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The paper presents a linear programming method of harvest volume determination including calculations of net present value (NPV) of standing timber. NPV was computed taking into account the costs of harvesting and skidding and a discount rate of 2.5%. Harvest volume was determined for three 10−year management periods according to the following four scenarios: (1) Vol_max – timber volume maximization within constraints concerning harvest area (4 ha), cutting interval (5 year), felling a maximum of two adjacent cutting plots over a 10−year period, combined harvest area per decade (a quarter of the total area of near−mature, mature, and overmature stands), and minimum stand age (starting with near−mature stands); (2) RA – as in the Vol_max scenario plus the harvest area per decade should be smaller than or equal to the regulated area; (3) NPV_max – NPV maximization while respecting all constraints from the Vol_max scenario; and (4) IUL – pursuant to the Instrukcja… [2012]. Calculations included allowable cuts by maturity for mature stands (the last age class) and near−mature and mature stands (two last age classes), as well as the allowable cut for mean age equalization. Subsequently, the optimum allowable cut was determined and particular stands were designated for felling, starting with the oldest ones, and taking into consideration spatial layout. An optimization case study was done for the Seredzice forest unit designated for clearcutting, consisting of pine stands or stands with a predominance of Scots pine growing on coniferous and mixed coniferous habitat types with a total area of 813.20 ha in the Marcule Forest District (C Poland). The total harvest volume determined using linear programming for a 30−year period was 81.17, 74.70, and 80.84 thousand m³ in the Vol_max, RA, and NPV_max scenarios, respectively, which was greater by 29%, 19%, and 28% than in the IUL scenario (62.95 thousand m³). The total NPV of stands designated for harvesting in the 30−year period was 9423, 8824, and 9483 thousand PLN for the Vol_max, RA, and NPV_max scenarios, respectively, as compared to 7492 thousand PLN in the IUL scenario. The simultaneous determination of harvest volume for several management periods by analyzing the parameters of individual stands and selecting the optimum harvest period for them makes it possible to better exploit the production potential of the forest and increase both the volume and value of the harvested timber over a long time horizon.
The objective of the study was to identify and analyze long−term trends as well as cyclic, seasonal, and irregular effects in timber volume and prices by means of time series decomposition. The adopted multiplicative model was the product of all the time series components identified using the Census X11 method. The study material consisted of quarterly data on the volume and prices of timber sold by the Marcule Forest District (central Poland) in the years 2006−2018. Analysis was performed for the overall timber production volume, as well as separately for pine timber (constituting 86% of the total) and oak timber (5%), taking into account large−diameter saw timber (WC0) and medium−diameter industrial and general purpose timber (pulpwood, S2A). Over the analyzed period, the nominal prices of timber rose on average by 39% from 165 to 229 PLN/m³. The greatest price increase was recorded for large−diameter oak timber (by 100%), while the prices of WC0 pine timber, which accounted for the greatest proportion of the production volume, increased by 23% on average. Production characterized marked seasonality throughout the year, depending on the species. Pine timber sales were the lowest in 1st quarter and the highest in 3rd one, while oak timber sales were the lowest in 3rd quarter and the highest in 4th one. The seasonal effect accounted for the largest proportion of variation in timber production volume. The seasonality of timber prices was negatively correlated with production. The highest prices for pine and oak timber were obtained in 1st and 2nd quarters respectively, when the production volume of those timber species was the lowest. Conversely, the lowest prices were recorded in seasons characterized by the greatest production volume, i.e., in 3rd quarter for pine and in 4th quarter for oak. The decomposition of overall variation in timber prices into its components, which can be predicted (cyclic and seasonal fluctuations as well as long−term trend), and those that are difficult to forecast (irregular effect) is of great significance for timber sales management as the results can be used to improve timber price forecasting.
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