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Silva Fennica vol. 35 no. 2 | 2001

Category: Research article

article id 598, category Research article
A. Maarit I. Kallio. (2001). Interdependence of the sawlog, pulpwood and sawmill chip markets: an oligopsony model with an application to Finland. Silva Fennica vol. 35 no. 2 article id 598. https://doi.org/10.14214/sf.598
The interdependence of the markets for pulpwood, sawlogs and sawmill chips is analysed using a short-run model, which accommodates the alternative competition structures of wood buyers. We propose that imperfect competition in the pulpwood market tends to make the sawmills owned by the pulp and paper companies larger than the independent ones, even in the absence of transactional economies of integration. The impact of the wood market competition pattern on the profits of the forest owners and forest industry firms depends upon a firm-capacity structure, wood supply elasticities, and business cycles in the output markets. The numerical application of the model to the Finnish softwood market suggests that inflexibility of production capacities tends to make the wood demand rather insensitive with respect to price. Only the large firms, which all produce both pulp and sawnwood, may have oligopsony power under some conditions. Integrated production can increase competition in the sawlog market via the wood chip market.
  • Kallio, Helsinki School of Economics, Department of Economics and Management Science, P.O. Box 1210, FIN-00101 Helsinki ORCID ID:E-mail: maarit.kallio@hkkk.fi (email)
article id 597, category Research article
Annika Kangas, Jyrki Kangas & Jouni Pykäläinen. (2001). Outranking methods as tools in strategic natural resources planning. Silva Fennica vol. 35 no. 2 article id 597. https://doi.org/10.14214/sf.597
Two outranking methods, ELECTRE III and PROMETHEE II, commonly used as decision-aid in various environmental problems, and their applications to decision support for natural resources management are presented. These methods represent ‘the European school’ of multi-criteria decision making (MCDM), as opposed to ‘the American school’, represented by, for instance, the AHP method. On the basis of a case study, outranking methods are compared to so far more usually applied techniques based on the ideas of multi attribute utility theory (MAUT). The outranking methods have been recommended for situations where there is a finite number of discrete alternatives to be chosen among. The number of decision criteria and decision makers may be large. An important advantage of outranking methods, when compared to decision support techniques most often applied in today’s natural resources management, is the ability to deal with ordinal and more or less descriptive information on the alternative plans to be evaluated. Furthermore, the uncertainty concerning the values of the criterion variables can be taken into account using fuzzy relations, determined by indifference and preference thresholds. The difficult interpretation of the results, on the other hand, is the main drawback of the outranking methods.
  • Kangas, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland ORCID ID:E-mail: annika.kangas@metla.fi (email)
  • Kangas, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland ORCID ID:E-mail:
  • Pykäläinen, University of Joensuu, Faculty of Forestry, P.O. Box 111, FIN-80101 Joensuu, Finland ORCID ID:E-mail:
article id 596, category Research article
Sakari Tuominen & Simo Poso. (2001). Improving multi-source forest inventory by weighting auxiliary data sources. Silva Fennica vol. 35 no. 2 article id 596. https://doi.org/10.14214/sf.596
A two-phase sampling design has been applied to forest inventory. First, a large number of first phase sample plots were defined with a square grid in a geographic coordinate system for two study areas of about 1800 and 4500 ha. The first phase sample plots were supplied by auxiliary data of Landsat TM and IRS-1C with principal component transformation for stratification and drawing the second phase sample (field sample). Proportional allocation was used to draw the second phase sample. The number of field sample plots in the two study areas was 300 and 380. The local estimates of five continuous forest stand variables, mean diameter, mean height, age, basal area, and stem volume, were calculated for each of the first phase sample plots. This was done separately by using one auxiliary data source at a time together with the field sample information. However, if the first phase sample plot for which the stand variables were to be estimated was also a field sample plot, the information of that field sample plot was eliminated according to the cross validation principle. This was because it was then possible to calculate mean square errors of estimates related to a specific auxiliary data source. The procedure produced as many estimates for each first phase sample plot and forest stand variable as was the number of auxiliary data sources, i.e. seven estimates: These were based on Landsat TM, IRS-1C, digitized aerial photos, ocular stereoscopic interpretation from aerial photographs, data from old forest inventory made by compartments, Landsat TM95–TM89 difference image and IRS96–TM95 difference image. The final estimates were calculated as weighted averages where the weights were inversely proportional to mean square errors. The alternative estimates were calculated by applying simple rules based on knowledge and the outliers were defined. The study shows that this kind of system for finding outliers for elimination and a weighting procedure improves the accuracy of stand variable estimation.
  • Tuominen, Finnish Forest Research Institute, Unioninkatu 40 A, FIN-00170 Helsinki, Finland ORCID ID:E-mail: sakari.tuominen@metla.fi (email)
  • Poso, Department of Forest Resource Management, P.O. Box 27, FIN-00014 University of Helsinki, Finland ORCID ID:E-mail:
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
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 ORCID ID:E-mail: kenneth.nystrom@resgeom.slu.se (email)
  • Ståhl, SLU, Department of Forest Resource Management and Geomatics, SE-901 83 Umeå, Sweden ORCID ID:E-mail:
article id 594, category Research article
Walter Zucchini, Matthias Schmidt & Klaus von Gadow. (2001). A model for the diameter-height distribution in an uneven-aged beech forest and a method to assess the fit of such models. Silva Fennica vol. 35 no. 2 article id 594. https://doi.org/10.14214/sf.594
This paper illustrates the application of a mixture model to describe the bivariate diameter-height distribution of trees growing in a pure, uneven-aged beech forest. A mixture of two bivariate normal distributions is considered but the methodology is applicable to mixtures of other distributions. The model was fitted to diameter-height observations for 1242 beech trees in the protected forest Dreyberg (Solling, Germany). A considerable advantage of the model, apart from the fact that it happens to fit this large data set unusually well, is that the individual parameters all have familiar interpretations. The bivariate Johnson SBB distribution was also fitted to the data for the purpose of comparing the fits. A second issue discussed in this paper is concerned with the general question of assessing the fit of models for bivariate data. We show how a device called ‘pseudo-residual’ enables one to investigate the fit of a bivariate model in new ways and in considerable detail. Attractive features of pseudo-residuals include the fact that they are not difficult to interpret; they can be computed using generally available statistical software and, most important of all, they enable one to examine the fit of a model by means of simple graphs.
  • Zucchini, Georg-August-Universität Göttingen, Institute for Statistics and Econometrics ORCID ID:E-mail: zucchini@wi-wiss.uni-goettingen.de (email)
  • Schmidt, Forest Research Station of Lower Saxony ORCID ID:E-mail:
  • Gadow, Georg-August-Universität Göttingen, Institute for Forest Management and Yield Sciences ORCID ID:E-mail:
article id 593, category Research article
Anneli Jalkanen. (2001). The probability of moose damage at the stand level in southern Finland. Silva Fennica vol. 35 no. 2 article id 593. https://doi.org/10.14214/sf.593
The probability of moose damage was studied in sapling stands and young thinning stands in southern Finland. Data from the eighth National Forest Inventory in 1986–92 were used for modelling. The frequency of damage was highest at the height of two to five meters and at the age of ten to twenty years (at the time of measurement). Moose preferred aspen stands the most and least preferred Norway spruce stands. Scots pine and silver birch were also susceptible to damage. Logistic regression models were developed for predicting the probability that moose damage is the most important damaging agent in a forest stand. The best predictive variables were the age and dominant species of the stand. Variables describing the site were significant as cluster averages, possibly characterizing the area as a food source (fertility and organic soil), as well as the lack of shelter (wall stand). When sample plot, cluster and municipality levels were compared, it was found that most of the unexplained variance was at the cluster level. To improve the model, more information should be obtained from that level. The regression coefficients for aspen as supplementary species, and for pine as dominant species, had significant variance from cluster to cluster (area to area). It was also shown that the occurrence of aspen is closely connected to the occurrence of moose damage in pine sapling stands.
  • Jalkanen, Finnish Forest Research Institute, Unioninkatu 40 A, FIN-00170 Helsinki, Finland ORCID ID:E-mail: anneli.jalkanen@metla.fi (email)
article id 592, category Research article
Marja-Liisa Juntunen. (2001). Use of pesticides in Finnish forest nurseries in 1996. Silva Fennica vol. 35 no. 2 article id 592. https://doi.org/10.14214/sf.592
In 1996 a questionnaire on seedling production and use of pesticides was sent to 33 forest nurseries in Finland. Twenty-eight nurseries answered the questionnaire; thus the survey covered about 80% of the Finnish production of forest seedlings. According to this study, the Finnish nurseries together are using about 1000 kilograms of pesticides (as active ingredient, a.i.) annually. The most used herbicide was terbutylazine (Gardoprim-Neste®), and half of the total amount of fungicide used was chlorothalonil (Bravo 500®). Three fourths of the insecticide products had permethrin as the active ingredient. The nurseries applied, on average, 1.7 kg pesticides (a.i.)/ha annually, although the amount varied considerably between nurseries. In production of container seedlings the highest mean amounts of pesticides were applied to pine seedlings (9.5 kg/ha) and the lowest to spruce seedlings (0.9 kg/ha). To the fields of bareroot seedlings the nurseries applied, on average, 3.9 kg pesticides (a.i.)/ha. Mean amounts of pesticide (a.i.) per 1000 seedlings grown in containers were almost the same for birch and pine production, 1.6 and 1.7 grams, respectively; for production of spruce seedlings the comparable values were less than 0.5 grams. For production of bareroot seedlings the nurseries used about four times more pesticides than for container seedlings.
  • Juntunen, The Finnish Forest Research Institute, Suonenjoki Research Station, FIN-77600 Suonenjoki, Finland ORCID ID:E-mail: marja-liisa.juntunen@metla.fi (email)
article id 591, category Research article
Qibin Yu. (2001). Can physiological and anatomical characters be used for selecting high yielding hybrid aspen clones? Silva Fennica vol. 35 no. 2 article id 591. https://doi.org/10.14214/sf.591
Stomatal, CO2 exchange parameters and several leaf and growth traits were recorded in a five-year-old hybrid aspen clone trial. The field trial consisted of four aspen hybrid clones (Populus tremula L. x P. tremuloides Michx.) and one local Populus tremula seedling source. The mean estimated height of hybrid aspen clones was 1.6 times higher than for P. tremula. Similarly, basal diameter was 1.5 times and breast diameter 1.8 times higher in the hybrids. Differences were observed for physiological and growth traits among hybrid clones and P. tremula, but, only stomatal characters of hybrid clones differed significantly from those in P. tremula. Hybrid clones had larger guard cells (22.9 mm) than P. tremula (19.2 mm), whereas P. tremula had a higher stomatal density (211.3/mm2) than the hybrid clones (164.4/mm2). Among four hybrid clones, net photosynthesis was strongly correlated with foliar nitrogen. Height correlated significantly with foliar nitrogen, but negatively with leaf fresh weight, leaf dry weight and stomatal density. The results suggested that yield components could be controlled by many genes, specific to each clone. No single gas exchange or morphological variable can provide a reliable indicator of yield potential.
  • Yu, Department of Plant Biology, P.O. Box 27, FIN-00014 University of Helsinki, Finland ORCID ID:E-mail: qibin.yu@helsinki.fi (email)

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