Analysis of genetic parameters and genetic trends for early growth and reproductive traits of Doyogena sheep managed under community-based breeding program
Impact factor: 3.776 (Year: 2022)
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Kebede H. Giorgis, Aynalem Haile, Tesfaye Getachew, Manzoor Kirmani, Deribe Gemiyu. (17/6/2022). Analysis of genetic parameters and genetic trends for early growth and reproductive traits of Doyogena sheep managed under community-based breeding program. Heliyon, 8 (6).
This study aims to estimate genetic parameters and genetic trends for early growth and reproductive traits of Doyogena sheep. Data used in the study were collected over 6 years (2013–2018). Studied traits were birth weight (BWT), weaning weight (WWT), 6-month weight (SMWT), average daily gains from birth to weaning (ADG0-3), average daily gains from weaning to 6-month age (ADG3-6), average daily gain from birth to 6-month age (ADG0- 6), litter size (LS), lambing interval (LI), age at first lambing (AFL), and annual reproductive rate (ARR). (Co) variance components and genetic parameters were estimated using restricted maximum likelihood (REML). The analyses were carried out using WOMBAT program. Univariate analysis was applied to estimate genetic parameters. Six different animal models were fitted by including or excluding maternal effects. The direct heritability estimates for BWT, WWT, SMWT, ADG0-3, ADG3-6 and ADG0-6 were 0.33 0.06, 0.31 0.06, 0.14 0.06, 0.13 0.04, 0.11 0.07, and 0.02 0.05 respectively. Direct heritability for LS, LI, and AFL were 0.28 0.12, 0.20 0.5, and 0.001 0.3, respectively. The maternal heritability estimates for BWT, WWT, and LS were 0.24 0.12, 0.60 0.07, and 0.24 0.08, respectively. The genetic correlation between BWT with WWT and BWT with SMWT were 0.21 0.07 and 0.21 0.09, respectively. Genetic progress for most of the studied traits has shown promising improvements. Thus, continuation of selection, therefore, suggested for more improvements in the performance of Doyogena sheep. Direct heritability estimates decrease as lamb age increases and selection based on earlier body weight will be more efficient.
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