Genetic and non-genetic parameter estimates for growth traits and Kleiber ratios in Dorper × indigenous sheep

Abstract

Genetic improvement programme will only be successful when accompanied by a good understanding of the influence of different environmental factors, knowledge of the genetic parameters and the genetic relationships between the traits of interest. This study aimed to evaluate the influence of non-genetic fac- tors on growth traits and Kleiber ratios and to estimate genetic parameters for early growth traits in Dorper indigenous crossbred sheep. The effects of fixed factors were analysed by the general linear model procedure of statistical analysis system, while the genetic parameters were estimated using a WOMBAT computer program fitted animal model. The overall least-square mean for birth weight (BRW), weaning weight (3MW), six-month weight, nine-month weight, and yearling weight were 3.03 ± 0.02, 14.5 ± 0.18, 20.4 ± 0.26, 24.8 ± 0.31, and 28.3 ± 0.40 kg, respectively. The overall least- square mean for Kleiber ratio from birth to weaning (KR1), weaning to six months, six to nine months and nine months to yearling age were 16.8 ± 0.10, 6.41 ± 0.17, 4.55 ± 0.21 and 3.38 ± 0.20 g/kg of meta- bolic weight, respectively. The inclusion of maternal genetic effect had a significant influence on BRW, and it explains 20% of the phenotypic variation. The total heritability estimates for BRW, 3MW, birth to weaning average daily weight gain and KR1 were 0.10, 0.14, 0.16 and 0.12, respectively. The pheno- typic correlation varied from 0.11 ± 0.05 to 0.98 ± 0.02, whereas the direct genetic correlation ranged from 0.32 ± 0.40 to 0.98 ± 0.17. The mean inbreeding coefficient was 0.105% with an annual rate of 0.02%. The heritability estimates for growth traits and Kleiber ratio suggest that slow genetic progress would be expected from the selection. However, the integration of selection with crossbreeding pro- gramme can enhance genetic gain. Therefore, selection should be conducted based on breeding values estimated from multiple information sources to increase the selection response.