A Comparison of Moment-Independent and Variance-Based Global Sensitivity Analysis Approaches for Wheat Yield Estimation with the Aquacrop-OS Model

文献类型： 外文期刊

第一作者： Upreti, Deepak

作者： Upreti, Deepak;Tolomio, Massimo;Casa, Raffaele;Pignatti, Stefano;Pascucci, Simone;Li, Zhenhai;Huang, Wenjiang

作者机构：

关键词： AOS; Morris; EFAST; PAWN; parameters; density-based

期刊名称：AGRONOMY-BASEL （ 影响因子：3.417； 五年影响因子：3.64 ）

ISSN：

年卷期： 2020 年 10 卷 4 期

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收录情况： SCI

摘要： The present work reports the global sensitivity analysis of the Aquacrop Open Source (AOS) model, which is the open-source version of the original Aquacrop model developed by the Food and Agriculture Organization (FAO). Analysis for identifying the most influential parameters was based on different strategies of global SA, density-based and variance-based, for the wheat crop in two different geographical locations and climates. The main objectives were to distinguish the model's influential and non-influential parameters and to examine the yield output sensitivity. We compared two different methods of global sensitivity analysis: the most commonly used variance-based method, EFAST, and the moment independent density-based PAWN method developed in recent years. We have also identified non-influential parameters using Morris screening method, so to provide an idea of the use of non-influential parameters with a dummy parameter approach. For both the study areas (located in Italy and in China) and climates, a similar set of influential parameters was found, although with varying sensitivity. When compared with different probability distribution functions, the probability distribution function of yield was found to be best approximated by a Generalized Extreme Values distribution with Kolmogorov-Smirnov statistic of 0.030 and lowest Anderson-Darling statistic of 0.164, as compared to normal distribution function with Kolmogorov-Smirnov statistic of 0.122 and Anderson-Darling statistic of 4.099. This indicates that yield output is not normally distributed but has a rather skewed distribution function. In this case, a variance-based approach was not the best choice, and the density-based method performed better. The dummy parameter approach avoids to use a threshold as it is a subjective question; it advances the approach to setting up a threshold and gives an optimal way to set up a threshold and use it to distinguish between influential and non-influential parameters. The highly sensitive parameters to crop yield were specifically canopy and phenological development parameters, parameters that govern biomass/yield production and temperature stress parameters rather than root development and water stress parameters.

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