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A model to estimate economic weight of tree survival relative to volume production taking patchiness into account

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Economic weights are needed for genetic selection to weigh different traits in multitrait breeding. The aim of this study was to develop, for commercial forest species, a model for estimating economic weights of survival relative to volume production per unit area. The model takes the patchiness across a stand and the polygenic nature of the factors influencing survival into account and consists of three submodels. The first submodel calculates volume production and a patchiness coefficient, using a production area that is divided into smaller units or production cells. The patchiness coefficient is defined in the model as the variance of survival between the production cells. The second submodel calculates volume production as a function of survival and the patchiness coefficient. The third submodel is a threshold model, which transforms the genetic change in survival from the observable scale to the underlying (liability) scale, thus providing a measure of economic weight. The model behaviour was studied using growth functions for Pinus sylvestris L. in northern Sweden. Both the absolute value of survival and its patchiness coefficient affected the projected production per unit area. When survival was higher than 50%, the relative economic weight of survival increased with decreasing survival and an increasing patchiness coefficient. The relative economic weight of survival was found to be insensitive to changes in site index and harvesting age, but was clearly affected by different initial stand densities.

Keywords: Economic weights; genetic selection; patchiness; tree breeding

Document Type: Research Article


Affiliations: 1: Department of Plant Biology and Forest Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden,Skogforsk (The Forestry Research Institute of Sweden), Uppsala Science Park, Uppsala, Sweden 2: Reindeer Husbandry Unit, Swedish University of Agricultural Sciences, Uppsala, Sweden 3: Skogforsk (The Forestry Research Institute of Sweden), Savar, Sweden 4: Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, Sweden

Publication date: August 1, 2009

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