Eckhardt Regina, Arablouei Reza, McCosker Kieren, Bishop-Hurley Greg, Bagnall Neil, Hayes Ben, Reverter Antonio, Ingham Aaron, Bernhardt Heinz
Chair of Agricultural Systems Engineering, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany.
Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, 4067, Australia.
Sci Rep. 2025 Jul 31;15(1):27941. doi: 10.1038/s41598-025-13264-0.
Cattle have been observed to change their behavior and location in response to thermal stress. This study employs a multimodal sensor-based approach to assess if the behavior of grazing cattle changed in response to thermal conditions that occurred during two trials conducted in Queensland, Australia, over late spring and early summer. Each trial involved sixty cattle (Brahman and Droughtmaster) fitted with eGrazor collars containing triaxial accelerometer and GNSS sensors. Cattle were genotyped and weighed weekly, and relevant meteorological data was collected. Accelerometer data was used to classify cattle behavior at five-second intervals into six distinct categories: grazing, walking, ruminating, resting, drinking, and other. GNSS data and satellite imagery were utilized to estimate time spent in open areas, while the Comprehensive Climate Index (CCI) was calculated from meteorological data and used to identify the two warmest and coolest weeks of both trials. Correlation analysis revealed that, during days of higher CCI, cattle increased time in the shade (correlation coefficient [Formula: see text]), reduced daytime grazing ([Formula: see text]), shifted grazing to nighttime ([Formula: see text]), and prolonged daytime resting ([Formula: see text]). Weather variations had a subtle influence on weight gain, while cattle with increased Bos indicus genetic proportion were more active during periods of higher CCI. These findings emphasize the potential of sensor-based monitoring to quantify cattle behavioral responses to variable weather conditions in relevant production environments.
据观察，牛会因热应激而改变其行为和位置。本研究采用基于多模式传感器的方法，以评估在澳大利亚昆士兰州春末和初夏进行的两次试验期间，放牧牛的行为是否会因热状况而发生变化。每次试验涉及60头牛（婆罗门牛和抗旱王牛），它们佩戴了装有三轴加速度计和全球导航卫星系统（GNSS）传感器的eGrazor项圈。每周对牛进行基因分型和称重，并收集相关气象数据。加速度计数据用于每隔5秒将牛的行为分类为六个不同类别：放牧、行走、反刍、休息、饮水和其他。利用GNSS数据和卫星图像估计在开阔区域花费的时间，同时根据气象数据计算综合气候指数（CCI），并用于确定两次试验中最温暖和最凉爽的两周。相关性分析表明，在CCI较高的日子里，牛在阴凉处停留的时间增加（相关系数[公式：见正文]），白天放牧时间减少（[公式：见正文]），放牧时间转移到夜间（[公式：见正文]），白天休息时间延长（[公式：见正文]）。天气变化对体重增加有微妙影响，而印度瘤牛基因比例增加的牛在CCI较高的时期更活跃。这些发现强调了基于传感器的监测在量化相关生产环境中牛对多变天气条件的行为反应方面的潜力。
