How Animals Communicate Quality of Life: The Qualitative Assessment of Behaviour

The notion ‘quality of life’ (QoL) suggests that welfare in animals encompasses more than just an absence of suffering; it concerns the quality of an animal’s entire relationship with its environment, of how it lives its life. Judgements of such quality are based on the integration of perceived details of how animals behave over time in different contexts. The scientific status of such judgements has long been ambiguous, but in recent decades has begun to be addressed by animal scientists. This paper starts with a brief review of qualitative approaches to the study of animal behaviour, which tend to address characteristics such as individuality, personality, and emotionality. The question then arises whether such characteristics involve a subjective, experiential aspect, and identify animals as sentient beings. The second half of this paper argues that taking the integrative nature of qualitative judgements seriously enables a ‘whole animal’ perspective, through which it becomes possible to view behaviour as a dynamic, expressive body language that provides a basis for assessing the quality of an animal’s experience (eg contented, anxious). Judging this quality is a skill that requires knowledge of species-specific behaviour, experience in observing and interacting with animals in different contexts, and a willingness to communicate with animals as sentient beings. A substantial body of research indicates that this skill can function reliably in a scientific context, and can be applied usefully as a practical welfare assessment tool. Thus qualitative approaches to the study of animal behaviour should make an important contribution to the growing interest in animal QoL

Applying ethological and health indicators to practical animal welfare assessment

There is a growing effort worldwide to develop objective indicators for animal welfare assessment, which provide information on an animal’s quality of life, are scientifically trustworthy, and can readily be used in practice by professionals. Animals are sentient beings capable of positive and negative emotion, and so these indicators should be sensitive not only to their physical health, but also to their experience of the conditions in which they live. This paper provides an outline of ethological research aimed at developing practical welfare assessment protocols. The first section focuses on the development and validation of welfare indicators generally, in terms of their relevance to animal well-being, their interobserver reliability, and the confidence with which the prevalence of described features can be estimated. Challenges in this work include accounting for the ways in which welfare measures may fluctuate over time, and identifying measures suited to monitoring positive welfare states. The second section focuses more specifically on qualitative welfare indicators, which assess the ‘whole animal’ and describe the expressive qualities of its demeanour (e.g. anxious, content). Such indicators must be validated in the same way as other health and behaviour indicators, with the added challenge of finding appropriate methods of measurement. The potential contribution of qualitative indicators, however, is to disclose an emotional richness in animals that helps to interpret information provided by other indicators, thus enhancing the validity of welfare assessment protocols. In conclusion, the paper emphasises the importance of integrating such different perspectives, showing that new knowledge of animals and new ways of relating to animals are bot

The qualitative assessment of responsiveness to environmental challenge in horses and ponies.

The responsiveness of 10 horses and 10 ponies to environmental challenge (represented by an open field test) was assessed using a qualitative approach based on free choice profiling methodology (FCP), which gives observers complete freedom to choose their own descriptive terms. Data were analysed with generalised Procrustes analysis (GPA), a multivariate statistical technique associated with FCP. A cross-validation of the outcomes of this approach to data recorded through quantitative behaviour analysis, and through a questionnaire given to the animals’ owner/riding instructor, was also performed using principal component analysis (PCA). Twelve undergraduate students generated their own descriptive vocabularies, by watching 20 horse/pony video clips lasting 2.5 min each. GPA showed that the consensus profile explained a high percentage of variation among the 12 observers, and differed significantly from the mean randomised profile ( p < 0.001). Two main dimensions of the consensus profile were identified, explaining 60% and 5.2% of the variation between animals, respectively. The 12 observer word charts interpreting these dimensions were semantically consistent, as they all converged towards the same meaning, albeit using different terms. The most used term to describe the positive end of axis 1 was ‘‘quiet’’, whereas ‘‘attentive’’ was the best positive descriptor of axis 2. The most frequently used descriptors for the negative ends of axes 1 and 2 were ‘‘nervous’’ and ‘‘bored’’, respectively. Thus, axis 1 was labelled as ‘‘quiet/nervous’’ and axis 2 was named as ‘‘attentive/bored’’. A marked effect of animal category was observed on the scores of the animals on the first dimension ( p < 0.001). Horses received significantly higher scores, and were thus assessed as more quiet and calm, than ponies. Conversely, ponies tended to receive lower scores on the second dimension ( p < 0.12), therefore they appeared less curious and attentive. The results of the PCA showed that the variables from different types of measurement clearly had meaningful relationships. For instance, the variables with the highest loading on the positive end of axis 1 were all indicative of tractable and docile animals, whereas axis 2 showed high loadings on the positive end for variables indicating attentive animals. Qualitative behaviour assessment proved to be an appropriate methodology for the study of horse behavioural responsiveness, in that it provided a multifaceted characterisation of horse behavioural expression that was in agreement with other quantitative and subjective assessments of the animals’ behaviour

Welfare assessment: correlations and integration between a Qualitative Behavioural Assessment and a clinical/health protocol applied in veal calves farms

This study is aimed at finding correlations and possible integration among Qualitative Behavioural Assessment (QBA) and a specific protocol of clinical/health evaluation. Both welfare assessment methods were based on direct animal observation and were applied in 24 Italian veal calves farms at 3 weeks (wks) of rearing. Principal component analysis (PCA) summarized 20 QBA descriptors on two main components (PC1 and PC2) with eigenvalues above 4 and explaining 29.6 and 20.3% of the variation respectively. PCA on residuals obtained after correcting for housing condition yielded highly similar results, indicating that the rearing environment of the calves was not an important determinant of the observer reliability of QBA. A relationship was found between QBA PC2 and the presence of signs of cross-sucking recorded during the clinical visit (presence PC2=1.11 vs. absence PC2=-1.55,

Qualitative behaviour assessment of dairy buffaloes (Bubalus bubalis)

This study applies qualitative behaviour assessment (QBA) for the first time to dairy buffaloes, using three groups of observers with different cultural backgrounds and different levels of experience in animal behaviour observation and buffalo farming. Eight buffalo heifers aged 16–18 months were subjected to two isolation tests, one performed in the indoor part of their home environment, and one in a novel outdoor paddock. Animals were filmed individually for 2.5 min, and the resulting 16 video clips were shown to three observer panels, consisting of 11 applied animal behaviour scientists from 6 European countries, 11 Italian animal scientists with a background in buffalo farming but no experience in behavioural observation, and 14 Italian undergraduate animal science students with no particular experience. A free choice profiling method was used to instruct observers in QBA, and data for the three panels were analysed separately using Generalised Procrustes Analysis. All three panels showed significant inter-observer agreement (p < 0.001) and generated two main consensus dimensions characterised as ‘calm-agitated’ and ‘curious-shy’. There were significant correlations between buffalo scores provided by each of the three observer panels on both these dimensions (dim1: Kendall W = 0.96, n = 3, 2 = 43.28, p < 0.001; dim2: W = 0.68, n = 3, 2 = 30.73, p < 0.01). Buffaloes viewed in the familiar indoor pen were assessed by all three panels as more calm and less agitated (dimension 1) than animals viewed in the novel outdoor pen (Wilcoxon z = −2.52, p < 0.01, z = −2.52, p < 0.01, z = −2.38, p < 0.01 for Panels 1, 2, and 3, respectively). Scores on dimension 1 for the same animals viewed in either indoor or outdoor pen were correlated at r = 0.60 (p < 0.10), 0.74 (p < 0.05) and 0.71 (p < 0.05) for Panels 1, 2, and 3, respectively. Quantitatively, buffalo in the outdoor pen displayed longer bouts of running and higher frequencies of sniffing (both p < 0.05) than those in the indoor pen. Principal component analysis showed meaningful associations between qualitative and quantitative assessments, allowing qualitative dimensions to play a valuable role in interpreting the animals’ state. The main outcomes of this study are that QBA can be usefully applied to scientific studies of dairy buffalo, and that substantial differences in observer background do not appear to diminish the reliability of QBA

Digital Livestock Technologies as boundary objects: Investigating impacts on farm management and animal welfare

Digital Livestock Technologies (DLTs) can assist farmer decision-making and promise benefits to animal health and welfare. However, the extent to which they can help improve animal welfare is unclear. This study explores how DLTs may impact farm management and animal welfare by promoting learning, using the concept of boundary objects. Boundary objects may be interpreted differently by different social worlds but are robust enough to share a common identity across them. They facilitate communication around a common issue, allowing stakeholders to collaborate and co-learn. The type of learning generated may impact management and welfare differently. For example, it may help improve existing strategies (single-loop learning), or initiate reflection on how these strategies were framed initially (double-loop learning). This study focuses on two case studies, during which two DLTs were developed and tested on farms. In-depth, semi-structured interviews were conducted with stakeholders involved in the case studies (n = 31), and the results of a separate survey were used to complement our findings. Findings support the important potential of DLTs to help enhance animal welfare, although the impacts vary between technologies. In both case studies, DLTs facilitated discussions between stakeholders, and whilst both promoted improved management strategies, one also promoted deeper reflection on the importance of animal emotional well-being and on providing opportunities for positive animal welfare. If DLTs are to make significant improvements to animal welfare, greater priority should be given to DLTs that promote a greater understanding of the dimensions of animal welfare and a reframing of values and beliefs with respect to the importance of animals’ well-being

Qualitative Behavioural Assessment of emotionality in pigs

AbstractScientific assessment of affective states in animals is challenging but vital for animal welfare studies. One possible approach is Qualitative Behavioural Assessment (QBA), a ‘whole animal’ methodology which integrates information from multiple behavioural signals and styles of behavioural expression (body language) directly in terms of an animal's emotional expression. If QBA provides a valid measure of animals’ emotional state it should distinguish between groups where emotional states have been manipulated. To test this hypothesis, QBA was applied to video-recordings of pigs, following treatment with either saline or the neuroleptic drug Azaperone, in either an open field or elevated plus-maze test. QBA analysis of these recordings was provided by 12 observers, blind to treatment, using a Free Choice Profiling (FCP) methodology. Generalised Procrustes Analysis was used to calculate a consensus profile, consisting of the main dimensions of expression. Dimension one was positively associated with terms such as ‘Confident’ and ‘Curious’ and negatively with ‘Unsure’ and ‘Nervous’. Dimension two ranged from ‘Agitated’/‘Angry’ to ‘Calm’/‘Relaxed’. In both tests, Azaperone pre-treatment was associated with a more positive emotionality (higher scores on dimension one reflecting a more confident/curious behavioural demeanour) than control pigs. No effect of drug treatment on dimension two was found. Relationships between qualitative descriptions of behaviour and quantitative behavioural measures, taken from the same recordings, were found. Overall, this work supports the use of QBA for the assessment of emotionality in animals