<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In the analysis of forestry experiments, there may be a need to adjust for competition between plots before predicting deployment performance in the field but there have been few attempts to investigate this. Our analysis looked at diameter data from a 19-year old Sitka spruce clonal trial growing in Scotland. Using a sequence of nested models, a likelihood ratio test indicated that fitting competition at both the genetic and residual level provided a significantly better fit than models which either ignored competition or fitted it at just the genetic or just the residual level. A strong negative genetic correlation of −0.93±0.05 was found between the direct genetic effects and competition effects. This was not significantly different from −1, indicating that competition is almost exactly proportional to the direct genetic effect and that a tree will exert a competitive effect which is closely related to its own genetic merit for size. At the residual level, the correlation between direct and competition effect was estimated as −0.17±0.03. We conclude that competition exists at both the genetic and environmental levels and including it in genetic evaluation systems gives a better prediction of future performance. Results also demonstrate that it is possible to obtain useful information about competition effects from a single-tree plot experiment.</p></abstract>

In the analysis of forestry experiments, there may be a need to adjust for competition between plots before predicting deployment performance in the field but there have been few attempts to investigate this. Our analysis looked at diameter data from a 19-year old Sitka spruce clonal trial growing in Scotland. Using a sequence of nested models, a likelihood ratio test indicated that fitting competition at both the genetic and residual level provided a significantly better fit than models which either ignored competition or fitted it at just the genetic or just the residual level. A strong negative genetic correlation of −0.93±0.05 was found between the direct genetic effects and competition effects. This was not significantly different from −1, indicating that competition is almost exactly proportional to the direct genetic effect and that a tree will exert a competitive effect which is closely related to its own genetic merit for size. At the residual level, the correlation between direct and competition effect was estimated as −0.17±0.03. We conclude that competition exists at both the genetic and environmental levels and including it in genetic evaluation systems gives a better prediction of future performance. Results also demonstrate that it is possible to obtain useful information about competition effects from a single-tree plot experiment.

Competition Effects in a Young Sitka Spruce (<italic>Picea sitchensis</italic>, Bong. Carr) Clonal Trial

United Kingdom of Great Britain and Northern Ireland

The Roslin Institute, Royal(Dick) School of Veterinary Studies

Silvae Genetica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Competition Effects in a Young Sitka Spruce (, Bong. Carr) Clonal Trial

In the analysis of forestry experiments, there may be a need to adjust for competition between plots before predicting deployment performance in the field but...

Competition Effects in a Young Sitka Spruce (Picea sitchensis, Bong. Carr) Clonal Trial

Published Online: Aug 05, 2017

Page range: 149 - 155

Received: Aug 02, 2010

DOI: https://doi.org/10.1515/sg-2011-0020

Keywords
genetic variance, genetic prediction, single-plot design, local spatial trends

© 2011 S. Brotherstone et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Competition Effects in a Young Sitka Spruce (Picea sitchensis, Bong. Carr) Clonal Trial

Published Online: Aug 05, 2017

Page range: 149 - 155

Received: Aug 02, 2010

DOI: https://doi.org/10.1515/sg-2011-0020

Keywordsgenetic variance, genetic prediction, single-plot design, local spatial trends

© 2011 S. Brotherstone et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
genetic variance, genetic prediction, single-plot design, local spatial trends