Global sensitivity analysis by means of EFAST and Sobol’ methods and calibration of reduced state-variable TOMGRO model using genetic algorithms

Vazquez-Cruz, Moises Alejandro; Guzmán-Cruz, Rosario; López‐Cruz, Irineo L.; Cornejo-Pérez, O.; Torres-Pacheco, Irineo; Guevara-Gonzalez, R.G.

doi:10.1016/j.compag.2013.10.006

Cited by 69 publications

(38 citation statements)

References 25 publications

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“…Various models have been developed for different crops, such as DSSAT (Jones et al, 2003), APSIM (Keating et al, 2003), AquaCrop (Vanuytrecht et al, 2014) and STICS (Brisson et al, 2003). These models have become useful and effective tools within the fields of irrigation and fertilizer (nitrogen) management (Asseng et al, 2012;, grain yield and quality forecasting (Asseng et al, 2002;Asseng and Milroy, 2006;de Wit and van Diepen, 2007), plant growth estimation (Araya et al, 2010;Thorp et al, 2012), precision agriculture (Thorp et al, 2008), breeding programs (Jeuffroy et al, 2014;Vazquez-Cruz et al, 2014) and climate change evaluation (Angulo et al, 2013;Fan and Shibata, 2014). The CERES-Wheat model is integrated within the Decision Support System of Agrotechnology Transfer modelling system (DSSAT;V 4.5) and is a well-known crop model developed for the simulation of winter wheat growth (Jones et al, 2003;Thorp et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Estimating genetic parameters of DSSAT-CERES model with the GLUE method for winter wheat (Triticum aestivum L.) production

et al. 2018

Computers and Electronics in Agriculture

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Crop growth models integrate genotype, environment and management and can serve as an analytical tool by which to study the influences of these factors on crop growth, production, and agricultural planning. Parameter calibration is the primary step taken before the local application of crop growth models. In this study, experimental field data were collected by way of a five-year (2008-2013) set of field experiments at a field site in Beijing, China. The DSSAT-CERES model was calibrated by integrating the generalized likelihood uncertainty estimation (GLUE) method and a systematic approach, and used experimental data relating to two

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Section: Introductionmentioning

confidence: 99%

Estimating genetic parameters of DSSAT-CERES model with the GLUE method for winter wheat (Triticum aestivum L.) production

et al. 2018

Computers and Electronics in Agriculture

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“…Many crop models have been subjected to SA, including the EPIC model (Wu et al 2009), DSSAT model (Pathak et al 2007;Jiang et al 2011;Dejonge et al 2012;Wei and Wei et al 2012;Song et al 2014;Wu et al 2015), ALMANAC model (Xie et al 2003;Liu et al 2008), WARM model (Confalonieri et al 2010a, b), APSIM model (Zhao et al 2014;He et al 2015), WOFOST model (Wang et al 2013;He et al 2016), RZWQM model (Fang et al 2012;Esmaeili et al 2014), and TOMOGO model (Vazquez-Cruz et al 2014), but only a single study has focused on SA of the AquaCrop model (Vanuytrecht et al 2014;Xing et al 2016Xing et al , 2017.…”

Section: Introductionmentioning

confidence: 99%

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

Huimin

et al. 2017

Journal of Integrative Agriculture

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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 no water stress. Two parameters sets were selected to calibrate the AquaCrop model, one group of parameters were under water stress, and the other were under no water stress, there were two more sensitive parameters (growing degree-days (GDD) from sowing to the maximum rooting depth (root) and 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

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“…Sensitivity for q can provide a reference for design. Hence, sensitivities of the four parameters were analyzed based on theoretical equation and method of EFAST (Extended Fourier Amplitude Sensitivity Test) (DeJonge et al, 2015;Sanadhya et al, 2014;Vazquez-Cruz et al, 2014). Filter height was limited by drain depth and hence topsoil depth was adopted to represent the sensitivity of filter height.…”

Section: Design Parameter Analysis Based On Theoretical Equationmentioning

confidence: 99%

Experiment and analysis on flow rate of improved subsurface drainage with ponded water

Tao

Wang

et al. 2016

Agricultural Water Management

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Global sensitivity analysis by means of EFAST and Sobol’ methods and calibration of reduced state-variable TOMGRO model using genetic algorithms

Cited by 69 publications

References 25 publications

Estimating genetic parameters of DSSAT-CERES model with the GLUE method for winter wheat (Triticum aestivum L.) production

Estimating genetic parameters of DSSAT-CERES model with the GLUE method for winter wheat (Triticum aestivum L.) production

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

Experiment and analysis on flow rate of improved subsurface drainage with ponded water

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