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

dc.bibliographicCitation.firstPage3905eng
dc.bibliographicCitation.issue9eng
dc.bibliographicCitation.journalTitleGeoscientific model development : GMDeng
dc.bibliographicCitation.lastPage3923eng
dc.bibliographicCitation.volume13eng
dc.contributor.authorLutz, Femke
dc.contributor.authorDel Grosso, Stephen
dc.contributor.authorOgle, Stephen
dc.contributor.authorWilliams, Stephen
dc.contributor.authorMinoli, Sara
dc.contributor.authorRolinski, Susanne
dc.contributor.authorHeinke, Jens
dc.contributor.authorStoorvogel, Jetse J.
dc.contributor.authorMüller, Christoph
dc.date.accessioned2021-07-12T12:16:55Z
dc.date.available2021-07-12T12:16:55Z
dc.date.issued2020
dc.description.abstractNo-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 great uncertainties as the processes producing the emissions are complex and strongly nonlinear. Previous findings have shown deviations between the LPJmL5.0-tillage model (LPJmL: Lund–Potsdam–Jena managed Land) 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, the representation of soil water dynamics or both. Model results were compared to observational data and outputs from field-scale DayCent model simulations. DayCent has been successfully applied for the simulation of N2O emissions and provides a richer database for comparison than noncontinuous measurements at 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 and 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 overestimate 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 and 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 and improvements in soil moisture highlights the potential to improve LPJmL5.0-tillage and global estimates of tillage effects on N2O emissions.eng
dc.description.fondsLeibniz_Fonds
dc.description.versionpublishedVersioneng
dc.identifier.urihttps://oa.tib.eu/renate/handle/123456789/6263
dc.identifier.urihttps://doi.org/10.34657/5310
dc.language.isoengeng
dc.publisherKatlenburg-Lindau : Copernicuseng
dc.relation.doihttps://doi.org/10.5194/gmd-13-3905-2020
dc.relation.essn1991-9603
dc.relation.issn1991-959X
dc.rights.licenseCC BY 4.0 Unportedeng
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/eng
dc.subject.ddc910eng
dc.subject.othernitrous oxide (N2O)eng
dc.subject.otherN2O emissionseng
dc.subject.otherLPJmLeng
dc.subject.otherno-tillageeng
dc.titleThe importance of management information and soil moisture representation for simulating tillage effects on N2O emissions in LPJmL5.0-tillageeng
dc.typeArticleeng
dc.typeTexteng
tib.accessRightsopenAccesseng
wgl.contributorPIKeng
wgl.subjectUmweltwissenschafteneng
wgl.typeZeitschriftenartikeleng
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