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1994
Sammendrag
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Forfattere
Kjell AndreassenSammendrag
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Forfattere
Tron Eid Andreas FitjeSammendrag
The aim of this work is to compare three models with respect to estimation of mean diameter (Dg), number of trees per ha (N), tariff number (Hkl), gross value (Pb) and logging costs (K) in forest stands. For Model I, both the number of trees per ha and tariff number are assumed to be recorded in the field. For Model II only the number of trees per ha are assumed to be recorded, and for Model III only the tariff number are assumed to be recorded. Basal area and basal area weighted mean height (HL) are assumed to be recorded in the field for all three models.Details with respect to functions and assumptions for estimation of the different stand variables are given in chapter 2 Figs. 1, 2 and 3. The models are tested on data from sample plot inventories in 157 stands. The material is described in Table 1. Both systematic errors, i.e. mean differences between stand variables from different models, and random errors, i.e. the standard deviations for the differences, are compared. The differences are settled as recorded value minus estimated value (see Formulae 1 and 2 in chapter 3.2). Table 2 shows that Model III underestimates the mean diameter (Dg) (significant, 3.7%) in Norway spruce dominated stands and overestimates the mean diameter (significant, 7.0%) in Scots pine dominated stands. The standard deviations for the differences are about 7% in spruce dominated stands and about 9% in pine dominated stands. Table 3 shows that Model III underestimates the number of trees per ha (significant, 10.4%) in pine-dominated stands. For spruce dominated stands there are no significant differences. The standard deviations for the differences are 14.5% in spruce dominated stands and about 16% in pine dominated stands. Table 4 shows that Model II overestimates the tariff number (Hkl) both in spruce dominated (significant, 7.1%) and in pine dominated (significant, 7.2%) stands. The standard deviations for the differences are 5.7% in spruce dominated stands and 4.1 % in pine dominated stands (see also Fig. 4). Gross values (Pb) in all models are compared to recorded gross values, i.e. gross values calculated according to the observed diameter distribution. Table 5 shows that there are no significant differences between the recorded value and the model values when the means for all spruce dominated stands are compared. In pine dominated stands there is a significant underestimation (1.6%) of gross value for Model II. For the other models no significant differences appear. Table 5 also shows that the standard deviations for the differences are about 4% for all models in spruce stands and about 5.5% for all models in pine stands. The costs according to Model I are regarded as recorded when the logging costs (K) are compared. In this model both the number of trees per ha and the tariff number are assumed to be recorded (see Fig. 1). Table 6 shows that Model II underestimates the logging costs (0.9%), while Model III overestimates them (2.4%) in spruce dominated stands. In pine dominated stands the logging costs are underestimated both by Model II (1.0%) and Model III (3.1%). The standard deviations for the differences are about 1-1.5% for Model II and about 5% for Model III. Comparisons for development class III basically give the same results as in development class IV-V. However, both the differences between the models and standard deviations for the differences, are larger (see Table 7). A general discussion, and a comparison of the models with respect to all stand variables, are carried out in chapter 5. Table 8 shows all systematic tendencies derived from the comparisons. Model I provide for the best results. Because of high inventory costs, however, this model will probably be out of question in most practical inventories. Model II and III, however, also seem to provide for satisfactory results with respect to gross values and logging costs. If the mean diameter and the number of trees per ha are going to be used as input in long term yield forecasts, Model II provides for better results than Model III. With this background it is therefore recommended to apply Model II rather than Model III. This conclusions holds true if all field sampled data are correct, and if the costs for collecting the data are the same for the two models. Such issues are discussed by Eid (1994).
Forfattere
Bernt-Håvard ØyenSammendrag
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Forfattere
Bjørn ØklandSammendrag
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Forfattere
Øystein Dale Hans E. AamodtSammendrag
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Sammendrag
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Forfattere
Tord K. RindalSammendrag
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Forfattere
Halvor SolheimSammendrag
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Forfattere
Øystein Dale J. StammSammendrag
The goal of the project was to collect technical data for selective harvesting operations. The main emphasis was on areas which had not earlier been studied, and in which there were a lack in knowledge. Gross operational statistics from 6 harvesters and 3 forwarders were collected throughout a period of 50 working days. This material is used as a reference representing ordinary harvesting operations. A model study of a forest estate in the Gardermoen area practicing selective harvesting was made. The main goal of the study was to see how much longer it took to administrate the selective harvesting compared to the reference material. A comparison between the motor-manual and the mechanized working methods was also made. Studies of gross operational statistics at Gardermoen showed that the portion of total working time used for planning increased from 2 % to 10 % for harvesters in the reference material and selective harvesting operations, respectively. The equivalent numbers for forwarders was 2 % and 4 %. The main reasons for the differences between harvesters and forwarders is that operational planning is normally done by the harvester operator. The differences in the portion of time required for planning were small between mechanized and motor-manual working methods in single-tree selection and small clear-cuts (1-2 daa). On the other hand, harvesters used 6 times the proportion of time for planning of group selection ( 1 daa) than that of motor-manual cutters. From the reference material and the sampled gross operational statistics from the Gardermoen study, we have worked out a system for estimating time consumption for moving forestry machines. A practical consequence of selective harvesting may be the division of harvested volume into smaller operational units and, therefore, more frequent moves between operational units.Moving forestry equipment by means of car transport: Y = 0.85 (X/32.5) Y = Time consumption in hours X = Moving distance in kilometres Moving forestry equipment by itselves along roads: Y = 0.41 (X/10.6) Y = Time consumption in hours X = Moving distance in kilometres In the Sessvollmoen area a study of harvester production in a pine shelterwood was made. The production of a two-grip harvester for cutting in a pine shelterwood can be described with the following function: Y= 14.819 8948 * (X*X) 0.356 * T Y = Production in m3 ob. per effective hour (E0) X = Average tree size (m3 ob.) T = Number of trees harvested per daa The production for mechanized cutting of windthrown trees will be reduced by 60-70 % compared with ordinary clear-cutting. Knowledge from other experiments shows that the frequency of wind thrown trees can be a considerable problem in shelterwoods. In the Rakkestad area we saw how the production of a forwarder varied with different levels of volume removal. The time consumption for forwarding in selection harvesting can be described as Y = ((0.0124*Rb0.0131*Lb0.017*Rt0.021*Lt)/Lv) 2.72 - 0.0166 * Ut Y = minutes per m3ob (E0) Rb = Return distance on base or strip road (m) Lb = Driving distance on the base or strip road while loaded (m) Rt = Return distance in the terrain (m) Lt = Driving distance in the terrain while loaded (m) Lv = Volume per load (m3 ob.) Ut = Volume removal in m3/daa (ob.)