• Title/Summary/Keyword: 유효딸소기여도

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Approximation of Multiple Trait Effective Daughter Contribution by Dairy Proven Bulls for MACE (젖소 국제유전능력 평가를 위한 종모우별 다형질 Effective Daughter Contribution 추정)

  • Cho, Kwang-Hyun;Choi, Tae-Jeong;Cho, Chung-Il;Park, Kyung-Do;Do, Kyoung-Tag;Oh, Jae-Don;Lee, Hak-Kyo;Kong, Hong-Sik;Lee, Joon-Ho
    • Journal of Animal Science and Technology
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    • v.55 no.5
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    • pp.399-403
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    • 2013
  • This study was conducted to investigate the basic concept of multiple trait effective daughter contribution (MTEDC) for dairy cattle sires and calculate effective daughter contribution (EDC) by applying a five lactation multiple trait model using milk yield test records of daughters for the Multiple-trait Across Country Evaluation (MACE). Milk yield data and pedigree information of 301,551 cows that were the progeny of 2,046 Korean and imported dairy bulls were collected from the National Agricultural Cooperative Federation and used in this study. For MTEDC approximation, the reliability of the breeding value was separated based on parents average, own yield deviation and mate adjusted progeny contribution. EDC was then calculated by lactation using these reliabilities. The average number of recorded daughters per sire by lactations were 140.57, 94.24, 55.14, 29.20 and 14.06 from the first to fifth lactation, respectively. However, the average EDC per sire by lactation using the five lactation multiple trait model was 113.49, 89.28, 73.56, 54.02 and 35.08 from the first to fifth lactation, respectively, while the decrease of EDC in late lactations was comparably lower than the average number of recorded daughters per sire. These findings indicate that the availability of daughters without late lactation records is increased by genetic correlation using the multiple trait model. Owing to the relatedness between the EDC and reliability of the estimated breeding value for sire, understanding the MTEDC algorithm and continuous monitoring of EDC is required for correct MACE application of the five lactation multiple trait model.