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1995
Forfattere
Erik ChristiansenSammendrag
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Forfattere
Kenneth F. RaffaSammendrag
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Forfattere
Trond MæhlumSammendrag
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Forfattere
A. HovdeSammendrag
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Forfattere
A. HovdeSammendrag
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Forfattere
Erik Trømborg Birger SolbergSammendrag
This report describes the model used in the project Modelling the Norwegian Forest Sector. The purpose of this project is to do consistent analyzes of how different changes in factors affecting the business environment of forestry and forest industries in Norway will effect the forest sector. The model used is developed by professor Markku Kallio at Helsinki School of Economics. The model is based on the same main principles as the model developed at the International Institute for Applied System Analysis in the 80s, IIASAs Global Trade Model, GTM. The model is named the Norwegian Trade Model, NTM. NTM is a regional partial equilibrium model with linear constraints (like production capacity limits and upper bounds on harvesting), and a non-linear object function (through non-linear timber supply functions). The model maximizes net social pay-off for all products and regions. Net social pay-off is calculated as the area under all demand functions, minus the sum of transportation costs resulting from trade with other regions, and minus productions costs in the forest industry including timber costs given by non-linear supply curves. This describes according to economic theory, a situation under perfect competition where all consumers and producers maximize their surplus. The model consists of four main parts: A model for timber supply, including the connection between harvesting level and timber costs, and between todays harvesting and future timber production and harvesting. A model for the forest industry that describes how timber is transformed to intermediate- and endproducts and how central factors such as capacity, locations and production costs change over time. A model for product demand that relates demand for forest products to factors such as price, volume, economic growth and exchange rates. A model for trade between regions that relates a fixed location of timber resources and forest industry to demand and supply of forest products. The model consists of 10 domestic regions and two regions for respectively export and import. 27 products are included in the model of which there are six roundwood assortments, 5 pulp grades, 2 board grades, 3 sawnwood products and 9 different paper and board products in addition to recycled paper and energy wood. The dynamics of the model is created through recursive programming where the equilibrium problem for the analyzing period is split into a number of equilibrium solutions, one for each time step. The equilibrium for the first time step t, gives together with changes in timber supply, production capacity, costs and demand, basis for the equilibrium solution in next time step t1. The model describes 5 time steps of 5 years each from year 1990 to year 2010, where the model estimates product prices, harvesting, production on plant and regional level and trade between regions in each time step. A model will always be a strong simplification of the real world. It is therefore important that results from the model are evaluated on the basis of assumptions within the model and the uncertainty of data used. It is our opinion that NTM is a appropriate model for analyzes of the Norwegian Forest Sector. Compared with other models we feel that NTM has the following advantages:The regional aspects is very well taken care of.The forest sector is well described as the forest sector is included at individual plant level. The optimization algorithms secure economic consistency in each scenario alternative. The non-linear timber supply equations used gives most likely a more realistic descriptions of the forest owners behaviour than linear supply equations.The algorithm applied is highly efficient, making possible solutions in short time. Every model has shortcomings towards the real world and it is important that the results from the model are evaluated in relation to these shortcomings. In NTM is it our opinion that the following factors are burdened with highest uncertainty:The linearization of the demand functions might give too large changes in demand when price change.The substitution between different timber assortments on both the supply and demand side is just described to a limited extent in the model.The model is not very user friendly.There will in general be significant uncertainty linked to the huge amount of data demanded by the model. The main purpose with the model is to quantify relative changes connected to certain assumptions and to clarify mechanisms. This purpose has to be emphasized when both results and model are evaluated. Used in this way, it is our opinion that NTM can give valuable insight in many aspects of the forest sector.
Forfattere
Tron EidSammendrag
Non-productive forest land is defined by a potential yield capacity of less than 1.0 m3/ha/year inclusive bark. The portion of non-productive forest areas in stands are usually recorded subjectively in practical inventories. The aim of this work has been to develop sampling methods which, as fare as possible, are based on objectivity. The problems related to non-productive forest areas are restricted to sites with occurrences of rock on surface, shallow soils and obvious productive areas within stands. Non-productivity in wetland areas and mountain areas was not considered. Three different methods for estimation of the portion of non-productive areas were investigated. Method 1 was based on an assumption of a link between actual and expected number of trees per ha, and the portion of non-productive area in stands, i.e. missing trees were assumed to be a result of non-productivity. Method 2 was based on classification on sample plots in a systematic grid within stands. Variables related to soil depth and vegetation types were used for classification on each plot. Method 3 was based on a prediction of site quality on sample plots systematically distributed within stands. Plots where the site quality was predicted to be less than H40=5.0 meter, i.e. a potential yield capacity of less than 1.0 m3/ha/year inclusive bark, were classified as non-productive. Site quality was predicted by means of regression functions developed for the purpose of classification. Site properties as soil depth, soil type and vegetation type were used as independent variables. 72 blocks located in Southeast Norway were selected for the investigation. The blocks were subjectively located in stands with occurrences of rock on surface, shallow soils and productive areas within stands. Longitude, latitude, height above sea level, slope and conditions with respect to water penetration and soil type were recorded for each block. All blocks were covered by a systematic grid of points (1x2 meter). Soil depth and vegetation type were recorded for each point. Height, diameter and coordinates were recorded for all trees on each block. In addition the age was recorded for trees suitable for site quality classification. Based on the experiences from the field work, and on the considerations around different sources of errors, a systematic sample plot inventory within stands, with classifications on each plot (Method 2), is recommended. The sample plot size should be small, e.g. circles with radius 1 meter. The following recommendations are given for classifications on each plot;The sample plot should always be classified as non-productive if the portion of rock on surface is larger than 50%.The sample plot should as a rule be classified as non-productive if the mean soil depth is less than 10 cm. If mean soil depth on the plot is 7-10 cm, the following considerations should be performed; - Vaccinio-Pinetum boreale, Eu-Piceetum myrtilletosum or a richer vegetation type on the plot indicate productivity, - trees within a plot which are a natural part of the stand with respect to size and species indicate productivity, - small areas within a plot with large soil depth (larger than 30 cm) indicate productivity. If mean soil depth on the plot is 10-15 cm, the following considerations should be performed; - Barbilophozio-Pinetum lapponicae or Cladonio-Pinetum boreale on the plot indicate non-productivity. Pure subjective judgments of the portion of non-productive forest land in stands should also in the future be the main element in practical inventories. As early as at the stand delineation phase, however, one should try to eliminate those areas which obviously are non-productive. In this way the amount areas with subjective judgments are reduced. Estimation of the portion of non-productive areas by means of systematic plot inventories within stands should be used from time to time to calibrate the subjective judgments. This is particularly important in sites with occurrences of rock on surface and shallow soils within stands. The results of such sample plot inventories might also be useful as reference data when non-productive forest land is estimated by means of photo interpretation.
Forfattere
Erik NæssetSammendrag
In Norway, about 5500 km2 are surveyed annually for forest management planning. Approximately 50 % of that area is recorded by aerial photo interpretation. In order to carry out economical planning by means of data collected by photo interpretation, the logging costs have to be computed. The logging costs can be determined utilizing cost functions (Anon. 1994). The number of trees per cubic meter of a stand is an important input variable of such functions. The first objective of the present study was to develop models for determination of number of trees per cubic meter, cutting cost, skidding cost, and total logging costs of mature forest stands of Norway spruce and Scots pine by means of photo interpretation. The second objective was to investigate the accuracy of practical use of these models. A material of 119 tallied stands of Norway spruce, Scots pine, and various mixtures of spruce and pine was applied in this study. The stands were distributed on four different sites in southeastern Norway. The cutting cost, skidding cost, and total logging costs were computed from the field measurements by means of the cost functions (Anon. 1994) (Table 1). Five photo interpreters measured and interpreted the stand mean height, the crown closure, and the tree species distribution of the individual stands by means of panchromatic photographs at the approximate scale 1:15000 and a stereo plotter of the second order (Wild B8). The site quality was determined from the site quality layer of the official Economic Map Series. First, the accuracy of the determination of number of trees per cubic meter from aerial photographs was investigated. The model for computation of number of trees per cubic meter is displayed in Fig. 1. Mean differences between photo estimated and field measured number of trees per cubic meter in the range -4.7 % to -43.1 % were found (Table 2). The standard deviations for the differences between photo estimates and field measurements varied between 14.9 % and 37.6 %. The large systematic deviations were partly due to calibration problems related to the interpretation of crown closure. The logging costs were determined according to three different models. In model I (Fig. 2), the logging costs are computed directly from the tariff number and the number of trees per cubic meter of the individual stands. In model II (Fig. 3), a stratified systematic sample plot inventory is used to correct the systematic errors of tariff number and number of trees per cubic meter. The corrected values of the individual stands are used for determination of the costs. In model III (Fig. 4), a stratified systematic sample plot inventory is used to correct the systematic errors of tariff number and stand volume, while the number of trees of the individual stands is recorded by field measurements. The number of trees per cubic meter is computed by means of the corrected stand volume and the number of trees. The corrected values of the individual stands are used for determination of the costs. For model I a mean difference between photo estimated and field measured logging costs of maximum 13.0 % was found (Tables 3, 6, and 9). For model II and model III the maximum mean differences were 4.8 % (Tables 4, 7, and 10) and 5.9 % (Tables 5, 8, and I 1), respectively. For practical use of model II and model III a somewhat larger systematic error than indicated by the present results should be expected. The standard deviations for the differences between photo estimated and field measured total logging costs were 4.6-13.0 % (Tables 9, 10, and 11). Model II and model III seem to yield systematic and random errors of a similar magnitude as field based relascope surveys that not record the number of trees, but basal area, stand mean height, and tariff number only.
Sammendrag
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Forfattere
Thomas J. Savage Rodney CroteauSammendrag
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