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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.

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.

National forest inventories (NFIs) are a primary source of data for carbon stock reporting. In Italy the most comprehensive assessment of forest carbon has been carried out under the second NFI. The NFI was conducted between 2003 and 2007 with an additional field exercise in 2008 and 2009, which collectively aimed to exhaustively survey four out of five forest carbon pools recognized by IPCC. The final estimates were published in 2013 and confirmed the major role of soil as a carbon store. Italian forests contain 1.24 × 109 Mg organic carbon, 57.6% of which is in soil (715.7 × 106 Mg), 38.1% in the above-ground biomass (472.7 × 106 Mg), 2.3% in litter (28.3 × 106 Mg) and 2.0% in deadwood (24.9 × 106 Mg). This article analyzed the variability of total above-ground living biomass and soil organic carbon stocks along with environmental gradients related to elevation and biogeographic regions; examined the carbon storage capacity of the different forest categories; investigated the relationships, at plot level, between both litter carbon and soil organic carbon, and other site/stand features, as well as the relationships between the four carbon pools investigated at the level of forest categories. Forest category proved to be the main driver of variability in carbon stocks, especially for the total above-ground biomass (total-AGB). The observed variation in total-AGB with altitude was primarily due to the distribution of forest categories across altitude classes. There was less variation in SOC between altitude classes than in total-AGB, and soil generally remains the main carbon pool at any altitude. The environmental parameters related to the biogeographic regions did not have a clear effect on total-AGB or soil carbon stocks. Significant correlations between both litter carbon and soil organic carbon and different site/stand features were found in the plot level data, but the amount of variation explained by the multiple regression analysis was too small for use in predictions. More variation was explained when fitting data at the forest category level.