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New Forests

, Volume 47, Issue 6, pp 783–799 | Cite as

Defining breeding and deployment regions for radiata pine in southern Australia

  • Gregory Dutkowski
  • Miloš IvkovićEmail author
  • Washington J. Gapare
  • T. A. McRae
Article

Abstract

Productivity of forest tree plantations can be maximised by matching genetically improved planting stock to environments where it performs best. Radiata pine (Pinus radiata D. Don) breeding and deployment of genetically improved stock in Australia are currently based on the National Plantation Inventory (NPI) regions. These regions are not based on environmental drivers and biological patterns of genotype by environment interaction (G × E), so they may not deliver optimal genetic gains across plantation areas in Australia. This study used diameter at breast height data from trial sites with common parents to estimate site–site and age–age additive genetic correlations, and compile them into a database. A custom-built script in R was developed, which models the correlation estimates by minimising the weighted error sum of squares from the model to the estimates. First, parameters for the Lambeth’s age–age correlation model were derived to adjust for differences in age between sites. Second, estimates of average site–site additive genetic correlations between and within NPI regions were compared with currently assumed values. Third, to identify new breeding and deployment regions, sites were sequentially divided into groups based on critical values of climate and soil variables. Sites were first split into two clusters based on mean daily minimum temperature of wettest quarter, at a threshold of 9.0 °C, and then within the cool cluster, based on rainfall in March, at a threshold of 68 mm. Variances among breeding values were compared for different site classifications as a measure of potential genetic gain. The results from this study are currently being used to redefine the breeding and deployment regions for radiata pine grown in Australia.

Keywords

Radiata pine Genetic correlations Genotype by environment interaction Breeding Climate variables Soil variables 

Notes

Acknowledgments

This research was jointly funded by the Commonwealth Scientific and Industrial Research Organisation (CSIRO), Forest and Wood Products Australia (FWPA, PNC246-1112), Southern Tree Breeding Association (STBA), Radiata Pine Breeding Company (RPBC) and Timberlands Pacific. We thank David Pilbeam and Peter Buxton of STBA for access to trial data; Carolyn Raymond, Southern Cross University and Forestry Corporation NSW for data access and in-kind contributions. David Jacquier helped us with the Australian Soil Resource Information System (CSIRO ASRIS). We thank Wayne Richardson Forestry SA Research Section, for soil data. Don Aurik of Timberlands Pacific Pty Ltd provided trial and environmental data. Thanks to Drs Shiming Liu and Ian Purvis of CSIRO for reviewing and providing useful comments on a draft of this paper. Special thanks to two anonymous reviewers for their constructive suggestions that greatly improved the manuscript.

Supplementary material

11056_2016_9544_MOESM1_ESM.docx (1.1 mb)
Supplementary material 1 (DOCX 1160 kb)
11056_2016_9544_MOESM2_ESM.docx (21 kb)
Supplementary material 2 (DOCX 20 kb)

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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Gregory Dutkowski
    • 1
    • 2
  • Miloš Ivković
    • 3
    Email author
  • Washington J. Gapare
    • 3
  • T. A. McRae
    • 1
    • 2
  1. 1.PlantPlan Genetics Pty Ltd.Mount GambierAustralia
  2. 2.Southern Tree Breeding Association (STBA)Mount GambierAustralia
  3. 3.Commonwealth Scientific and Industrial Research Organisation (CSIRO), AgricultureCanberraAustralia

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