牛奶生物标志物和乳牛群福利状况:一种机器学习方法
Daniela Elena Babiciu1, Anamaria Blaga Petrean1, Sorana Daina1
1Faculty of Veterinary Medicine, University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, 400372 Cluj-Napoca, Romania.
Veterinary sciences
|January 28, 2026
概括
常规的牛奶分析,包括体细胞计数 (SCC) 和体细胞计数差异 (DSCC),可以帮助评估奶牛在农场层面的福利. 这些生物标志物在数据驱动的福利评估中显示出作为补充指标的潜力.
科学领域:
- 动物科学动物科学
- 兽医医学 兽医医学 兽医医学
- 数据科学数据科学数据科学
背景情况:
- 常规牛奶记录数据为乳牛福利提供了潜在的见解.
- 牛奶生物标志物的准确性反映群体级福利结果需要进一步调查.
研究的目的:
- 探索常规收集的牛奶生物标志物与农场级福利状况之间的关联.
- 评估机器学习算法在从牛奶数据中预测福利状况方面的实用性.
主要方法:
- 使用福利质量®协议将43家奶牛场分为福利类别 (增强,可接受,未分类).
- 分析的牛奶变量包括体细胞计数 (SCC),体细胞计数差异 (DSCC),脂肪,蛋白质,乳糖,尿素和牛奶产量.
- 训练了六个具有十倍交叉验证的机器学习算法,其中Elastic-Net (ENET) 显示了最高的性能.
主要成果:
- 在福利阶级中观察到DSCC,SCC,乳糖和牛奶产量的显著差异 (FDR调整后p < 0.05).
- 该ENET模型实现了最高的精度 (0.72±0.19),其次是随机森林和多层感知器 (0.70).
- 关键影响变量包括SCC,DSCC,乳糖,牛奶产量,脂肪与蛋白质的比率 (FPR),脂肪和尿素.
结论:
- 常规的牛奶生物标志物捕捉到群体层面的与福利相关的模式.
- 这些生物标志物在数据驱动的乳牛福利评估框架中显示出作为补充指标的潜力.
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