Assessment of the AquaCrop Model under different irrigation scenarios in the North China Plain
文献类型: 外文期刊
第一作者: Shirazi, Sana Zeeshan
作者: Shirazi, Sana Zeeshan;Mei, Xurong;Liu, Buchun;Liu, Yuan
作者机构:
关键词: AquaCrop; Crop simulation; Winter wheat; Summer maize; North China Plain
期刊名称:AGRICULTURAL WATER MANAGEMENT ( 影响因子:4.516; 五年影响因子:5.12 )
ISSN: 0378-3774
年卷期: 2021 年 257 卷
页码:
收录情况: SCI
摘要: Crop simulation models play an essential role in evaluating irrigation management strategies for improving agricultural water use in crop production. In this study, the AquaCrop model was used to calibrate and validate the grain yield and biomass of 11 cultivars (9 winter wheat and 2 summer maize) under full and water-saving irrigation practices. The model results were verified with the available data from the published literature. Under the full irrigation practice, widely used by farmers in the region for winter wheat, the Normalized Root Mean Square Error (NRMSE) was found to be between 2.00-9.90% for grain yield, 2.40-10.85% for biomass, and 0.42-19.61% for water productivity. Under water-saving irrigation for winter wheat, the NRMSE range was found to be between 5.80-16.00% for grain yield, 3.70-17.30% for biomass, and 3.80-13.79% for water productivity. For summer maize, the NRMSE was 5.95%, 6.08%, and 16.41% under full irrigation, and 9.51%, 8.41%, and 3.60% under water-saving irrigation for grain yield, biomass, and water productivity, respectively. For winter wheat and summer maize, the simulation accuracy of grain yield was high under full irrigation, with percent deviations under +/- 11.00%. In this study, the simulation accuracy for winter wheat was low under water-saving irrigation, while the model simulated the yield and biomass for summer maize to acceptable accuracy. The model can be reliably used as a tool to simulate grain yield and biomass across the NCP region for winter wheat and summer maize. However, the limitations of the model must be considered when simulating winter wheat under water-stressed conditions.
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