Estimation of genetic parameters and trends for production traits of dairy cattle in Thailand using a multiple-trait multiple-lactation test day model.

IF 2.2 2区 农林科学 Asian-Australasian Journal of Animal Sciences Pub Date : 2020-09-01 Epub Date: 2019-11-12 DOI:10.5713/ajas.19.0141
Sayan Buaban, Somsook Puangdee, Monchai Duangjinda, Wuttigrai Boonkum
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引用次数: 11

Abstract

Objective: The objective of this study was to estimate the genetic parameters and trends for milk, fat, and protein yields in the first three lactations of Thai dairy cattle using a 3-trait,- 3-lactation random regression test-day model.

Methods: Data included 168,996, 63,388, and 27,145 test-day records from the first, second, and third lactations, respectively. Records were from 19,068 cows calving from 1993 to 2013 in 124 herds. (Co) variance components were estimated by Bayesian methods. Gibbs sampling was used to obtain posterior distributions. The model included herd-year-month of testing, breed group-season of calving-month in tested milk group, linear and quadratic age at calving as fixed effects, and random regression coefficients for additive genetic and permanent environmental effects, which were defined as modified constant, linear, quadratic, cubic and quartic Legendre coefficients.

Results: Average daily heritabilities ranged from 0.36 to 0.48 for milk, 0.33 to 0.44 for fat and 0.37 to 0.48 for protein yields; they were higher in the third lactation for all traits. Heritabilities of test-day milk and protein yields for selected days in milk were higher in the middle than at the beginning or end of lactation, whereas those for test-day fat yields were high at the beginning and end of lactation. Genetics correlations (305-d yield) among production yields within lactations (0.44 to 0.69) were higher than those across lactations (0.36 to 0.68). The largest genetic correlation was observed between the first and second lactation. The genetic trends of 305-d milk, fat and protein yields were 230 to 250, 25 to 29, and 30 to 35 kg per year, respectively.

Conclusion: A random regression model seems to be a flexible and reliable procedure for the genetic evaluation of production yields. It can be used to perform breeding value estimation for national genetic evaluation in the Thai dairy cattle population.

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利用多性状多泌乳试验日模型估计泰国奶牛生产性状的遗传参数和趋势
目的:本研究的目的是利用3性状- 3泌乳随机回归试验日模型,估计泰国奶牛泌乳前3期奶、脂肪和蛋白质产量的遗传参数和趋势。方法:数据包括第一、第二、第三泌乳的168,996、63,388和27,145个测试日记录。记录来自1993年至2013年124个牛群的19068头奶牛。(Co)方差成分采用贝叶斯方法估计。Gibbs抽样得到后验分布。模型以待测乳组的畜群-年-月、待测乳组产犊月的品种群-季、产犊年龄为固定效应,以遗传和永久环境效应的线性和二次回归系数为随机回归系数,分别定义为修正常数、线性、二次、三次和四次勒让德系数。结果:牛奶的平均日遗传率为0.36 ~ 0.48,脂肪为0.33 ~ 0.44,蛋白质产量为0.37 ~ 0.48;在第三次泌乳时,所有性状均较高。试验日产奶量和选定日产奶量的遗传力在泌乳中期高于泌乳开始和结束时,试验日产奶量的遗传力在泌乳开始和结束时较高。产奶量之间的遗传相关性(305 d产量)在哺乳期内(0.44 ~ 0.69)高于哺乳期间(0.36 ~ 0.68)。在第一次和第二次泌乳之间观察到最大的遗传相关性。305 d产奶量、脂肪量和蛋白质产量的遗传趋势分别为230 ~ 250 kg /年、25 ~ 29 kg /年和30 ~ 35 kg /年。结论:随机回归模型是一种灵活可靠的产量遗传评价方法。该方法可用于泰国奶牛种群遗传评价的育种价值估算。
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来源期刊
Asian-Australasian Journal of Animal Sciences
Asian-Australasian Journal of Animal Sciences AGRICULTURE, DAIRY & ANIMAL SCIENCE-
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审稿时长
3 months
期刊介绍: Asian-Australasian Journal of Animal Sciences (AJAS) aims to publish original and cutting-edge research results and reviews on animal-related aspects of the life sciences. Emphasis will be placed on studies involving farm animals such as cattle, buffaloes, sheep, goats, pigs, horses, and poultry. Studies for the improvement of human health using animal models may also be publishable. AJAS will encompass all areas of animal production and fundamental aspects of animal sciences: breeding and genetics, reproduction and physiology, nutrition, meat and milk science, biotechnology, behavior, welfare, health, and livestock farming systems.
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