Harvesters produce one third of timber in Czechia. The aim of this study was to analyze the over- and under-bark volume estimates of Scots pine (Pinus sylvestris L.) timber produced by a mid-performance harvester. The data were collected between March 2017 and June 2018. In total, 4661 stems cut into 29,834 logs were analyzed. For volume estimation, StanForD offers several price categories using various algorithms. Three of these price categories are relevant for Czech forestry—M3s, M3toDE, and M3miDE. The M3s price category is based on the estimation of partial volumes of 10 cm long sections, which are summed. Therefore, this price category represents the volume estimation closest to the true volume. By comparison, the M3toDE and M3miDE price categories use the same algorithm for volume estimation, which is based on the Huber formula using a midspan diameter rounded down to the nearest whole centimeter. The M3toDE price category underestimated the over-bark volume by 6.48% compared to the reference price category M3s. The mean log volume estimated through the M3s price category was significantly higher than the M3toDE volume both in individual grades and without grading. We found significant differences between under-bark volume estimates by the diameter band bark deduction method (DBM) and the parametric linear bark deduction method (PLM) used in harvester’s systems according to the Guidelines for Electronic Scaling of Timber for Harvesters in Czechia (GEH) for Scots pine butt logs with rough bark, and also for other logs with normal bark thickness. To obtain under-bark volume estimates of Scots pine timber that are comparable with the Guidelines for Timber Scaling in Czechia (GTS) using the parametric nonlinear bark deduction method (PNM), we recommend using the algorithm of the M3toDE price category, with double bark thickness determined by the diameter band bark deduction method.

Timber is the most important source of revenue in forestry and, therefore, is necessary to precisely estimate its volume. The share of timber volume produced by harvesters is annually growing in many European countries. Suitable settings of harvesters will allow us to achieve the most accurate volume estimates of the produced timber. In this study, we compared the different methods of log volume estimation applied by control and information systems of harvesters. The aim was to analyze the price categories that can be set up in the StanForD standard and to determine the differences between the algorithms used for log volume estimations. We obtained the data from *.STM files collected from March 2017 until June 2018 on a medium-size harvester. We analyzed price categories and found seven different algorithms used to estimate the log volumes. Log volume estimates according to Algorithm A2 were considered as standard because these estimates should be closest to the true log volumes. Significant differences, except the difference between Algorithm A2 and Algorithm A3, were found between log volumes estimated by different algorithms. After categorization of logs to assortments, the results showed that significant differences existed between algorithms in each assortment. In the roundwood assortment, which contains the most valuable logs, a difference of more than 6% was found between the log volumes estimated by Algorithm A5 and Algorithm A2. This is interesting because Algorithm A5 is widely used in some Central European countries. To obtain volumes closest to the true volumes, we should use Algorithm A2 for the harvester production outputs. The resulting differences between the algorithms can be used to estimate the volume difference between harvester outputs using the different price categories. Understanding this setting of harvesters and the differences between the price categories will provide users useful information in applied forest management.

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