Genetic Relationships for Milk and Yields Betweem Brazilian and UnitedStates Holstein Cattle Populations PDF Download
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Author: C. N. Costa
Publisher:
ISBN:
Category :
Languages : en
Pages : 175
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Book Description
Author: C. N. Costa
Publisher:
ISBN:
Category :
Languages : en
Pages : 175
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Book Description
Author: Claudio Napolis Costa
Publisher:
ISBN:
Category :
Languages : en
Pages : 394
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Book Description
Genetic relationships between Brazilian and U.S.A. Holstein cattle populations were studies using firstlactation 305-d ME milk and fat records of 29,413 daughters of 705 sires in Brazil and 726,932 daughters of 701 sires in the U.S.A. There were 358 sires with daughter records in both countries. Data sets consisted of all herds and low and high herds stratified by within-year standar deviation for milk (HYSD) computed within counthin country. Components of (co) variance, heritability, and genetic correlations for milk and fat yields were estimated using three data groupings: all, low and high HYDS data sets form within-country and between-country bivariete and multivariate analyses, simutaneous milk and fat yields in both countries. A sire model was solved with a restricted Maximum Likelihood (REML) derivative-free algorithm. Heritabilies for milk and fat yields using all the data were smaller in Brazil (.26 and .23) than in the in the U.S.A. (.33 and .36). Genetic correlations between milk and fat were .79 in Brazil and .62 in the U.S.A. Sire and residual variances for milk yield in Brazil were 59% and 81% (all herds), 51% and 58% (low HYSD herds), and 59% and 101% (high HYSD herds) as large as those obtained for all the U.S.A. data. corresponding percentage values for fat yield were 39% and 65%, and 48%, and 41 and 80%, respectively. Genetic correlations obtained from biavariate analyses between countries ranged from .79 to .87 for milk yield and were .89 or .90 for fat yield. Parameter estimates using all data for bibariate and multivariate analyses were essentially the same. Correlated response from sire selection using the U.S.A. information increased with average HYSD Brazil: daughter response was greatest (.77 kg/kg for milk yield and .63 kg/kg for fat yield) based on information from U.S.A. half-sisters in low HYSD herds. Unequal daughter responses from unequal genetic (co) variances under restrictive Brazilian conditions support the presence of an interaction between genetype and environment, which affects the yield expectation of daughters of U.S.A. sires performing in Brazilian herd enrironments.
Author: Emiliano Raffrenato
Publisher:
ISBN:
Category :
Languages : en
Pages : 362
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Author: James Amon Grantham
Publisher:
ISBN:
Category :
Languages : en
Pages : 66
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Author: Arden Ray Hardie
Publisher:
ISBN:
Category :
Languages : en
Pages : 142
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Author:
Publisher:
ISBN:
Category : Animal genetics
Languages : en
Pages : 710
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Author: Hector Juarez Castillo
Publisher:
ISBN: 9780591924169
Category :
Languages : en
Pages : 135
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A total of 248,230 first parity records of Holstein cows calving from 1987 to 1994, daughters of 588 sires in 3,042 herds were used to evaluate genotype by environment interactions in mature equivalent milk yield (MEM), lactation mean somatic cell score (LMSCS), and conception rate at first service (CRFS). Herds were classified into low and high management groups using three different criteria to differentiate production environments. Phenotypic means for MEM and body weight at first calving were greater in high than in low management herds, while low management herds had higher phenotypic means for LMSCS, CRFS, and age at first calving than high management herds. Genetic parameters were modeled using multiple trait linear models. For the complete data set heritability estimates for MEM, LMSCS, and CRFS, were 0.276, 0.103, and 0.015. Heritabilities and genetic and phenotypic correlations were consistent regardless of the environmental classification criteria. For low management herds, heritabilities for MEM, LMSCS, and CRFS averaged 0.232, 0.101, and 0.020, while for high management herds average heritabilities were 0.283, 0.097, and 0.009. For the low management herds, genetic (and phenotypic) correlations between MEM and LMSCS, MEM and CRFS, LMSCS and CRFS averaged 0.247, $-$0.407, and $-$0.228, ($-$0.055, $-$0.174, and $-$0.037), while for the high management herds they averaged 0.178, $-$0.304, and $-$0.139, ($-$0.089, $-$0.171, and $-$0.034). The genetic correlations between pairs of traits were consistently lower in high than in low management groups, suggesting a genotype by environment interaction. These changes suggest that differences of management between the two management levels reduces considerably the antagonistic genetic association between the traits studied. Breeding programs designed to increase milk yield at the same time that to reduce the genetic increase of LMSCS and the rate of genetic deterioration of CRFS must take into account the magnitude of the differences in the expected correlated responses in LMSCS and CRFS in the two levels of management.
Author:
Publisher:
ISBN:
Category : Medicine
Languages : en
Pages : 1696
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Book Description
Vols. for 1963- include as pt. 2 of the Jan. issue: Medical subject headings.
Author: David W. Blackmore
Publisher:
ISBN:
Category : Cattle
Languages : en
Pages : 160
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Author: Fikret Isik
Publisher: Springer
ISBN: 3319551779
Category : Science
Languages : en
Pages : 409
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Book Description
This book fills the gap between textbooks of quantitative genetic theory, and software manuals that provide details on analytical methods but little context or perspective on which methods may be most appropriate for a particular application. Accordingly this book is composed of two sections. The first section (Chapters 1 to 8) covers topics of classical phenotypic data analysis for prediction of breeding values in animal and plant breeding programs. In the second section (Chapters 9 to 13) we provide the concept and overall review of available tools for using DNA markers for predictions of genetic merits in breeding populations. With advances in DNA sequencing technologies, genomic data, especially single nucleotide polymorphism (SNP) markers, have become available for animal and plant breeding programs in recent years. Analysis of DNA markers for prediction of genetic merit is a relatively new and active research area. The algorithms and software to implement these algorithms are changing rapidly. This section represents state-of-the-art knowledge on the tools and technologies available for genetic analysis of plants and animals. However, readers should be aware that the methods or statistical packages covered here may not be available or they might be out of date in a few years. Ultimately the book is intended for professional breeders interested in utilizing these tools and approaches in their breeding programs. Lastly, we anticipate the usage of this volume for advanced level graduate courses in agricultural and breeding courses.
