Qualitative behavioural assessment (QBA) has been used to assess welfare in dairy cattle; however, it has not been evaluated longitudinally over extended time periods. A group of approximately 270 housed dairy cattle were monitored using QBA and activity sensors over a 13-month period (June 2024–July 2025) to explore whether factors such as temperature and season are associated with changes in affective state. The QBA scores were analysed using principal component analysis. Principal component 1 (PC1), with 39.3% of the variance, was negatively associated with ‘positively occupied’, ‘relaxed’, ‘calm’, ‘content’, and ‘happy’ and was positively associated with terms such as ‘indifferent’, ‘bored’, and ‘irritable’, suggesting that a decrease in PC1 shows an improved emotional state. Principal component 2 (PC2), which contributed 13.4% of the variance, was positively associated with ‘lively’ and ‘active’. The principal component scores varied over time: less variation and a lower PC1 score were observed in the winter. Time series analysis was conducted with the outcomes PC1 and PC2 using autoregressive integrated moving average (ARIMA) models to account for potential correlation in the outcome over time. An ARIMA model for PC1 showed that increased temperature (0.18, p 0.001) and mean days in milk (0.10, p 0.001) and reduced milk per cow per day (−0.67, p 0.001) were significantly associated with an increase in PC1. These results suggest that the herd was more content and happier in cooler conditions and that warmer conditions were associated with a more agitated state. Given that the changes in QBA (considered as a proxy for affective state) are observed in year-round-housed dairy cows and appear to be linked to temperature, milking status, and activity, QBA performed at a single time point in housed dairy cattle should be interpreted with caution rather than used as a herd baseline.
Craven et al. (2026) studied this question.