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Phenotypic and genetic parameter estimates for early growth, growth rate and growth efficiency‐related traits of Fogera cattle in Ethiopia

BACKGROUND: Understanding the phenotypic and genetic parameter estimates of growth traits is important for an effective livestock genetic improvement programme. OBJECTIVES: In this study, we evaluated the phenotypic performances and estimated genetic parameters for birthweight (BWT), weaning weight...

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Detalles Bibliográficos
Autores principales: Kassahun, Demelash, Taye, Mengistie, Kebede, Damitie, Tilahun, Mekonen, Tesfa, Assemu, Bitew, Addisu, Kebede, Adebabay, Meseret, Mulugeta, Lakew, Eyasu, Bimrow, Tewodros, Haile, Aynalem
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8788963/
https://www.ncbi.nlm.nih.gov/pubmed/34480429
http://dx.doi.org/10.1002/vms3.628
Descripción
Sumario:BACKGROUND: Understanding the phenotypic and genetic parameter estimates of growth traits is important for an effective livestock genetic improvement programme. OBJECTIVES: In this study, we evaluated the phenotypic performances and estimated genetic parameters for birthweight (BWT), weaning weight (WWT), pre‐weaning average daily gain (PADG), pre‐weaning Kleiber ratio (PKR), pre‐weaning growth efficiency (PGE) and pre‐weaning relative growth rate (PRGR) of Fogera cattle in Ethiopia. METHODS: Growth data collected from 2000 to 2018 in Andassa Livestock Research Center were used for the study. General linear model of SAS 9.1 was used to estimate the least squares mean (LSM) ± standard error (SE) for phenotypic performances, and AI‐REML of Wombat software combined with a series of five single‐trait animal models to estimate phenotypic variance and its direct, maternal and residual components. Calf sex, calf birth season and calf birth year were the fixed effects considered. RESULTS: The overall LSM ± SE BWT, WWT, PADG, PKR, PGE and PRGR were 21.28 ± 0.05 kg, 97.99 ± 0.67 kg, 320.29 ± 2.79 g, 10.10 ± 0.04, 3.51 ± 0.35 and 1.95 ± 0.00, respectively. All the fixed effects considered significantly (p < 0.001) affected all the traits. The direct heritability estimates for BWT, WWT, PADG, PKR, PGE and PRGR were 0.21 ± 0.07, 0.26 ± 0.01, 0.55 ± 0.19, 0.53 ± 0.18, 0.33 ± 0.00 and 0.50 ± 0.00, respectively. The genetic correlations among the traits ranged from negative (−0.20 ± 0.04; BWT‐PKR) to positive (0.99 ± 0.00; BW‐PGE, BW‐GR, WWT‐PGE, WWT‐PGR, ADG‐PGR, PKR‐PGR, PKR‐PGE and PGE‐PGR). Similarly, the phenotypic correlations ranged from −0.03 ± 0.20 to 0.99 ± 0.01; BWT‐PGE, BWT‐PRGR, WWT‐PGE, WWT‐PRGR, PKR‐PGE, PKR‐PRGR and PGE‐PRGR). CONCLUSION: The positive and larger phenotypic and genetic correlations between most of the traits implied that selection based on one trait could improve the other traits. However, the negative phenotypic and genetic correlation between BWT‐PKRA implies that selection of Fogera calves based on either of the traits has an adverse effect on the other. Therefore, caution should be taken when designing the selection criteria for growth improvement.