Using multivariate statistical analysis to measure ovine temperament; stability of factor construction over time and between groups of animals

Sheep's coping style can be identified by unsupervised machine learning from unlabeled data

The objective of this study was to define coping style of sheep by using unsupervised machine learning approaches. A total of 105 Norduz sheep (age 3-5 years) were subjected to a 5-minute arena test. Agglomerative Hierarchical Clustering (HCA) was performed on scores of selected principal components retained from Principal Components Analysis (PCA) on arena behaviors to identify sheep coping style. Initially, the variables retained for the PCA were determined with Bartlett's test for sphericity and Kaiser-Meyer-Olkin (KMO) measure of sample adequacy. Seven behavioral variables with KMO values greater than 0.5 were used for final PCA: the average distance to group sheep (DTG), the average distance to stimulus (DTS), the duration of locomotion (LOC), the total number of zone boundaries crossed during the test (CRS), the total number of times that tested sheep sniffed stimulus (NSS), latency to the first sniff the stimulus (LSS), and subjective scores (SCR) scored by an observer on a scale from 1 to 5 (1: extremely calm, 5: extremely restless). The first two components, which were the only ones with an eigenvalue greater than one, accounted for 70.32% of the total variation and were used for clustering analysis. Clustering tendency showed that the scores for the first two components were suitable for clustering (Hopkins' H = 0.852). Several cluster validity indexes were used to obtain aggregated results to determine the most appropriate clustering method and number of clusters. Five different clustering methods: k-means and hierarchical clustering with Ward, average, single and complete linkage were compared. Bootstrap resampling was used to evaluate the stability of a given cluster using the Jaccard coefficient. The clustering method and number of clusters corresponding to the highest rank aggregation score from the bootstrap resampling indicate that the hierarchical clustering method with average linkage and 5 clusters is the most suggested clustering method. However, Ward's algorithm identified the strongest clustering structure for hierarchical clustering, as it had the highest agglomerative coefficient value (0.98). When both Jaccard and aggregation scores are considered together, Ward's method with 3 clusters was selected as the most appropriate method. Sheep were classified into three coping styles (CS) based on HCA results as reactive (Cluster 1, n = 71), intermediate (Cluster 2, n = 22) or proactive (Cluster 3, n = 12). Coping style had significant effect on behavioral variables, DTG, DTS, LOC, CRS and NSS (P < 0.05). The individuals that have proactive coping style had the highest mean values for the variables DTG, DTS and LOC and SCR (P < 0.0001). This indicates that proactive sheep are more active then reactive sheep. The CRS, LOC and NSS mean values were higher for intermediate sheep compared to reactive sheep (P < 0.05). The NSS values were higher for intermediate sheep compare to proactive sheep (P < 0.0001). The findings of the current study show that distinct coping styles in sheep may be identified based on behaviors recorded in an arena test. The findings also revealed that sheep's coping style can be objectively identified by unsupervised machine learning from unlabeled behavioral data.

Personality Research in Mammalian Farm Animals: Concepts, Measures, and Relationship to Welfare

Measuring and understanding personality in animals is a rising scientific field. Much research has been conducted to assess distinctive individual differences in behavior in a large number of species in the past few decades, and increasing numbers of studies include farm animals. Nevertheless, the terminology and definitions used in this broad scientific field are often confusing because different concepts and methods are used to explain often synonymously applied terms, such as personality, temperament and coping style. In the present review we give a comprehensive overview of the concepts and terms currently used in animal personality research and critically reveal how they are defined and what they measure. First, we shortly introduce concepts describing human personality and how these concepts are used to explain animal personality. Second, we present which concepts, methods and measures are applied in farm animal personality research and show that the terminology used seems to be somehow species-related. Finally, we discuss some findings on the possible impact of personality on the welfare of farm animals. The assessment of personality in farm animals is of growing scientific and practical interest. Differences in theoretical frameworks and methodological approaches may also entail the diverse use of the different concepts between basic and applied research approaches. We conclude that more consistency is needed in using different theoretical concepts, terms and measures, especially in farm animal personality research. The terms coping style and temperament, which are used in different ways, should not be examined as independent concepts, but rather should be considered as different aspects of the whole personality concept. Farm animal personality should be increasingly considered for the improvement of animal housing, management, breeding and welfare.

A comparison of commonly used and novel electronic techniques for evaluating cattle temperament

Schwartzkopf-Genswein, K. S., Shah, M. A., Church, J. S., Haley, D. B., Janzen, K., Truong, G., Atkins, R. P. and Crowe, T. J. 2012. A comparison of commonly used and novel electronic techniques for evaluating cattle temperament. Can. J. Anim. Sci. 92: 21–31. The temperament of steers (n=28) was assessed using five quantitative techniques including: flight time, flight distance, electronic (strain-gauge and accelerometer) tests, and three visual scores (VS) made during entry, restraint and exit from a squeeze chute. The objective of this study was to determine the most important predictive parameters based on those measurements and evaluate the relationship between the techniques. Flight time and distance were correlated with exit VS (r=−0.51, and 0.41, P<0.05; n=56), but were not related to restraint VS. Data from strain-gauge and accelerometer sensors were used to generate parameters such as peak response and area under the curve that were correlated with all three VS. Regression models using VS as the dependent variable and a combination of 2 to 5 parameters from the strain-gauge and accelerometer tests as independent variables predicted temperament with values of 29 to 65 or 41 to 57%, respectively. When all techniques, excluding VS, were used as independent variables, model accuracy increased to 72, 81 and 77% for restraint, exit and the sum of all VS, respectively. These findings suggest the objective measures of temperament assessed in this study could be used to identify highly reactive animals.

A behavioural scale to measure ease of farrowing in sows

Farrowing is one of the most critical phases in pig production, as it has an impact on neonatal pig survival. Assessing the ease of farrowing can improve the management of sows and thus increase litter survival. The aims of this study were: 1) to develop an ease of farrowing score (EFS) in sows based on the behaviour of the sows and their piglets, and 2) to determine the relationship between the EFS and productive, physiological, and subjective parameters. Eighty hybrid (Large White × Landrace) sows from first to seventh parity housed in individual crates were used. An EFS was constructed using the total duration of farrowing, the birth interval, the total time standing or sitting, the number of position changes during the day before and the day of farrowing, the sow posture at birth, the viability and the position of the piglets at birth (head or back born). Moreover, rectal temperature at 90 min after farrowing, a four categorical subjective visual assessment (VA) of farrowing and litter size (piglets born alive, stillborn and mummified foetus) were recorded. A common factor analysis model yielded five factors with an eigenvalue higher than 0.95 that accounted for 75.05% of the total variation between individuals. The three main factors were "farrowing duration", "sow posture", and "sow activity" of sows and explained 23.44%, 15.67%, and 14.23% of the variance, respectively. Primiparous sows had higher values for factor 3 (sow activity) than multiparous sows (P = 0.02). Sows without stillborn or mummification foetus showed higher values of EFS than sows with at least one stillborn or mummification foetus (P = 0.06 and 0.01, respectively). Sows that received a visual assessment of 3 and 4 showed higher values of EFS than sows that received a VA of 1 and 2 (P = 0.0017). The EFS appears to be a good behavioural scale to measure ease of farrowing in sows kept in individual farrowing crates. Duration of farrowing, sow position, and presence of stillborn piglets and mummified foetuses appear to be important ease of farrowing indicators.