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从部分日奶记录中识别牛奶成分测量的数据异常

Xiao-Lin Wu^1,2, Malia J Caputo¹, Chip Donatone¹

¹Council on Dairy Cattle Breeding, Bowie, MD 20716.

JDS communications

|March 6, 2026

概括

一个新的指标,个人级别的类内相关性,有效地检测牛日级每日牛奶成分数据中的错误. 这种方法提高了基因评估和群体管理的数据质量.

科学领域:

乳制品科学乳制品科学
动物遗传学动物遗传学
数据分析数据分析数据分析.

背景情况:

准确的牛奶和牛奶成分数据对于遗传评估和群体管理至关重要.
数据记录错误可能会对这些决定的有效性产生重大影响.
以前的方法评估了农场层面的数据一致性,但检测牛日水平的异常仍然是一个挑战.

研究的目的:

引入和评估一种新的指标,个人级别的类内相关性,用于评估牛日级数据的一致性.
将这种新指标的性能与现有的异常检测方法进行比较.
开发一种强大的方法来识别可疑的牛奶成分记录.

主要方法:

发展个人级别的类内相关性作为牛日数据一致性的指标.
根据三个常见的异常检测技术对新指标的评估.
实施两步方法来估计标记异常值的百分点值.
检查数据混对异常检测的影响.

主要成果:

与传统的单变量和多变量方法相比,个人级别的类内相关度指标在识别异常方面表现优越.
拟议的方法有效地检测到可疑的牛奶成分记录在单个牛日的水平.
该研究还分析了数据混对异常检测准确性的有害影响.

结论:

个人级别的类内相关性提供了一个强大而实用的工具,用于提高牛日级别的牛奶成分数据质量.
这些方法对于提高遗传评估和群体管理决策的准确性至关重要.
这些发现为乳制品数据质量保证协议提供了宝贵的见解.

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