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结合短期呼吸测量来开发牛奶中红外光谱中的甲预测方程
S Fresco1, A Vanlierde2, D Boichard3
1Eliance, 149 rue de Bercy, 75595 Paris cedex 12, France; Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France.
Animal : an international journal of animal bioscience
|June 13, 2024
概括
使用牛奶中红外光谱预测甲 (CH4) 排放对于基因组选择至关重要. 对CH4数据进行日间变化的校正可以提高预测准确度,并减少与标准平均方法相比的数据损失.
科学领域:
- 动物科学动物科学
- 农业科学 农业科学
- 频谱学是一种光谱学.
背景情况:
- 对乳牛的甲 (CH4) 排放的准确预测对于基因组选择至关重要.
- 目前使用GreenFeed系统的方法涉及CH4测量的平均值,由于动物不经常访问,导致大量数据丢失.
- 牛奶的中红外光谱 (MIR) 为CH4排放预测提供了高通量数据源.
研究的目的:
- 评估使用日间变化纠正数据校准CH4预测方程是否与标准平均化方法相比提高了预测准确性.
- 通过利用所有可用的光谱数据来减少CH4排放预测中的数据损失.
- 开发基于不同牛群的CH4排放的新的,强大的预测方程.
主要方法:
- 使用了来自235头奶牛 (荷尔斯坦,蒙贝利亚德,丰富) 的1822个牛奶MIR光谱的校准数据集和来自46头奶牛的104个光谱的验证数据集.
- 用原始MIR光谱校准的方程的比较预测能力 (R^2_v,RMSE) 与使用CH4平均值进行日间变化预校正的方程相比.
- 评估了预校正对数据利用的影响,以及对GreenFeed系统最低点位测量的需求.
主要成果:
- 仅基于MIR光谱的方程的预测能力低至中等 (R^2_v = 0.22-0.36,RMSE = 57-70 g/d).
- 使用CH4平均值预先对日间变化进行校正的方程显示出更好的预测性能,特别是在减少预测误差方面.
- 预校正允许使用所有可用的数据,消除了对平均化最低数量的GreenFeed随机测量的要求.
结论:
- 在校准之前对CH4排放数据的日间变化进行校正,可显著提高牛奶MIR光谱的预测准确性.
- 这种方法最大限度地减少了数据损失,并优化了在乳牛中进行基因组选择的高通量光谱数据的使用.
- 该研究提供了实用建议和一套新的CH4排放预测方程,适用于各种乳制品种群.
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