Doing nothing and what it looks like... Inactivity in fattening cattle

Abstract

Animals kept in barren environments often show increased levels of inactivity and first studies indicate that inactive behaviour may reflect boredom or depression-like states. However, inactivity does not necessarily reflect negative welfare and can even be a sign of positive welfare, for example in terms of relaxation. To date, knowledge of how to reliably differentiate between positive and negative states associated with inactivity is scarce and methods to identify different forms of inactivity are thus warranted. To this end, we developed an Inactivity Ethogram including detailed information on the postures of different body parts (Standing/Lying, Head, Ears, Eyes, Tail) for fattening cattle, a farm animal category often kept in barren environments. The Inactivity Ethogram was applied to Austrian Fleckvieh heifers from intensive, semi-intensive and pasture-based husbandry systems. Three farms per husbandry system were visited twice; once in the morning and once in the afternoon to cover most of the daylight hours with our observations. During each visit, 16 focal animals were continuously observed for 15 minutes each (96 heifers per husbandry system, 288 in total). Moreover, the focal animals groups were video recorded to later determine the inactivity level on the group level. Group level and focal animal data were analysed with (generalised) linear mixed-effect models with husbandry system as fixed effect and (group nested in) farm visit nested in farm as random effects. Husbandry system did not affect group level inactivity or the time the different postures were adopted (with the exception of asymmetrical ears, which were more prevalent in intensive than in semi-intensive than in pasture systems). In addition to the analysis of the time the single postures were observed for, simultaneous occurrences of postures of different body parts (Standing/Lying, Head, Ears and Eyes) were analysed using the machine learning algorithm cspade to provide insight into co-occurring postures of inactivity. Frequently co-occurring postures were generally similar between husbandry systems, but with subtle differences. The most frequently observed combination in intensive and semi-intensive systems was Lying with Head up, Ears backwards and Eyes open whereas in pasture systems it was Lying with Head up, ears low and eyes closed. To conclude, both the Inactivity Ethogram (including the description of detailed postures) and the machine learning algorithm cspade (for identifying frequently co-occurring posture combinations) are promising tools to understand how combinations of postures may be used to distinguish between different affective states associated with inactivity.