Machine Learning-Based Smart Prediction of Future Suitability of Maize in Northern Regions of Turkey Under CMIP6 Climate Scenarios

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dc.contributor.authorKilic, Mirac
dc.contributor.authorBirol, Murat
dc.contributor.authorGunal, Hikmet
dc.contributor.authorBayrakli, Betul
dc.contributor.authorKilic, Orhan Mete
dc.contributor.authorTariq, Aqil
dc.date.accessioned2026-06-19T06:40:55Z
dc.date.available2026-06-19T06:40:55Z
dc.date.issued2026
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractThis study examines the effects of climate change on maize (Zea mays L.) cultivation in Turkey's Bart & imath;n and Zonguldak provinces. The objective was to assess current and future land suitability for maize under changing climatic conditions to support adaptive agricultural planning. We developed an integrated approach combining Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model data with a fuzzy logic system to incorporate expert knowledge and manage environmental uncertainties. Environmental variables included climate parameters, soil properties, and topographic factors. Two machine learning (ML) algorithms, Random Forest and LightGBM, were employed to analyze land suitability for three periods: current (1981-2023), mid-century (2040-2070), and late-century (2070-2100). Current climate analysis reveals that approximately 50% of the study region is moderately suitable for maize cultivation, with less than 1% classified as highly suitable. Under the CMIP6 SSP5-8.5 scenario, projections indicate marginally suitable areas will expand by mid-century due to rising temperatures and altered rainfall patterns. By late-century, a dramatic shift is expected, with approximately 71% of the area becoming highly suitable, demonstrating significant changes in cultivation potential. Random Forest outperformed LightGBM, achieving 97.4% accuracy compared to 82.9%, with superior precision, recall, and F1-scores across all suitability classes. This research provides critical insights for developing adaptive agricultural strategies in response to climate change. The integration of ML techniques with high-resolution climate models offers robust predictions essential for policymakers and land managers to optimize agricultural productivity and ensure food security.
dc.description.sponsorshipTarimsal Arascedil;tirmalar ve Politikalar Genel Mdrlgbreve;, Trkiye Cumhuriyeti Tarim Ve Orman Bakanligbreve;i [TAGEM/TSKAD/17/A09/P02/02]; General Directorate of Agricultural Research And Policies
dc.description.sponsorshipAuthors would like to acknowledge the General Directorate of Agricultural Research And Policies (grant agreement No. TAGEM/TSKAD/17/A09/P02/02).
dc.identifier.doi10.1007/s41748-025-00764-2
dc.identifier.endpage3027
dc.identifier.issn2509-9426
dc.identifier.issn2509-9434
dc.identifier.issue3
dc.identifier.orcid0000-0003-1196-1248
dc.identifier.orcid0000-0002-6723-1984
dc.identifier.orcid0000-0002-4648-2645
dc.identifier.orcid0000-0001-8026-5540
dc.identifier.scopus2-s2.0-105013582569
dc.identifier.scopusqualityQ1
dc.identifier.startpage3007
dc.identifier.urihttps://doi.org/10.1007/s41748-025-00764-2
dc.identifier.urihttps://hdl.handle.net/20.500.12899/5986
dc.identifier.volume10
dc.identifier.wosWOS:001553028500001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Int Publ Ag
dc.relation.ispartofEarth Systems and Environment
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20260612
dc.subjectCmip6 Climate Data
dc.subjectClimate Change
dc.subjectLand Suitability
dc.subjectMachine Learning
dc.subjectFao Novel Methodological Framework
dc.titleMachine Learning-Based Smart Prediction of Future Suitability of Maize in Northern Regions of Turkey Under CMIP6 Climate Scenarios
dc.typeArticle

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