使用行为监测传感器,表型和天气数据与监督机器学习来预测霍尔斯坦·吉尔 (Holstein × Gyr) 奶牛的个人干物质摄入量
Camila S da Silva1, Tadeu E da Silva2, Anna L L Sguizatto3
1Brazilian Agricultural Research Company, Embrapa Mid-North, Teresina, PI 64006-220, Brazil.
JDS communications
|March 6, 2026
概括
准确的每日干物质摄入量 (DMI) 预测对于杂交的奶牛至关重要. 机器学习,特别是梯度增强,显示了使用行为和环境数据改善个人DMI估计的前景.
科学领域:
- 动物科学动物科学
- 机器学习 机器学习
- 乳制品营养 乳制品营养
背景情况:
- 准确的干物质摄入量 (DMI) 估计对于奶牛群管理,优化营养和经济表现至关重要.
- 现有的DMI预测方程通常仅限于纯种荷尔斯坦奶牛的群体级预测,因此需要对不同种群进行改进的方法.
研究的目的:
- 评估机器学习 (ML) 算法的准确性和精确性,用于预测霍尔斯坦×吉尔杂交母乳母牛的每日个体DMI.
- 整合行为监测,牛表型和天气数据,以创建一个全面的DMI预测模型.
主要方法:
- 采用监督和整合方法,使用18天内31只Holstein × Gyr杂交牛的数据.
- 训练有素的线性回归和整体ML算法 (包括梯度提升),使用22只奶牛的离开一组排除交叉验证.
- 在9头牛的外部测试组上验证了模型,包括行为,表型和天气特征.
主要成果:
- 梯度增强在测试数据上的评估算法中表现最好.
- 在预测个人每日DMI时获得了中等精度 (R2 = 0.68) 和准确性 (RMSE = 1.60 kg/d).
- 指示梯度提升适用于捕捉DMI预测中的复杂,非线性关系的适用性.
结论:
- 与传统方法相比,机器学习,特别是梯度增强,提供了更准确的方法来预测交叉品种奶牛的个体DMI.
- 未来的ML模型应纳入养行为和动物特异性影响的个体内和个体间的变异性,以提高DMI预测.
- 这项研究为开发乳牛养殖中先进的,数据驱动的营养管理策略提供了基础.
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