用Wood模型拟合南方中国荷斯坦牛产奶量、乳脂率、乳蛋白率和体细胞评分及其效果分析

摘要/Abstract

摘要：

旨在了解南方中国荷斯坦牛测定日产奶量、乳脂率、乳蛋白率和体细胞评分（Somatic cell score，SCS）变化趋势，并进行准确预测。利用Wood模型对南方5个大中型奶牛场(20082010年1～3胎)中国荷斯坦牛的33 194条测定日产奶量、乳脂率、乳蛋白率和SCS数据进行曲线拟合。结果表明，测定日产奶量为标准泌乳曲线，乳脂率、乳蛋白率和SCS变化与标准泌乳曲线正好相反。Wood模型对乳蛋白率和产奶量变化曲线拟合度最高，各胎次拟合度均为0.99，误差均方也较低；其次为乳脂率，各胎次拟合度均为0.98，而对SCS的拟合度最低，均在0.7以下，同时误差均方也最大。各胎次产奶高峰日出现的时间与乳蛋白率和SCS最低值出现的时间相近，而最低乳脂率出现的时间较晚。一胎牛高峰产奶量相对较低（30.4 kg·d^-1），但泌乳后期泌乳持续力及维持低SCS能力较强；二胎和三胎牛高峰产奶量较大，分别为35.9和36.2 kg·d^-1，二胎奶牛在泌乳后期同时维持高乳脂率和乳蛋白率的能力较强。Wood模型适合于南方中国荷斯坦牛测定日产奶量、乳脂率、乳蛋白率变化曲线的拟合分析，而不适合于SCS的拟合分析。

Abstract:

This study aimed to reveal the variations of daily milk yield, milk fat percentage, milk protein percentage and somatic cell score (SCS), and to establish the prediction models for these parameters in the lactation period for Chinese Holstein in southern China. A 33194-test-day dairy herd complete data from 5 Chinese Holstein dairy farms were collected in the southern China from first lactation to third lactation between 2008 to 2010 and fitted to nonlinear curve of test-day milk yield, milk fat percentage, milk protein percentage and SCS with the Wood’s incomplete gamma function model. The curve of test-day milk yield for Chinese Holstein was the standard lactation curve, and the curves of milk fat percentage, milk protein percentage and SCS were the reversed standard lactation curve. The best fitness of the Wood’s model occurred for milk protein percentage and daily milk yield with the lowest residual mean square, then following for milk fat percentage. The poor model fitness (R2≤0.7) was observed for SCS which residual mean square was highest. Daily milk yield peak day was accompanied with occurrences of minimal milk protein and SCS in the estimated lactation model. The minimal milk fat percentage came at the latest time of 18^th week to 21^th week in lactation curve. The peak milk yield was 30.4 kg·d^-1 for first-parity cows, but the persistence for maintaining high milk yield and low SCS were greater than those of second- and third- parity cows in the latter lactation period, and the maximal milk yields for second- and third-parity dairy cows were 35.9 and 36.2 kg·d^-1, respectively. The persistence for keeping high milk fat percentage and milk protein percentage was greater for second-parity cows than those of first- and third-parity cows in the latter lactation period. The Wood’s incomplete gamma function model was appropriate to predict the variation for test-day milk yield, milk fat percentage, milk protein percentage, and was not appropriate for SCS for Chinese Holstein dairy cows in southern China.

中图分类号:

S823
Q348

毛永江，张美荣，许兆君，刘姗，张亚琴，陈丹，王杏龙，杨章平. 用Wood模型拟合南方中国荷斯坦牛产奶量、乳脂率、乳蛋白率和体细胞评分及其效果分析[J]. 畜牧兽医学报, doi: .

MAO Yong-jiang, ZHANG Mei-rong, XU Zhao-jun, LIU Shan, ZHANG Ya-qin, CHEN Dan, WANG Xing-long, YANG Zhang-ping. Lactation Curve Fittings of Wood’s Nonlinear Model for Milk Yield, Milk Fat, Milk Protein and Somatic Cell Score for Chinese Holstein in Southern China[J]. ACTA VETERINARIA ET ZOOTECHNICA SINICA, doi: .

0
/ / 推荐

导出引用管理器 EndNote|Reference Manager|ProCite|BibTeX|RefWorks

链接本文: https://www.xmsyxb.com/CN/

https://www.xmsyxb.com/CN/Y2012/V43/I12/1904

参考文献

［1］GARCIA S C, HOLMES C W. Lactation curves of autumn- and spring-calved cows in pasture-based dairy systems ［J］. Livest Prod, 2001, 68: 189-203.

［2］MACCIOTTA N P P, VICARIO D, CAPPIO-BORLINE A. Detection of different shapes of lactation curve for milk yield in dairy cattle by empirical mathematical models ［J］. J Dairy Sci, 2005, 88: 1178-1191.

［3］WOOD P D P. Algebraic model of the lactation curve in cattle ［J］ . Nature,1967,216(10): 164-165.

［4］ALI T E, SCHAEFFER L R. Accounting for covariance among test day milk yields in dairy cow ［J］. Can J Anim Sci, 1987, 67: 637-644.

［5］NELDER J A. Inverse polynomials, a useful group of multifactor response functions ［J］. Biometrics. 1966, 22: 128-141.

［6］WILMINK J B M. Adjustment of test day milk, fat and protein yield for age, season and stage of lactation ［J］. Livest Prod Sci, 1987, 16: 335-348.

［7］GUO Z, SWALVE H H. Modelling of the lactation curve as a sub-model in the evaluation of test day records［C］. Interbull open meeting, 7-8 September, 1995, Prague, Czech Republic.

［8］刘绍贵, 雷衡, 李春平, 等. 云南省某奶牛场奶牛泌乳曲线拟合及泌乳规律分析［J］. 中国牛业科学,2009, 35(6): 28-32.

［9］程郁昕, 胡春晖, 田丰. 不同模型拟合奶牛泌乳曲线的效果分析［J］.中国奶牛, 2010, 4: 24-26.

［10］尹召华，毛永江，邢淑芳, 等. 不同胎次中国荷斯坦牛泌乳曲线及其拟合的初步研究［J］. 畜牧与兽医, 2010, 42(4): 41-44.

［11］罗清尧，熊本海，马毅，等. 中国荷斯坦奶牛第二泌乳期泌乳曲线模型的研究［J］. 中国农业科学, 2010,43(23):4910-4916.

［12］熊本海，马毅，罗清尧，等. 中国荷斯坦奶牛第三泌乳期泌乳曲线模型的研究［J］. 中国农业科学, 2011，44(2)：402-408.

［13］VAL-ARREOLA D, KEBREAB E, DIJSTRA J, et al. Study of the lactation curve in dairy cattle on farms in central Mexico ［J］. J Dairy Sci, 2004, 87: 3789-3799.

［14］SANTOS A S, SILVESTRE A M. A study of lusitano mare lactation curve with wood's model ［J］. J Dairy Sci, 2008, 91(2): 760-766.

［15］POLLOTT G E. A biological approach to lactation curve analysis for milk yield ［J］. J Dairy Sci, 2000, 83: 2448-2458.

［16］SILVESTRE A M，MARTINS A M，SANTOS V A，et al. Lactation curves for milk, fat and protein in dairy cows: A full approach ［J］.Livest Sci，2009， 122：308-313.

［17］WOOD P D P. Algebraic models of the lactation curves for milk, fat and protein production, with estimates of seasonal variation ［J］. Anim Prod, 1976, 22: 35-40.

［18］POLOTT G E. Deconstructing milk yield and composition during lactation using biologically based lactation models ［J］. J Dairy Sci, 2004, 87: 2375-2387.

［19］STANTON T L, JONES L R, EVERETT R W, et al. Estimating milk, fat and protein lactation curves with a test day model ［J］. J Dairy Sci, 1992, 75: 1691-1700.

［20］OLORIA V E, BROTHERSTONEA S, HILLA W G, et al. Fit of standard models of the lactation curve to weekly records of milk production of cows in single herd ［J］. Livest Prod Sci, 1999, 58: 55-63.

［21］GOLEBIEWSKI M, BRZOZOWSKI P, GOLEBIEWSKI L. Analysis of lactation curves, milk constituents, somatic cell count and urea in milk of cows by the mathematical model of Wood ［J］. Acta Vet Brno, 2011, 80: 73-80.

［22］王雅春,陈幼春,柏荣, 等. 奶牛泌乳曲线的拟合及其模型参数的遗传分析［J］. 畜牧兽医学报, 1999, 30(5): 399-404.

[1]	曹建华, 杨柏高, 张培培, 冯肖艺, 张航, 余洲, 牛一凡, 郝海生, 杜卫华, 朱化彬, 杨凌, 赵学明. 能量负平衡影响奶牛卵泡发育的机制[J]. 畜牧兽医学报, 2024, 55(1): 22-30.
[2]	苗舒, 安济山, 王祚, 肖定福, 兰欣怡, 刘磊, 沈维军, 万发春. 亮氨酸通过PI3K-AKT信号通路促进牛成肌细胞的增殖[J]. 畜牧兽医学报, 2024, 55(1): 142-152.
[3]	刘益丽, 唐娇, 闵奇, 杨露, 王泽宁, 胡莲, 赵迪, 江明锋. 基于转录组数据挖掘牦牛皱胃发育代谢的关键候选基因[J]. 畜牧兽医学报, 2024, 55(1): 153-168.
[4]	姚颖, 周应聪, 杜培岩, 李一娟, 钱文洁, 李柳杨, 余志鹏, 崔燕, 余四九, 樊江峰. 基于TMT技术的牦牛妊娠期血清蛋白质组学分析[J]. 畜牧兽医学报, 2024, 55(1): 192-206.
[5]	张颖, 袁莉刚, 陈国娟, 张芳, 杨大鹏. ET-1/eNOS表达差异在牦牛隐睾发生中的作用分析[J]. 畜牧兽医学报, 2024, 55(1): 207-217.
[6]	张文涛, 刘晨阳, 朱炳霖, 柳丽, 田媛, 姚宇航, 成功. Snail1对牛脂肪细胞增殖分化影响及作用机制的研究[J]. 畜牧兽医学报, 2023, 54(12): 5008-5019.
[7]	牛一凡, 杨柏高, 张培培, 张航, 冯肖艺, 曹建华, 余洲, 郝海生, 杜卫华, 邹惠影, 朱化彬, 马友记, 赵学明. 牛胚胎基因组选择研究进展[J]. 畜牧兽医学报, 2023, 54(11): 4449-4457.
[8]	王文翔, 杜丽丽, 胡俊伟, 张琰翔, 马铭昊, 段蕊, 钱聪, 王欣玥, 李三禄, 张长庆, 张路培, 高雪, 徐凌洋, 李俊雅, 高会江. 基于转录组分析挖掘平凉红牛背最长肌肉质性状候选基因[J]. 畜牧兽医学报, 2023, 54(11): 4589-4604.
[9]	张倩, 崔燕, 余四九, 何俊峰, 潘阳阳, 王萌. 初生及成年牦牛大脑皮质的转录组分析[J]. 畜牧兽医学报, 2023, 54(11): 4605-4614.
[10]	董智豪, 时玉新, 郭冠华, 原开敏, 修豪宇, 汪超, 白俊艳, 王栋. 母牛不同发情阶段唾液化合物的比较分析[J]. 畜牧兽医学报, 2023, 54(11): 4636-4652.
[11]	孙东晓, 张胜利, 张勤, 李姣, 张桂香, 刘丑生, 郑伟杰. 我国奶牛基因组选择技术应用进展[J]. 畜牧兽医学报, 2023, 54(10): 4028-4039.
[12]	冯肖艺, 郝海生, 杜卫华, 朱化彬, 崔凯, 赵学明. 能量负平衡导致奶牛繁殖力下降的机制进展[J]. 畜牧兽医学报, 2023, 54(10): 4050-4060.
[13]	马浩然, 张路培, 金生云, 宝金山, 李红艳, 高会江, 徐凌洋, 王泽昭, 李俊雅. 利用高密度SNP芯片评估中国地方肉牛品种基因组亲缘关系[J]. 畜牧兽医学报, 2023, 54(10): 4174-4185.
[14]	郭逸芯, 王之盛, 胡瑞, 王俊梅, 王森, 施丽媛, 张晓红, 邹华围, 左家学, 彭全辉, 薛白, 王立志. 亮氨酸对黄牛皮下脂肪细胞棕色化的影响[J]. 畜牧兽医学报, 2023, 54(8): 3286-3298.
[15]	褚楚, 张静静, 丁磊, 樊懿楷, 包向男, 向世馨, 刘锐, 罗雪路, 任小丽, 李春芳, 刘文举, 王亮, 刘莉, 李永青, 江汉, 李委奇, 孙伟, 李喜和, 温万, 周佳敏, 张淑君. 基于中红外光谱的牛奶中三种氨基酸含量预测模型的建立及应用[J]. 畜牧兽医学报, 2023, 54(8): 3299-3312.