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

cg.contactsatispss@gmail.comen_US
cg.contributor.centerInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.centerPalli Siksha Bhavana (Institute of Agriculture) - PSBVBen_US
cg.contributor.centerPrajukti Research Private Limiteden_US
cg.contributor.crpResilient Agrifood Systems - RAFSen_US
cg.contributor.funderCGIAR Trust Funden_US
cg.contributor.initiativeExcellence in Agronomyen_US
cg.contributor.project-lead-instituteInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.coverage.countryEGen_US
cg.coverage.regionNorthern Africaen_US
cg.creator.idGovind, Ajit: 0000-0002-0656-0004en_US
cg.date.embargo-end-dateTimelessen_US
cg.identifier.doihttps://dx.doi.org/10.1016/j.asr.2025.03.058en_US
cg.isijournalISI Journalen_US
cg.issn0273-1177en_US
cg.issue11en_US
cg.journalAdvances in Space Researchen_US
cg.subject.actionAreaResilient Agrifood Systemsen_US
cg.subject.agrovocnile riveren_US
cg.subject.agrovocmachine learningen_US
cg.subject.agrovocwheaten_US
cg.volume75en_US
dc.contributorSahoo, Satiprasaden_US
dc.contributorGovind, Ajiten_US
dc.creatorSingha, Chiranjiten_US
dc.date.accessioned2025-07-03T18:53:27Z
dc.date.available2025-07-03T18:53:27Z
dc.description.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_US
dc.formatPDFen_US
dc.identifierhttps://mel.cgiar.org/dspace/limiteden_US
dc.identifier.citationC. 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_US
dc.identifier.statusTimeless limited accessen_US
dc.identifier.urihttps://hdl.handle.net/20.500.11766/70011
dc.languageenen_US
dc.publisherElsevier (12 months)en_US
dc.sourceAdvances in Space Research;75,(2025)en_US
dc.subjectpcaen_US
dc.subjectwheat yielden_US
dc.subjectsoil quality index (sqi)en_US
dc.titleTransforming soil quality index predictions in the Nile River Basin using hybrid stacking machine learning techniquesen_US
dc.typeJournal Articleen_US
dcterms.available2025-05-31en_US
dcterms.issued2025-06-01en_US
mel.impact-factor2.8en_US

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