Combining national forest inventory (NFI) data with digital site maps of high resolution enables spatially explicit predictions of site productivity. The aim of this study is to explore the possibilities and limitations of this database to analyze the environmental dependency of height-growth of Norway spruce and to predict site index (SI) on a scale that is relevant for local forest management. The study region is the German federal state of Bavaria. The exploratory methods comprise significance tests and hypervolume-analysis. SI is modeled with a Generalized Additive Model (GAM). In a second step the residuals are modeled using Boosted Regression Trees (BRT). The interaction between temperature regime and water supply strongly determined height growth. At sites with very similar temperature regime and water supply, greater heights were reached if the depth gradient of base saturation was favorable. Statistical model criteria (Double Penalty Selection, AIC) preferred composite variables for water supply and the supply of basic cations. The ability to predict SI on a local scale was limited due to the difficulty to integrate soil variables into the model.

Climate analogues provide forestry practice with empirical evidence of how forests are managed in “twin” regions, i.e., regions where the current climate is comparable to the expected future climate at a site of interest. As the twin regions and their silvicultural evidence change with each climate scenario and model, we focus our investigation on how the uncertainty in future climate affects tree species prevalence. We calculate the future climate from 2000 to 2100 for three ensemble variants of the mild (representative concentration pathway (RCP) 4.5) and hard (RCP 8.5) climate scenarios. We determine climatic distances between the future climate of our site of interest ‘Roth’ and the current climate in Europe, generating maps with twin regions from 2000 to 2100. From forest inventories in these twin regions we trace how the prevalence of 23 major tree species changes. We realize that it is not the ‘how’ but the ‘how fast’ species’ prevalence changes that differs between the scenario variants. We use this finding to develop a categorization of species groups that integrates the uncertainty in future climate. Twin regions provide further information on silvicultural practices, pest management, product chains etc.

Aim of study: (i) To estimate site productivity based on German national forest inventory (NFI) data using above-ground wood biomass increment (ΔB) of the stand and (ii) to develop a model that explains site productivity quantified by ΔB in dependence on climate and soil conditions as well as stand characteristics for Norway spruce (Picea abies (L.) Karst.).Area of study: Germany, which ranges from the North Sea to the Bavarian Alps in the south encompassing lowlands in the north, uplands in central Germany and low mountain ranges mainly in southern Germany.Material and methods: Biomass increment of the stand between the 2nd and 3rd NFI was calculated as measure for site productivity. Generalized additive models were fitted to explain biomass increment in dependence on stand age, stand density and environmental variables.Main results: Great part of the variation in biomass increment was due to differences in stand age and stand density. Mean annual temperature and summer precipitation, temperature seasonality, base saturation, C/N ratio and soil texture explained further variation. External validation of the model using data from experimental plots showed good model performance.Research highlights: The study outlines both the potential as well as the restrictions in using biomass increment as a measure for site productivity and as response variable in statistical site-productivity models: biomass increment of the stand is a comprehensive measure of site potential as it incorporates both height and basal area increment as well as stem number. However, it entails the difficulty of how to deal with the influence of management on stand density.Keywords: Site index; site potential; biomass increment; statistical model; climate.