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عنوان
Predicting Breeding Values with Applications in Forest Tree Improvement

پدید آورنده
by Timothy L. White, Gary R. Hodge.

موضوع
Human genetics.,Life sciences.,Trees.

رده
SD399
.
5
B985
1989

کتابخانه
Center and Library of Islamic Studies in European Languages

محل استقرار
استان: Qom ـ شهر: Qom

Center and Library of Islamic Studies in European Languages

تماس با کتابخانه : 32910706-025

INTERNATIONAL STANDARD BOOK NUMBER

(Number (ISBN
9401578338
(Number (ISBN
9789401578332

NATIONAL BIBLIOGRAPHY NUMBER

Number
b591879

TITLE AND STATEMENT OF RESPONSIBILITY

Title Proper
Predicting Breeding Values with Applications in Forest Tree Improvement
General Material Designation
[Book]
First Statement of Responsibility
by Timothy L. White, Gary R. Hodge.

.PUBLICATION, DISTRIBUTION, ETC

Place of Publication, Distribution, etc.
Dordrecht
Name of Publisher, Distributor, etc.
Springer Netherlands
Date of Publication, Distribution, etc.
1989

PHYSICAL DESCRIPTION

Specific Material Designation and Extent of Item
(xi, 369 pages)

SERIES

Series Title
Forestry sciences, 33.

CONTENTS NOTE

Text of Note
1 Matrix Algebra --; 2 Statistics --; 3 Concepts of Progeny Test Analysis --; 4 Theory of Best Linear Prediction (BLP) --; 5 BLP with Half-sib Progeny Test Data --; 6 BLP with Full-sib and Multiple Sources of Data --; 7 BLP: Further Topics --; 8 BLP: An Operational Example --; 9 Selection Index Theory --; 10 Selection Index Applications --; 11 Best Linear Unbiased Prediction: Introduction --; 12 Best Linear Unbiased Prediction: Applications --; Literature Cited --; Appendices --; Answers to Problems.

SUMMARY OR ABSTRACT

Text of Note
In most breeding programs of plant and animal species, genetic data (such as data from field progeny tests) are used to rank parents and help choose candidates for selection. In general, all selection processes first rank the candidates using some function of the observed data and then choose as the selected portion those candidates with the largest (or smallest) values of that function. To make maximum progress from selection, it is necessary to use a function of the data that results in the candidates being ranked as closely as possible to the true (but always unknown) ranking. Very often the observed data on various candidates are messy and unbalanced and this complicates the process of developing precise and accurate rankings. For example, for any given candidate, there may be data on that candidate and its siblings growing in several field tests of different ages. Also, there may be performance data on siblings, ancestors or other relatives from greenhouse, laboratory or other field tests. In addition, data on different candidates may differ drastically in terms of quality and quantity available and may come from varied relatives. Genetic improvement programs which make most effective use of these varied, messy, unbalanced and ancestral data will maximize progress from all stages of selection. In this regard, there are two analytical techniques, best linear prediction (BLP) and best linear unbiased prediction (BLUP), which are quite well-suited to predicting genetic values from a wide variety of sources, ages, qualities and quantities of data.

TOPICAL NAME USED AS SUBJECT

Human genetics.
Life sciences.
Trees.

LIBRARY OF CONGRESS CLASSIFICATION

Class number
SD399
.
5
Book number
B985
1989

PERSONAL NAME - PRIMARY RESPONSIBILITY

by Timothy L. White, Gary R. Hodge.

PERSONAL NAME - ALTERNATIVE RESPONSIBILITY

Gary R Hodge
Timothy L White

ELECTRONIC LOCATION AND ACCESS

Electronic name
 مطالعه متن کتاب 

[Book]

Y

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