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Journal Article

Application of DNDC biogeochemistry model to estimate greenhouse gas emissions from Italian agricultural areas at high spatial resolution

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Lugato, E., Zuliani, M., Alberti, G., Vedove, G. D., Gioli, B., Miglietta, F., et al. (2010). Application of DNDC biogeochemistry model to estimate greenhouse gas emissions from Italian agricultural areas at high spatial resolution. Agriculture, Ecosystems & Environment, 139, 546-556. doi:10.1016/j.agee.2010.09.015.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0014-4B12-9
Abstract
Agriculture is responsible for 13.5% of total greenhouse gas (GHG) emissions (IPCC, 2007), in particular accounting for more than 80% of global anthropogenic nitrous oxide (N2O) emissions. However uncertainties in GHG inventories are still high as biogeochemical cycles are strongly influenced by climatic and environmental conditions and also depend on local agricultural practices. In order to improve the GHG balance assessment in cropland, higher order methods (Tier 3) are recommended such as, for example, model applications at spatial level. In the present study, a GIS-model integration was performed with the aim of improving the national GHG inventory in Italy. The de-nitrification decomposition (DNDC) model, chosen due to its ability to simulate carbon (C) and nitrogen (N) cycles, has been tested against measured data coming from eddy-covariance stations and soil flux chambers (for CO2 and N2O fluxes) belonging to Carbo-Italy network. Despite the varying site specific parameters, the results confirmed the ability of the model to represent the real C balance in irrigated maize crop under both conventional and minimum tillage. Modelled N2O emissions fitted the measured data well, but the corresponding emission factor from fertilizers was much lower than the IPCC default (0.008 vs 0.0125 kgN2O-N kg−1 N, respectively). A platform of simulation was then built to run DNDC for the entire national territory, linking the model with geographical databases. To implement the model, a high spatial resolution grid (1km×1 km) was adopted in order to develop a tool that could be used by local administrations and easily upload information at high spatial resolution (e.g. remote sensed information). A tree management (e.g. a combination of different management and land use) was also built to simulate crops with a ‘business as usual’ (BaU) scenario and with alternative management practices (AMP) and potentially create infinite combinations in each cell simply by varying a relative land use weight. Although the total area under agriculture has not been simulated so far, this platform of simulation appears promising to improve the national GHG inventory and derive C credits from the agricultural sector.