• For the first time the harmonized forest availability for wood supply is quantified for Europe. • Economic viability is the main restriction to availability of forests for wood supply. • The harmonized forests available for wood supply (FAWS) represent 75–95 % of forest area and biomass. • The reference definition of FAWS is suitable for international reporting.

For national and international reporting on forest available for wood supply (FAWS), harmonized data are needed. This information is required as forests provide goods for various sectors like the timber industry or the bioenergy sector. The effect of applying different thresholds to the three restriction classes (environmental, social and economic restriction) within the proposed reference definition for FAWS was evaluated. We applied the reference definition for FAWS to national data sets provided by five European National Forest Inventories using harmonized thresholds. The effects on FAWS area and growing stock were evaluated for each restriction and threshold. All countries within this study could report on protected areas. Social restrictions were not applied in any country, data on other restrictions are available but definitions vary. The application of common thresholds for restrictions proved difficult as effects vary between countries. The economic restriction is the most challenging to assess as many countries do not have corresponding data for direct calculation of, e.g., harvesting costs. Using proxies for harvesting costs was difficult, as common thresholds may not be applied in different countries. For standardized reporting, a FAWS definition should be developed that utilizes existing, harmonized indicators to describe restrictions.

• Key message A dataset of forest resource projections in 23 European countries to 2040 has been prepared for forest-related 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 .

Awareness of exhaustible forest resources is not recent in human history; rather, it dates back to the late Middle Ages, when it became clear that some kind of planning was needed to utilise forest resources and to do so, assessment was necessary. Postponed in time, enlarged to a national scale and based on statistical sampling, compared to the inventory methods adopted at that time, modern NFIs are assigned to produce sound information necessary to support forest policies. Forest areas and composition, ownership, growing stock and increment, as well as management, silviculture and structural characters are among the variables assessed by NFIs. This chapter provides statistics on those variables. For areas, estimates are shown for Total wooded area, Forest, Other wooded land, and their distribution among inventory categories and forest types, which describe species composition. In addition, the chapter also addresses distribution by altitude classes. For stands characters, areas are shown by crown coverage, development stage and age class. Lastly, inventory statistics are given on the presence and amount of small trees and shrubs.

Aims of the study. Assessment of growth is essential to support sustainability of forest management and forest policies. The objective of the study was to develop a species-specific model to predict the annual increment of tree basal area through variables recorded by forest surveys, to assess forest growth directly or in the context of more complex forest growth and yield simulation models.Area of the study. Italy.Material and methods. Data on 34638 trees of 31 different forest species collected in 5162 plots of the Italian National Forest Inventory were used; the data were recorded between 2004 and 2006. To account for the hierarchical structure of the data due to trees nested within plots, a two-level mixed-effects modelling approach was used.Main results. The final result is an individual-tree linear mixed-effects model with species as dummy variables. Tree size is the main predictor, but the model also integrates geographical and topographic predictors and includes competition. The model fitting is good (McFadden’s Pseudo-R2 0.536), and the variance of the random effect at the plot level is significant (intra-class correlation coefficient 0.512). Compared to the ordinary least squares regression, the mixed-effects model allowed reducing the mean absolute error of estimates in the plots by 64.5% in average.Research highlights. A single tree-level model for predicting the basal area increment of different species was developed using forest inventory data. The data used for the modelling cover 31 species and a great variety of growing conditions, and the model seems suitable to be applied in the wider context of Southern Europe. Keywords: Tree growth; forest growth modelling; forest inventory; hierarchical data structure; Italy.Abbreviations used: BA - basal area; BAI – five-year periodic basal area increment; BALT - basal area of trees larger than the subject tree; BASPratio - ratio of subject tree species basal area to stand basal area; BASTratio - ratio of subject tree basal area to stand basal area; CRATIO - crown ratio; DBH – diameter at breast height ; DBH0– diameter at breast height corresponding to five years before the survey year; DBHt– diameter at breast height measured in the survey year; DI5 - five-year, inside bark, DBH increment; HDOM - dominant height; LULUCF - Land Use, Land Use Changes and Forestry; ME - mean error; MAE - mean absolute error; MPD - mean percent deviation; MPSE - mean percent standard error; NFI(s) - National Forest Inventory/ies; OLS - ordinary least squares regression; RMSE - root mean squared error; UNFCCC - United Nation Framework Convention on Climate Change.