The use of a Bivariate Random Regression Model for Genetic Analysis of Milk Yield in Iranian Native Buffalo
Abstract:
Bivariate random regression models were used to estimate variance components of test-day milk yields (TDMY) in the first and second lactations of Iranian buffaloes. Data included 10,133 TDMY records from 862 Iranian buffaloes for first lactation and 786 for second lactation which were collected from 1993 to 2011 by the animal breeding centre of Iran. The models of analysis included the fixed effects of herd-test-date (HTD), year-season (YS) and age at calving as covariate were fitted in the model of analysis. The random variables of model were the additive genetic and animal permanent environmental effects and residual effects. The (co)variance components and the genetic parameters were estimated using the REML method with the Wombat program. In bi-variate model, each parity was treated as a separate trait. In the first lactation, Heritability estimates were low to moderate and ranged from 0.05 to 0.26 and had an erratic pattern. In the second lactation, the heritability estimates increased from the first (0.29) to the second test day (0.31), and then with a slight decrease during lactation, again increased on the last two test days (0.14 and 0.29). The range of genetic correlation between the first day of the first lactation period and the whole day of the test days in the second lactation period were reported between -0.07 and .73. Heritability estimates test-day milk yield are higher at the beginning of the lactation period for first and second lactation which indicates that milk yield in the early months of lactation can be used as a selection criterion in Iranian native buffalo.
Article Type:
Research/Original Article
Language:
Persian
Published:
Research On Animal Production, Volume:9 Issue:19, 2018
Pages:
102 - 112
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