Machine Learning-Based Smart Prediction of Future Suitability of Maize in Northern Regions of Turkey Under CMIP6 Climate Scenarios
Küçük Resim Yok
Tarih
2026
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Int Publ Ag
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This 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.
Açıklama
Anahtar Kelimeler
Cmip6 Climate Data, Climate Change, Land Suitability, Machine Learning, Fao Novel Methodological Framework
Kaynak
Earth Systems and Environment
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
10
Sayı
3
