预测模型的偏离预期的牛奶产量在过渡期的牛在自动挤奶系统
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.
Preventive veterinary medicine
|March 7, 2024
概括
早期哺乳期奶牛的健康状况可以通过自动挤奶系统的牛奶产量和行为数据来预测. 虽然模型看起来有前途,但它们需要提高灵敏度,以便可靠的过渡牛管理.
科学领域:
- 动物科学动物科学
- 乳制品生产 乳制品生产
- 兽医医学 兽医医学 兽医医学
背景情况:
- 过渡期 (分娩到早期哺乳期) 对奶牛至关重要,30%至50%的奶牛患病.
- 代谢和传染病减少了早期哺乳期的牛奶产量,影响了农场的经济.
- 偏离预期的牛奶产量被追溯用于评估过渡期母牛的健康状况.
研究的目的:
- 评估自动挤奶系统 (AMS) 的早期哺乳期 (1-3天的牛奶) 数据是否可以预测30天的累积牛奶产量偏差.
- 评估预测的产量偏差是否可以将牛分类为改进过渡管理.
主要方法:
- 一项回顾性队列研究分析了31个商业AMS的生产,反和活动数据.
- 一项三步分析涉及计算预期产量,确定30天产量偏差 (YD),并使用机器学习从早期哺乳期数据预测YD.
- 根据年龄的差异,奶牛被分为红色 (~15%年龄差异),珀色 (-14%至0%年龄差异) 和绿色 (>0%年龄差异).
主要成果:
- 机器学习模型在外部验证后预测了30天收益率偏差,平均绝对误差为9%.
- 将奶牛归类为RED组 (较大的负偏差) 实现了99%的特异性,35%的灵敏性和67%的平衡准确性.
- 早期哺乳期牛奶产量,反和从牛奶中1-3天开始的活动模式在预测产量偏差方面具有实用性.
结论:
- 奶牛的牛奶产量,反和在牛奶中1-3天的活动可以预测30天累计牛奶产量的偏差.
- 目前的预测模型缺乏必要的灵敏度来可靠地分类奶牛,以促进改进过渡期奶牛管理.
相关概念视频
Variation
6.8K
An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
6.8K
Multiple Regression
3.0K
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...
3.0K
Prediction Intervals
2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.3K
Regression Toward the Mean
6.3K
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...
6.3K
Estimating Population Mean with Unknown Standard Deviation
7.7K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
William S. Gosset (1876–1937) of the...
7.7K
Mean Absolute Deviation
2.6K
The mean absolute deviation is also a measure of the variability of data in a sample. It is the absolute value of the average difference between the data values and the mean.
Let us consider a dataset containing the number of unsold cupcakes in five shops: 10, 15, 8, 7, and 10. Initially, calculate the sample mean. Then calculate the deviation, or the difference, between each data value and the mean. Next, the absolute values of these deviations are added and divided by the sample size to...
Let us consider a dataset containing the number of unsold cupcakes in five shops: 10, 15, 8, 7, and 10. Initially, calculate the sample mean. Then calculate the deviation, or the difference, between each data value and the mean. Next, the absolute values of these deviations are added and divided by the sample size to...
2.6K


