Bucking at Landing by a Single-Grip Harvester: Fuel Consumption, Productivity, Cost and Recovery Rate

Abstract

Operating at high efficiency is one of the prerequisites of sustainable forest operations. Typically, harvesters are designed to operate in tree felling and processing operations under the cut-to-length method. However, reasons such as limited business opportunities during the year may negatively affect the machine utilization rate. Coupled with high ownership costs, this results in economic sustainability problems. With this in mind, some practitioners have started to replace the traditional bucking operations conducted motor-manually at the landing with full mechanization using harvesters. This study evaluates the productive performance, fuel consumption, cost, and recovery rate in bucking wood at the landing by a single-grip harvester. A total of 27 bunches were taken into study, containing a total of 232 stems and stem parts, which were processed in 913 logs. A processing work cycle averaged 1.5 min but varied widely between 0.3 and 7 min. Dominant in the productive time were elements such as swinging to grab the stems, swinging to process, and piling the logs, which accounted for 86% of the productive time. Fuel consumption was estimated at 21 L/h and 0.78 L/m3, respectively. Efficiency was estimated at 0.047 PMH/m3, and productivity was estimated at 21.41 m3/PMH; the global recovery rate was 95%, and costs were in the range of 10 to 11 €/m3. Although there were attempts to develop dependence models of time consumption, efficiency, productivity, and recovery rate as functions of stem length, input volume, and the number of processed logs, this worked only for efficiency and productivity. The best-fitted models explained efficiency and productivity by power and linear equations, taking the input volume as a predictor. In addition to their practical application, the results of this study may be important for the planning and control of operations, for comparison to other ways of implementing the landing operations, or for optimizing larger systems.