DIGITALNA ARHIVA ŠUMARSKOG LISTA
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| ŠUMARSKI LIST 11-12/2016 str. 34 <-- 34 --> PDF |
due to breakdowns on truck and carriage. Timber recorded data include diameter and log length. Variables consist of load volume (m³), yarding distance (m), lateral dragging (m) and number of logs per load. In this time measurement study, 30 work cycles were recorded for Urus MIII skyline yarder in given stand and terrain conditions. RESULTS REZULTATI In this study, SPSS 21.0 statistical programmer has been applied for developing regression equation of time measurements (Anonymous 2012). Regression analysis has been realized with ENTER method. Summary for the total yarding cycles can be found in Table 2. A linear regression was developed from the time study using Urus MIII skyline yarder with total 30 yarding cycle. This regression model was as follow; T = – 0,832 + 0,024 x YD + 0,480 x V + 0,025 x LD where; T = Yarding time with delays (min/cycle) YD = Yarding Distance (m) V = Volume (m³) LD = Lateral Dragging (m) Yarding distance, load volume and lateral dragging per cycle were at significant level of 0.05. The multiple correlation coefficients (R) are interpreted as 83% of total variability. Autocorrelation was determined with Durbin Watson test, which indicated a positive autocorrelation (Table 3 and 4). The histogram of standardized residuals is shown in Figure 3. The average total cycle time was 6.43 minute for average length of 253 meters. The hourly production with delay times for Urus MIII skyline yarder was 10.60 m³ for this distance. The daily production was 84.80 m³. Yarder production without delay times was 12.31 m³/hour. Two most |