous animals, suggesting that real barriers exist for farmers when implementing biosecurity measures on dairy farms. We observed heterogeneity in expert opinion on biosecurity measures; for example, veterinarians rated the effectiveness of consulting the veterinarian on biosecurity significantly more highly than dairy farmers, suggesting a greater need for veterinarians to promote their services on-farm. Still, both groups rated it as a practical measure, suggesting that the farmer-veterinarian relationship holds some advantages for the promotion of biosecurity.

The tales of contradiction: A thematic analysis of British sheep farmers' perceptions of managing sheep scab in their flocks.

Sheep scab is endemic in Great Britain with an estimated national herd-level prevalence of 10.9% from a surveyed population of sheep farms. Previous studies have investigated how sheep farmers manage sheep scab on their farms in Great Britain, but there have not been any qualitative studies investigating sheep farmers perceptions on the roles different stakeholders have in the management of sheep scab. This qualitative study aims to explore how sheep farmers perceive their role and the different stakeholders' roles in the management of sheep scab, and how they would like sheep scab to be managed going forward. Semi-structured interviews were conducted with 43 sheep farmers from England, Scotland, and Wales. The data were analysed using reflexive thematic analysis, through the theoretical lens of Foucault's notion of 'biopower'. Two themes were generated: 'The feeling of powerlessness leads to a need of rules and regulations', with sub-themes: 'The need for governmental rules and regulations' and 'The need for rules and regulation at livestock markets', and 'An apparent lack of sheep scab surveillance', with sub-themes: 'The farmers perceive that the veterinarians have control over surveillance' and 'The farmers have control over surveillance on their farms'. In the first theme, the respondents suggested that more rules and regulations to control the management of sheep scab was required. This included reinstating of sheep scab as a notifiable disease in England and Wales, as well as more regulations at livestock markets to prevent the trading of infested sheep; both of which would subject the farmers to regulatory power mechanisms. The second theme centred around who has the control of surveillance on the farms. Most of the respondents perceived that the veterinarians had knowledge and expertise of the local area on sheep scab, which they were able to relay to the farmers. Thus, veterinarians exerted disciplinary power by creating 'docile' bodies. However, it also appeared that veterinarians were not regularly called onto farms. Although disciplinary power flows through the interactions between the farmer and veterinarian, the techniques currently used are not always having their desired effect. The study demonstrated that how sheep farmers want sheep scab to be managed is, at times, conflicting and contradictory, which highlights the complexity of sheep scab as a disease to manage.

Early detection of bovine respiratory disease in pre-weaned dairy calves using sensor based feeding, movement, and social behavioural data.

Previous research shows that feeding and activity behaviours in combination with machine learning algorithms has the potential to predict the onset of bovine respiratory disease (BRD). This study used 229 novel and previously researched feeding, movement, and social behavioural features with machine learning classification algorithms to predict BRD events in pre-weaned calves. Data for 172 group housed calves were collected using automatic milk feeding machines and ultrawideband location sensors. Health assessments were carried out twice weekly using a modified Wisconsin scoring system and calves were classified as sick if they had a Wisconsin score of five or above and/or a rectal temperature of 39.5 °C or higher. A gradient boosting machine classification algorithm produced moderate to high performance: accuracy (0.773), precision (0.776), sensitivity (0.625), specificity (0.872), and F1-score (0.689). The most important 30 features were 40% feeding, 50% movement, and 10% social behavioural features. Movement behaviours, specifically the distance walked per day, were most important for model prediction, whereas feeding and social features aided in the model's prediction minimally. These results highlighting the predictive potential in this area but the need for further improvement before behavioural changes can be used to reliably predict the onset of BRD in pre-weaned calves.

Familiarity, age, weaning and health status impact social proximity networks in dairy calves.

Social network analysis in dairy calves has not been widely studied, with previous studies limited by the short study duration, and low number of animals and replicates. In this study, we investigated social proximity interactions of 79 Holstein-Friesian calves from 5 cohorts for up to 76 days. Networks were computed using 4-day aggregated associations obtained from ultrawideband location sensor technology, at 1 Hz sampling rate. The effect of age, familiarity, health, and weaning status on the social proximity networks of dairy calves was assessed. Networks were poorly correlated (non-stable) between the different 4-day periods, in the majority of them calves associated heterogeneously, and individuals assorted based on previous familiarity for the whole duration of the study. Age significantly increased association strength, social time and eigenvector centrality and significantly decreased closeness and coefficient of variation in association (CV). Sick calves had a significantly lower strength, social time, centrality and CV, and significantly higher closeness compared to the healthy calves. During and after weaning, calves had significantly lower closeness and CV, and significantly higher association strength, social time, and eigenvector centrality. These results indicate that age, familiarity, weaning, and sickness have a significant impact on the variation of social proximity interaction of calves.

A survey of British sheep farmers: Practices, opinions and knowledge surrounding the management of sheep scab.

Ovine psoroptic mange (commonly known as sheep scab) is, arguably, one of the most important ectoparasitic diseases of sheep in the UK. Although research has been conducted on the practices and knowledge of British sheep farmers regarding the management of sheep scab, recent data is limited. During October 2021 an online survey was completed by 725 British sheep farmers. Most respondents farmed in England (63.0%, 457/725), followed by Wales (20.6%, 149/725) and Scotland (16.4%, 119/725). Over half (53.0%, 384/725) of the respondents had experienced sheep scab in their flocks before, and 38.3% (147/384) of respondents experienced sheep scab twice or more in the previous ten years. The estimated prevalence of sheep scab from these surveyed farmers was 10.9% (79/725). Of the farmers who had experienced sheep scab before, only 36.2% (139/384) reported using skin scrapes and 12.5% (48/384) reported using blood tests for diagnosis. Over three-quarters of farmers quarantined all new sheep (76.6%; 498/650), but fewer quarantined returning sheep (28.6%; 83/290). Many of the respondents (43.7%, 252/577) did not quarantine new sheep for the suggested minimum 21 days. In addition, only 53.0% (384/725) of respondents answered all three of the knowledge questions regarding sheep scab correctly. The analysis illustrates that there is scope to improve the use of diagnostics, which suggests that misdiagnosis of sheep scab may be high and thus inappropriate treatments may be used, or treatments may not be used at all. It also demonstrated that there is scope for improvement in farmers' knowledge regarding sheep scab, particularly that sheep scab could be detected via a blood test and that the disease could present without visible signs. Our results further support that sheep scab is a large-scale endemic issue, potentially with an increasing prevalence in Great Britain. Future control interventions need to focus on improving knowledge exchange, raising awareness of best practice controls to ensure that better management strategies are employed by farmers, including improved control at livestock markets to reduce the national transmission and thus focus on coordinated control interventions.

Methods to develop and evaluate attitudinal scales to measure farmer perceptions: Using sheep scab as an example.

There is limited information on the attitudes and opinions of British sheep farmers when preventing and managing sheep scab in their flocks. Attitudes are examples of latent traits, which are not directly observable. They must be measured indirectly which can be achieved using measurement scales. Attitudinal measurement scales are used frequently in research, but are seldom evaluated prior to use, particularly in veterinary research. Therefore, the aim of this study was to identify and assess latent traits which influence the control of sheep scab with the use of exploratory factor analysis, and then further evaluate these factors with the use of item response theory to determine how they perform as scales. An online survey was completed by 726 British sheep farmers during October 2021 (725 useable responses), which included a section with 37 belief statements related to the opinions and attitudes of sheep farmers around the management of sheep scab. These belief statements were developed and adapted from previous research. During the analysis, they were subjected to exploratory factor analysis, which suggested that the scales performed reasonably well. The scales were then evaluated further by item response theory. The graded response models for each attitudinal scale were assessed by graphical evaluation. They demonstrated that the scales were able to distinguish those respondents with low perceptions of control, responsibility, and trust, and those who had high perceptions of luck. This study provides insights into some of the factors influencing the management of sheep scab, and how well these scales perform. Additionally, it further highlights the importance of evaluating attitudinal scales for use in research, and that exploratory factor analysis alone does not provide enough information on how well they perform.

Evidence of personality-dependent plasticity in dairy calf movement behaviours derived from automated data collection.

Individual consistency in behaviour, known as animal personality, and behavioural plasticity in response to environmental changes are important factors shaping individual behaviour. Correlations between them, called personality-dependent plasticity, indicate that personality can affect individual reactions to the environment. In farm animals this could impact the response to management changes or stressors but has not yet been investigated. Here we use ultra-wideband location sensors to measure personality and plasticity in the movement of 90 dairy calves for up to 56 days starting in small pair-housing enclosures, and subsequently moved to larger social housings. For the first time calves were shown to differ in personality and plasticity of movement when changing housing. There were significant correlations between personality and plasticity for distance travelled (0.57), meaning that individuals that travelled the furthest in the pair housing increased their movement more in the social groups, and for residence time (- 0.65) as those that stayed in the same area more decreased more with the change in housing, demonstrating personality-dependent plasticity. Additionally, calves conformed to their pen-mate's behaviour in pairs, but this did not continue in the groups. Therefore, personality, plasticity and social effects impact how farm animals respond to changes and can inform management decisions.

Social and ethical implications of data and technology use on farms: a qualitative study of Swedish dairy and pig farmers.

Introduction: Livestock farmers are being increasingly encouraged to adopt digital health technologies on their farms. Digital innovations may have unintended consequences, but there tends to be a pro-innovation bias in previous literature. This has led to a movement towards "responsible innovation," an approach that questions the social and ethical challenges of research and innovation. This paper explores the social and ethical issues of data and technologies on Swedish dairy and pig farms from a critical perspective. Methods: Six focus groups were conducted with thirteen dairy and thirteen pig farmers. The data were analysed using reflexive thematic analysis and a digital critical health lens, which focuses on concepts of identity and power. Results and discussion: The analysis generated four themes: extending the self, sense of agency, quantifying animals, and managing human labour. The findings suggest that technologies can change and form the identities of farmers, their workers, and animals by increasing the visibility of behaviours and bodies through data collection. Technologies can also facilitate techniques of power such as conforming to norms, hierarchical surveillance, and segregation of populations based on data. There were many contradictions in the way that technology was used on farms which suggests that farmers cannot be dichotomised into those who are opposed to and those that support adoption of technologies. Emotions and morality played an important role in the way animals were managed and technologies were used by farmers. Thus, when developing innovations, we need to consider users' feelings and attachments towards the technologies. Technologies have different impacts on farmers and farm workers which suggests that we need to ensure that we understand the perspectives of multiple user groups when developing innovations, including those that might be least empowered.

Accurate prediction of calving in dairy cows by applying feature engineering and machine learning.

Prediction of calving is key to dairy cow management. Current trends of increasing herd sizes globally can directly impact the time that farmers spend monitoring individual animals. Automated monitoring on behavioural and physiological changes prior to parturition can be used to develop machine learning solutions for calving prediction. In this study, we developed a machine learning algorithm for the prediction of calving in dairy cows. We demonstrated that temperature and activity index information retrieved from a commercial reticuloruminal bolus sensor can accurately predict calving from 1-day to 5-days in advance. The best prediction solution using data from 82 dairy cows, achieved up to 87.81 % in accuracy, 92.99 % in specificity, 75.84 % in sensitivity, 82.99 % in positive predictive value (PPV), 78.85 % in F-score, and 90.02 % in negative predictive value (NPV) on the test dataset when using information from 2-days in advance and all the subsets of feature characteristics (temperature + drinking + activity). The performance only decreased by 2.45 % points in accuracy, 0.74 % points in specificity, 6.41 % points in sensitivity, 2.45 % points in positive predictive value, 4.91 % points in F-score, and 2.44 % points in negative predictive value on the test dataset when using all feature characteristics and 5-days in advance information compared to using all features and information from 2-days in advance. Full evaluation of the performance of the prediction showed an improvement when using all the different subsets of feature characteristics together (temperature, activity, and drinking) compared to using temperature features only. When adding activity and drinking to the subset of temperature features, an average increase of 2.70, 1.52, 5.40, 4.39, 5.02, 2.13 % points in accuracy, specificity, sensitivity, PPV, F-score, and NPV, respectively, was obtained. Notably, evaluation of feature importance (i.e., relative weight of any given feature in relation to model prediction) showed that 3-5 (depending on the selected days in advance model) of the top ten features were derived from drinking behaviour, showing the relevance that this behaviour can have in the prediction of calving. This algorithm can provide a useful tool for automated calving prediction in dairy cows which has potential for improvement of health, welfare, and productivity in the dairy industry.

Use of qualitative behavioural assessment to investigate affective states of housed dairy cows under different environmental conditions.

In addition to the reduction of suboptimal welfare, there is now a need to provide farmed animals with positive opportunities to provide confidence that they have experienced a life worth living. Diversification of the environment through environmental enrichment strategies is one suggested avenue for providing animals with opportunities for positive experiences. The provision of more stimulating environmental conditions has been widely implemented in other animal production industries, based on evidenced welfare benefits. However, the implementation of enrichment on dairy farms is limited. In addition to this, the relationship between enrichment and dairy cows' affective states is an under-researched area. One specific welfare benefit of enrichment strategies which has been observed in a number of species, is increased affective wellbeing. This study investigated whether the provision of different forms of environmental enrichment resources would impact the affective states of housed dairy cows. This was measured by Qualitative Behavioural Assessment, currently a promising positive welfare indicator. Two groups of cows experienced three treatment periods; (i) access to an indoor novel object, (ii) access to an outdoor concrete yard and (iii) simultaneous access to both resources. Principal component analysis was used to analyse qualitative behavioural assessment scores, which yielded two principal components. The first principal component was most positively associated with the terms "content/relaxed/positively occupied" and had the most negative associations with the terms 'fearful/bored'. A second principal component was most positively associated with the terms "lively/inquisitive/playful" and was most negatively associated with the terms "apathetic/bored". Treatment period had a significant effect on both principal components, with cows being assessed as more content, relaxed and positively occupied and less fearful and bored, during periods of access to additional environmental resources. Similarly, cows were scored as livelier, more inquisitive and less bored and apathetic, during treatment periods compared to standard housing conditions. Concurrent with research in other species, these results suggest that the provision of additional environmental resources facilitates positive experiences and therefore enhanced affective states for housed dairy cows.