In forest tree species with large natural ranges, there are usually several to many separate breeding populations, each designed to capture elite material suited to a particular geographic region. Separate test series are often dedicated to each population. Because the aim is to optimise gain in the meta-population, it is important to ensure that test series are linked so that individuals can be compared across test series as well as within. Computer simulation was used to determine the most efficient strategy for obtaining linkage. The average accuracy of a genetic value contrast between individuals in the same and in different test series was used as the criterion for assessing the optimal level of linkage. Accuracy is a function of the elements of the inverse coefficient matrix for a mixed linear model within a best linear unbiased prediction framework (BLUP). Material used to link test series was either common test families, common check-lots such as seed orchard bulks, or families generated by inter-crossing parents from different test series. Use of common test families was the most efficient strategy for the scenarios tested, which included having 50 parents crossed to produce 50 test families in each of three populations. For a low-heritability scenario, the amount of linkage material, relative to test material, needed to be 8 and 12%, for progeny and parents, respectively, in order for a contrast between individuals in different test series to have equivalent accuracy as a contrast between individuals in the same test series. Other strategies were less efficient in terms of the amount of linkage material needed to obtain this equivalency.

Item Type:	Refereed Article
Keywords:	connectedness, genetic evaluation, BLUP, genetic progress
Research Division:	Agricultural and Veterinary Sciences
Research Group:	Forestry Sciences
Research Field:	Forestry Management and Environment
Objective Division:	Plant Production and Plant Primary Products
Objective Group:	Forestry
Objective Field:	Forestry not elsewhere classified
UTAS Author:	Kerr, RJ (Dr Richard Kerr)
UTAS Author:	Dutkowski, GW (Mr Greg Dutkowski)
ID Code:	106617
Year Published:	2015
Web of Science® Times Cited:	1
Deposited By:	Plant Science
Deposited On:	2016-02-16
Last Modified:	2017-11-09
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