Department of Population Medicine, University of Guelph, Guelph, ON, Canada N1G 2W1.
Department of Animal and Food Science, University of Kentucky, Lexington 40506.
J Dairy Sci. 2021 Jun;104(6):7177-7194. doi: 10.3168/jds.2020-19645. Epub 2021 Mar 23.
Automated milk feeders (AMF) are computerized systems that provide producers with a tool that can be used to more efficiently raise dairy calves and allow for easier implementation of a high plane of nutrition during the milk feeding phase. Automated milk feeders also have the ability to track individualized behavioral data, such as milk consumption, drinking speed, and the number of rewarded and unrewarded visits to the feeder, that could potentially be used to predict disease development. The objective of this scoping review was to characterize the body of literature investigating the use of AMF data to predict morbidity and mortality in dairy calves during the preweaning stage. This review lists the parameters that have been examined for associations with disease in calves and identify discrepancies found in the literature. Five databases and relevant conference proceedings were searched. Eligible studies focused on the use of behavioral parameters measured by AMF to predict morbidity or mortality in preweaned dairy calves. Two reviewers independently screened titles and abstracts from 6,675 records identified during the literature search. After title and abstract screening, 382 studies were included and then assessed at the full-text level. Of these, 56 studies fed calves using an AMF and provided some measure of morbidity or mortality. Thirteen examined AMF parameters for associations with morbidity or mortality. The studies were completed in North America (n = 6), Europe (n = 6), and New Zealand (n = 1). The studies varied in sample size, ranging from 30 to 1,052 calves with a median of 100 calves. All 13 studies included enteric disease as an outcome and 11 studies evaluated respiratory disease. Of the studies measuring enteric disease, 8 provided disease definitions (n = 8/13, 61.2%); however, for respiratory disease, only 5 provided a disease definition (n = 5/11, 45.5%). Disease definitions and thresholds varied greatly between studies, with 10 using some form of health scoring. When evaluating feeding metrics as indicators of disease, all 13 studies investigated milk consumption and 6 and 7 studies investigated drinking speed and number of rewarded and unrewarded visits, respectively. Overall, this scoping review identified that daily milk consumption, drinking speed, and rewarded and unrewarded visits may provide insight into early disease detection in preweaned dairy calves. However, the disparity in reporting of study designs and results between included studies made comparisons challenging. In addition, to aid with the interpretation of studies, standardized disease outcomes should be used to improve the utility of this primary research.
自动化牛奶喂养器(AMF)是一种计算机化系统,为生产者提供了一种工具,可以更有效地饲养奶牛犊牛,并在牛奶喂养阶段更容易实施高营养水平。自动化牛奶喂养器还能够跟踪个性化的行为数据,例如牛奶消耗、饮用速度以及奖励和未奖励访问喂养器的次数,这些数据可能有助于预测疾病的发展。本范围综述的目的是描述使用 AMF 数据来预测哺乳期前奶牛发病率和死亡率的文献。本综述列出了与犊牛疾病相关的已检查参数,并确定了文献中的差异。五个数据库和相关会议记录被搜索。合格的研究侧重于使用 AMF 测量的行为参数来预测未断奶奶牛的发病率或死亡率。两名审查员独立筛选了文献检索中确定的 6675 条记录的标题和摘要。标题和摘要筛选后,有 382 项研究被纳入,并在全文水平进行评估。其中,56 项研究使用 AMF 喂养犊牛,并提供了一些发病率或死亡率的衡量标准。13 项研究检查了 AMF 参数与发病率或死亡率的关系。这些研究分别在北美(n = 6)、欧洲(n = 6)和新西兰(n = 1)完成。研究在样本量上存在差异,从 30 到 1052 头犊牛不等,中位数为 100 头犊牛。所有 13 项研究都将肠道疾病作为一种结果,11 项研究评估了呼吸道疾病。在研究肠道疾病的研究中,有 8 项提供了疾病定义(n = 8/13,61.2%);然而,对于呼吸道疾病,只有 5 项提供了疾病定义(n = 5/11,45.5%)。疾病定义和阈值在研究之间差异很大,其中 10 项使用了某种形式的健康评分。在评估作为疾病指标的喂养指标时,所有 13 项研究都研究了牛奶消耗,6 项和 7 项研究分别研究了饮用速度和奖励和未奖励访问的次数。总的来说,这项范围综述确定,每日牛奶消耗、饮用速度和奖励和未奖励访问次数可能提供了早期发现哺乳期前奶牛疾病的线索。然而,纳入研究之间在研究设计和结果报告方面的差异使得比较具有挑战性。此外,为了帮助解释研究,应使用标准化的疾病结果来提高这项基础研究的实用性。