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

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dc.contributor.authorOlivera, A.
dc.contributor.authorVisser, R.
dc.contributor.authorAcuna, M.
dc.contributor.authorMorgenroth, J.
dc.date.accessioned2017-01-09T19:28:49Z
dc.date.available2017-01-09T19:28:49Z
dc.date.issued2016en
dc.description.abstractUruguay 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 and mildly 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.en
dc.identifier.citationOlivera, A., Visser, R., Acuna, M., Morgenroth, J. (2016) Automatic GNSS-enabled harvester data collection as a tool to evaluate factors affecting harvester productivity in a Eucalyptus spp. harvesting operation in Uruguay. International Journal of Forest Engineering, (early access online).en
dc.identifier.doihttps://doi.org/10.1080/14942119.2015.1099775
dc.identifier.urihttp://hdl.handle.net/10092/13007
dc.language.isoen
dc.publisherUniversity of Canterbury. School of Forestryen
dc.rights.urihttps://hdl.handle.net/10092/17651
dc.subjectStanForD filesen
dc.subjectGNSSen
dc.subjectEucalyptus spp.en
dc.subjectUruguayen
dc.subjectharvesteren
dc.subjectproductivity modelen
dc.subject.anzsrcField of Research::07 - Agricultural and Veterinary Sciences::0705 - Forestry Sciencesen
dc.titleAutomatic GNSS-enabled harvester data collection as a tool to evaluate factors affecting harvester productivity in a Eucalyptus spp. harvesting operation in Uruguayen
dc.typeJournal Article
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