Articles containing the keyword 'basal area'

Especially in forest vegetation studies, the light climate below the canopy is of great interest. In extensive forest inventories, direct measurement of the light conditions is too time-consuming. Often only the standard tree stand parameters are available. The present study was undertaken with the aim to develop methods for estimation of the light climate on the basis of readily measurable tree stand characteristics. The study material includes 40 sample plots representing different kinds of more or less mature forest stands of Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) H. Karst.).

In each forest stand, a set of hemipherical photographs was taken and standard tree stand measurements were performed. A regression approach was applied in order to elaborate linear models for predicting the canopy coverage. The total basal area of the stand explained 63% of variance in the canopy coverage computed from hemipherical photographs. A coefficient representing the relative proportion of Norway spruce in the stand increased the explanatory power into 75%. When either the stand density (stems/unit area) or dominant age of the stand was included into the model, increment of the explanatory power into 80% was achieved. By incorporating both of the preceding predictors, an explanatory power of 85% was reached.

The PDF includes a summary in Finnish.

• Kuusipalo, E-mail: jk@mm.unknown

Thinning models are generally based on the density of the stand measured by the average basal area per hectare, for instance. These models are handicapped by the uneven structure of the stands. In uneven stands the averages are inadequate indicators for the need and amount of thinnings.

Small relascope plots were tested in the measurement of the spatial distribution of trees and in the determination of the need and amount of thinnings. The thinning quantity was determined as the difference between the actual distribution of the relascope plots into basal area classes and the ideal distribution after thinning. Sequential sampling was used in the derivation of the decision equations. A respective BASIC-program for a programmable pocket calculator is given.

The PDF includes a summary in English.

• Kilkki, E-mail: pk@mm.unknown
• Pohjola, E-mail: tp@mm.unknown
• Pohtila, E-mail: ep@mm.unknown

Dependence of the growth increase given by fertilization on different stand characteristics is examined in this article. The aim was to determine whether the volume growth increase can be accurately determined beforehand when fertilization is carried out on mineral soil sites at a dosage of 120 kg N/ha. The material consisted mostly on of mature stands ready for cutting, a total of 22 Scots pine (Pinus sylvestris L.) and 20 Norway spruce (Picea abies (L.) H. Karst.) stands. Increase in basal area, height quality class and basal area of the stand were found to best explain the increment and its increase in the regression equations calculated for different types of fertilizer and the control level.

The PDF includes a summary in English.

• Jokinen, E-mail: rj@mm.unknown

Models that predict forest development are essential for sustainable forest management. Constructing growth models via regression analysis or fitting a family of sigmoid equations to construct compatible growth and yield models are two ways these models can be developed. In this study, four species-specific models were developed and compared. A compatible growth and yield stand basal area model and a five-year stand basal area growth model were developed for Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.). The models were developed using data from permanent inventory plots from the Swedish national forest inventory and long-term experiments. The species-specific models were compared, using independent data from long-term experiments, with a stand basal area growth model currently used in the Swedish forest planning system Heureka (Elfving model). All new models had a good, relatively unbiased fit. There were no apparent differences between the models in their ability to predict basal area development, except for the slightly worse predictions for the Norway spruce growth model. The lack of difference in the model comparison showed that despite the simplicity of the compatible growth and yield models, these models could be recommended, especially when data availability is limited. Also, despite using more and newer data for model development in this study, the currently used Elfving model was equally good at predicting basal area. The lack of model difference indicate that future studies should instead focus on model development for heterogeneous forests which are common but lack in growth and yield modelling research.

• Goude, Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences, SE-230 53 Alnarp, Sweden https://orcid.org/0000-0002-2179-292X E-mail: martin.goude@slu.se
• Nilsson, Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences, SE-230 53 Alnarp, Sweden E-mail: urban.nilsson@slu.se
• Mason, School of Forestry, University of Canterbury, Private Bag 4800, Christchurch, New Zealand E-mail: euan.mason@canterbury.ac.nz
• Vico, Department of Crop Production Ecology, Swedish University of Agricultural Sciences, SE-750 07 Uppsala, Sweden E-mail: giulia.vico@slu.se

Investing in planting genetically improved silver birch (Betula pendula Roth) in Swedish plantations requires understanding how birch stands will develop over their entire rotation. Previous studies have indicated relatively low production of birch compared to Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.). This could result from using unrepresentative basic data, collected from unimproved, naturally-regenerated birch (Betula spp.) growing on inventory plots often located in coniferous stands. The objective of this study was to develop a basal area development function of improved silver birch and evaluate production over a full rotation period. We used data from 52 experiments including planted silver birch of different genetic breeding levels in southern and central Sweden. The experimental plots were established on fertile forest sites and on former agricultural lands, and were managed with different numbers of thinnings and basal area removal regimes. The model best describing total stand basal area development was a dynamic equation derived from the Korf base model. The analysis of the realized gain trial for birch showed a good stability of the early calculated relative differences in basal area between tested genotypes over time. Thus, the relative difference in basal area might be with cautious used as representation of the realized genetic gain. On average forest sites in southern Sweden, improved and planted silver birch could produce between 6–10.5 m³ ha^–1 year^–1, while on fertile agriculture land the average productivity might be higher, especially with material coming from the improvement program. The performed analysis provided a first step toward predicting the effects of genetic improvement on total volume production and profitability of silver birch. However, more experiments are needed to set up the relative differences between different improved material.

• Liziniewicz, The Forestry Research Institute of Sweden, Ekebo, SE-268 90 Svalöv, Sweden E-mail: mateusz.liziniewicz@skogforsk.se
• Barbeito, Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences, Alnarp, Sweden; Université de Lorraine, AgroParisTech, INRAE, UMR Silva, Nancy, France E-mail: ignacio.barbeito@slu.se
• Zvirgzdins, Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences, Box 49, 23053 Alnarp, Sweden E-mail: andis.zvirgzdins@slu.se
• Stener, The Forestry Research Institute of Sweden, Ekebo, SE-268 90 Svalöv, Sweden E-mail: lg.stener@telia.com
• Niemistö, Natural Resources In-stitute Finland (Luke), Natural resources, Seinäjoki, Finland E-mail: pentti.niemisto@luke.fi
• Fahlvik, The Forestry Research Institute of Sweden, Ekebo, SE-268 90 Svalöv, Sweden E-mail: nils.fahlvik@skogforsk.se
• Johansson, Tönnersjöheden Experimental Forest, SLU, Simlångsdalen, Sweden E-mail: ulf.johansson@slu.se
• Karlsson, The Forestry Research Institute of Sweden, Ekebo, SE-268 90 Svalöv, Sweden E-mail: curly.birch@gmail.com
• Nilsson, Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences, Box 49, 23053 Alnarp, Sweden E-mail: urban.nilsson@slu.se

• Fahlvik, Department of Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences, P.O. Box 49, SE-230 53 Alnarp, Sweden E-mail: nils.fahlvik@slu.se
• Elfving, Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, SE-901 83 Umeå, Sweden E-mail: bjorn.elfving@slu.se
• Wikström, Peder Wikström Skogsanalys AB, c/o Peder Wikström, Huldrans väg 1, SE-907 52 Umeå, Sweden E-mail: peder.wikstrom@slu.se

• Pulkkinen, E-mail: minna.pulkkinen@iki.fi

• Lowe, Centre d’étude de la foret, Département des sciences du bois et de la foret, Pavillon Abitibi-Price, 2405 rue de la Terrasse, Université Laval, Québec, Québec, G1V 0A6, Canada E-mail: jeovannalowe@gmail.com
• Pothier, Centre d’étude de la foret, Département des sciences du bois et de la foret, Pavillon Abitibi-Price, 2405 rue de la Terrasse, Université Laval, Québec, Québec, G1V 0A6, Canada E-mail: dp@nn.ca
• Savard, Wildlife Research, Science and Technology, Québec Region, 1141 Route de l’Église, P.O. Box 10100, Québec, Québec, G1V 4H5, Canada E-mail: jpls@nn.ca
• Rompré, Centre d’étude de la foret, Département des sciences du bois et de la foret, Pavillon Abitibi-Price, 2405 rue de la Terrasse, Université Laval, Québec, Québec, G1V 0A6, Canada & Department of Biology and Health Sciences, 84 West South Street, Wilkes University, PA 18766, USA E-mail: gr@nn.ca
• Bouchard, Ministère des Ressources naturelles et de la Faune, Direction de l’Environnement et de la Protection des Forets, 880 Chemin Ste-Foy, Quebec, Québec, G1S 4X4, Canada E-mail: mb@nn.ca

• Nyström, SLU, Department of Forest Resource Management and Geomatics, SE-901 83 Umeå, Sweden E-mail: kenneth.nystrom@resgeom.slu.se
• Ståhl, SLU, Department of Forest Resource Management and Geomatics, SE-901 83 Umeå, Sweden E-mail: gs@nn.se

The research was carried out in unmanaged middle-aged (75–85 years) Northern taiga Scots pine (Pinus sylvestris L.) forests in the Kola peninsula. It was established that forests of green moss-lichen and green moss site types are characterised by a predominance (65–70% by stand volume) of moderately and strongly weakened trees. Trees of differing vitality have significant differences in annual increment. Healthy trees had a radial increment (RI) 70–75% greater than that of dying trees, and a basal area increment (BAI) 85–90% greater. The dynamics of the RI and BAI of Scots pine trees for the 70-year period (from 1945 to 2015) is different. The RI of all individuals in the communities studied decreases consistently. The decrease is expressed more strongly in green moss Scots pine forests (80–95% from 1945 to 2015) compared to green moss-lichen forests (60–80%); it manifests itself more in strongly weakened and dying individuals (75–95%) than in healthy and moderately weakened ones (60–80%). Annual basal area increment in green moss Scots pine forests increases by 45–65% from stand establishment until the trees are 25 to 35 years old and subsequently decreases by 50–80% to 70–80 years of age. In green moss-lichen pine forests the BAI of Scots pine remains rather stable in healthy and moderately weakened trees and decreases in strongly weakened and dying individuals by 45% and 75–80%, respectively throughout the studied period.

• Katjutin, Komarov Botanical Institute of the Russian Academy of Sciences, Professora Popova str. 2, 197376, Saint-Petersburg, Russia E-mail: paurussia@binran.ru
• Stavrova, Komarov Botanical Institute of the Russian Academy of Sciences, Professora Popova str. 2, 197376, Saint-Petersburg, Russia E-mail: nstavrova@binran.ru
• Gorshkov, Komarov Botanical Institute of the Russian Academy of Sciences, Professora Popova str. 2, 197376, Saint-Petersburg, Russia; Saint-Petersburg State Forest Technical University, letter U, 5, Institutsky per., 194021, Saint-Petersburg, Russia E-mail: vgorshkov@binran.ru
• Lyanguzov, Saint-Petersburg State University, 7/9 Universitetskaya Emb., 199034, Saint-Petersburg, Russia E-mail: andrewlyanguzov@gmail.com
• Bakkal, Komarov Botanical Institute of the Russian Academy of Sciences, Professora Popova str. 2, 197376, Saint-Petersburg, Russia E-mail: bakkal@binran.ru
• Mikhailov, Komarov Botanical Institute of the Russian Academy of Sciences, Professora Popova str. 2, 197376, Saint-Petersburg, Russia E-mail: smikhailov@binran.ru