Marina H, Fikse W F, Rönnegård L
Department of Animal Biosciences, Swedish University of Agricultural Sciences, SE-750 07 Uppsala, Sweden.
Växa, Swedish University of Agricultural Sciences, SE-756 51 Uppsala, Sweden.
JDS Commun. 2024 Apr 20;5(6):608-612. doi: 10.3168/jdsc.2023-0507. eCollection 2024 Nov.
Dairy cattle are frequently housed in freestalls with limited space, affecting social interactions between individuals. Social behavior in dairy cattle is gaining recognition as a valuable tool for identifying sick animals, but its application is hampered by the complexities of analyzing social interactions in intensive housing systems. In this context, precision livestock technologies present the opportunity to continuously monitor dyadic spatial associations on dairy farms. The aim of this study is to evaluate the accuracy of predicting social behavior of dairy cows using social network analysis. Daily social networks were built using the position data from 149 cows over 14 consecutive days of the study period. We applied the separable temporal exponential random graph models to estimate the likelihood of formation and persistence of social contacts between dairy cows individually and to predict the social network on the subsequent day. The correlation between the individual degree centrality values, the number of established social contacts per individual, between the predicted and observed networks ranged from 0.22 to 0.49 when the structural information from network triangles was included in the model. This study presents a novel approach for predicting animal social behavior in intensive housing systems using spatial association information obtained from a real-time location system. The results indicate the potential of this approach as a crucial step toward the larger goal of identifying disruptions in dairy cows' expected social behavior.
奶牛经常被饲养在空间有限的自由牛舍中，这会影响个体之间的社交互动。奶牛的社会行为正逐渐被视为识别患病动物的一种有价值的工具，但其应用受到集约化养殖系统中社交互动分析复杂性的阻碍。在这种背景下，精准畜牧技术为持续监测奶牛场中的二元空间关联提供了机会。本研究的目的是使用社会网络分析评估预测奶牛社会行为的准确性。在研究期间连续14天，利用149头奶牛的位置数据构建每日社会网络。我们应用可分离的时间指数随机图模型来估计奶牛个体之间社交接触形成和持续的可能性，并预测次日的社会网络。当模型中包含来自网络三角形的结构信息时，预测网络与观察网络之间个体度中心性值（即每个个体建立的社交接触数量）的相关性在0.22至0.49之间。本研究提出了一种新颖的方法，利用从实时定位系统获得的空间关联信息来预测集约化养殖系统中的动物社会行为。结果表明，这种方法有潜力朝着识别奶牛预期社会行为中断这一更大目标迈出关键一步。
