Document Type : Original Article
Authors
1 Ph.D. Graduate, Department of Water Science and Engineering, Imam Khomeini International University, Qazvin, Iran.
2 Department of Water Science and Engineering, Imam Khomeini International University, Qazvin, Iran.
Abstract
Climate change poses a significant challenge to agricultural production, and corn yield, a strategic crop in the Qazvin Plain, is highly sensitive to climatic variability. This study investigated the impacts of climate change on forage corn yield under the SSP2-4.5 and SSP5-8.5 scenarios for the periods 2026–2050, 2051–2075, and 2076–2100. To reduce climatic uncertainty, outputs from multiple General Circulation Models (GCMs) were integrated using a linear-weighted ensemble approach. In the second stage, machine learning algorithms, including Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest (RF), were combined within an ensemble framework based on weighted averaging to improve yield simulation accuracy. Climatic variables, including precipitation, minimum and maximum temperature, and evapotranspiration, derived from the CNRM-CM6-1, GFDL-ESM4, MIROC6, and HadGEM3 models, were evaluated individually and collectively against station observations for the baseline period of 1986–2014. The evaluation results showed that the ensemble climate model outperformed individual GCMs, achieving a high coefficient of determination (R² = 0.95) and a low RMSE, thereby reducing simulation errors. Future projections indicate increasing minimum and maximum temperatures, as well as evapotranspiration, along with decreasing precipitation across the study area. Yield simulations using the ensemble machine learning models revealed a decline in forage corn yield under both scenarios, with reductions of approximately 4.72% under SSP2-4.5 and 8.72% under SSP5-8.5 during the 2026–2050 period. Overall, the results suggest that effective adaptation strategies and improved water resource management are crucial for mitigating the impacts of climate change on corn production in the Qazvin Plain.
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