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Journal of Applied Sciences
  Year: 2016 | Volume: 16 | Issue: 8 | Page No.: 380-387
DOI: 10.3923/jas.2016.380.387
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Model Comparisons and Genetic Parameters Estimates of Productive Traits in Holstein Cows

Eman A. ABO Elfadl and Hend A. Radwan

Objective: The study aimed at selecting the accurate model helps in improving productive traits in dairy population through compare between inclusion and exclusion of maternal genetic effect in estimating variance components and genetic parameters of those traits. Methodology: Data set and pedigree information were collected from 3464 Holstein-Friesian dairy records for 991 cows descended from 99 bulls and 691 dams calved over a period of 17 years (1994-2011) for three consecutive lactations. Three multi-trait animal models were fitted to estimate covariance components and corresponding genetic parameters. Results: The highest direct and maternal genetic variances were in model 2 which account for covariance between direct and maternal genetic effect. Mostly, phenotypic variances were high in full animal model for first and second lactations but in third lactation the highest being in model 3. Direct and maternal heritability were high in model 2 in three lactations except direct heritability for days in milk in second lactation and days in milk, 305-day milk yeild mature equivalent, fat yield and protein yield in third lactation. Ranges and standard deviations of breeding value varied between different models but often they were high in model 1 and 3. Conclusion: Results highlight the importance of inclusion of maternal genetic effect in designing an appropriate selective and breeding program to obtain accurate estimates of genetic parameters especially if the variation between animals is high.
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  •    Different Single-trait Animal Models for Estimating Direct-maternal Covariance Components in Holstein Cows
How to cite this article:

Eman A. ABO Elfadl and Hend A. Radwan, 2016. Model Comparisons and Genetic Parameters Estimates of Productive Traits in Holstein Cows. Journal of Applied Sciences, 16: 380-387.

DOI: 10.3923/jas.2016.380.387






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