Study on lactation curve models of chinese holstein for the second parity | 中国荷斯坦奶牛第二泌乳期泌乳曲线模型的研究

Chino. 【目的】满足对奶牛个体营养需要量的精确预测,探索乳成分的分泌规律。【方法】以中国荷斯坦奶牛第二泌乳期305 d产奶量为基础,从6 000―13 000 kg,按1 000 kg为间隔分为7个区间对289头奶牛进行分组, 分别采用Wood、Gompertz经验模型和Dijkstra机理模型对每月测定并校正到标准日期的产奶数据进行模拟。通过SAS非线性参数估计和均方误差(MSE)分析,筛选得到了21套泌乳产量区间的泌乳曲线模型,并通过模型的参数揭示了每个区间的起始产量(y0)、达到泌乳高峰时的天数(tm)、高峰产量(ym)和泌乳持续力(r(th))等泌乳特性指标。【结果】通过分析模型参数及特性指标的规律表明,上述3类数学模型均能较好反映中国荷斯坦奶牛第二泌乳期不同产量区间的泌乳规律,但是3种模型中的估测参数及其派生的泌乳特性参数表现出不同的特征：Wood和Gompertz的模型参数a、b、c基本与泌乳区间平均产量(Yavg)呈现规律性变化(模型14例外),但Dijkstra模型的参数a、b、c和d与Yavg 未表现出规律性;由模型参数决定的泌乳特性参数对y0的估计Ｗood模型有局限性,由Gompert模型决定的y0的规律性最优,而Dijkstra模型得到的y0值变异大,不能反映实际结果;对tm 的计算结果, 总体上Wood模型值均高于其它2种模型的计算结果,而后两种模型除在最低产量与最高产量区间上出现较大差异及变异外,其它5个泌乳区间上的计算值基本一致;而3种模型在不同泌乳区间上对Ym 和r(th)的计算结果的吻合性高度一致。【结论】上述建立在中国荷斯坦奶牛第二胎不同泌乳区间上泌乳曲线模型为准确预测奶牛营养需要、实施奶牛的精细饲养提供了依据。

Inglés. 【Objective】 An experiment was made in order to precisely predict nutrient requirements of individual dairy cattle and study lactating characteristics of milk compositions. 【Method】 Two hundred and eighty-nine dairy cows were divided into 7 groups from 6 000 kg to 10 000 kg with an interval of 1 000 kg according to 305-day milk yield in the second parity. The adjusted month-milk-yields were fitted using 3 equations (Wood, Gompertz and Dijkstra equations). Twenty-one sets of lactation curve models for different milk yield ranges were obtained by SAS nonlinear parameter estimation and mean square error (MSE) analysis and lactation feature indices of each yield range including initial yield (y0), time to peak yield (tm), peak yield (ym) and relative rate of decline at the point halfway between peak yield and the end of lactation (r(th)) were revealed by model parameters. 【Result】 Analyses of the model parameters and feature indices showed that the above three mathematic equations are all able to describe lactation characteristics of Chinese Holstein dairy cows among different milk yield ranges for the second parity well,but their characteristics were different: a,b and c from Wood and Gompertz model changed regularly with the average yield (Yavg) of yield ranges (except for model 14 ); Y0 estimated by Gompertz was the best by means of regularity while Y0 of Wood had limitations, that of Dijkstra was different widely and could not reflect practical situations. Results of tm estimated by Wood was better than that of the other models which the variation was big in the lowest and highest yield ranges. Values of ym and r(th) of 3 models were highly consistent. 【Conclusion】 Above lactation curve models for different lactation ranges could provide reference for the accurate prediction of nutrient requirements and precise feeding for dairy cattle.