Publication:
Soil Quality Dynamics in Natural Pine Forests Lands Integrating Fuzzy-AHP and Prediction Based on Random Forest Algorithms

dc.authorscopusid60165311300
dc.authorscopusid57223127769
dc.authorscopusid16052385200
dc.authorscopusid57579342200
dc.authorscopusid58199448800
dc.authorwosidSafli, Muhammet Emin/Jtu-0274-2023
dc.authorwosidAbebaw, Wudu Abiye/Abf-5300-2021
dc.authorwosidDengiz, Orhan/Abg-7284-2020
dc.contributor.authorPoudel, Dikshya
dc.contributor.authorAbiye, Wudu
dc.contributor.authorDengiz, Orhan
dc.contributor.authorPacci, Sena
dc.contributor.authorSafli, Muhammet Emin
dc.date.accessioned2025-12-11T00:46:40Z
dc.date.issued2025
dc.departmentOndokuz Mayıs Üniversitesien_US
dc.department-temp[Poudel, Dikshya; Abiye, Wudu; Dengiz, Orhan; Pacci, Sena; Safli, Muhammet Emin] Ondokuz Mayis Univ, Fac Agr, Dept Soil Sci & Plant Nutr, Samsun, Turkiyeen_US
dc.description.abstractSoil quality assessment is crucial for monitoring and restoring soil functions and ensuring the soil's ability to support sustainable production. The present study aimed to assess the soil quality of a pine-forested region in the Engiz basin of Samsun province, T & uuml;rkiye. We applied the minimum dataset (MDS) and total dataset (TDS) indicator selection method and linear and non-linear scoring approach and integrated with the Fuzzy-analytical hierarchical approach (F-AHP) to evaluate the soil quality of the region. Principal component analysis (PCA) reduced the initial set of 28 soil quality indicators to 12 most representative indicators, namely, sand, silt, structural stability index, organic matter, calcium, potassium, calcium carbonate, copper, manganese, soil respiration, and carbon-to-nitrogen ratio. Regardless of scoring techniques, soil quality obtained based on MDS adequately represented the TDS approach, with a significant correlation coefficient (r > 0.85, P < 0.01) and strong linear association (R-2 > 0.64). Non-linear (NL) models consistently performed better than linear models, and TDS_NL (sensitivity index (SI): 2.29) emerged as the best model in representing the soil quality of the study area, followed by MDS_NL (SI: 2.23). Repeated 10-fold cross-validation (with 3 random repeats) results showed that random forest models accurately predicted soil quality across all soil types (R-2 > 0.75), emphasizing their utility in soil quality evaluation studies. Both the observed and predicted soil quality maps, regardless of the indicator selection or scoring method, showed a consistent spatial trend, with lower soil quality mainly concentrated in the southern and southwestern areas, moderate soil quality in the central area, and higher soil quality in the north and northeastern regions. The results of our study approach are expected to offer valuable insights into sustainable forest soil and forest use management in forest-dominated landscapes in similar ecological and climatic conditions.en_US
dc.description.woscitationindexScience Citation Index Expanded
dc.identifier.doi10.1038/s41598-025-21943-1
dc.identifier.issn2045-2322
dc.identifier.issue1en_US
dc.identifier.pmid41168412
dc.identifier.scopus2-s2.0-105020419999
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1038/s41598-025-21943-1
dc.identifier.urihttps://hdl.handle.net/20.500.12712/39133
dc.identifier.volume15en_US
dc.identifier.wosWOS:001606739500014
dc.identifier.wosqualityQ1
dc.language.isoenen_US
dc.publisherNature Portfolioen_US
dc.relation.ispartofScientific Reportsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAnalytical Hierarchy Processen_US
dc.subjectFuzzyen_US
dc.subjectIndicatorsen_US
dc.subjectPredictionen_US
dc.subjectSoil Quality Indexen_US
dc.titleSoil Quality Dynamics in Natural Pine Forests Lands Integrating Fuzzy-AHP and Prediction Based on Random Forest Algorithmsen_US
dc.typeArticleen_US
dspace.entity.typePublication

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