Hintze Sara, Maulbetsch Freija, Asher Lucy, Winckler Christoph
University of Natural Resources and Life Sciences, Vienna, Division of Livestock Sciences, Department of Sustainable Agricultural Systems, Vienna, Austria.
School of Natural and Environmental Sciences, Newcastle University, Newcastle, United Kingdom.
PeerJ. 2020 Jul 21;8:e9395. doi: 10.7717/peerj.9395. eCollection 2020.
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, to date, knowledge of what inactivity looks like in different species is scarce and methods to precisely describe and analyse inactive behaviour are thus warranted.
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 kept in intensive, semi-intensive and pasture-based husbandry systems to record inactive behaviour in a range of different contexts. Three farms per husbandry system were visited twice; once in the morning and once in the afternoon to cover most of the daylight hours. 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 inactivity on the group level. Since our study was explorative in nature, we refrained from statistical hypothesis testing, but analysed both the individual- and group-level data descriptively. Moreover, 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.
Inspection of graphs indicated that with increasing intensity of the husbandry system, more animals were inactive (group-level data) and the time the focal animals were inactive increased (individual-level data). Frequently co-occurring postures were generally similar between husbandry systems, but with subtle differences. The most frequently observed combination on farms with intensive and semi-intensive systems was lying with head up, ears backwards and eyes open whereas on pasture it was standing with head up, ears forwards and eyes open.
Our study is the first to explore inactive behaviour in cattle by applying a detailed description of postures from an Inactivity Ethogram and by using the machine learning algorithm cspade to identify frequently co-occurring posture combinations. Both the ethogram created in this study and the cspade algorithm may be valuable tools in future studies aiming to better understand different forms of inactivity and how they are associated with different affective states.
饲养在贫瘠环境中的动物通常表现出更高水平的不活动，初步研究表明，不活动行为可能反映出无聊或类似抑郁的状态。然而，迄今为止，对于不同物种的不活动表现的了解还很匮乏，因此需要有精确描述和分析不活动行为的方法。
我们开发了一种不活动行为图谱，其中包含了育肥牛（一种经常饲养在贫瘠环境中的农场动物）不同身体部位姿势（站立/躺卧、头部、耳朵、眼睛、尾巴）的详细信息。该不活动行为图谱被应用于饲养在集约化、半集约化和基于牧场的饲养系统中的奥地利弗莱维赫小母牛，以记录一系列不同环境下的不活动行为。每个饲养系统访问三个农场，每个农场访问两次，一次在上午，一次在下午，以覆盖大部分白天时间。每次访问期间，对16只焦点动物各连续观察15分钟（每个饲养系统96头小母牛，总共288头）。此外，对焦点动物所在的群体进行视频记录，以便稍后在群体层面确定不活动情况。由于我们的研究本质上是探索性的，我们没有进行统计假设检验，而是对个体和群体层面的数据进行了描述性分析。此外，使用机器学习算法cspade分析不同身体部位姿势（站立/躺卧、头部、耳朵和眼睛）的同时出现情况，以深入了解不活动时共同出现的姿势。
图表检查表明，随着饲养系统强度的增加，不活动的动物数量增多（群体层面数据），焦点动物不活动的时间增加（个体层面数据）。不同饲养系统中经常共同出现的姿势总体上相似，但存在细微差异。在集约化和半集约化系统的农场中，最常观察到的组合是躺卧，头向上，耳朵向后，眼睛睁开，而在牧场上则是站立，头向上，耳朵向前，眼睛睁开。
我们的研究首次通过应用不活动行为图谱中对姿势的详细描述，并使用机器学习算法cspade来识别经常共同出现的姿势组合，探索了牛的不活动行为。本研究中创建的行为图谱和cspade算法可能都是未来研究中的宝贵工具，旨在更好地理解不同形式的不活动以及它们与不同情感状态的关联。
