On-board computers (OBC) of harvesting machines can now provide optimized bucking (task of cutting stems into different log lengths) by relying on value and demand matrices. Despite existing benefits of these systems in certain countries, they remain largely underutilized and generally poorly understood in German mechanized forest operations. The study aimed to compare and quantify the differences in harvesting productivity and value recovery between two treatments: quality bucking (OFF) and automatic bucking (ON). A mature forest stand with a high proportion of Scots pine (Pinus sylvestris L.) was divided into plots (30 m × 100 m) where initial tests of both treatments were randomly distributed and replicated 10 times for a total of 11 plots per treatment. Pre-harvest inventory was performed on each tree targeted for removal via a commercial thinning silvicultural treatment. Mechanized harvesting was performed with an excavator-based Atlas Kern T23 Königstiger single-grip harvester. The same assortment specifications and prices were used for both treatments but on-board optimized bucking solutions were applied in the ON plots, whereas the operator had full control of the products to be recovered in the OFF plots. During harvesting operations, continuous time and motion was performed in all plots. Average harvesting productivity was higher—but not statistically significant—in OFF plots compared to ON plots by 2.0 and 0.46 m3/PMH0 for pine and spruce trees, respectively. Even if there was no difference detected in volume recovery for both treatments and tree species, value recovery was more than 1.60 € per cubic meter higher for pine in larger diameter classes when using quality bucking. This may be due to the fact that the algorithm of the OBC is designed for pine trees with a simpler crown architecture than trees harvested in this study. Results supporting quality bucking over automatic bucking in a Scots pine-dominated stands provide important forest operational information to managers.
European Journal of Forest Research – Springer Journals
Published: Jul 11, 2017
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