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Introducing a mechanistic model in digital soil mapping to predict soil organic matter stocks in the Cantabrian region (Spain)

dc.contributor.authorHendriks, C.M.J.
dc.contributor.authorStoorvogel, J.J.
dc.contributor.authorÁlvarez‐Martínez, J.M.
dc.contributor.authorClaessens, L.
dc.contributor.authorPérez‐Silos, I.
dc.contributor.authorBarquín, J.
dc.date.accessioned2021-04-29T13:29:34Z
dc.date.available2021-04-29T13:29:34Z
dc.date.issued2021-03
dc.identifier.citationHendriks, C.M.J., Stoorvogel, J.J., Álvarez‐Martínez, J.M., Claessens, L., Pérez‐Silos, I. & Barquín, J. (2021). Introducing a mechanistic model in digital soil mapping to predict soil organic matter stocks in the Cantabrian region (Spain). European Journal of Soil Science, 72(2), 704-719.
dc.identifier.issn1351-0754
dc.identifier.urihttps://hdl.handle.net/20.500.12478/7113
dc.description.abstractDigital soil mapping (DSM) is an effective mapping technique that supports the increased need for quantitative soil data. In DSM, soil properties are correlated with environmental characteristics using statistical models such as regression. However, many of these relationships are explicitly described in mechanistic simulation models. Therefore, the mechanistic relationships can, in theory, replace the statistical relationships in DSM. This study aims to develop a mechanistic model to predict soil organic matter (SOM) stocks in Natura2000 areas of the Cantabria region (Spain). The mechanistic model is established in four steps: (a) identify major processes that influence SOM stocks, (b) review existing models describing the major processes and the respective environmental data that they require, (c) establish a database with the required input data, and (d) calibrate the model with field observations. The SOM stocks map resulting from the mechanistic model had a mean error (ME) of −2 t SOM ha−1 and a root mean square error (RMSE) of 66 t SOM ha−1. The Lin's concordance correlation coefficient was 0.47 and the amount of variance explained (AVE) was 0.21. The results of the mechanistic model were compared to the results of a statistical model. It turned out that the correlation coefficient between the two SOM stock maps was 0.8. This study illustrated that mechanistic soil models can be used for DSM, which brings new opportunities. Mechanistic models for DSM should be considered for mapping soil characteristics that are difficult to predict by statistical models, and for extrapolation purposes.
dc.description.sponsorshipUniversidad de Cantabria
dc.description.sponsorshipCGIAR Research Programme on Climate Change, Agriculture and Food Security
dc.format.extent704-719
dc.language.isoen
dc.subjectSoil Surveys
dc.subjectSoils
dc.subjectSoil Organic Matter
dc.subjectSpain
dc.subjectSoil Mapping
dc.titleIntroducing a mechanistic model in digital soil mapping to predict soil organic matter stocks in the Cantabrian region (Spain)
dc.typeJournal Article
cg.contributor.crpMaize
cg.contributor.affiliationWageningen University and Research Centre
cg.contributor.affiliationUniversity of Cantabria
cg.contributor.affiliationInternational Institute of Tropical Agriculture
cg.coverage.regionACP
cg.coverage.regionEurope
cg.coverage.countrySpain
cg.coverage.hubEastern Africa Hub
cg.researchthemeNatural Resource Management
cg.identifier.bibtexciteidHENDRIKS:2021
cg.isijournalISI Journal
cg.authorship.typesCGIAR and advanced research institute
cg.iitasubjectAgronomy
cg.iitasubjectNatural Resource Management
cg.iitasubjectPlant Breeding
cg.iitasubjectPlant Production
cg.iitasubjectSoil Surveys and Mapping
cg.journalEuropean Journal of Soil Science
cg.notesOpen Access Article; Published online: 12 Jun 2020
cg.accessibilitystatusOpen Access
cg.reviewstatusPeer Review
cg.usagerightslicenseCreative Commons Attribution 4.0 (CC BY 0.0)
cg.targetaudienceScientists
cg.identifier.doihttps://dx.doi.org/10.1111/ejss.13011
cg.iitaauthor.identifierLieven Claessens: 0000-0003-2961-8990
cg.futureupdate.requiredNo
cg.identifier.issue2
cg.identifier.volume72


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