Analysis of social interactions in group-housed animals using dyadic linear models

Understanding factors affecting social interactions among animals is important for applied animal behavior research. Thus, there is a need to elicit statistical models to analyze data collected from pairwise behavioral interactions. In this study, we propose treating social interaction data as dyadic observations and propose a statistical model for their analysis. We performed posterior predictive checks of the model through different validation strategies: stratified 5-fold random cross-validation, block-by-social-group cross-validation, and block-by-focal-animals validation. The proposed model was applied to a pig behavior dataset collected from 797 growing pigs freshly remixed into 59 social groups that resulted in 10,032 records of directional dyadic interactions. The response variable was the duration in seconds that each animal spent delivering attacks on another group mate. Generalized linear mixed models were fitted. Fixed effects included sex, individual weight, prior nursery mate experience, and prior littermate experience of the two pigs in the dyad. Random effects included aggression giver, aggression receiver, dyad, and social group. A Bayesian framework was utilized for parameter estimation and posterior predictive model checking. Prior nursery mate experience was the only significant fixed effect. In addition, a weak but significant correlation between the random giver effect and the random receiver effect was obtained when analyzing the attacking duration. The predictive performance of the model varied depending on the validation strategy, with substantially lower performance from the block-by-social-group strategy than other validation strategies. Collectively, this paper demonstrates a statistical model to analyze interactive animal behaviors, particularly dyadic interactions

Approachability to a Human in Gilts Divergently Selected for Feed Efficiency

The objective of this study was to evaluate approachability of gilts divergently selected for residual feed intake (RFI) to a novel human. Twenty low-RFI and 19 high-RFI gilts were tested using a human approach test. Testing occurred over two consecutive weeks between 1300 and 1900 hours. Gilts were tested individually within a 4.9 x 2.4 m test arena. Throughout the test, latency to first enter, duration of time spent, and frequency of entrances within 1 m and 0.5 m of the human were recorded. These results suggest that divergent selection for RFI did not alter gilt approach behavior to a novel human

Effects of Genetic Selection for Residual Feed Intake on Behavioral Reactivity of Castrated Male Pigs to Novel Stimuli Tests

Increasing feed efficiency in swine is important for increasing sustainable food production and profitability for producers; therefore, this is often selected for at breeding. Residual feed intake (RFI) can be used for the genetic selection of pigs for feed efficiency. In our selection project, low-RFI pigs consume less feed for equal weight gain compared to their less efficient, high-RFI counterparts. However, little is known about how feed efficiency influences the pig\u27s behavioral reactivity toward fear-eliciting stimuli. In this study, behavioral reactivity of pigs divergently selected for RFI was evaluated using human approach- (HAT) and novel object tests (NOT). Forty low-RFI (more feed efficient) and 40 high-RFI (less feed efficient) castrated male pigs (barrows; 46.5 ± 8.6 kg) from 8th generation Yorkshire RFI selection lines were randomly selected and evaluated once using HAT and once using NOT over a four week period utilizing a crossover experimental design. Each pig was individually tested within a 4.9 × 2.4 m test arena for 10 min; behavior was evaluated using live and video observations. The test arena floor was divided into four zones; zone 1 being oral, nasal, and/or facial contact with the human (HAT) or orange traffic cone (NOT) and zone 4 being furthest from the human or cone and included the point where the pig entered the arena. During both HAT and NOT, low-RFI pigs crossed fewer zones (P \u3c 0.0001), had fewer head movements (P ≤ 0.02), defecated less frequently (P ≤ 0.03), displayed a shorter duration of freezing (P = 0.05), and froze less frequently (HAT: low-RFI = 4.9 ± 0.65 vs. high-RFI = 7.5 ± 0.96; NOT: low-RFI = 4.7 ± 0.66 vs. high-RFI = 7.2 ± 0.96; P \u3c 0.0001) compared to high-RFI pigs. During HAT, low-RFI pigs also attempted to escape less frequently (low-RFI = 0.4 ± 0.14 vs. high-RFI = 1.1 ± 0.30; P = 0.001) compared to high-RFI pigs. In contrast, compared to the high-RFI pigs, low-RFI pigs took 48 s longer during HAT and 52 s longer during NOT to approach zone 1 (P ≤ 0.04). These results indicate that low-RFI pigs had decreased behavioral reactivity during HAT and NOT compared to high-RFI pigs. This may suggest that reducing a pig\u27s behavioral reactivity is an important component of improving feed efficiency; however, it may have implications for animal handling and facility design

Recording behaviour of indoor-housed farm animals automatically using machine vision technology: a systematic review

Large-scale phenotyping of animal behaviour traits is time consuming and has led to increased demand for technologies that can automate these procedures. Automated tracking of animals has been successful in controlled laboratory settings, but recording from animals in large groups in highly variable farm settings presents challenges. The aim of this review is to provide a systematic overview of the advances that have occurred in automated, high throughput image detection of farm animal behavioural traits with welfare and production implications. Peer-reviewed publications written in English were reviewed systematically following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. After identification, screening, and assessment for eligibility, 108 publications met these specifications and were included for qualitative synthesis. Data collected from the papers included camera specifications, housing conditions, group size, algorithm details, procedures, and results. Most studies utilized standard digital colour video cameras for data collection, with increasing use of 3D cameras in papers published after 2013. Papers including pigs (across production stages) were the most common (n = 63). The most common behaviours recorded included activity level, area occupancy, aggression, gait scores, resource use, and posture. Our review revealed many overlaps in methods applied to analysing behaviour, and most studies started from scratch instead of building upon previous work. Training and validation sample sizes were generally small (mean±s.d. groups = 3.8±5.8) and in data collection and testing took place in relatively controlled environments. To advance our ability to automatically phenotype behaviour, future research should build upon existing knowledge and validate technology under commercial settings and publications should explicitly describe recording conditions in detail to allow studies to be reproduced

The Hitchhiker's Guide to Integration of Social and Ethical Awareness in Precision Livestock Farming Research

While fully automated livestock production may be considered the ultimate goal for optimising productivity at the farm level, the benefits and costs of such a development at the scale at which it needs to be implemented must also be considered from social and ethical perspectives. Automation resulting from Precision Livestock Farming (PLF) could alter fundamental views of human-animal interactions on farm and, even further, potentially compromise human and animal welfare and health if PLF development does not include a flexible, holistic strategy for integration. To investigate topic segregation, inclusion of socio-ethical aspects, and consideration of human-animal interactions within the PLF research field, the abstracts from 644 peer-reviewed publications were analysed using the recent advances in the Natural Language Processing (NLP). Two Latent Dirichlet Allocation (LDA) probabilistic models with varying number of topics (13 and 3 for Model 1 and Model 2, respectively) were implemented to create a generalised research topic overview. The visual representation of topics produced by LDA Model 1 and Model 2 revealed prominent similarities in the terms contributing to each topic, with only weight for each term being different. The majority of terms for both models were process-oriented, obscuring the inclusion of social and ethical angles in PLF publications. A subset of articles (5%, n = 32) was randomly selected for manual examination of the full text to evaluate whether abstract text and focus reflected that of the article as a whole. Few of these articles (12.5%, n = 4) focused specifically on broader ethical or societal considerations of PLF or (9.4%, n = 3) discussed PLF with respect to human-animal interactions. While there was consideration of the impact of PLF on animal welfare and farmers in nearly half of the full texts examined (46.9%, n = 15), this was often limited to a few statements in passing. Further, these statements were typically general rather than specific and presented PLF as beneficial to human users and animal recipients. To develop PLF that is in keeping with the ethical values and societal concerns of the public and consumers, projects, and publications that deliberately combine social context with technological processes and results are needed

US Swine Industry Stakeholder Perceptions of Precision Livestock Farming Technology: A Q-Methodology Study

This study used the Q-methodology approach to analyze perceptions of precision livestock farming (PLF) technology held by stakeholders directly or indirectly involved in the US swine industry. To see if stakeholders’ perceptions of PLF changed over time as PLF is a rapidly evolving field, we deliberately followed up with stakeholders we had interviewed 6 months earlier. We identified three distinct points of view: PLF improves farm management, animal welfare, and laborer work conditions; PLF does not solve swine industry problems; PLF has limitations and could lead to data ownership conflict. Stakeholders with in-depth knowledge of PLF technology demonstrated elevated levels of optimism about it, whereas those with a basic understanding were skeptical of PLF claims. Despite holding different PLF views, all stakeholders agreed on the significance of training to enhance PLF usefulness and its eventual adoption. In conclusion, we believe this study’s results hold promise for helping US swine industry stakeholders make better-informed decisions about PLF technology implementation

Pilot Study: A Guide to Equine Welfare Assessment

In response to growing interest in equine welfare and the need for 4-H curriculum, a pilot study of equine welfare curriculum was conducted with 4-H clubs (N=15). An overall low response rate of 26.67% was experienced. An online survey was then conducted in order to determine factors affecting involvement. Time constraints for both youth and leaders were among the most commonly cited deterrents to completion. Survey responses also suggested that following a hybrid (of hard copy and online instruction) may increase usability and effectiveness of A Guide to Equine Welfare Assessment