Section for Science of Complex Systems, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, 1090, Vienna, Austria.
Complexity Science Hub Vienna, Josefstädterstraße 39, 1080, Vienna, Austria.
Sci Rep. 2021 Oct 27;11(1):21152. doi: 10.1038/s41598-021-00469-2.
In this study we present systematic framework to analyse the impact of farm profiles as combinations of environmental conditions and management practices on common diseases in dairy cattle. The data used for this secondary data analysis includes observational data from 166 farms with a total of 5828 dairy cows. Each farm is characterised by features from five categories: husbandry, feeding, environmental conditions, housing, and milking systems. We combine dimension reduction with clustering techniques to identify groups of similar farm attributes, which we refer to as farm profiles. A statistical analysis of the farm profiles and their related disease risks is carried out to study the associations between disease risk, farm membership to a specific cluster as well as variables that characterise a given cluster by means of a multivariate regression model. The disease risks of five different farm profiles arise as the result of complex interactions between environmental conditions and farm management practices. We confirm previously documented relationships between diseases, feeding and husbandry. Furthermore, novel associations between housing and milking systems and specific disorders like lameness and ketosis have been discovered. Our approach contributes to paving a way towards a more holistic and data-driven understanding of bovine health and its risk factors.
在本研究中，我们提出了一个系统框架，用于分析奶牛常见疾病的农场特征（即环境条件和管理实践的组合）的影响。这项二次数据分析使用了来自 166 个农场的观测数据，共有 5828 头奶牛。每个农场的特征来自五个类别：饲养、喂养、环境条件、住房和挤奶系统。我们结合降维和聚类技术来识别相似的农场属性组，我们称之为农场特征。通过多元回归模型对农场特征及其相关疾病风险进行统计分析，研究疾病风险与特定聚类的农场成员以及描述特定聚类的变量之间的关联。五种不同农场特征的疾病风险是环境条件和农场管理实践之间复杂相互作用的结果。我们证实了之前记录的疾病、喂养和饲养之间的关系。此外，还发现了住房和挤奶系统与跛行和酮病等特定疾病之间的新关联。我们的方法为更全面和数据驱动的理解牛的健康及其风险因素铺平了道路。
