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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.

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Purpose of Review Mechanized logging operations with ground-based equipment commonly represent European production forestry but are well-known to potentially cause soil impacts through various forms of soil disturbances, especially on wet soils with low bearing capacity. In times of changing climate, with shorter periods of frozen soils, heavy rain fall events in spring and autumn and frequent needs for salvage logging, forestry stakeholders face increasingly unfavourable conditions to conduct low-impact operations. Thus, more than ever, planning tools such as trafficability maps are required to ensure efficient forest operations at reduced environmental impact. This paper aims to describe the status quo of existence and implementation of such tools applied in forest operations across Europe. In addition, focus is given to the availability and accessibility of data relevant for such predictions. Recent Findings A commonly identified method to support the planning and execution of machine-based operations is given by the prediction of areas with low bearing capacity due to wet soil conditions. Both the topographic wetness index (TWI) and the depth-to-water algorithm (DTW) are used to identify wet areas and to produce trafficability maps, based on spatial information. Summary The required input data is commonly available among governmental institutions and in some countries already further processed to have topography-derived trafficability maps and respective enabling technologies at hand. Particularly the Nordic countries are ahead within this process and currently pave the way to further transfer static trafficability maps into dynamic ones, including additional site-specific information received from detailed forest inventories. Yet, it is hoped that a broader adoption of these information by forest managers throughout Europe will take place to enhance sustainable forest operations.

• Key message A dataset of forest resource projections in 23 European countries to 2040 has been prepared for forestrelated policy analysis and decision-making. Due to applying harmonised definitions, while maintaining country-specific forestry practices, the projections should be usable from national to international levels. The dataset can be accessed at https://doi.org/10.5061/dryad.4t880qh. The associated metadata are available at https://metadata-afs.nancy.inra.fr/ geonetwork/srv/eng/catalog.search#/metadata/8f93e0d6-b524-43bd-bdb8-621ad5ae6fa9.