预测肉牛的干物质摄入量
Nathan E Blake1,2, Matthew Walker2,3,4, Shane Plum2
1School of Agriculture and Food, Davis College of Agriculture, Natural Resources and Design, West Virginia University, Morgantown, WV 26506, USA.
Journal of animal science
|August 10, 2023
概括
机器学习使用水摄入量和性能数据准确预测个体动物干物质摄入量 (DMI). 这项技术可以改善群体住房环境中的牲畜管理和效率.
科学领域:
- 动物科学动物科学
- 农业技术 农业技术
- 机器学习应用 机器学习应用
背景情况:
- 准确估计个体动物干物质摄入量 (DMI) 对畜牧生产效率至关重要.
- 目前用于监测DMI的方法在集体住房或牧场环境中通常是不切实际的.
- 使用代理变量的预测算法是需要在具有挑战性的环境中估计DMI的.
研究的目的:
- 通过水摄入量,动物表现和环境数据来确定机器学习是否可以预测DMI.
- 开发和评估DMI估计的预测算法,在集体养的牛群中.
- 将机器学习方法与用于DMI预测的传统统计方法进行比较.
主要方法:
- 使用随机森林回归 (RFR) 和重复测量随机森林 (RMRF) 机器学习模型.
- 收集的数据包括178头牛 (125头公牛,53头牛) 的每日DMI,水摄入量,体重和平均每日增益 (ADG).
- 记录了气候数据,并使用重复测量ANOVA (RMANOVA) 作为传统的比较.
主要成果:
- 该RMRF模型成功预测DMI,准确度为0.75公斤.
- 机器学习方法证明了在干旱牛中准确预测DMI的潜力.
- 开发的算法显示了未来在牧场环境中的应用的前景.
结论:
- 机器学习,特别是RMRF,为预测个体动物DMI提供了一种可行的方法.
- 准确的DMI预测可以提高畜牧管理和生产效率.
- 通过对各种数据集的进一步改进,这些预测算法将能够得到更广泛的应用.
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