| Titre : | Multi-crop yield estimation and spatial analysis of agro-climatic indices based on high-resolution climate simulations in Türkiye's lakes region, a typical Mediterranean biogeography (2026) |
| Auteurs : | F. Kaya ; S. Demir ; M. Dedeoglu ; L. Basayigit ; Y. Ünal ; C. Yürük Sonuç ; T. Dogan Güzel ; E.G. Çakmak |
| Type de document : | Article |
| Dans : | Agronomy (vol. 16, n. 3, February 2026) |
| Article en page(s) : | p. 321 |
| Langues : | Anglais |
| Langues du résumé : | Anglais |
| Catégories : |
Catégories principales 06 - AGRICULTURE. FORÊTS. PÊCHES ; 6.4 - Production Agricole. Système de ProductionThésaurus IAMM RENDEMENT DES CULTURES ; CHANGEMENT CLIMATIQUE ; EVALUATION DE L'IMPACT ; MODELE ; REGION MEDITERRANEENNE ; TURQUIE |
| Résumé : | Mediterranean biogeography is characterized as a global “hotspot” for climate change; understanding the impacts of these changes on local agricultural systems through high-resolution analyses has thus become a critical need. This study addresses this gap by evaluating the holistic effects of climate change on site-specific agriculture systems, focusing on the Egirdir–Karacaören (EKB) and Beysehir (BB) lake basins in the Lakes Region of Türkiye. This study employed machine learning modeling techniques to forecast changes in the yields of key crops, such as wheat, maize, apple, alfalfa, and sugar beet. Detailed spatial analyses of changes in agro-climatic conditions (heat stress, chilling requirement, frost days, and growing degree days for key crops) between the reference period (1995–2014) and two decadal periods projected for 2040–2049 and 2070–2079 were conducted under the Shared Socioeconomic Pathways (SSP3-7.0). Daily temperature, precipitation, relative humidity, and solar radiation data, derived from high-resolution climate simulations, were aggregated into annual summaries. These datasets were then spatially matched with district-level yield statistics obtained from the official data providers to construct crop-specific data matrices. For each crop, Random Forest (RF) regression models were fitted, and a Leave-One-Site-Out (LOSOCV) cross-validation method was used to evaluate model performance during the reference period. Yield prediction models were evaluated using the mean absolute error (MAE). The models achieved low MAE values for wheat (33.95 kg da-1 in EKB and 75.04 kg da-1 in BB), whereas the MAE values for maize and alfalfa were considerably higher, ranging from 658 to 986 kg da-1. Projections for future periods indicate declines in relative yield across both basins. For 2070–2079, wheat and maize yields are projected to decrease by 10–20%, accompanied by wide uncertainty intervals. Both basins are expected to experience a substantial increase in heat stress days (>35 °C), a reduction in frost days, and an overall acceleration of plant phenology. Results provided insights to inform region-specific, evidence-based adaptation options, such as selecting heat-tolerant varieties, optimizing planting calendars, and integrating precision agriculture practices to improve resource efficiency under changing climatic conditions. Overall, this study establishes a scientific basis for enhancing the resilience of agricultural systems to climate change in two lake basins within the Mediterranean biogeography. |
| Cote : | En ligne |
| URL / DOI : | https://doi.org/10.3390/agronomy16030321 |
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