Since the late nineteenth century when high-cost equipment was introduced into forestry there has been a need to calculate the cost of this equipment in more detail with respect to, for example, cost of ownership, cost per hour of production, and cost per production unit. Machine cost calculations have been made using various standard economic methods, where costs have been subdivided into capital costs and operational costs. Because of differences between methods and between national regulations, mainly regarding tax rules and subsidies, international comparisons of machine costs are difficult. To address this, one of the goals of the European Cooperation in Science and Technology (COST) Action FP0902 was to establish a simple format for transparent cost calculations for machines in the forest biomass procurement chain. A working group constructed a Microsoft Excel–based spreadsheet model which is easy to understand and use. Input parameters are easy to obtain or possible to estimate by provided rules of thumb. The model gives users a simultaneous view of the input parameters and the resulting cost outputs. This technical note presents the model, explains how the calculations are made, and provides future users with a guide on how to use the model. Prospective users can view the model in the Supplementary Material linked to this article online.

Productivity of a mechanized P. patula cut-to-length harvesting operation was estimated and modelled using two methods of data collection: manual time study and follow-up study using StanForD stem files. The objective of the study was to compare the productivity models derived using these two methods to test for equivalence. Manual time studies were completed on four different machines and their operators. Two Ponsse Bear harvesters fitted with H8 heads, and two Ponsse  Beaver harvesters, fitted with H6 heads, were included. All machines were equipped with Ponsse Opti2 information system. All four operators had approximately 1 year of experience working with their respective machines. The four machines worked in separate four-tree-wide harvesting corridors, and they each harvested 200 trees.  Individual tree diameter at breast height (DBH), and height measurements were made manually. Subsequently, data on the trees in each study were extracted from the StanForD stem reports from each of the harvesters. Cycle times in the stem reports were determined based on the difference between consecutive harvest timestamps. The two methods were compared in terms of their abilities to estimate equivalent measures for tree DBH, volume, and productivity. In all four cases, significant differences were found between the DBH and volume measures derived using the two methods. Subsequently, the volume measures from the manual methods were used as the basis for productivity calculations. Results of the productivity comparisons found no significant differences between the models developed from the two methods. These results suggest that equivalent productivity models can be developed in terms of time using either method, however volume discrepancies indicate a need to reconcile bark and volume functions with the high variability experienced in the country.Â

Climate impacts of forest bioenergy result from a multitude of warming and cooling effects and vary by location and technology. While past bioenergy studies have analysed a limited number of climate-altering pollutants and activities, no studies have jointly addressed supply chain greenhouse gas emissions, biogenic CO₂ fluxes, aerosols and albedo changes at high spatial and process detail. Here, we present a national-level climate impact analysis of stationary bioenergy systems in Norway based on wood-burning stoves and wood biomass-based district heating. We find that cooling aerosols and albedo offset 60–70% of total warming, leaving a net warming of 340 or 69 kg CO₂e MWh⁻¹ for stoves or district heating, respectively. Large variations are observed over locations for albedo, and over technology alternatives for aerosols. By demonstrating both notable magnitudes and complexities of different climate warming and cooling effects of forest bioenergy in Norway, our study emphasizes the need to consider multiple forcing agents in climate impact analysis of forest bioenergy.

Whole trees from energy thinnings constitute one of many forest fuel sources, yet ten widely applied supply chains could be defined for this feedstock alone. These ten represent only a subset of the real possibilities, as felling method was held constant and only a single market (combustion of whole tree chips) was considered. Stages included in-field, roadside landing, terminal, and conversion plant, and biomass states at each of these included loose whole trees, bundled whole trees or chipped material. Assumptions on prices, performances, and conversion rates were based on field trials and published literature in similar boreal forest conditions. The economic outcome was calculated on the basis of production, handling, treatment and storage costs and losses. Outcomes were tested for robustness on a range of object volumes (50–350 m³_solid), extraction distances (50–550 m) and transport distances (10–70 km) using simulation across a set of discrete values. Transport was calculated for both a standard 19.5 m and an extended 24 m timber truck. Results showed that the most expensive chain (roadside bundling, roadside storage, terminal storage and delivery using a 19.5 m timber truck) at 158 € t_d⁻¹ was 23% more costly than the cheapest chain (roadside chipping and direct transport to conversion plant with container truck), at 128 € t_d⁻¹. Outcomes vary at specific object volumes and transport distances, highlighting the need to verify assumptions, although standard deviations around mean supply costs for each chain were small (6%–9%). Losses at all stages were modelled, with the largest losses (23 € t_d⁻¹) occurring in the chains including bundles. The study makes all methods and assumptions explicit and can assist the procurement manager in understanding the mechanisms at work.

Bipolar surface EMG (sEMG) signals of the trapezius muscles bilaterally were recorded continuously with a frequency of 800 Hz during full-shift field-work by a four-channel portable data logger. After recordings of 60 forest machine operators in Finland, Norway and Sweden, we discovered erroneous data. In short of any available procedure to handle these data, a method was developed to automatically discard erroneous data in the raw data reading files (Discarding Erroneous EPOchs (DESEPO) method. The DESEPO method automatically identifies, discards and adjusts the use of signal disturbances in order to achieve the best possible data use. An epoch is a 0.1 s period of raw sEMG signals and makes the basis for the RMS calculations. If erroneous signals constitute more than 30% of the epoch signals, this classifies for discharge of the present epoch. Non-valid epochs have been discarded, as well as all the subsequent epochs. The valid data for further analyses using the automatic detection resulted in an increase of acceptable data from an average of 2.15-6.5 h per day. The combination of long-term full-shift recordings and automatic data reduction procedures made it possible to use large amount of data otherwise discarded for further analyses.

Root and butt rot caused by pathogenic fungi in the genera Heterobasidion and Armillaria is a pressing issue in managed Norway spruce forests. The disease results in financial losses for the forest owners and reduces the volume of wood that can be used in long‐lived products. Pathogenic wood decay fungi spread either with the aid of airborne spores or via mycelial growth among neighbouring trees, the latter leading to clustering (tendency of decayed trees to be in close proximity relative to their neighbouring trees) of decay‐affected trees in forests. Understanding the spatial patterns of the decay‐affected trees at the forest stand level is vital for designing management strategies to address this problem. We examined decay clustering in 273 clear‐cut Norway spruce stands in Norway using harvester‐recorded data on spatial occurrence of decayed and healthy Norway spruce trees. We tested clustering using three global‐cluster tests that account for population density and distribution, evaluating clustering without identifying specific cluster locations. The proportions of clustered and non‐clustered stands differed depending on the statistical test used for clustering assessment, resulting in overall agreement of 32.8% for clustered and 36.9% for non‐clustered. Clustered stands exhibited a median cluster distance (maximum distance between the decay‐affected trees within a cluster) of 12 m (Inter‐Quantile Range, IQR, 6–20 m) and a median of 6 (IQR 3–16) nearest neighbour trees (number of decayed trees forming a cluster), estimates comparable with prior studies focused on assessment of trees infected by mycelial spread of the same fungal individual. The decay incidence in the clustered stands was 16.24%, while the non‐clustered stands had a butt‐rot incidence of 20.97%. In clustered stands the average number of trees per hectare was higher (693) than in non‐clustered stands (553). Synthesis and applications: Our study demonstrates that Norway spruce stands display a diverse range of spatial patterns of butt rotted trees. We found that higher densities of Norway spruce trees probably facilitate the vegetative spread of pathogenic wood decay fungi, leading to clustering of decay‐affected trees. To disrupt the spread of decay fungi between tree generations, precision planting of trees other than Norway spruce around infested stumps of prior generation trees has been recommended by earlier studies. We discussed the potential of using harvester‐derived geoposition data for butt‐rotted trees upon planning and execution of forest regeneration.