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Estimates of Variance Components and Genetic Parameters for Weekly Weight in Juvenile Dairy Goats Using Random
Chrilukovian B Wasike1, Ruth Wambui Waineina2, Kiplangat Ngeno3
1Livestock Efficiency Enhancement Group (LEEG), Department of Animal and Fisheries Sciences, Maseno University, Maseno, Kenya.
Abstract:
Growth performance of juvenile dairy goats influences their rate of growth and consequently (re)productive performance as mature goats. However, dairy goat improvement programmes focus on milk and fertility traits and seldom growth traits. This study aimed at estimating variance components and genetic parameters for growth in juvenile dairy goats to avail these estimates for inclusion in the national dairy goat improvement program. 4072 weekly weights records (from 1 week to 16th week of age) were collected on 453 goats born of 35 dams and 15 sires of Saanen, Toggenburg, Alpine and their crossbred genotypes. The records were subjected to univariate random regression analysis fitting direct additive, maternal genetic and permanent environmental [PE] effects as random effects to estimate variance components and genetic parameters. Phenotypic, additive genetic, maternal genetic and PE variances increased along the growth trajectory. Estimates of variance ratios ranged from 0.015 ± 0.14 to 0.469 ± 0.15, 0.097 ± 0.06 to 0.204 ± 0.08 and 0.407 ± 0.13 to 0.641 ± 0.08 for additive genetic, maternal genetic and PE effects, respectively. Correlation estimates were positive. Additive genetic correlations between the weekly weights were high and near unity. Maternal genetic correlations between the weekly weights were equally high. PE correlations were low between early weights and later weights, although correlations between later weights were high. The correlation estimates decreased as the interval between the weights increased for all the random effects. There is sufficient scope of variance in weekly weights to enable selective breeding. Selection to improve later weights could be done based on early weekly weights given the high additive genetic correlations.
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