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To evaluate the mitigation potential provided by the SOC pool, we investigated the impact of woody encroachment in the 0–30 cm depth of mineral soil across a natural succession from abandoned pastures and croplands to broadleaves forests on the central Apennine in Italy. In parallel, to assess the effect of the land use change (LUC) from cropland to pasture, a series of pastures established on former agricultural sites, abandoned at different time in the past, were also investigated. Our results show that woody encroachment on former pastures and croplands contributes largely to mitigate climate change, with an increase of the original SOC stock of 45% (40.5 Mg C ha−1) and 120% (66.5 Mg C ha−1), respectively. Also the LUC from croplands to pastures, greatly contributes to climate change mitigation trough a SOC increase of about 80% of the original SOC (45.9 Mg C ha−1). The management of abandoned lands represent a crucial point in the mitigation potential of agriculture and forestry activities, and particularly the role of the SOC pool. A policy effort should focus on minimizing the risk of speculative management options, particularly when the value of woody biomass become convenient to supply new energy systems allowing monetizing a long term forests productivity. In conclusion, despite both the land abandonment and the LUC can have a different impact on the SOC pool under different climatic conditions, these results can be useful to improve the SOC estimates in the National greenhouse gases Inventory at country level.

Understanding the dynamics of organic carbon mineralization is fundamental in forecasting biosphere to atmosphere net carbon ecosystem exchange (NEE). With this perspective, we developed 3D-CMCC-PSM, a new version of the hybrid process based model 3D-CMCC FEM where also heterotrophic respiration (R h ) is explicitly simulated. The aim was to quantify NEE as a forward problem, by subtracting ecosystem respiration (R eco ) to gross primary productivity (GPP). To do so, we developed a simplification of the soil carbon dynamics routine proposed in the DNDC (DeNitrification-DeComposition) computer simulation model. The method calculates decomposition as a function of soil moisture, temperature, state of the organic compartments, and relative abundance of microbial pools. Given the pulse dynamics of soil respiration, we introduced modifications in some of the principal constitutive relations involved in phenology and littering sub-routines. We quantified the model structure-related uncertainty in NEE, by running our training simulations over 1000 random parameter-sets extracted from parameter distributions expected from literature. 3D-CMCC-PSM predictability was tested on independent time series for 6 Fluxnet sites. The model resulted in daily and monthly estimations highly consistent with the observed time series. It showed lower predictability in Mediterranean ecosystems, suggest

The most of the global organic carbon is stored in the forest soil. The large quantity of CO2 emitted from soil is a consequence of land use change, both through human activities (e.g. agriculture) or natural hazards (e.g. flooding, landslides, erosion). A net carbon loss from soils adds to the increase in the atmospheric CO2 concentration, probably leading to higher global temperatures. On the other hand, a net soil CO2 sequestration could help to mitigate the greenhouse effect and to improve soil quality. In this purpose, identifying areas suitable for projects aimed to reduce emissions through SOC sequestration, is of the most importance for both developed and developing countries. This report describes the sources and procedures used to develop a global database for SOC content. The FAO approaches and methodologies provided by using Geographic Information Systems (GIS) software, play a crucial role in this process. Global Soil Organic Carbon Database (at 30 arcsec). Available from: https://www.researchgate.net/publication/317958234_Global_Soil_Organic_Carbon_Database_at_30_arcsec [accessed Sep 5, 2017].

The assessment of forest biomass has important consequences for climate-related policies, including conservation and activities related to deforestation and forest degradation. Tree and forest biomass are most efficiently assessed by a combination of remote-sensing, ground-based measurements and allometric models to predict tree and forest characteristics such as tree volume and biomass. The accuracy of these allometric models and therefore of biomass estimates can be improved greatly if wood density is used as an input variable. It is the most commonly used species functional trait in multispecies biomass models, which are frequently used in tropical ecosystems. However, wood density is difficult to measure accurately and thus is not included in large-scale forest inventories. This issue can be bypassed by using species average values, but often wood density values are only available at coarse taxonomic and/or spatial scales. Applying regional multi-species averages reintroduces to the models the potential bias originally removed by the use of wood density values. Therefore more wood density data is needed to cover the diversity of species and environments. This paper presents the work carried out to complement current initiatives aiming to collect, harmonize and share existing wood density data stored in national research institutes across the tropics. The database compiles raw data and calculated averages, with 70 information fields including GPS coordinates, measurement methods and sample size, when available, for a wide range of taxonomic and environmental values. Where precision in both taxonomic and environmental scales remains difficult to meet, the trade-off between the two can be better understood as the database includes both very accurate data and coarse averages. This database will improve forest biomass estimates, towards increasing the potential of climate change mitigation initiatives in the forestry sector.