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预测模型用于实施针对性的繁殖管理在多雌性母牛在自动挤奶系统的实施
Fergus P Hannon1, Martin J Green1, Luke O'Grady2
1School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington Campus, Leicestershire LE12 5RD, United Kingdom.
Journal of dairy science
|November 9, 2024
概括
通过预测奶牛的生育能力,可以改善乳牛群的有针对性的生殖管理 (TRM). 仅来自自动挤奶系统 (AMS) 的数据就显示了有限的准确性,并且添加辅助数据并没有显著提高预测.
科学领域:
- 乳制品科学 乳制品科学
- 动物繁殖 动物繁殖
- 机器学习在农业中的应用
背景情况:
- 有针对性的生殖管理 (TRM) 依赖于准确预测奶牛群的生育能力.
- 自动挤奶系统 (AMS) 产生了大量的数据,但其用于预测生殖表现的实用性尚未得到充分探索.
- 经常可以获得辅助数据源,如反和活动监测器.
研究的目的:
- 使用AMS数据评估预测雌性表达和怀孕到第一次授精的准确性.
- 评估添加辅助数据源对预测模型性能的影响.
- 确定使用AMS数据用于TRM实施的可行性.
主要方法:
- 使用AMS数据 (RBT数据集) 开发了二进制随机森林分类模型.
- 模型性能与包含AMS和辅助数据 (RBT+数据集) 的模型进行了比较.
- 预测的准确性是使用接受器运营商曲线下的面积 (AUC-ROC) 来评估的.
主要成果:
- 仅使用AMS数据就预测了雌性表达 (AUC-ROC=0.6) 和受孕 (AUC-ROC=0.56).
- 添加辅助数据略有改善了预测 (期AUC-ROC=0.65,怀孕AUC-ROC=0.62),但没有显著改善.
- 在添加辅助数据时,没有观察到统计学上显著的分类准确度的改善.
结论:
- AMS数据为预测奶牛生殖表现提供了基线.
- 目前使用AMS和辅助数据的预测模型由于子组准确性差,对TRM的实用性有限.
- 需要进一步的研究,以提高预测准确度,同时保持模型的节.
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