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Estimation of Variance Component of Micro-environmental Sensitivity of Milk Yield and Somatic Cell Score in Iranian Holstein cows

Document Type : Research Paper

Authors

1 Agriculture Group,Minab Higher Education Center,University of Hormozgan, Minab, Iran

2 Department of Animal Science,Faculty of Agriculture, University of Guilan, Rasht, Iran

Abstract

The genetic difference in micro-environmental sensitivities are measurable through the heterogeneity of the residual variance between animals and it can be seemed that these differences could be inherited. The objective of current study was to estimate the genetic variance components and heritability of micro-environmental sensitivities for milk yield and somatic cell score (SCS) traits in the first lactation of Iranian Holstein cows. Data included the 1,466,498 and 875,416 test day records for milk yield and somatic cell score, respectively, that were collected by the Animal Breeding Center and Promotion of Animal Products of Iran from 1987 to 2015. The GLM procedure of SAS software was used to determine the fixed effects which were fitted in the statistical model of analysis. Estimation of variance components and genetic parameters of micro-environmental sensitivity was performed using ASReml software, applying the double hierarchical generalized linear model (DHGLM). Heritability of micro-environmental sensitivity for milk yield (0.00201±0.00014) and somatic cell score (0.00188±0.00018) was lower compared with the heritability of milk yield (0.16±0.08) and somatic cell score (0.034±0.007). However, the genetic coefficient variation (GCV) for residual variance of the mentioned traits was estimated to be 0.18 and 0.16, respectively, which indicating a substantial potential for selection responses in both traits. The results of this study indicate that heterogeneity of residual variation in milk yield and somatic cell score of Iranian Holstein cows was partly under genetic control and therefore uniformity of these traits could be improved by selection for residual variance.

Keywords

References
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Research Journal of Livestock Science
Volume 32, Issue 125
March 2020
Pages 121-130
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