Global sensitivity analysis of the AquaCrop model for winter wheat under different water treatments based on the extended Fourier amplitude sensitivity test

Abstract Sensitivity analysis (SA) is an effective tool for studying crop models; it is an important link in model localization and plays an important role in crop model calibration and application. The objectives were to (i) determine influential and non-influential parameters with respect to above ground biomass (AGB), canopy cover (CC), and grain yield of winter wheat in the Beijing area based on the AquaCrop model under different water treatments (rainfall, normal irrigation, and over-irrigation); and (ii) generate an AquaCrop model that can be used in the Beijing area by setting non-influential parameters to fixed values and adjusting influential parameters according to the SA results. In this study, field experiments were conducted during the 2012–2013, 2013–2014, and 2014–2015 winter wheat growing seasons at the National Precision Agriculture Demonstration Research Base in Beijing, China. The extended Fourier amplitude sensitivity test (EFAST) method was used to perform SA of the AquaCrop model using 42 crop parameters, in order to verify the SA results, data from the 2013–2014 growing season were used to calibrate the AquaCrop model, and data from 2012–2013 and 2014–2015 growing seasons were validated. For AGB and yield of winter wheat, the total order sensitivity analysis had more sensitive parameters than the first order sensitivity analysis. For the AGB time-series, parameter sensitivity was changed under different water treatments; in comparison with the non-stressful conditions (normal irrigation and over-irrigation), there were more sensitive parameters under water stress (rainfall), while root development parameters were more sensitive. For CC with time-series and yield, there were more sensitive parameters under water stress than under no water stress. Two parameters sets were selected to calibrate the AquaCrop model, one group of parameters were under water stress, and the others were under no water stress, there were two more sensitive parameters (growing degree-days (GDD) from sowing to the maximum rooting depth (root) and the maximum effective rooting depth (rtx)) under water stress than under no water stress. The results showed that there was higher accuracy under water stress than under no water stress. This study provides guidelines for AquaCrop model calibration and application in Beijing, China, as well providing guidance to simplify the AquaCrop model and improve its precision, especially when many parameters are used.

[1]  Patrick Willems,et al.  Global sensitivity analysis of yield output from the water productivity model , 2014, Environ. Model. Softw..

[2]  A. Saltelli,et al.  A quantitative model-independent method for global sensitivity analysis of model output , 1999 .

[3]  Yanjun Shen,et al.  Evaluation of the FAO AquaCrop model for winter wheat on the North China Plain under deficit irrigation from field experiment to regional yield simulation. Agric Water Manag , 2014 .

[4]  Su Wei,et al.  Sensitivity analysis of CERES-Wheat model parameters based on EFAST method. , 2012 .

[5]  Jing Wang,et al.  Parameter sensitivity analysis of crop growth models based on the extended Fourier Amplitude Sensitivity Test method , 2013, Environ. Model. Softw..

[6]  Xingang Xu,et al.  Global sensitivity analysis of winter wheat yield and process-based variable with AquaCrop model , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[7]  James W. Jones,et al.  USE OF GLOBAL SENSITIVITY ANALYSIS FOR CROPGRO COTTON MODEL DEVELOPMENT , 2007 .

[8]  Tannecia S. Stephenson,et al.  Parameterizing the FAO AquaCrop Model for Rainfed and Irrigated Field‐Grown Sweet Potato , 2015 .

[9]  Xin-gang Xu,et al.  Assessment of the AquaCrop Model for Use in Simulation of Irrigated Winter Wheat Canopy Cover, Biomass, and Grain Yield in the North China Plain , 2014, PloS one.

[10]  D. Raes,et al.  AquaCrop-The FAO Crop Model to Simulate Yield Response to Water: I. Concepts and Underlying Principles , 2009 .

[11]  M. Janat,et al.  Simulating cotton yield response to deficit irrigation with the FAO AquaCrop model , 2011 .

[12]  Ilya M. Sobol,et al.  Sensitivity Estimates for Nonlinear Mathematical Models , 1993 .

[13]  Marco Acutis,et al.  Model simplification and development via reuse, sensitivity analysis and composition: A case study in crop modelling , 2014, Environ. Model. Softw..

[14]  B. Bryan,et al.  Sensitivity and uncertainty analysis of the APSIM-wheat model: interactions between cultivar, environmental, and management parameters. , 2014 .

[15]  Stefano Tarantola,et al.  Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models , 2004 .

[16]  D. Raes,et al.  AquaCrop — The FAO Crop Model to Simulate Yield Response to Water: II. Main Algorithms and Software Description , 2009 .

[17]  Jiang Zhiwei,et al.  Global sensitivity analysis of CERES-Wheat model parameters , 2011 .

[18]  Chen Zhongxin,et al.  Global sensitivity analysis of growth simulation parameters of winter wheat based on EPIC model. , 2009 .

[19]  Colin T. Whittemore,et al.  Calibration and sensitivity analysis of a model of the growing pig for weight gain and composition , 2005 .

[20]  A. Saltelli,et al.  Sensitivity analysis: Could better methods be used? , 1999 .

[21]  Dirk Raes,et al.  Use of the FAO AquaCrop model in developing sowing guidelines for rainfed maize in Zimbabwe , 2014 .

[22]  G. Ovando,et al.  Aquacrop Model Calibration in Potato and Its Use to Estimate Yield Variability under Field Conditions , 2013 .

[23]  Xiying Zhang,et al.  Effects of irrigation frequency under limited irrigation on root water uptake, yield and water use efficiency of winter wheat , 2009 .

[24]  Gianni Bellocchi,et al.  Comparison of sensitivity analysis techniques: A case study with the rice model WARM , 2010 .

[25]  Max D. Morris,et al.  Factorial sampling plans for preliminary computational experiments , 1991 .

[26]  A. Saltelli,et al.  Sensitivity Anaysis as an Ingredient of Modeling , 2000 .

[27]  John R. Williams,et al.  The ALMANAC model's sensitivity to input variables , 2003 .

[28]  Stefano Tarantola,et al.  Sensitivity analysis of the rice model WARM in Europe: Exploring the effects of different locations, climates and methods of analysis on model sensitivity to crop parameters , 2010, Environ. Model. Softw..

[29]  K. Shuler,et al.  Nonlinear sensitivity analysis of multiparameter model systems , 1977 .

[30]  R. Guevara-González,et al.  Global sensitivity analysis by means of EFAST and Sobol' methods and calibration of reduced state-variable TOMGRO model using genetic algorithms , 2014 .

[31]  M. Mkhabela,et al.  Performance of the FAO AquaCrop model for wheat grain yield and soil moisture simulation in Western Canada , 2012 .

[32]  P. Steduto,et al.  Validation and testing of the AquaCrop model under full and deficit irrigated wheat production in Iran , 2011 .

[33]  S. Evett,et al.  Validating the FAO AquaCrop Model for Irrigated and Water Deficient Field Maize , 2009 .

[34]  Olivier Klepper,et al.  Multivariate aspects of model uncertainty analysis: tools for sensitivity analysis and calibration , 1997 .

[35]  R. Stričević,et al.  Assessment of the FAO AquaCrop model in the simulation of rainfed and supplementally irrigated maize, sugar beet and sunflower , 2011 .

[36]  Bryan A. Tolson,et al.  Quantitative global sensitivity analysis of the RZWQM to warrant a robust and effective calibration , 2014 .

[37]  D. Raes,et al.  AquaCrop—The FAO Crop Model to Simulate Yield Response to Water: III. Parameterization and Testing for Maize , 2009 .

[38]  A. Nemes,et al.  Validation of the AquaCrop model for irrigated rice production under varied water regimes in Bangladesh , 2015 .

[39]  Qiang Yu,et al.  Parameters optimization of WOFOST model by integration of global sensitivity analysis and Bayesian calibration method , 2016 .

[40]  Mazdak Arabi,et al.  Global sensitivity and uncertainty analysis of a dynamic agroecosystem model under different irrigation treatments , 2012 .