通过模拟比例的每日产量来估计测试日的牛奶产量:超越线性
Xiao-Lin Wu1, George R Wiggans2, H Duane Norman2
1Council on Dairy Cattle Breeding, Bowie, MD 20716; Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706.
Journal of dairy science
|August 29, 2023
概括
对奶牛来说,准确估计每天的牛奶产量至关重要. 这项研究评估了非线性模型,显示它们比测试日牛奶产量计算的传统方法提高了准确性,特别是在不均的挤奶间隔下.
科学领域:
- 乳制品科学 乳制品科学
- 动物养殖 动物养殖
- 统计建模 统计建模
背景情况:
- 美国的哺乳乳产量是用测试日的牛奶产量计算的,而不是直接测量.
- 目前的方法通常依赖部分产量,并与不平等的挤奶间隔作斗争.
- 维根斯模型,一个标准,假设线性,这是不准确的每天两次挤奶.
研究的目的:
- 审查和评估用于估计测试日牛奶产量的非线性模型.
- 将非线性模型的准确性与传统方法进行比较.
- 提出一种一般的方法来推导所有奶间隔时间的牛奶产量调整系数.
主要方法:
- 审查和评估扩展维根模型的非线性模型.
- 与传统方法比较非线性模型 (例如,局部加权回归,一般添加模型).
- 开发一种通用方法来推导可适应各种模型的校正因子.
主要成果:
- 非线性模型,不包括步骤函数,在估计测试日牛奶产量时显示出更少的错误和更高的准确性.
- 局部加权回归和一般添加模型提供灵活和准确的估计.
- 建议的通用方法允许在所有挤奶间隔内准确的校正因子.
结论:
- 与传统方法相比,非线性模型显著提高了测试日牛奶产量估计的准确性.
- 拟议的通用方法提供了一个灵活的框架,用于改善奶牛的牛奶产量计算.
- 准确估计牛奶产量对于有效管理乳牛群至关重要.
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