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.

A comparison of two methods of data collection for modelling productivity of harvesters: manual time study and follow-up study using on-board-computer stem records. Ann. For. Res. 61(1): 109-124.

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 m 3 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) 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. OPEN ACCESSForests 2014, 5 2085

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.

Belbo, H. 2010. Comparison of two working methods for small tree harvesting with a multi tree felling head mounted on farm tractor. Silva Fennica 44(3): 453-464.In this study, the efficiency of a small multi-tree felling head, mounted on a farm tractor with a timber trailer was studied, when harvesting small trees for energy in thinnings. Both separate loading and direct loading of the felled trees was studied.Time studies were carried out in a mixed stand of Norway spruce (Picea abies (L.) Karst) and birch (Betula pubescens Ehrh.). The time consumption of the work elements in the different work methods was formulated by regression analysis, where the independent variables were tree size and degree of accumulation. The average size of the harvested trees was 0.035 m 3 . The time consumption for the harvesting and loading were similar for the two studied methods, 20 minutes per m 3 at a tree size of 0.035 m 3 , but the two methods showed different characteristics for different tree sizes and level of accumulation. The direct loading method had the highest productivity when more than 0.1 m 3 were collected in the felling cycle, whereas the separate loading method had the highest productivity when less than 0.05 m 3 were collected in the felling cycle. The total effective time consumption for harvesting and forwarding the biomass 300 meters to roadside landing was 27 minutes per m 3 . The efficiency of the initial felling and collecting of the small trees was the main challenge. Both the harvesting technique and harvesting technology needs further development to provide a feasible production chain for woodfuel from energy thinning.