Document Type : Research Article
Authors
1 Department of Agronomy and Plant Breeding, Faculty of Agriculture, Ferdowsi University of Mashhad
2 Department of Water Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad
Abstract
Abstract
Development and evaluation of mitigation strategies are very crucial to manage climate change risk. Research objectives of this study were (1) to quantify the response of maize grain yield to potential impacts of climate change and (2) to investigate the effectiveness of changing sowing date of maize as a mitigation option for Khorasan Province which is located in northeast of Iran. Two type of General Circulation Models (HadCM3 and IPCM4) and three scenarios (A1B, A2 and B1) at four locations (Mashhad, Birjand, Bojnourd and Sabzevar) employed in this study. Statistical downscaling method was applied for developing quantitative relationship between large scale atmospheric variables (predictors) and local variables (observes), and generating daily climatological variables performed by LARS-WG stochastic weather generator. The CSM-CERES-Maize model was used to achieve study objectives. The result showed that the simulated grain yields of maize gradually would decrease (ranged from -1% to -39%) during future 100 years compared to baseline under different scenarios and two GCM at all study locations. In general, Bojnourd experienced the highest simulated grain yields of maize under A1B scenario (12234 Kg/ha), A2 scenario (12662 Kg/ha) and B1 scenario (12653 Kg/ha) during the period of 2010-2039 by planting date of 19 June. Sabzevar experienced the lowest simulated grain yields of maize under A1B scenario (3320 Kg/ha), A2 scenario (2370 Kg/ha) and B1 scenario (3582 Kg/ha) during the period of 2070-2099 by planting date of 4 June. Delayed sowing of maize crop (from May to June) at all locations, except for Sabzevar is the most effective management factor in mitigating the thermal detrimental effects.
Keywords: Climate change scenarios, Crop growth simulation, General Circulation Model, Maize yield
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