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Articles by Kenneth Nyström

Category : Research article

article id 7738, category Research article
Samuel Egbäck, Bo Karlsson, Karl-Anders Högberg, Kenneth Nyström, Mateusz Liziniewicz, Urban Nilsson. (2018). Effects of phenotypic selection on height-diameter ratio of Norway spruce and Scots pine in Sweden. Silva Fennica vol. 52 no. 2 article id 7738. https://doi.org/10.14214/sf.7738
Keywords: Pinus sylvestris; Picea abies; genetic correlations; heritability; Genetic selection; slenderness
Highlights: Swedish plus-tree selection promoted less slender Norway spruce trees and more slender Scots pine trees compared to neighboring trees; Similar results were also found for progeny trials which indicated that genetics played a prominent role in phenotypic appearance.
Abstract | Full text in HTML | Full text in PDF | Author Info

Genetically improved Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.) are extensively used in operational Swedish forestry plantations. However, relatively little is known about the stem slenderness (height-diameter ratio) of genetically improved material. Thus, in this study we investigated effects of plus-tree selection on stem slenderness of Norway spruce and Scots pine in Sweden by evaluating both the plus-tree selection and a large number of progeny trials. Species-specific models for predicting the height-diameter ratio were estimated using regression and mixed model approach. Our results show that phenotypic plus-tree selection promoted less slender Norway spruce trees and more slender Scots pine trees compared to neighboring trees. Similar results were also found for the progeny trials which indicated that genetics played a prominent role in the phenotypic appearance. Compared to the progeny of neighboring trees, Norway spruce plus-tree progenies had a 5.3% lower height-diameter ratio, while Scots pine plus-tree progenies had a 1.5% greater height-diameter ratio. The narrow sense heritability for height-diameter ratio was 0.19 for Norway spruce and 0.11 for Scots pine, indicating that it is possible to modify the height-diameter ratio by breeding. Correlation coefficients between breeding values for height-diameter ratio and diameter were negative for Scots pine (–0.71) and Norway spruce (–0.85), indicating that selection for diameter only would result in less slender stems of both species. Similar correlations were also found between breeding values for height-diameter ratio and height of Scots pine (–0.34) and Norway spruce (–0.74).

  • Egbäck, Swedish University of Agricultural Sciences, Southern Swedish Forest Research Centre, P.O. Box 49, 230 53 Alnarp, Sweden E-mail: samuel.egback@slu.se (email)
  • Karlsson, Skogforsk, Ekebo, 268 90 Svalöv, Sweden E-mail: bo.karlsson@skogforsk.se
  • Högberg, Skogforsk, Ekebo, 268 90 Svalöv, Sweden E-mail: karl-anders.hogberg@skogforsk.se
  • Nyström, Swedish University of Agricultural Sciences, Department of Forest Resource Management, Skogsmarksgränd, 901 83 Umeå, Sweden E-mail: kenneth.nystrom@slu.se
  • Liziniewicz, Skogforsk, Ekebo, 268 90 Svalöv, Sweden E-mail: Mateusz.Liziniewicz@skogforsk.se
  • Nilsson, Swedish University of Agricultural Sciences, Southern Swedish Forest Research Centre, P.O. Box 49, 230 53 Alnarp, Sweden E-mail: urban.nilsson@slu.se
article id 5662, category Research article
Samuel Egbäck, Urban Nilsson, Kenneth Nyström, Karl-Anders Högberg, Nils Fahlvik. (2017). Modeling early height growth in trials of genetically improved Norway spruce and Scots pine in southern Sweden. Silva Fennica vol. 51 no. 3 article id 5662. https://doi.org/10.14214/sf.5662
Keywords: Pinus sylvestris; Picea abies; individual tree growth model; genetic component; genetic multiplier; unimproved material; improved material
Highlights: The developed height growth model based on unimproved material predicted the development relatively well for genetically improved Norway spruce; For genetically improved Scots pine, however, the model needed to be modified; By incorporating a genetic component into the Scots pine model, the prediction errors were reduced.
Abstract | Full text in HTML | Full text in PDF | Author Info

Genetically improved Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.) are used extensively in operational Swedish forestry plantations to increase production. Depending on the genetic status of the plant material, the current estimated genetic gain in growth is in the range 10–20% for these species and this is expected to increase further in the near future. However, growth models derived solely from data relating to genetically improved material in Sweden are still lacking. In this study we investigated whether an individual tree growth model based on data from unimproved material could be used to predict the height increment in young trials of genetically improved Norway spruce and Scots pine. Data from 11 genetic experiments with large genetic variation, ranging from offspring of plus-trees selected in the late 1940s to highly improved clonal materials selected from well performing provenances were used. The data set included initial heights at the age of 7–15 years and 5-year increments for almost 2000 genetic entries and more than 20 000 trees. The evaluation indicated that the model based on unimproved trees predicted height development relatively well for genetically improved Norway spruce and there was no need to incorporate a genetic component. However, for Scots pine, the model needed to be modified. A genetic component was developed based on the genetic difference recorded within each trial, using mixed linear models and methods from quantitative genetics. By incorporating the genetic component, the prediction errors were significantly reduced for Scots pine. This study provides the first step to incorporate genetic gains into Swedish growth models and forest management planning systems.

  • Egbäck, Swedish University of Agricultural Sciences, Southern Swedish Forest Research Centre, P.O. Box 49, 230 53 Alnarp, Sweden E-mail: samuel.egback@slu.se (email)
  • Nilsson, Swedish University of Agricultural Sciences, Southern Swedish Forest Research Centre, P.O. Box 49, 230 53 Alnarp, Sweden E-mail: urban.nilsson@slu.se
  • Nyström, Swedish University of Agricultural Sciences, Department of Forest Resource Management, Skogsmarksgränd, 901 83 Umeå, Sweden E-mail: kenneth.nystrom@slu.se
  • Högberg, Skogforsk, Ekebo, 268 90 Svalöv, Sweden E-mail: karl-anders.hogberg@skogforsk.se
  • Fahlvik, Swedish University of Agricultural Sciences, Southern Swedish Forest Research Centre, P.O. Box 49, 230 53 Alnarp, Sweden E-mail: nils.fahlvik@slu.se
article id 595, category Research article
Kenneth Nyström, Göran Ståhl. (2001). Forecasting probability distributions of forest yield allowing for a Bayesian approach to management planning. Silva Fennica vol. 35 no. 2 article id 595. https://doi.org/10.14214/sf.595
Keywords: basal area growth model; mixed-model; uncertainty of predictions; Monte Carlo simulation
Abstract | View details | Full text in PDF | Author Info
Probability distributions of stand basal area were predicted and evaluated in young mixed stands of Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst.) and birch (Betula pendula Roth and Betula pubescens Ehrh.) in Sweden. Based on an extensive survey of young stands, individual tree basal area growth models were estimated using a mixed model approach to account for dependencies in data and derive the variance/covariance components needed. While most of the stands were reinventoried only once, a subset of the stands was revisited a second time. This subset was used to evaluate the accuracy of the predicted stand basal area distributions. Predicting distributions of forest yield, rather than point estimates, allows for a Bayesian approach to planning and decisions can be made with due regard to the quality of the information.
  • Nyström, SLU, Department of Forest Resource Management and Geomatics, SE-901 83 Umeå, Sweden E-mail: kenneth.nystrom@resgeom.slu.se (email)
  • Ståhl, SLU, Department of Forest Resource Management and Geomatics, SE-901 83 Umeå, Sweden E-mail: gs@nn.se

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