Current issue: 56(2)

Under compilation: 56(3)

Scopus CiteScore 2021: 2.8
Scopus ranking of open access forestry journals: 8th
PlanS compliant
Silva Fennica 1926-1997
1990-1997
1980-1989
1970-1979
1960-1969
Acta Forestalia Fennica
1953-1968
1933-1952
1913-1932

Articles containing the keyword 'tree growth model'

Category: Research article

article id 10055, category Research article
Jaakko Repola, Hannu Hökkä, Hannu Salminen. (2018). Models for diameter and height growth of Scots pine, Norway spruce and pubescent birch in drained peatland sites in Finland. Silva Fennica vol. 52 no. 5 article id 10055. https://doi.org/10.14214/sf.10055
Keywords: Pinus sylvestris; Picea abies; Betula pubescens; drained peatlands; forest drainage; mixed model; tree growth model
Highlights: Tree growth strongly correlated with site drainage status; Between-tree competition had a higher impact on tree diameter growth than on height growth; Growth predicted by the constructed models were calibrated using NFI11 data to ensure generally applicable growth predictions level in whole country.
Abstract | Full text in HTML | Full text in PDF | Author Info

The aim of this study was to develop individual-tree diameter and height growth models for Scots pine, Norway spruce, and pubescent birch growing in drained peatlands in Finland. Trees growing in peatland sites have growth patterns that deviate from that of trees growing in mineral soil sites. Five-year growth was explained by tree diameter, different tree and stand level competition measures, management operations and site characteristics. The drainage status of the site was influencing growth directly or in interaction with other variables. Site quality had a direct impact but was also commonly related to current site drainage status (need for ditch maintenance). Recent thinning increased growth of all species and former PK fertilization increased growth of pine and birch. Temperature sum was a significant predictor in all models and altitude for spruce and birch. The data were a subsample of the 7th National Forest Inventory (NFI) sample plots representing northern and southern Finland and followed by repeated measurements for 15–20 yrs. Growth levels predicted by the models were calibrated using NFI11 data to remove bias originating from the sample of the modelling data. The mixed linear models technique was used in model estimation. The models will be incorporated into the MOTTI stand simulator to replace the current peatlands growth models.

  • Repola, Natural Resources Institute Finland (Luke), Natural resources, Eteläranta 55, FI-96300 Rovaniemi, Finland E-mail: jaakko.repola@luke.fi (email)
  • Hökkä, Natural Resources Institute Finland (Luke), Natural resources, Paavo Havaksen tie 3, FI-90014 University OF Oulu, Finland E-mail: hannu.hokka@luke.fi
  • Salminen, Natural Resources Institute Finland (Luke), Natural resources, Eteläranta 55, FI-96300 Rovaniemi, Finland E-mail: hannu.salminen@luke.fi
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 580, category Research article
Susanna Sironen, Annika Kangas, Matti Maltamo, Jyrki Kangas. (2001). Estimating individual tree growth with the k-nearest neighbour and k-Most Similar Neighbour methods. Silva Fennica vol. 35 no. 4 article id 580. https://doi.org/10.14214/sf.580
Keywords: pine; spruce; single tree growth models; non-parametric models; local estimates
Abstract | View details | Full text in PDF | Author Info
The purpose of this study was to examine the use of non-parametric methods in estimating tree level growth models. In non-parametric methods the growth of a tree is predicted as a weighted average of the values of neighbouring observations. The selection of the nearest neighbours is based on the differences between tree and stand level characteristics of the target tree and the neighbours. The data for the models were collected from the areas owned by Kuusamo Common Forest in Northeast Finland. The whole data consisted of 4051 tally trees and 1308 Scots pines (Pinus sylvestris L.) and 367 Norway spruces (Picea abies Karst.). Models for 5-year diameter growth and bark thickness at the end of the growing period were constructed with two different non-parametric methods: the k-nearest neighbour regression and k-Most Similar Neighbour method. Diameter at breast height, tree height, mean age of the stand and basal area of the trees larger than the subject tree were found to predict the diameter growth most accurately. The non-parametric methods were compared to traditional regression growth models and were found to be quite competitive and reliable growth estimators.
  • Sironen, University of Joensuu, Faculty of Forestry, P.O. Box 111, FIN-80101 Joensuu, Finland E-mail: susanna.sironen@forest.joensuu.fi (email)
  • Kangas, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland E-mail: ak@nn.fi
  • Maltamo, University of Joensuu, Faculty of Forestry, P.O. Box 111, FIN-80101 Joensuu, Finland E-mail: mm@nn.fi
  • Kangas, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland E-mail: jk@nn.fi

Register
Click this link to register to Silva Fennica.
Log in
If you are a registered user, log in to save your selected articles for later access.
Contents alert
Sign up to receive alerts of new content
Your selected articles