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使用不同泌乳曲线模型对水摄入量和干物质摄入量进行分析。

Analysis of water intake and dry matter intake using different lactation curve models.

作者信息

Kramer E, Stamer E, Spilke J, Thaller G, Krieter J

机构信息

Institute of Animal Breeding and Husbandry, Christian-Albrechts-University, D-24118 Kiel, Germany.

出版信息

J Dairy Sci. 2009 Aug;92(8):4072-81. doi: 10.3168/jds.2008-1957.

Abstract

The objective was to evaluate 6 different lactation curve models for daily water and dry matter intake. Data originated from the Futterkamp dairy research farm of the Chamber of Agriculture of Schleswig-Holstein in Germany. A data set of about 23,000 observations from 193 Holstein cows was used. Average daily water and dry matter intake were 82.3 and 19.8 kg, respectively. The basic linear mixed model included the fixed effects of parity and test-day within feeding group. Additionally, 6 different functions were tested for the fixed effect of lactation curve and the individual (random) effect of cow-lactation curve. Furthermore, the autocorrelation between repeated measures was modeled with the spatial (power) covariance structure. Model fit was evaluated by the likelihood ratio test, Akaike's and Bayesian information criteria, and the analysis of mean residual at different days in milk. The Ali and Schaeffer function was best suited for modeling the fixed lactation curve for both traits. A Legendre polynomial of order 4 delivered the best model fit for the random effect of cow-lactation. Applying the error covariance structure led to a significantly better model fit and indicated that repeated measures were autocorrelated. Generally, the best information criteria values were yielded by the most complex model using the Ali and Schaeffer function and Legendre polynomial of order 4 to model the average lactation and cow-specific lactation curves, respectively, with inclusion of the spatial (power) error covariance structure. This model is recommended for the analysis of water and dry matter intake including missing observations to obtain estimation of correct statistical inference and valid variance components.

摘要

目的是评估6种不同的泌乳曲线模型用于每日水和干物质摄入量。数据来源于德国石勒苏益格 - 荷尔斯泰因州农业商会的富特坎普奶牛研究农场。使用了来自193头荷斯坦奶牛的约23,000条观察数据。平均每日水和干物质摄入量分别为82.3千克和19.8千克。基本线性混合模型包括胎次和饲养组内试验日的固定效应。此外,针对泌乳曲线的固定效应和奶牛 - 泌乳曲线的个体(随机)效应测试了6种不同的函数。此外,重复测量之间的自相关性采用空间(幂)协方差结构进行建模。通过似然比检验、赤池信息准则和贝叶斯信息准则以及对不同泌乳天数的平均残差分析来评估模型拟合度。阿里和谢弗函数最适合对这两个性状的固定泌乳曲线进行建模。4阶勒让德多项式对奶牛 - 泌乳的随机效应给出了最佳的模型拟合。应用误差协方差结构导致模型拟合显著改善,并表明重复测量存在自相关性。一般来说,使用阿里和谢弗函数以及4阶勒让德多项式分别对平均泌乳曲线和特定奶牛的泌乳曲线进行建模,并包含空间(幂)误差协方差结构的最复杂模型产生了最佳的信息准则值。推荐使用该模型分析水和干物质摄入量,包括缺失观测值,以获得正确的统计推断估计和有效的方差分量。

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