Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/82268
Type of publication: research article
Type of publication (PDB): Straipsnis Clarivate Analytics Web of Science / Article in Clarivate Analytics Web of Science (S1)
Field of Science: Miškotyra / Forestry (A004)
Author(s): Danusevičius, Darius;Lindgren, Dag
Title: Strategies for optimal deployment of related clones into seed orchards
Is part of: Silvae genetica. , Vol. 57, iss. 3 (2008)
Extent: p. 119-127
Date: 2008
Keywords: Co-ancestry;Gene diversity;Genetic gain;Inbreeding;Seed orchard;Status number
Abstract: This study deals with how the deployed proportion of each candidate clone can be decided at the establishment of a seed orchard when the breeding values are available for each candidate in a population of unrelated half-sib families. The following deployment strategies were compared: (a) truncation selection by selecting the clones with the breeding values exceeding certain threshold and deploying equal number of ramets (Truncation strategy); (b) truncation selection by selecting only one best individual within each family (Truncation unrelated); (c) maximizing gain at a given effective clone number (Linear deployment); (d) linear deployment by selecting one best individual within each family (Linear deployment unrelated) and (e) maximizing net gain at a given gene diversity (Optimal proportions). The study focused on the latest alternative and described its superiority and characteristics for a number of possible typical cases. The genetic gain adjusted for predicted inbreeding depression (Net gain), gene diversity and effective clone number were considered as the main ranking criteria. The strategies optimizing the number of related individuals and the linear deployment strategy with restriction on relatedness returned the highest Net gain. If there is a large diversity to select from (the status number of the candidates is more than 8 times greater than the stateus number desired in the seed orchard), a relatively simple advice is to select the best individual within the best families and deploy the clones linearly according to their breeding values (the number of families selected depends on the desired status number)
Internet: https://hdl.handle.net/20.500.12259/82268
Affiliation(s): Vytauto Didžiojo universitetas
Žemės ūkio akademija
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications

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