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A Chemometric Analysis of Soil Health Indicators Derived from Mid-Infrared Spectra

2025Artigo científico Acesso aberto
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datacite.subject.fosCiências Agrárias::Agricultura, Silvicultura e Pescas
datacite.subject.fosEngenharia e Tecnologia::Engenharia Química
datacite.subject.fosEngenharia e Tecnologia::Engenharia do Ambiente
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
datacite.subject.sdg12:Produção e Consumo Sustentáveis
datacite.subject.sdg04:Educação de Qualidade
dc.contributor.authorAlmendros, Gonzalo
dc.contributor.authorLópez-Pérez, Antonio
dc.contributor.authorHernandez Hernandez, Zulimar
dc.date.accessioned2026-05-28T18:14:36Z
dc.date.available2026-05-28T18:14:36Z
dc.date.issued2025
dc.description.abstractSignificant models predicting Soil Organic Carbon (SOC) and other chemical and biological indicators of soil health in an experimental farm with semi-arid Mediterranean Calcisol have been obtained by partial least squares (PLS) regression, with mid-infrared (MIR) spectra of whole soil samples used as independent variables (IVs). The dependent variables(DVs) included SOC, pH, electric conductivity, N, P2O5, K, Ca2+, Mg2+, Na+, Fe, Mn, Cu and Zn. The DVs also included free-living nematodes and microbivores, such as Rhabditids and Cephalobids, and phytoparasitics, such as Xiphinema spp. and other Dorylaimids. More importantly, an attempt was made to determine which spectral patterns allowed each dependent variable (DV) to be predicted. For this purpose, a number of statistical indices were plotted between 4000 and 450 cm−1, e.g., variable importance for prediction (VIP) and beta coefficients from PLS, loading factors from principal component analysis (PCA) and correlation and determination indices. The most effective plots, however, were the “scaled subtraction spectra” (SSS) obtained by subtracting the averages of groups of spectra in order to reproduce the spectral patterns typical in soils where the values of each DV are higher, or vice versa. For instance, distinct SSS resembled the spectra of carbonate, clay, oxides and SOC, whose varying concentrations enabled the prediction of the different DVs.eng
dc.description.sponsorshipFinancial support by the European Union (EJP Soil SANCHOSTHIRST grant agreement N.862695, INCO-DC, PL-972698) is gratefully acknowledged.
dc.identifier.citationAlmendros, Gonzalo; López-Pérez, Antonio; Hernandez Hernandez, Zulimar (2025). A Chemometric Analysis of Soil Health Indicators Derived from Mid-Infrared Spectra. Agronomy. ISSN 2073-4395. 15:7, p. 1-23
dc.identifier.doi10.3390/agronomy15071592
dc.identifier.issn2073-4395
dc.identifier.urihttp://hdl.handle.net/10198/36800
dc.language.isoeng
dc.peerreviewedyes
dc.publisherMDPI
dc.relationTowards climate-smart sustainable management of agricultural soils - 862695
dc.relation.ispartofAgronomy
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectInfrared spectroscopy
dc.subjectPartial least squares
dc.subjectPhytoparasites
dc.subjectSoil organic carbon
dc.titleA Chemometric Analysis of Soil Health Indicators Derived from Mid-Infrared Spectraeng
dc.typejournal article
dspace.entity.typePublication
oaire.awardNumber862695
oaire.awardTitleTowards climate-smart sustainable management of agricultural soils - 862695
oaire.awardURIhttp://hdl.handle.net/10198/36795
oaire.citation.endPage23
oaire.citation.issue7
oaire.citation.startPage1
oaire.citation.titleAgronomy
oaire.citation.volume15
oaire.fundingStreamH2020
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameHernandez Hernandez
person.givenNameZulimar
person.identifier.ciencia-id5815-8F1B-70F4
person.identifier.orcid0000-0002-7790-8397
person.identifier.scopus-author-id36084226300
relation.isAuthorOfPublication077df6f8-4a5e-4b46-8a8b-327f436c2653
relation.isAuthorOfPublication.latestForDiscovery077df6f8-4a5e-4b46-8a8b-327f436c2653
relation.isProjectOfPublication4a44da0e-e57f-40d6-afe5-77ce73dc57c3
relation.isProjectOfPublication.latestForDiscovery4a44da0e-e57f-40d6-afe5-77ce73dc57c3

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