Institute of Behavioural Physiology, Leibniz Institute for Farm Animal Biology (FBN), Wilhelm-Stahl-Allee 2, D-18196 Dummerstorf, Germany.
Institute of Reproductive Biology, Leibniz Institute for Farm Animal Biology (FBN), Wilhelm-Stahl-Allee 2, D-18196 Dummerstorf, Germany.
Animal. 2020 Jan;14(1):198-205. doi: 10.1017/S1751731119001733. Epub 2019 Aug 1.
Oestrus detection remains a problem in the dairy cattle industry. Therefore, automatic detection systems have been developed to detect specific behavioural changes at oestrus. Vocal behaviour has not been considered in such automatic oestrus detection systems in cattle, though the vocalisation rate is known to increase during oestrus. The main challenge in using vocalisation to detect oestrus is correctly identifying the calling individual when animals are moving freely in large groups, as oestrus needs to be detected at an individual level. Therefore, we aimed to automate vocalisation recording and caller identification in group-housed dairy cows. This paper first presents the details of such a system and then presents the results of a pilot study validating its functionality, in which the automatic detection of calls from individual heifers was compared to video-based assessment of these calls by a trained human observer, a technique that has, until now, been considered the 'gold standard'. We developed a collar-based cattle call monitor (CCM) with structure-borne and airborne sound microphones and a recording unit and developed a postprocessing algorithm to identify the caller by matching the information from both microphones. Five group-housed heifers, each in the perioestrus or oestrus period, were equipped with a CCM prototype for 5 days. The recorded audio data were subsequently analysed and compared with audiovisual recordings. Overall, 1404 vocalisations from the focus heifers and 721 vocalisations from group mates were obtained. Vocalisations during collar changes or malfunctions of the CCM were omitted from the evaluation. The results showed that the CCM had a sensitivity of 87% and a specificity of 94%. The negative and positive predictive values were 80% and 96%, respectively. These results show that the detection of individual vocalisations and the correct identification of callers are possible, even in freely moving group-housed cattle. The results are promising for the future use of vocalisation in automatic oestrus detection systems.
发情检测仍然是奶牛养殖业中的一个问题。因此,已经开发出自动检测系统来检测发情时的特定行为变化。尽管发情时发声率已知会增加,但在牛的这种自动发情检测系统中,尚未考虑发声行为。使用发声来检测发情的主要挑战是在动物在大群中自由移动时正确识别发声个体,因为需要在个体水平上检测发情。因此,我们旨在实现群体饲养奶牛的发声自动记录和发声者识别。本文首先介绍了该系统的详细信息,然后介绍了验证其功能的初步研究结果,该研究将自动检测个体小母牛的叫声与受过训练的人类观察者基于视频的这些叫声评估进行了比较,直到现在,这种方法一直被认为是“黄金标准”。我们开发了一种基于项圈的奶牛叫声监测器(CCM),该监测器具有结构传播和空气传播的麦克风和一个记录单元,并开发了一种后处理算法,通过匹配来自两个麦克风的信息来识别发声者。五个处于发情前期或发情期的群体饲养小母牛每个都配备了一个 CCM 原型,持续了 5 天。随后对记录的音频数据进行了分析,并与视听记录进行了比较。总体而言,获得了 1404 次来自焦点小母牛的发声和 721 次来自群体小母牛的发声。评估中省略了项圈更换或 CCM 故障期间的发声。结果表明,CCM 的灵敏度为 87%,特异性为 94%。阴性和阳性预测值分别为 80%和 96%。这些结果表明,即使在自由移动的群体饲养牛中,也可以实现个体发声的检测和发声者的正确识别。这些结果为未来在自动发情检测系统中使用发声提供了希望。