Akademik Veri Yönetim Sistemi
CROATIAN JOURNAL OF FOREST ENGINEERING, cilt.41, sa.1, ss.109-117, 2020 (SCI-Expanded)
Wood-harvesting activities are conducted by contractors through tendering based on prices determined by the amount of transported wood, land conditions and transport method parameters. Managers should determine the average completion time of the work and the base price accurately to prevent both work and contractor losses prior to the tender and note the same in the tender contract. Thus, prediction of productivity in wood production is of great importance in the determination of the work duration and cost. In this context, the aim of the present study was to determine the most accurate estimation model that would predict productivity (P-e) based on log volume (V-t), route slope (P) and winching distance (D) in uphill cable skidding activities with a drum tractor. In the current study, estimation models were developed that use both linear regression through SPSS employing all data and the robust regression method that minimizes the effect of outliers. Harvesting units were selected among pure spruce (Picea orientalis (L.) Link) stands via the uphill cable-skidding method with a tractor in the North-East of Turkey. Route slope, winching distance, log volume and time-consumption data were collected in the chosen harvesting units and productivity prediction models were developed with these data. In this study, the productivity estimation was performed using linear regression in SPSS and robust regression methods prepared in MATLAB environment. The coefficients calculated by these methods were statistically tested, and it was determined that the winching distance coefficient was insignificant with both methods. Thus, the productivity estimation model was re-determined with both methods based on the slope and log volume parameters, and the findings were compared. Additionally, the standard errors of the coefficients of both models were compared and it was concluded that the robust method was more sensitive than the SPSS regression method.