Skip to main content
ARS Home » Plains Area » Fort Collins, Colorado » Center for Agricultural Resources Research » Soil Management and Sugarbeet Research » Research » Publications at this Location » Publication #370733

Research Project: Management Practices for Long Term Productivity of Great Plains Agriculture

Location: Soil Management and Sugarbeet Research

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

Author
item LUTZ, FEMKE - Potsdam Institute
item Del Grosso, Stephen - Steve
item OGLE, STEPHEN - Colorado State University
item WILLIAMS, STEVEN - Colorado State University
item MINOLI, SARA - Potsdam Institute
item ROLINSKI, SUSSANE - Potsdam Institute
item HEINKE, JEAN - Potsdam Institute
item STOORVOGEL, JETSE - Wageningen University
item MULLER, CHRISTOPHER - Potsdam Institute

Submitted to: Geoscientific Model Development
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/28/2020
Publication Date: 9/1/2020
Citation: Lutz, F., Del Grosso, S.J., Ogle, S., Williams, S., Minoli, S., Rolinski, S., Heinke, J., Stoorvogel, J., Muller, C. 2020. The importance of management information and soil moisture representation for simulating tillage effects on N2O emissions in LPJmL5.0-tillage. Geoscientific Model Development. https://doi.org/10.5194/gmd-13-3905-2020.
DOI: https://doi.org/10.5194/gmd-13-3905-2020

Interpretive Summary: Converting croplands from plowing to 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 involve physical, biological, and chemical interactions. Previous findings have shown deviations between the LPJmL5.0-tillage model and results from field studies 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 limited 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 plowing 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.

Technical 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 nonlinear. Previous findings have shown deviations between LPJmL5.0-tillage model and the 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.