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dc.contributor.authorCarbajal, Mariella-
dc.contributor.authorRamirez, David A.-
dc.contributor.authorTurin Canchaya, Cecilia Claudia-
dc.contributor.authorSchaeffer, Sean M.-
dc.contributor.authorKonkel, Julie-
dc.contributor.authorNinanya, Johan-
dc.contributor.authorRinza, Javier-
dc.contributor.authorDe Mendiburu, Felipe-
dc.contributor.authorZorogastua, Percy-
dc.contributor.authorVillaorduña, Liliana-
dc.contributor.authorQuiroz, Roberto-
dc.date.accessioned2024-09-30T18:24:09Z-
dc.date.available2024-09-30T18:24:09Z-
dc.date.issued2024-09-09-
dc.identifier.citationCarbajal, M.; Ramirez, D.A.; Turin-Canchaya, C.C.; Schaeffer, S.M.; Konkel, J.; Ninanya, J.; Rinza, J.; De Mendiburu, F.; Zorogastua, P.; Villaordun, L.; & Quiroz, R. (2024). From rangelands to cropland, land-use change and its impact on soil organic carbon variables in a Peruvian Andean highlands: a machine learning modeling approach. Ecosystems (2024). doi: 10.1007/s10021-024-00928-7es_PE
dc.identifier.issn1435-0629-
dc.identifier.urihttps://hdl.handle.net/20.500.12955/2576-
dc.description.abstractAndean highland soils contain significant quantities of soil organic carbon (SOC); however, more efforts still need to be made to understand the processes behind the accumulation and persistence of SOC and its fractions. This study modeled SOC variables—SOC, refractory SOC (RSOC), and the 13C isotope composition of SOC (d13CSOC)—using machine learning (ML) algorithms in the Central Andean Highlands of Peru, where grasslands and wetlands (‘‘bofedales’’) dominate the landscape surrounded by Junin National Reserve. A total of 198 soil samples (0.3 m depth) were collected to assess SOC variables. Four ML algorithms—random forest (RF), support vector machine (SVM), artificial neural networks (ANNs), and eXtreme gradient boosting (XGB)—were used to model SOC variablesusing remote sensing data, land-use and landcover (LULC, nine categories), climate topography, and sampled physical–chemical soil variables. RF was the best algorithm for SOC and d13CSOC prediction, whereas ANN was the best to model RSOC. ‘‘Bofedales’’ showed 2–3 times greater SOC (11.2 ± 1.60%) and RSOC (1.10 ± 0.23%) and more depleted d13CSOC (- 27.0 ± 0.44 &) than other LULC, which reflects high C persistent, turnover rates, and plant productivity. This highlights the importance of ‘‘bofedales’’ as SOC reservoirs. LULC and vegetation indices close to the near-infrared bands were the most critical environmental predictors to model C variables SOC and d13CSOC. In contrast, climatic indices were more important environmental predictors for RSOC. This study’s outcomes suggest the potential of ML methods, with a particular emphasis on RF, for mapping SOC and its fractions in the Andean highlands.es_PE
dc.formatapplication/pdfes_PE
dc.language.isoenges_PE
dc.publisherSpringeres_PE
dc.relation.ispartofurn:issn:1435-0629es_PE
dc.relation.ispartofseriesEcosystemses_PE
dc.rightsinfo:eu-repo/semantics/openAccesses_PE
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/es_PE
dc.sourceInstituto Nacional de Innovación Agrariaes_PE
dc.source.uriRepositorio Institucional - INIAes_PE
dc.subjectArtificial neural networkses_PE
dc.subjectBofedaleses_PE
dc.subject13C isotope compositiones_PE
dc.subjectExtreme gradient boostinges_PE
dc.subjectGrasslandses_PE
dc.subjectRandom forestes_PE
dc.subjectRefractory C fractiones_PE
dc.subjectSupport vector machinees_PE
dc.titleFrom rangelands to cropland, land-use change and its impact on soil organic carbon variables in a Peruvian Andean highlands: a machine learning modeling approaches_PE
dc.typeinfo:eu-repo/semantics/articlees_PE
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#4.01.04es_PE
dc.publisher.countryUSes_PE
dc.identifier.doihttps://doi.org/10.1007/s10021-024-00928-7-
dc.subject.agrovocRedes de neuronases_PE
dc.subject.agrovocFishing netses_PE
dc.subject.agrovocTierra húmedaes_PE
dc.subject.agrovocWetlandses_PE
dc.subject.agrovocIsótopoes_PE
dc.subject.agrovocIsotopeses_PE
dc.subject.agrovocGradiente de temperaturaes_PE
dc.subject.agrovocTemperature gradientses_PE
dc.subject.agrovocGrasslandses_PE
dc.subject.agrovocPraderaes_PE
dc.subject.agrovocMachine learninges_PE
dc.subject.agrovocAprendizaje automáticoes_PE
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