Zur Kurzanzeige

Description and prediction of copper contents in soils using different modeling approaches - Results of long-term monitoring of soils of northern Germany

dc.date.accessioned2023-01-04T14:37:50Z
dc.date.available2023-01-04T14:37:50Z
dc.date.issued2022-10-03
dc.identifierdoi:10.17170/kobra-202301047303
dc.identifier.urihttp://hdl.handle.net/123456789/14332
dc.description.sponsorshipGefördert im Rahmen des Projekts DEAL
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectcoppereng
dc.subjectmixed-effects modelingeng
dc.subjectrule-based modelingeng
dc.subjectsoil monitoringeng
dc.subject.ddc570
dc.subject.ddc580
dc.subject.ddc630
dc.titleDescription and prediction of copper contents in soils using different modeling approaches - Results of long-term monitoring of soils of northern Germanyeng
dc.typeAufsatz
dcterms.abstractBackground: Different regression approaches may be useful to predict dynamics of copper (Cu), an essential element for plants and microorganisms that becomes toxic at increased contents, in soils. // Aim: Our objective was to explore the usefulness of mixed-effects modeling and rule-based models for a description and prediction of Cu contents in aqua regia (CuAR) in surface soils using site, pH, soil organic carbon (SOC), and the cation exchange capacity (CEC) as predictors. // Methods: Three sites in northern Germany were intensively monitored with respect to CuAR and SOC contents, pH, and CEC. Data analysis consisted of calibrations using the entire data set and of calibration/validation approaches with and without spiking. // Results: There was no consistent temporal trend, so data could be combined for the subsequent regressions. Calibration using the entire data set and calibration/validation after random splitting (i.e., pseudo-independent validation) were successful for mixed-effects and cubist models, with Spearman's rank correlation coefficients rs ranging from 0.83 to 0.91 and low root mean squared errors (RMSEs). Both algorithms included SOC, CEC, and pH as essential predictors, whereas site was important only in the mixed-effects models. Three-fold partitioning of the data according to site to create independent validations was again successful for the respective calibrations, but validation results were variable, with rs ranging from 0.04 to 0.76 and generally high RMSEs. Spiking the calibration samples resulted in generally marked improvements of the validations, with rs ranging from 0.45 to 0.67 and lower RMSEs. // Conclusions: Overall, the information provided by SOC, pH, and CEC is beneficial for predicting CuAR contents in a closed population of sites using either mixed-effects or cubist models. However, for a prediction of CuAR dynamics at new sites in the region, spiking is required.eng
dcterms.accessRightsopen access
dcterms.creatorLudwig, Bernard
dcterms.creatorKlüver, Karen
dcterms.creatorFilipinski, Marek
dcterms.creatorGreenberg, Isabel
dcterms.creatorPiepho, Hans-Peter
dcterms.creatorCordsen, Eckhard
dc.relation.doidoi:10.1002/jpln.202200075
dc.subject.swdNorddeutschlandger
dc.subject.swdKupferbelastungger
dc.subject.swdLängsschnittuntersuchungger
dc.subject.swdBodenanalyseger
dc.subject.swdMethodenmixger
dc.type.versionpublishedVersion
dcterms.source.identifiereissn:1522-2624
dcterms.source.issueIssue 6
dcterms.source.journalJournal of Plant Nutrition and Soil Scienceeng
dcterms.source.pageinfo876-887
dcterms.source.volumeVolume 185
kup.iskupfalse


Dateien zu dieser Ressource

Thumbnail
Name:
license_rdf
Größe:
805Bytes
Format:
application/rdf+xml
Öffnen
Thumbnail
Name:
Journal of Plant Nutrition and ...
Größe:
1.636Mb
Format:
PDF
Öffnen

Das Dokument erscheint in:

Zur Kurzanzeige

Attribution-NonCommercial-NoDerivatives 4.0 International
Solange nicht anders angezeigt, wird die Lizenz wie folgt beschrieben: Attribution-NonCommercial-NoDerivatives 4.0 International