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

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.

Keywords: Genetic Parameter; Multiple-lactation; Multiple-traits; Random Regression Model; Thai Dairy Cattle.

Figure 1

Lactation curve based on 15-day moving average by days in milk (DIM) for the first (square), second (triangle) and third (circle) lactations of Thai dairy cattle: (A) milk yield (kg); (B) fat yield (kg); and (C) protein yield (kg).

Figure 2

Additive genetic variance of milk, fat and protein yields for the first (square), second (triangle), and third (circle) lactations of Thai dairy cattle.

Figure 3

Permanent environmental variance of milk, fat and protein yields for the first (square), second (triangle), and third (circle) lactations of Thai dairy cattle.

Figure 4

Heritability estimates of test day of milk, fat and protein yields for the first (square), second (triangle), and third (circle) lactations of Thai dairy cattle.

Figure 5

Genetic correlations of milk, fat and protein yields between days in milk (DIM) across the first and second (square), the first and third (triangle), and the second and third lactations (circle) of Thai dairy cattle.

Figure 6

Genetic trend for different 305-day production yield in the first (square), second (triangle), and third (circle) lactations for Thai dairy cattle.