ZTHZ, Division of Animal Welfare, University of Bern, Bern, Switzerland.
Veterinary Public Health Institute, University of Bern, Bern, Switzerland.
Sci Rep. 2018 Aug 17;8(1):12303. doi: 10.1038/s41598-018-29962-x.
We sought to objectively quantify and compare the recorded movement and location patterns of laying hens within a commercial system. Using a custom tracking system, we monitored the location within five zones of a commercial aviary for 13 hens within a flock of 225 animals for a contiguous period of 11 days. Most hens manifested a hen-specific pattern that was (visually) highly consistent across days, though, within that consistency, manifested stark differences between hens. Three different methods were used to classify individual daily datasets into groups based on their similarity: (i) Linear Discriminant Analysis based on six summary variables (transitions into each zone) and total transitions; (ii) Hierarchical Clustering, a naïve clustering analysis technique, applied to summary variables and iii) Hierarchical Clustering applied to dissimilarity matrices produced by Dynamic Time Warping. The three methods correctly classified more than 85% of the hen days and provided a unique means to assess behaviour of a system indicating a considerable degree of complexity and structure. We believe the current effort is the first to document these location and movement patterns within a large, complex commercial system with a large potential to influence the assessment of animal welfare, health, and productivity.
我们试图客观地量化和比较商业系统中产卵鸡的运动和位置模式。使用定制的跟踪系统,我们在商业禽舍的五个区域内监测了 13 只母鸡在 225 只鸡群中的位置,连续监测了 11 天。大多数母鸡表现出一种母鸡特有的模式,这种模式在几天内(视觉上)高度一致,但在这种一致性中,母鸡之间表现出明显的差异。我们使用三种不同的方法将个体每日数据集根据其相似性分类为组:(i) 基于六个摘要变量(进入每个区域的过渡)和总过渡的线性判别分析;(ii) 应用于摘要变量的朴素聚类分析技术——层次聚类;以及 (iii) 应用于动态时间扭曲生成的不相似性矩阵的层次聚类。这三种方法正确地对超过 85%的母鸡日进行了分类,并提供了一种独特的方法来评估系统的行为,表明系统具有相当程度的复杂性和结构。我们相信,目前的努力是首次在一个具有很大潜力影响动物福利、健康和生产力评估的大型复杂商业系统中记录这些位置和运动模式。