Application of QBA to Assess the Emotional State of Horses during the Loading Phase of Transport

SIMPLE SUMMARY: The traditional approach to animal welfare has been primarily based on assessing negative factors. However, over the past few years, due to changes in societal beliefs and values and improved scientific understanding of animals, researchers are being asked to develop tools to identify positive animal-based measures and, more in general, to evaluate animals’ emotional states. Transportation, mainly the loading phase, is a crucial welfare concern in horses and the development, enhancement, and implementation of positive animal-based indicators during transport are of critical importance. This study aimed at investigating the accuracy of Qualitative Behaviour Assessment (QBA), using both free choice profiling and fixed list methodologies, in horses during the loading phase of transport. A total of 13 stakeholders were asked to assess different sets of videos of horses being loaded for road transport using both their own descriptors and a list of descriptors. Our results showed that giving first the possibility to observer to use their own descriptor is useful to the development and implementation of a fixed list with terms that perfectly fit with the specific conditions we want to evaluate. This would allow the observers to better understand and quantify each given descriptor and therefore obtain a good QBA accuracy level. Furthermore, a specific descriptors list was developed to be used in pre-transport loading situations. ABSTRACT: To identify feasible indicators to evaluate animals’ emotional states as a parameter to assess animal welfare, the present study aimed at investigating the accuracy of free choice profiling (FCP) and fixed list (FL) approach of Qualitative Behaviour Assessment (QBA) in horses during the loading phase of transport. A total of 13 stakeholders were trained to score 2 different sets of videos of mixed breed horses loaded for road transport, using both FCP and FL, in 2 sessions. Generalized Procustes Analysis (GPA) consensus profile explained a higher percentage of variation (80.8%) than the mean of 1000 randomized profiles (41.2 ± 1.6%; p = 0.001) for the FCP method, showing an excellent inter-observer agreement. GPA identified two main factors, explaining 65.1% and 3.7% of the total variation. Factor 1 ranging from ‘anxious/ to ‘calm/relaxed’, described the valence of the horses’ emotional states. Factor 2, ranging from ‘bright’ to ‘assessing/withdrawn’, described the arousal. As for FL, Principal Component Analysis (PCA) first and second components (PC1 and PC2, respectively), explaining on average 59.8% and 12.6% of the data variability, had significant agreement between observers. PC1 ranges from relaxed/confident to anxious/frightened, while PC2 from alert/inquisitive to calm. Our study highlighted the need for the use of descriptors specifically selected, throughout a prior FCP process for the situation we want to evaluate to get a good QBA accuracy level.