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 ...
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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.
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Add this copy of Predicting Breeding Values With Applications in Forest to cart. $56.50, good condition, Sold by Marlowes Books rated 1.0 out of 5 stars, ships from Ferny Hills, Brisbane, QLD, AUSTRALIA, published 1989 by Kluwer Academic Publishers.
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Seller's Description:
Good in None Issued jacket. 367 pages. Ex-Library. Book is in general good condition. There is some light reading wear present, but still a presentable copy. This Book, For Quantitative Geneticists And Plant And Animal Breeders, Describes The Theory And Applcations Of Three Analytical Techniques Useful In Plant And Animal Breeding Programs.
Add this copy of Predicting Breeding Values With Applications in Forest to cart. $162.72, good condition, Sold by Bulrushed Books rated 5.0 out of 5 stars, ships from Moscow, ID, UNITED STATES, published 1989 by Springer.
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Good. Good or better condition. Pages have scattered marks and notes, but completely legible, binding is good. Covers mostly clean, minor scuffing or stickers. Solid reading copies.
Add this copy of Predicting Breeding Values With Applications in Forest to cart. $178.95, like new condition, Sold by THE OREGON ROOM rated 5.0 out of 5 stars, ships from Phoenix, OR, UNITED STATES, published 1989 by Springer.
Add this copy of Predicting Breeding Values with Applications in Forest to cart. $215.66, new condition, Sold by Ingram Customer Returns Center rated 5.0 out of 5 stars, ships from NV, USA, published 1989 by Springer.
Add this copy of Predicting Breeding Values With Applications in Forest to cart. $235.24, new condition, Sold by Ria Christie Books rated 5.0 out of 5 stars, ships from Uxbridge, MIDDLESEX, UNITED KINGDOM, published 1989 by Springer.
