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首页> 外文期刊>Indian Journal of Dairy Science >Evaluation of Sires Using Different Multitrait Sire Evaluation Methods in Crossbred Cattle
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Evaluation of Sires Using Different Multitrait Sire Evaluation Methods in Crossbred Cattle

机译:杂交牛不同杂种优势评价方法对杂种的评价

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摘要

The data for the present investigation were obtained from the performance records of 1081 crossbred cattle progeny of 65 sires maintained at Instructional Dairy Farm Nagla of G.B, Pant University of Agriculture & Technology Pantnagar, distributed overa period of 40 years from 1966-2005. A total of 65 sires were evaluated on the basis of single and multi traits of their progeny. For single trait sire evaluation, FLMY was considered as principal trait under univariate models viz. least squares method(LSM), DFREML-I method and BLUP-I method. However, for evaluation of sires by multi traits FLMY+AFC, FLMY+FLP and FLMY+AFC+FLP, the multivariate models viz. DFREML-II, DFREML-III, DFREML-IV, BLUP-II, BLUP-IH and BLUP-IV method were used. The accuracy, efficiency and stability of different sire indices were compared to judge their effectiveness. The error variance of single trait BLUP method was found lowest and, therefore, it was considered to be most efficient out of all sire evaluation methods. Rank correlations between different sire evaluation methods ranged between 0.882 (DEFEML-IV with BLUP III) and 0.998 (DFREML-I with DFREML-III and BLUP-I). All the values were positive and highly significant (P<0.01) indicating that there was higher degree ofsimilarity in ranking of sires by different methods.
机译:本次调查的数据来自于1966年至2005年40年间分布在潘特农业技术大学潘纳加尔分校G.B的纳格拉教学奶牛场的65个公牛的1081头杂交牛后代的性能记录。根据后代的单性状和多性状,共评估了65个父本。对于单性状父亲评估,在单变量模型下,将FLMY视为主要性状。最小二乘法(LSM),DFREML-I方法和BLUP-I方法。但是,对于通过多性状FLMY + AFC,FLMY + FLP和FLMY + AFC + FLP进行的父系评估,多元模型即为。使用了DFREML-II,DFREML-III,DFREML-IV,BLUP-II,BLUP-IH和BLUP-IV方法。比较不同父亲指数的准确性,效率和稳定性,以判断其有效性。发现单特征BLUP方法的误差方差最低,因此,在所有父评估方法中,它被认为是最有效的。不同父亲评估方法之间的等级相关性介于0.882(DEFEML-IV,BLUP III)和0.998(DFREML-I,DFREML-III和BLUP-1)之间。所有值均为阳性且极显着(P <0.01),表明通过不同方法对公牛进行排名的相似度更高。

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