人工智能预测的牛奶产量残留与多母乳母牛的行为模式和过渡成功的关联
C Kemel1, M Salamone2, B Aernouts3
1Department of Internal Medicine, Reproduction, and Population Medicine, Faculty of Veterinary Medicine, Ghent University, 9820 Merelbeke, Belgium.
Journal of dairy science
|July 17, 2025
概括
使用乳产剩余值 (MRT) 监测牛的健康状况,揭示了过渡期内的行为变化. 负的MRT表明牛的健康受到损害,这表明MRT是早期检测的潜在代谢指标.
科学领域:
- 动物科学动物科学
- 乳制品科学 乳制品科学
- 兽医医学 兽医医学 兽医医学
背景情况:
- 基于数据的乳牛健康监测对于早期发现健康问题至关重要.
- 过渡期 (产前到产后) 是牛健康和生产力的关键阶段.
- 牛奶产量是一个关键指标,但通过考虑预期产量,可以增强其解释.
研究的目的:
- 为了研究牛奶产量残留量 (MRT) 与牛在过渡期的行为之间的关联.
- 为了确定MRT是否可以作为牛健康状况的指标.
- 探索活动传感器的潜力,以监测与牛奶产量相关的牛行为.
主要方法:
- 在荷兰8个奶牛场的奶牛上使用加速计传感器来记录时间预算 (吃饭,反,躺着,站立).
- 通过将实际的牛奶产量与人工智能预测的预期产量对2,689次哺乳进行比较,计算了剩余的牛奶产量 (MRT).
- 将奶牛分为低,中和高MRT组,并分析行为差异.
主要成果:
- 阴性MRT (低分类) 的奶牛表现出减少反和增加产后站立时间.
- 在高MRT类别中,与低MRT类别产后相比,观察到更长的进食时间.
- 躺时间有所不同,高MRT类别在某些天的躺时间更长,而低MRT类别在分娩后的第1天和第18-20天的躺时间更长.
结论:
- 产后期的负牛奶产量残留与改变的行为模式有关,这表明过渡期受到损害.
- 乳产剩余值 (MRT) 显示出作为过渡期牛的代谢指标的潜力.
- 这些发现支持开发乳牛群的新型健康监测工具.
相关概念视频
01:25Multiple Regression
3.2K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.2K
01:52Regression Toward the Mean
6.5K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.5K