Development of validated cognitive and behavioural indicators of welfare in pigs towards a predictive early warning system for poor welfare.

Statistics, physics, engineering and psychology utilise a wide-range of methods that can be adapted for use as animal-based welfare measures if only they could be validated in a model animal system. Validated indicators could then be used to develop a predictive early warning system so that potential welfare problems can be detected and mitigated in advance. Previous research has shown pigs to be an ideal model system for such a project, showing significant welfare improvements when provided with enrichment, and extent of skin injuries sustained reliably indicating welfare state. In this study, categories of pigs of different welfare status will be created using both physical and environmental factors to develop and validate novel welfare indicators, based methods used in statistics, physics, engineering and psychology.
Large White x Landrace pigs will be kept in groups of 20 in one of two environmental conditions: (1) deep straw pen, and (2) part-slatted floor. A total of 800 pigs (10 batches of 4 groups of 20) will be observed for 6 week periods. CCTV video cameras will record the pens for 12 hours per day once a week throughout the experiment. Every pig will be individually marked so that they are identifiable from the camera. At intervals over the first 4 weeks, each pig will be assessed and given a 'welfare' score based on measures of injury (to body and tail separately). The 4 pigs from each pen with the highest average % body injury score will be the 'Low welfare' group and the 4 with the lowest average % score will be the 'High welfare' group. This will give four groups: (a) High welfare on deep straw; (b) High welfare on part-slatted floor; (c) Low welfare on deep straw; (d) Low welfare on part-slatted floor.
Before each trial starts, 5 pigs will be selected at random from each pen and will be trained and tested throughout the trial using 3 cognitive behaviour tests: cognitive bias (to determine the individual's level of optimism or pessimism), functional memory (to assess how well individuals can keep track of a pattern) and interval timing (to determine the perceived passage of time by different individuals). Cognitive bias has been shown to be affected by the welfare state of the animal. Functional memory and interval timing have not been utilised in animal welfare studies; this project will aim to validate these methods as new useful tools to assessing the impact of welfare on cognitive processing. In addition, the 4 pigs with the highest and lowest % injury from each pen will be tested using the three cognitive behaviour tests at the end of the trial. We will also record acoustic communication both within a pen and of selected individuals within a pen.
From the recorded video footage, each of the focal pigs will be retrospectively tracked and their behaviours recorded for differences in individual activity budgets over the course of the trial. In addition, a number of tools will be used to investigate the social group dynamics within each pen. Fractal analysis of movement and semi-hidden Markov-chain analysis will identify repeating sequences of behaviour, social network analysis will determine the relative positions of each pig within its pen network and the overall level of connectedness within the group, levels of clustering will be assessed to determine whether individuals in a pen are crowding together more than expected given the space available to them, and finally we will assess the levels of synchronous behaviour within each pen. We will analyse each of indicator to look for differences between the 4 stated group types.
Finally, a predictive statistical tool will be developed based on the entire data set, which can be used as an early warning system for welfare problems. The aim of the predictive model will be to predict which pens and which individuals within them, are likely to develop welfare problems at a later stage and hence permit mitigation strategies to be put in place.

We propose to develop a range of novel, validated welfare indicators and a predictive statistical tool for use as an early warning system for aggression-related welfare problems. We will use pigs kept in different environmental conditions: high welfare (deep straw) and low welfare (part-slatted floor) as a model system. Each pig will be assessed and given a 'welfare' score based on measures of injury (to body and tail). The four pigs from each pen with the highest and lowest average % score will be the 'Low welfare' and 'High welfare' groups respectively. This will give four group types for comparison: (a) High welfare on deep straw; (b) High welfare on part-slatted floor; (c) Low welfare on deep straw; (d) Low welfare on part-slatted floor. We will use a series of novel cognitive indicators (cognitive bias, functional memory and interval timing tests) and quantitative behavioural indicators (Markov chain analyses, fractal analyses of movement, spatial clustering analysis, temporal synchrony analysis and social network analysis) and validate these against physical health indicators and a range of traditional, validated behavioural indicators of welfare. We will compare the group and individual level indicators for each of the identified four group types. Developing validated tools to assess animal welfare remains one of applied ethology's greatest challenges, so we have designed a wide-ranging experiment that will incorporate a large amount of detail from each animal that will be observed. Secondly, we will conduct risk factor analysis to formally investigate associations between different groups of indicators and injury status (looking at both aggression-related body injuries and tail biting injuries separately). Finally, we will develop a statistical tool as an early warning system for socially-induced injury, predicting which groups and which individuals within them are likely to have a welfare problem at a later stage.

This project will be of interest to animal behaviour and welfare scientists, particularly those involved in developing and using welfare measures. Many individual aspects of this research are novel, for example cluster scores have not previously been investigated in pigs. In addition, the concepts of interval time and functional memory are relatively unexplored in the context of animal welfare science as a whole. These data, and the relationship between cognitive measures and quantitative assessments of animal welfare, will also be of interest to psychologists. However, perhaps the greatest impact in terms of the novelty of this proposed project lies in the combination of such wide-range of indicators at both the individual and group level. This will potentially provide a suite of validated and reliable welfare indicators applicable to pigs, with potential for application in other species also.

Data generated from this research (both from on-farm research work or generated through statistical analyses) will be made available online through a university shared area. Links to this online data resource will be provided in all published papers and communications. The raw data will allow other researchers to verify our findings or apply different models to the data.

This research is also highly relevant to policy makers at national, European and global level. There is an increasing interest in developing welfare measures that are animal-based and repeatable, and in developing early warning systems for poor welfare, and the development of such a system is one of the principal objectives of this project. This not only applies to government policy makers, but also to retailers, quality assurance schemes and welfare charity organizations. The implications of being tail bitten on behavioural indicators of welfare will also be assessed as part of this research. This will provide useful information for competent authorities and government advisory staff that may assist in enforcing pig welfare legislation in relation to providing environmental enrichment. Furthermore, as this project will produce and openly share a large and varied dataset, we believe this will contribute considerably to designing the implementation strategies for putting welfare into practice. One of that one of the main current set-backs in terms of animal welfare policy is the lack of data that is readily available for use in meta-analyses and risk assessments (for example, conducted by the European Food Safety Authority for the European Commission). This means that quantitative assessment of welfare priorities by bodies such as the European Commission are rarely achieved in practice, although this would be far preferable to the qualitative, opinion-led assessments that priorities are currently assessed on. This could lead to priorities that are based more directly on science.

Research findings will be communicated to industry and government representatives, and to the wider scientific community, through published papers and at conferences, through press releases (the university has a dedicated press centre for publicising research conducted at Queen's University Belfast) and the university website. Findings will also be communicated directly to Department of Agriculture and Rural Development (DARD) policy and advisory staff through annual 'link' meetings, which Co-Investigator Niamh O'Connell chairs. Likewise, findings will be communicated to the European Commission through the European Food Safety Authority, with whom the Principal Investigator Lisa Collins frequently acts as an Expert on various working groups.