Current issue: 56(4)
Under compilation: 57(1)
A time study was conducted to determine whether stem crowding had any impact on harvester productivity in Eucalyptus grandis stands. This represents an important element when trying to balance the advantages and disadvantages of coppice management in fast growing plantations designated for mechanized harvesting (i.e. machine felling, delimbing, debarking and cross-cutting). The study material consisted of 446 coppice stems, half of which grew as single stems per stool and half as double stems per stool as a result of different coppice reduction strategies. The dataset was balanced and randomized, with both subsets replicating exactly the same stem size distribution and the single and double stems alternating randomly. Harvester productivity ranged between 6 and 50 m3 under bark per productive machine hour, following the variation of tree diameter from 10 to 40 cm at breast height (1.37 m according to South African standards). Regression analysis indicated that both tree size and stem crowding (e.g. one or two stems per stool) had a significant effect on harvester productivity, which increased with stem size and decreased with stem crowding. However, operator experience may overcome the effect of stem crowding, which was not significant when the harvester was manned by a highly experienced operator. In any case, the effect of stem size was much greater than that of stem crowding, which resulted in a cost difference of less than 10%. However, this figure excludes the possible effects of stem crowding on volume recovery and stem development, which should be addressed in the future.
The present research focuses on the productivity of energy wood chipping operations at several sites in Italy. The aim was to assess the productivity and specifically the effect attributed to the operator in the chipping of wood biomass. The research included 172 trials involving 67 operators across the country that were analysed using a mixed model approach, in order to assess productivity, and to isolate the operator effect from other potential variables. The model was constructed using different predictors aiming to explain the variability due to the machines and the raw-materials. The final model included the average piece weight of raw material chipped as well as the power of the machine. The coefficients of determination (R2) were 0.76 for the fixed part of the model, and 0.88 when the effects due to the operators were included. The operators’ performance compared to their peers was established, and it was compared to a subjective classification based on the operator’s previous experience. The results of this study can help to the planning and logistics of raw material supply for bioenergy, as well as to a more effective training of future forest operators.