Abstract
Two objectives of the Common Agricultural Policy post-2013 (CAP, 2014–2020) in the European Union (EU) are the sustainable management of natural resources and climate smart agriculture. To understand the CAP impact on these priorities, the Land Use/Cover statistical Area frame Survey (LUCAS) employs direct field observations and soil sub-sampling across the EU. While a huge amount of information can be retrieved from LUCAS points for monitoring the environmental status of agroecosystems and assessing soil carbon sequestration, a fundamental aspect relating to climate change action is missing, namely nitrous oxide (N2O) soil emissions. To fill this gap, we ran the DayCent biogeochemistry model for more than 11’000 LUCAS sampling points under agricultural use, assessing also the model uncertainty. The results showed that current annual N2O emissions followed a skewed distribution with a mean and median values of 2.27 and 1.71 kg N ha-1 yr-1, respectively. Using a Random Forest regression for upscaling the modelled results to the EU level, we estimated direct soil emissions of N2O in the range of 171–195 Tg yr-1 of CO2eq. Moreover, the direct regional upscaling using modelled N2O emissions in LUCAS points was on average 0.95 Mg yr-1 of CO2eq. per hectare, which was within the range of the meta-model upscaling (0.92–1.05 Mg ha-1 yr-1 of CO2eq). We concluded that, if information on management practices would be made available and model bias further reduced by N2O flux measurement at representative LUCAS points, the combination of the land use/soil survey with a well calibrated biogeochemistry model may become a reference tool to support agricultural, environmental and climate policies.
Highlights
Following decisions 1445/2000/EC and 2066/2003/EC [1,2], the statistical office of the European Union (Eurostat) has established a regular survey to monitor changes in land use over time across the European Union (EU)
Other than for sugar beet, the deviation from the 1:1 line was noted in NUTS regions with a lower number of simulated values, indicating a possible lack of statistical representativeness rather than a model bias
The Mean Absolute Error (MAE), which is less sensitive to outliers than Root Mean Square Error (RMSE), was 0.67 Mg C ha-1
Summary
The aim of this research was to demonstrate a framework that can be used to improve estimates of N2O emissions from agricultural soils at continental scales using the original DayCent model with adequate input data
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