Validating the predicted saw log and pulpwood proportions and gross value of Scots pine and Norway spruce harvest at stand level by Most Similar Neighbour analyses and a stem quality database

Abstract

Detailed pre-harvest information about the volumes and properties of growing stocks is needed for increased precision in wood procurement planning for just-in-time wood deliveries by cut-to-length (CTL) harvesters. In the study, the non-parametric Most Similar Neighbour (MSN) methodology was evaluated for predicting external quality of Scots pine and Norway spruce, expressed as stem sections fulfilling the saw log dimension and quality requirements of Finnish forest industry, as they affect the recovery of timber assortments and the value of a pre-harvest stand. Effects of external tree quality were evaluated using saw log recovery and saw log reduction caused by stem defects, as well as total timber value (€) and average unit value (€ m) in a stand. Root mean square error (RMSE) of saw log recovery and reduction were 9.12 percentile points (pp) for Scots pine and 6.38 pp for Norway spruce stands. In the unit value considerations, the predictions compared with measurements resulted in the RMSE of 3.50 € m and the bias of 0.58 € m in Scots pine stands and 2.60 € m, and 0.35 € m in Norway spruce stands, respectively. The presented MSN based approach together with the utilization of the external stem quality database included in the ARVO software could provide dimension and external quality predictions usable for pre-harvest assessment of timber stock at a stand level. This prediction methodology is usable especially in analyses where timber assortment recoveries, values and unit prices are compared when different bucking objectives are used.–3–3–3–3–3

Demonstrating the Effect of Height Variation on Stand-Level Optimization with Diameter-Structured Matrix Model

“…In brief, a tree shorter than an arithmetic average tree height within a size category was labeled with a lower unit price of saw logs. The rationale was that the shorter the tree the less allowance there is for bucking, which in turn has a negative effect on a saw log price [31]. Our results indicated that including variation into the matrix model has a notable impact on financial performance.…”

“…Further, if the height of a tree in diameter class i, is less than µ i − 0.5 m then the unit price of the saw log for the tree is lower than the original saw log price, where µ i denotes the mean height of the diameter class i. Technically, the term µ i − 0.5 m is a sanction which degrades the saw log price for trees which are shorter (by 0.5 m) than the mean height of the particular diameter class i. The rationale is that the smaller the tree the less allowance there is for bucking, which in turn has a negative effect on a saw log price [31]. In this connection it should be highlighted that although the height variation does not have a direct correspondence to wood quality it has bearing on the saw log recovery.…”

Integrating Detailed Timber Assortments into Airborne Laser Scanning (ALS)-Based Assessments of Logging Recoveries

“…Species-specific taper curve models using DBH and height as the other inputs were used to taper the stems in the tree lists from the harvester data and from the ABA [12]. When quality was not taken into account, the bucking-to-value simulator used the tapering of the stems, the tree species and the species-wise bucking objectives, whereas when quality was considered, the same simulator employed external quality expressed in terms of vertical stem sections fulfilling different timber assortment quality requirements as specified by the Finnish forest companies [30]. The external quality that affected bucking was estimated in Scenarios 3 and 4, in which a stem quality database was used with the MSN method [30,31] (see Figure 2).…”

“…When quality was not taken into account, the bucking-to-value simulator used the tapering of the stems, the tree species and the species-wise bucking objectives, whereas when quality was considered, the same simulator employed external quality expressed in terms of vertical stem sections fulfilling different timber assortment quality requirements as specified by the Finnish forest companies [30]. The external quality that affected bucking was estimated in Scenarios 3 and 4, in which a stem quality database was used with the MSN method [30,31] (see Figure 2). For these two scenarios, technical defects of the target stems were estimated by selecting the most similar stem from the quality database in accordance with the stand variables, diameter at breast height, and height of the stem.…”

“…The quality assessment was based on visual estimation of the occurrence of technical defects (scars, crooks, sweeps, forks, knots, etc.). The database was collected in various research projects at the Finnish Forest Research Institute between 1998 and 2010 (for a more detailed description and the geographic coverage of the database, see [30]).…”