Transforming soil quality index predictions in the Nile River Basin using hybrid stacking machine learning techniques

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cg.contributor.affiliationInternational Center for Agricultural Research in the Dry Areasen
cg.contributor.affiliationPalli Siksha Bhavana (Institute of Agriculture)en
cg.contributor.affiliationPrajukti Research Private Limiteden
cg.contributor.donorCGIAR Trust Funden
cg.contributor.initiativeExcellence in Agronomy
cg.coverage.countryEgypt
cg.coverage.iso3166-alpha2EG
cg.coverage.regionNorthern Africa
cg.creator.identifierGovind, Ajit: 0000-0002-0656-0004
cg.identifier.doihttps://doi.org/10.1016/j.asr.2025.03.058en
cg.isijournalISI Journalen
cg.issn0273-1177en
cg.issue11en
cg.journalAdvances in Space Researchen
cg.reviewStatusPeer Reviewen
cg.subject.actionAreaResilient Agrifood Systems
cg.volume75en
dc.contributor.authorSingha, Chiranjiten
dc.contributor.authorSahoo, Satiprasaden
dc.contributor.authorGovind, Ajiten
dc.date.accessioned2025-07-03T18:58:42Z
dc.date.available2025-07-03T18:58:42Z
dc.identifier.urihttps://hdl.handle.net/10568/175482
dc.titleTransforming soil quality index predictions in the Nile River Basin using hybrid stacking machine learning techniquesen
dcterms.abstractThis study highlights the importance of sustainable land management in preserving soil health and agricultural productivity, particularly in mitigating land degradation. Soil Quality Index (SQI) was assessed in Egypt’s Nile River Basin using 266 surface samples (0–30 cm depth) collected between 2021 and 2022. Eleven key soil quality indicators such as bulk density (BD), sand, silt, clay, pH, electrical conductivity (EC), organic carbon (OC), calcium (Ca), nitrogen (N), phosphorus (P), and potassium (K) were analyzed to estimate the observed SQI (SQIobs) using a PCA-based scoring method and geostatistical techniques. The SQIobs were validated against in-situ wheat yield. Various hybrid stacking ensemble (SE) machine learning models including Random Forest (SE-RF), Extreme Gradient Boosting (SE-XGB), Gradient Boosting Machine (SE-GBM), Multivariate Adaptive Regression Splines (SE-MARS), Support Vector Machine (SE-SVM), and SE-Cubist was applied to predict soil quality (SQIpred) in data-scarce regions. The SE-RF and SE-Cubist models demonstrated the highest predictive accuracy (R2 = 0.830 and 0.824, respectively). Results showed that “very high” and “very low” SQI classes covered 24.25 % and 14.70 % of the study area, respectively. Future projections using CMIP6 models indicate a decline in SQI, from 24.25 % to 19.15 % (SSP2-4.5) and 10.85 % (SSP5-8.5) between 1990 and 2030. SHAP analysis identified BD, clay, sand, OC, and N as key drivers of SQIobs, while SM, Tmax, FC, ST, and NDVI significantly influenced SQIpred. This study provides a robust framework for assessing soil quality, offering valuable insights for land use planning, sustainable agriculture, and combating soil degradation.en
dcterms.accessRightsLimited Access
dcterms.available2025-05-31
dcterms.bibliographicCitationC. Singha, S. Sahoo and A. Govind, Transforming soil quality index predictions in the Nile River Basin using hybrid stacking machine learning techniques, Advances in Space Research, https://doi.org/10.1016/j.asr.2025.03.058en
dcterms.formatPDFen
dcterms.issued2025-06-01
dcterms.languageen
dcterms.publisherElsevieren
dcterms.subjectnile riveren
dcterms.subjectmachine learningen
dcterms.subjectwheaten
dcterms.subjectpcaen
dcterms.subjectwheat yielden
dcterms.subjectsoil quality index (sqi)en
dcterms.typeJournal Article

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