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Genetic parameters and trends for production traits of dairy cattle in Thailand using a multiple-trait multiple-lactation test day model |
Sayan Buaban1 
, Somsook Puangdee2
, Monchai Duangjinda3
, Wuttigrai Boonkum3,4,*
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1The Bureau of Biotechnology in Livestock Production, Department of Livestock Development, Pratumtani 12000, Thailand
2Mahidol University, Nakhonsawan Campus, Nakhonsawan 60130, Thailand
3Department of Animal Science, Khon Kaen University, Meaung, Khon Kaen 40002, Thailand
4Thermo-tolerance Dairy Cattle Research Group, Khon Kaen University, Khon Kaen 40002, Thailand |
Correspondence:
Wuttigrai Boonkum, Tel: +66-43-202360, Fax: +66-43-202361, Email: wboonkum@gmail.com
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Received: 19 February 2019 • Revised: 26 May 2019 • Accepted: 19 February 2019 |
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| 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 DIM 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 230–250, 25–29, and 30–35 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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| Keywords:
Genetic Parameter; Multiple-traits; Multiple-lactation; Random Regression Model; Thai Dairy Cattle |
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