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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/gmd-2019-364
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-364
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  14 Feb 2020

14 Feb 2020

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A revised version of this preprint was accepted for the journal GMD and is expected to appear here in due course.

The importance of management information and soil moisture representation for simulating tillage effects on N2O emissions in LPJmL5.0-tillage

Femke Lutz1,2, Stephen DelGrosso3, Stephen Ogle4, Stephen Williams4, Sara Minoli1, Susanne Rolinski1, Jens Heinke1, Jetse J. Stoorvogel2, and Christoph Müller1 Femke Lutz et al.
  • 1Potsdam Institute for Climate Impact Research (PIK), member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
  • 2Wageningen University, Soil Geography and Landscape Group, P.O. Box 47, 6700 AA Wageningen, the Netherlands
  • 3USDA-ARS, Soil management and Sugar Beet Research Unit, 2150 Centre Ave. Bldg. D, Fort Collins, CO 80526, USA
  • 4NREL, Colorado State University, Fort Collins, CO 80523, USA

Abstract. No-tillage is often suggested as a strategy to reduce greenhouse gas emissions. Modeling tillage effects on nitrous oxide (N2O) emissions is challenging and subject to large uncertainties, as the processes producing the emissions are complex and strongly non-linear. Previous findings have shown deviations between the LPJmL5.0-tillage model and results from meta-analysis on global estimates of tillage effects on N2O emissions. Here we tested LPJmL5.0-tillage at four different experimental sites across Europe and the USA, to verify whether deviations in N2O emissions under different tillage regimes result from a lack of detailed information on agricultural management and/or the representation of soil water dynamics. Model results were compared to observational data and outputs from field-scale DayCent simulations. DayCent has been successfully applied for the simulation of N2O emissions and provides a richer data base for comparison than non-continuous measurements at the experimental sites. We found that adding information on agricultural management improved the simulation of tillage effects on N2O emissions in LPJmL. We also found that LPJmL overestimated N2O emissions as well as the effects of no-tillage on N2O emissions, whereas DayCent tended to underestimate the emissions of no-tillage treatments. LPJmL showed a general bias to over-estimate soil moisture content. Modifications of hydraulic properties in LPJmL in order to match properties assumed in DayCent, as well as of the parameters related to residue cover, improved the overall simulation of soil water as well as the N2O emissions simulated under tillage and no-tillage separately. However, the effects of no-tillage (shifting from tillage to no-tillage) did not improve. Advancing the current state of information on agricultural management as well as improvements in soil moisture highlight the potential to improve LPJmL5.0-tillage and global estimates of tillage effects on N2O emissions.

Femke Lutz et al.

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Interactive discussion

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Femke Lutz et al.

Model code and software

LPJmL5.0-tillage: Original source code as used in Lutz et al., 2019 F. Lutz, C. Müller, J. Heinke, S. Minoli, and S. Rolinski https://doi.org/10.5281/zenodo.3592381

Femke Lutz et al.

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