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Consistent knowledge about the increment in European forests gained amplified importance in European policies and decision processes related to forest-based bioeconomy, carbon sequestration, sustainable forest management and environmental changes. Until now, large-area increment information from European countries was lacking international comparability. In this study we present a harmonisation framework in accordance with the principles and the approach established for the harmonisation of National Forest Inventories (NFIs) in Europe. 11 European NFIs, representing a broad range of increment measurement and estimation methods, developed unified reference definitions and methods that were subsequently implemented to provide harmonised increment estimates by NUTS regions (Nomenclature of territorial units for statistics of the European Union), main forest types and tree species groups, and to rate the impact of harmonisation measures. The main emphasis was on gross annual increment (GAI), however, also annual natural losses (ANL) and net annual increment (NAI) were estimated. The data from the latest available NFI cycles were processed. The participating countries represent a forest area of about 130 million ha, and 82% of the European Unions' (EU) forest area, respectively. The increments were estimated in terms of volume (m³ year−1, m³ ha−1 year−1) and above-ground biomass (t year−1, t ha−1 year−1). The harmonised GAI volume estimates deviate in a range of +12.3% to −26.5% from the estimates according to the national definitions and estimation methods. Within the study area, the harmonised estimates show a considerable range over the NUTS regions for GAI, from 0.6 to 12.3 m³ ha−1 year−1, and 0.8–6.4 t ha−1 year−1, of volume and above-ground biomass, respectively. The largest increment estimates are found in Central Europe and gradually decrease towards the North, South, West and East. In most countries coniferous forests show larger increment estimates per hectare than broadleaved forests while mixed forests are at an intermediate level. However, in some instances, the differences were small or mixed forests revealed the largest increment estimates. The most important tree species groups in the study area are Pinus spp. and Picea spp., contributing 29% and 26% of the estimated total GAI volume, respectively. The shares of the prevalent broadleaved species are smaller with contributions of 9%, 7% and 6% by Quercus spp., Fagus sylvatica and Betula spp. The results underline the importance of harmonisation in international forest statistics. Looking ahead, harmonised large-area increment estimation is pivotal for accurate monitoring and evidence-based policy decisions in the changing context of future forest ecosystems dynamics, management strategies and wood availability. [Display omitted] • Increment estimation approaches differ among European NFIs. • Harmonisation framework developed for gross and net annual increment. • Common increment estimation method implemented by 10 European countries. • Harmonised increments estimated for NUTS regions, forest types and tree species. • Harmonised increments improve the comparability of international forest statistics. [ABSTRACT FROM AUTHOR]

N is known to be the most limiting element for vegetation growth in temperate and boreal forests. The expected increases in global temperature are predicted to accelerate N mineralization, therefore incrementing N availability in the soil and affecting the soil C cycle as well. While there is an abundance of C data collected to fulfill the requirements for national GHG accounting, more limited information is available for soil N accumulation and storage in relation to forest categories and altitudinal gradients. The data collected by the second Italian National Forest Inventory, spanning a wide range of temperature and precipitation values (10° latitudinal range), represented a unique opportunity to calculate N content and C/N ratio of the different soil layers to a depth of 30 cm. Boosted Regression Tree (BRT) models were applied to investigate the main determinants of soil N distribution and C/N ratio. Forest category was shown to be the main explanatory factor of soil N variability in seven out of eight models, both for forest floor and mineral soil layers. Moreover latitude explained a larger share of variability than single climate variables. BRT models explained, on average, the 49% of the data variability, with the remaining fraction likely due to soil-related variables that were unaccounted for. Accurate estimations of N pools and their determinants in a climate change perspective are consequently required to predict the potential impact of their degradation on forest soil N pools. [ABSTRACT FROM AUTHOR]

European National Forest Inventories (NFIs) are well established in most of the European countries. In some NFIs, core samples are taken to estimate volume increments at annual resolution. However, the potential of the NFI for dendroecological investigations has not been evaluated so far. Therefore, the aim of this study was to test the potential of dendrochronological data collected during the Italian NFI in 2006. Silver fir was selected as a test species. Core samples were taken from eighty-one trees including one sample per inventory plot (IP). The 81 IPs were distributed across the Italian Alps and the northern Apennine thus well representing the site conditions of the study area. The effects of the detrending method and sample size on detecting middle- to long-term growth trends and high-frequency climate signals were tested. Further, cluster analysis was applied to find dissimilarities among tree-ring width (TRW) series. Results suggest the detrending method to be the most important factor for detecting growth trends, but not for identification of high-frequency climate signals. Sample size essentially influences the final mean chronology, but it does not bring new information when larger than 30 series. Two groups of trees were recognised with distinct middle-term TRW patterns, although being the same in terms of climate sensitivity. Results are mostly in line with recent findings of other authors, which suggest the potential of the NFI data for dendroecological investigations. [ABSTRACT FROM AUTHOR]

Interest in the accurate measurement of forest biomass has increased remarkably in the decades following the agreements made at the United Nations Framework Convention on Climate Change, particularly because of forest biomass’ role in carbon storage. Information on forest biomass over large areas is generally obtained from National Forest Inventory (NFI) statistics; however, these data are usually estimates of stem volumes and only rarely estimate tree biomass through direct measurements. Conversion from stem volumes to biomass is commonly achieved through factors known as biomass expansion factors (BEFs). BEFs may also expand the estimation of tree components and, in such cases, are also referred to as conversion and expansion factors (BCEFs). Experience has proven that biomass factors vary with stand characteristics such as composition, age, volume and others. For this reason, models that predict such factors as a function of stand variables have been developed. In Italy, the total above-ground tree biomass (AGB) of forests was estimated by the second Italian NFI through direct measurements and, on the basis of the stock estimates of tree volume and biomass, BCEFs representative of Italian broad-spectrum forest conditions (e.g., composition, density, silviculture treatment and others) for reference year 2005 were published. This paper presents a stand-level model developed to predict AGB density (Mg ha −1 ) from growing stock volume (GSV) density (m 3 ha −1 ) by forest category. The model was calibrated using NFI plot level data. The fitted model was tested to estimate total AGB from aggregated data, which was represented as the mean GSV per hectare at the plot level, and two broader aggregation scales using the mean GSV per hectare of forest category in each administrative region and at the national level. The accuracy of the estimates generally decreased with the level of data aggregation, but the upscaling exercises only overestimated the national overall AGB value by a maximum of 2.00%, an acceptable difference given the broad datum used as the independent variable. The upscaling exercises proved that the model is also suitable for predicting AGB by forest category, particularly given that the estimated values always fell within the 95% confidence interval of the NFI reference estimates. [ABSTRACT FROM AUTHOR]