Volume 10, Issue 25 (11-2019)                   rap 2019, 10(25): 104-111 | Back to browse issues page


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kamel K, hafezian H, gholizadeh M. Estimation of Genetic Parameters for Production and Reproduction Traits of Holstein Cows of Mazandaran Province using Bayesian Approach. rap. 2019; 10 (25) :104-111
URL: http://rap.sanru.ac.ir/article-1-981-en.html
Department of Animal Science, Sari Agriculture Sciences and Natural Resources University
Abstract:   (628 Views)
      The present study was conducted to estimate the genetic and phenotypic parameters of productive and reproductive traits of Holstein cows in Mazandaran Holstein dairy cattle. To this purpose, data 39792 records of Holstein cows collected at Mahdasht Milk and Meat Company in sari during 1986 to 2015 were used. Productive traits included corrected 305 days milk records, fat and protein production, and reproductive traits were age at first calving, calving interval, dry period and days open. Genetic parameters were estimated by THRGIBBS1F90 software under single trait animal model using Bayesian statistical method based on Gibbs sampling approach. A total of 200000 cycles were implemented among which the first, 20000 cycles were considered as burn-in period. Heritabilities obtained as 0.34, 0.68, 0.65, 0.17, 0.007, 0.07 and 0.07 for milk production, fat production, protein production, age at first calving, calving interval, dry period and days open, respectively. The genetic correlation between milk production and fat production was 0.48. Genetic correlation between milk production, fat production and protein production with age at first calving was -0.003, 0.14 and 0.39, respectively, Genetic correlation between milk and protein production with calving interval was estimated to be 0.23 and 0.55, respectively. According to the results, correlations between production and reproduction traits could be considered in breeding programs.
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Type of Study: Research | Subject: ژنتیک و اصلاح نژاد دام
Received: 2018/12/31 | Revised: 2019/11/25 | Accepted: 2019/07/8 | Published: 2019/11/25

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