Automatic GNSS-enabled harvester data collection as a tool to evaluate factors affecting harvester productivity in a Eucalyptus spp. harvesting operation in Uruguay

摘要

Uruguay has adopted cut-to-length (CTL) machines in forest harvesting operations, especially in large scale, fast-growing plantations. The majority of modern CTL machines have on-board computers that capture individual tree data and can be coupled with global navigation satellite systems (GNSS). This provides the opportunity to collect data for research purposes and to improve operations. In this study, we retrieved data (StanForD stm and drf files) from a GNSS-enabled harvester working in CTL operations in Eucalyptus spp. plantations in Uruguay. With two thirds of this data we fitted a mixed effects model to evaluate harvester productivity as a function of stem diameter at breast height (DBH), species, shift (day/ night), slope, and operator. A slope surface derived from a digital terrain model was overlaid with GNSS stem records. Slope values were assigned to each stem using the Spatial Analyst toolbox in ArcGIS. The reserved third of the data were used to validate the model. DBH was the most influential variable in harvester productivity, showing a positive correlation and a R2 value of 0.73 in the validation model. Operator and species also had significant effects. There was no significant slope effect, whereby the study area only had flat andmildly sloping terrain. Shift did not have a significant effect, indicating there was no drop in night shift productivity. The model developed constitutes the first published harvester productivity model in South America based on data automatically collected by harvesters. In addition, the forestry company may benefit from using the model for operator management.