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GLOBAL SENSITIVITY ANALYSIS OF WINTER WHEAT YIELD AND PROCESS-BASED VARIABLE WITH AQUACROP MODEL

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文献类型：外文期刊

作者： Xing, Huimin ¹ ; Xu, Xingang ² ; Yang, Fuqin ¹ ; Feng, Haikuan ² ; Yang, Guijin ² ;

作者机构： 1.China Univ Min & Technol, Coll Geosci & Surveying Engn, 11 Xueyuan Rd, Beijing 100083, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

3.Natl Engn Res Ctr Informat Technol Agr, Beijing 10097, Peoples R China

4.Natl Eng

关键词： Sensitivity analysis;winter wheat;AquaCrop;Crop parameters

期刊名称：2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)

ISSN： 2153-6996

年卷期： 2016 年

页码：

收录情况： SCI

摘要： Sensitivity analysis (SA) is an effective tool for studying the crop model; it is an important link in model localization, and plays an important role in correction and application of crop model. In this study, the field experiment was conducted during the 2013/2014 growing seasons of winter wheat, and the EFAST GSA method was used to examine the above ground dry biomass and grain yield output sensitivity of the AquaCrop model with changing the crop parameters for winter wheat and 42 crop parameters were chosen in Yangling, Shaanxi, Province, China. The main objectives of this study were to determine the sensitive parameters of the dry biomass and yield based EFAST method for the AquaCrop model in ShanXi province, China. The results show that (1) for dry biomass, there was little influence for the results of SA under different variation ranges of crop parameters; some crop parameters had higher sensitivity in the whole or some key growing period of winter wheat, which do not affected by variation ranges of crop parameters and growth surroundings; (2) for yield, the SA results were a little difference under different irrigation conductions (normal irrigation and rainfall). Above research results show that, we can set fixed values for the insensitive parameters, and adjust the sensitive parameters when the AquaCrop crop model is localized. It is helpful to simplify model, reduce the complexity of the model, reduce workload and improve the precision of the model, especially for the model which has large number of parameters.

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