全球敏感性分析先进的ORYZA-N模型与不同的水类型和灌制度
Ya Gao1, Chen Sun2, Tiago B Ramos3
1State Key Laboratory of Efficient Utilization of Agricultural Water Resources, China Agricultural University, Beijing 100083, China.
Plants (Basel, Switzerland)
|January 23, 2024
概括
确定关键的米模型参数对于准确的作物模拟至关重要. 这项研究发现,RGRLMX,FRPAR和FLV0.5在不同条件下显著影响米生长和产量.
科学领域:
- 农业科学 农业科学
- 农业学是一种农业学.
- 作物建模作物建模
背景情况:
- 精确的作物建模对于优化农业实践和确保粮食安全至关重要.
- ORYZA-N模型是模拟大米生长和产量的广泛使用的工具.
- 了解参数灵敏度对于提高模型可靠性和应用至关重要.
研究的目的:
- 在ORYZA-N大米模型中对23个参数进行灵敏度分析.
- 为了评估不同类型和灌方案的参数灵敏度.
- 为了比较节水灌对参数灵敏性的影响.
主要方法:
- 使用扩展FAST (eFAST) 方法进行灵敏度分析.
- 在传统洪水灌 (TFI) 和浅湿灌 (SWI) 下对单季和双季大米的参数敏感性进行了研究.
- 对四个发展阶段和最终产量的六种作物生长输出分析了灵敏度.
主要成果:
- 确定了RGRLMX,FRPAR和FLV0.5参数在所有模型输出和开发阶段都有很大的影响.
- 由SWI引起的水应力对单季米的参数灵敏度的影响更大,而不是双季米.
- 在不同的发育阶段和灌场景中,参数灵敏度差异很大.
结论:
- RGRLMX,FRPAR和FLV0.5是ORYZA-N模型的关键参数,需要仔细校准.
- 节水灌策略,如SWI,可以改变大米模型的灵敏度,特别是单季大米.
- 这些发现为完善水作物模型和为灌管理决策提供了宝贵的见解.
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