Modelling the response of yields and tissue C : N to changes in atmospheric CO2 and N management in the main wheat regions of western Europe
- 1Department of Physical Geography and Ecosystem Science, Lund University, 223 62 Lund, Sweden
- 2Department of Geosciences and Natural Resource Management, University of Copenhagen, Øster Voldgade 10, 1350 Copenhagen, Denmark
- 3CSIRO Sustainable Agricultural Flagship, CSIRO Agriculture, GPO Box 1666, Black Mountain, Canberra, ACT 2601, Australia
- 4Centre for Environmental and Climate Research, Lund University, 223 62 Lund, Sweden
- 5Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research/Atmospheric Environmental Research, 82467 Garmisch-Partenkirchen, Germany
Abstract. Nitrogen (N) is a key element in terrestrial ecosystems as it influences both plant growth and plant interactions with the atmosphere. Accounting for carbon–nitrogen interactions has been found to alter future projections of the terrestrial carbon (C) cycle substantially. Dynamic vegetation models (DVMs) aim to accurately represent both natural vegetation and managed land, not only from a carbon cycle perspective but increasingly so also for a wider range of processes including crop yields. We present here the extended version of the DVM LPJ-GUESS that accounts for N limitation in crops to account for the effects of N fertilisation on yields and biogeochemical cycling.
The performance of this new implementation is evaluated against observations from N fertiliser trials and CO2 enrichment experiments. LPJ-GUESS captures the observed response to both N and CO2 fertilisation on wheat biomass production, tissue C to N ratios (C : N) and phenology.
To test the model's applicability for larger regions, simulations are subsequently performed that cover the wheat-dominated regions of western Europe. When compared to regional yield statistics, the inclusion of C–N dynamics in the model substantially increase the model performance compared to an earlier version of the model that does not account for these interactions. For these simulations, we also demonstrate an implementation of N fertilisation timing for areas where this information is not available. This feature is crucial when accounting for processes in managed ecosystems in large-scale models. Our results highlight the importance of accounting for C–N interactions when modelling agricultural ecosystems, and it is an important step towards accounting for the combined impacts of changes in climate, [CO2] and land use on terrestrial biogeochemical cycles.