Coping styles in Holstein Heifers: Relationship between proactivity and habituation to automatic milking system

Research on the influence of personality on animal welfare, health, and productivity is gaining significant attention in both the scientific community and the dairy industry. This study explores how proactivity, a coping style associated with risk-taking, influences heifers’ adaptation to the Automatic Milking System (AMS). Prior to calving, the behavioural responses of twenty-three heifers were assessed through three tests: the Individual Novel Human Test (NHi), the Individual Novel Object Test (NOi), and the Novel Object Test in Group (NOg). Following parturition, each heifer was video-recorded during their first ten automated milkings, and the videos were analysed using the BORIS© software. Statistical analysis distinguished proactive and reactive individuals in each test, revealing key differences. Proactive animals in the NOi test exhibited a significantly lower stepping rate (p < 0.05) during milking. In contrast, reactive heifers in the NHi test displayed a higher kicking rate (p < 0.05). Additionally, proactive subjects in the NOg test spent significantly less time in the robot (p < 0.05) and were less likely to pull the teat cup off (p < 0.05). These findings suggest that proactive animals, with differences between tests, were generally less nervous during the automated milking process, indicating better habituation. This highlights the importance of considering coping styles in animal management to improve both welfare and productivity in dairy farming. Science4Impact Statement (S4IS). This study highlights how personality in heifers, particularly considering the coping style in response to the environment, influence their ability to adapt to Automatic Milking Systems (AMS). Proactive animals exhibited better adaptability, showing less stressful behaviours (e.g., reduced kicking and stepping rates), suggesting lower stress levels during milking. These findings provide practical evidence to support decision-making processes for stakeholders, including farmers and policymakers, in the development of precision livestock farming systems. By identifying behavioural profiles that influence heifers’ adaptation to automatic milking, our results highlight specific traits that could be integrated into sensor-based monitoring and decision-support algorithms (Lamanna, Bovo, et al., 2025; Lamanna, Bovo, & Cavallini, 2025; Cavallini et al., 2025). This integration can help Precision Livestock Farming (PLF) technologies predict stress responses, optimise training strategies, and reduce human intervention, thereby contributing to improved welfare and labour efficiency. Such systems not only enhance animal welfare but also promote greater sustainability, aligning with global goals to reduce the environmental impact of intensive agriculture.