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ŠUMARSKI LIST 7-8/2017 str. 47     <-- 47 -->        PDF

terrain gradient is 37.4%, and altitudes range from 1,300 m to 1,900 m above sea level. (Figure 1a).
Study area has a total of 666.7 ha forested and 285.1 ha open lands. The forest within the study area belongs to government and is managed by Dereli State Forest Enterprise. The vegetation type of the study area is primarily composed of the association of Oriental Spruce (Picea orientalis L.), Oriental beech (Fagus orientalis Lipsky), and Caucasian fir (Abies nordmanniana subsp. nordmanniana) (GDF, 2013). The road length is 25,170 m within the study area, means that road density is 26.6 ha/m which is close to desired value as 20 m/ha (GDF, 2008). Therefore, road density and road spacing is sufficient for harvesting and other forestry activities. (Figure 1b).
Method of Approach – Metoda pristupa
In this study a two stage modelling approach was developed. The first stage was determined on the level of harvesting unit using linear programming with the aim of timber cut volume maximization. On the other hand, new forest road construction cost is very high nowadays considering steep terrain conditions (Sessions, et al., 1987; Allison et al., 2004; Enache et al., 2015). The mean forest road construction cost is approximately 7,238 €/km throughout the country, however, this cost is nearly two times higher as 13,442 € in the Blacksea region due to the mountainous and rocky conditions with high slopes (Çağlar and Türk, 2008). In another study, Acar and Eker (2001) found the road construction costs in Blacksea region two times more than costs in Lake region (near Isparta city) based on the six-year data, similar with previous study. Therefore, forest enterprise is not willing to spent more money because of budget constraints at least for the first period. A constraint was added to the linear programming model with minimizing the distance from the regeneration areas to the nearest forest road to reflect willingness of the forest enterprise. After obtaining the outputs, the results of the first period in terms of regeneration or thinning compartments and the level of harvest belong to those harvest areas were taken. Those parameters or outputs were used in the second stage as determining the timber extraction system.
Six main timber extraction systems are in use throughout the country as man power, animal power, skidder, small size cable crane, medium size cable crane and sledge yarder (Acar et al., 2000; Eker and Acar, 2006; Çalışkan and Karahalil, 2015). Oxen are used as animal power and MB Trac is used as skidder. Koller K 300, Urus MIII and Gantner were also used as small size cable crane, medium size cable crane and sledge yarder respectively. Due to the terrain conditions and high purchase costs, harvester machines are not used and other timber extraction systems such as plastic channels or monorail are not common and very limited. Therefore, six types of timber extraction systems were considered in this study. In order to achieve the integration of different timber extraction systems in operation planning with a number of different scenario analyses considering time, quantity, economic and environment was tried. The developed conceptual framework is presented in the Figure 2.
Harvest Scheduling Stage – Faza planiranja sječe
To determine the level of harvest and assign the compartments to final felling or thinning, a 50 year linear programming model was developed for the study area. Stands were taken as the basic components of the model. MODEL I approach was used to develop linear programming model (Davis et. al., 2001). Planning period is determined for 10 years. Natural stands younger than 100 years were exempted from regeneration. Bare lands were allowed for forestation in any period during the planning horizon. The level of thinning of any stand was determined as the 10% of the growing stock of the related stand. On the other hand, degraded and loose canopy stands (canopy <40%) were only subject to thinning (GDF, 2014). Regenerated stands grow according to normal yield tables developed by Akalp (1978) and Carus (1998). Forest inventory data were updated to determine the current forest structure (forest composition) using İkisu forest management plan (GDF, 2013). Mid points of planning periods were used in calculation of yield curve data.