|
|
|
|
|
| Global sensitivity analysis of the AquaCrop model for winter wheat under different water treatments based on the extended Fourier amplitude sensitivity test |
| XING Hui-min1, 2, 3, 4, XU Xin-gang2, 3, 4, LI Zhen-hai2, 3, 4, CHEN Yi-jin1, FENG Hai-kuan2, 3, 4, YANG Gui-jun2, 3, 4, CHEN Zhao-xia2, 3, 4 |
1 College of Geoscience and Surveying Engineering, China University of Mining & Technology (Beijing), Beijing 100083, P.R.China
2 National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, P.R.China
3 Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, P.R.China
4 Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, P.R.China |
|
|
|
|
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.
|
|
Received: 07 November 2016
Accepted:
|
| Fund: This study was supported by the National Natural Science Foundation of China (41571416), the Natural Science Foundation of Beijing, China (4152019), and the Beijing Academy of Agricultural and Forestry Sciences Innovation Capacity Construction Specific Projects, China (KJCX20150409). |
|
Corresponding Authors:
Correspondence XU Xin-gang, Tel: +86-10-51503441, E-mail: xxg2007@aliyun.com
|
| About author: XING Hui-min, Tel: +86-10-51503215, E-mail: hmxing1980a @163.com |
Cite this article:
XING Hui-min, XU Xin-gang, LI Zhen-hai, CHEN Yi-jin, FENG Hai-kuan, YANG Gui-jun, CHEN Zhao-xia.
2017.
Global sensitivity analysis of the AquaCrop model for winter wheat under different water treatments based on the extended Fourier amplitude sensitivity test. Journal of Integrative Agriculture, 16(11): 2444-2458.
|
| Andarzian B, Bannayan M, Steduto P, Mazraeh H, Barati M E, Barati M A, Rahnama A. 2011. Validation and testing of the AquaCrop model under full and deficit irrigated wheat production in Iran. Agricultural Water Management, 100, 1–8. Casa A D L, Ovando G, Bressanini L, Martinez J. 2013. Aquacrop model calibration in potato and its use to estimate yield variability under field conditions. Atmospheric & Climate Sciences, 3, 397–407. Confalonieri R, Bellocchi G, Bregaglio S, Donatelli M, Acutis M. 2010a. Comparison of sensitivity analysis techniques: A case study with the rice model WARM. Ecological Modelling, 221, 1897–1906. Confalonieri R, Bellocchi G, Tarantola S, Acutis M, Donatelli M, Genovese G. 2010b. 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. Environmental Modelling & Software, 25, 479–488. Cukier R I, Levine H B, Shuler K E. 1974. Nonlinear sensitivity analysis of multiparameter model systems. Journal of Computational Physics, 26, 1–42.Dejonge K C, Ii J C A, Ahmadi M, Andales A A, Arabi M. 2012. Global sensitivity and uncertainty analysis of a dynamic agroecosystem model under different irrigation treatments. Ecological Modelling, 231, 113–125. Esmaeili S, Thomson N R, Tolson B A, Zebarth B J, Kuchta S H, Neilsen D. 2014. Quantitative global sensitivity analysis of the rzwqm to warrant a robust and effective calibration. Journal of Hydrology, 511, 567–579.Fang Q X. 2012. Optimizing and uncertainty evaluation of soil and crop parameters in root zone water quality model. Transactions of the Chinese Society of Agricultural Engineering, 28, 118–123.FAO (Food and Agriculture Organization). 2012. Reference Manual, Annex I - AquaCrop Crop Parameters. Food and Agriculture Organization of the United Nations, Rome. pp. 39–42.Green D M, Whittemore C T. 2005. Calibration and sensitivity analysis of a model of the growing pig for weight gain and composition. Agricultural Systems, 84, 279–295.He L, Hou Y Y, Zhao G, Wu D R, Wu Q. 2016. Parameters optimization of WOFOST model by integration of global sensitivity analysis and Bayesian calibration method. Transactions of the Chinese Society of Agricultural Engineering, 32, 169–179.He L, Zhao G, Jin N, Zhuang W, Yu Q. 2015. Global sensitivity analysis of APSIM-wheat parameters in different climate zones and yield levels. Transactions of the Chinese Society of Agricultural Engineering, 31, 148–157.Hsiao T C, Lee H, Steduto P. 2009. AquaCrop - the FAO crop model to simulate yield response to water: III. parameterization and testing for maize. Agronomy Journal, 101, 448–459.Hussein F, Janat M, Yakoub A. 2011. Simulating cotton yield response to deficit irrigation with the FAO AquaCrop model. Spanish Journal of Agricultural Research, 9, 1319–1330.Iqbal M A, Shen Y, Stricevic R, Pei H, Sun H, Amiri E. 2014. Evaluation of the FAO AquaCrop model for winter wheat on the north china plain under deficit irrigation from field experiment to regional yield simulation. Agricultural Water Management, 135, 61–72.Jiang Z W, Chen Z X, Zhou Q B, Ren J Q. 2011. Global sensitivity analysis of CERES-Wheat model parameters. Transactions of the CSAE, 27, 236–242. (in Chinese)Jin X L, Feng H K, Zhu X K, Li Z H, Song S N, Song X Y. 2014. Assessment of the AquaCrop model for use in simulation of irrigated winter wheat canopy cover, biomass, and grain yield in the North China Plain. PLOS ONE, 9, e86938.Klepper O. 1997. Multivariate aspects of model uncertainty analysis: Tools for sensitivity analysis and calibration. Ecological Modelling, 101, 1–13.Leekheng H, Hsiao T, Evett S, Howell T, Steduto P. 2009. Validating the FAO AquaCrop model for irrigated and water deficient field maize. Agronomy Journal, 101, 488–498.Liu G, Xie Y, Gao X F, Feng Y J. 2008. Sensitivity analysis on parameters of almanac crop model. Chinese Journal of Agrometeorology, 29, 259–263.Maniruzzaman M, Talukder M S U, Khan M H, Biswas J C, Nemes A. 2015. Validation of the AquaCrop model for irrigated rice production under varied water regimes in Bangladesh. Agricultural Water Management, 159, 331–340.Mhizha T, Geerts S, Vanuytrecht E, Makarau A, Raes D. 2014. Use of the FAO AquaCrop model in developing sowing guidelines for rainfed maize in Zimbabwe. Water, 40, 233–244.Mkhabela M S, Bullock P R. 2012. Performance of the FAO AquaCrop model for wheat grain yield and soil moisture simulation in western canada. Agricultural Water Management, 110, 16–24.Monod H, Naud C, Makowski D. 2006. Uncertainty and Sensitivity Analysis for Crop Models. Working with Dynamic Crop Models, India. pp. 55–99.Morris M D. 1991. Factorial Sampling plans for preliminay computational experiments. Technometrics, 33, 161–174.Pathak T B, Fraisse C W, Jones J W, Messina C D, Hoogenboom G. 2007. Use of global sensitivity analysis for CROPGRO cotton model development. Transactions of the Asabe, 50, 2295–2302.Raes D, Steduto P, Hsiao T C, Elias F, 2009. AquaCrop The FAO crop model to simulate yield response to water, II. Main algorithms and software description. Agronomy Journal, 101, 438–447.Rankine D R, Cohen J E, Taylor M A, Coy A D, Simpson L A, Stephenson T. 2014. Parameterizing the FAO AquaCrop model for rainfed and irrigated field-grown sweet potato. Agronomy Journal, 107, 375–387.Saltelli A. 1999a. Sensitivity analysis: Could better methods be used? Journal of Geophysical Research Atmospheres, 104, 3789–3793.Saltelli A, Tarantola S, Campolongo F. 2000. Sensitivity anaysis as an ingredient of modeling. Statistical Science, 15, 377–395.Saltelli A, Tarantola S, Chan P S. 1999b. A quantitative model-independent method for global sensitivity analysis of model output. Technometrics, 41, 39–56.Saltelli A, Tarantola S, Campolongo F, Ratto M. 2006. Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models. Wiley-VCH Verlag GmbH, USA. pp. 398–399.Shao L, Zhang X, Chen S, Wang Z. 2009. Effects of irrigation frequency under limited irrigation on root water uptake yield and water use efficiency of winter wheat. Irrigation & Drainage, 58, 393–405.Sobol’ I M. 1993. Sensitivity estimates for nonlinear mathematical models. Mathematical Modelling and Computational Experiments, 2, 112–118.Song M D, Feng H, Li Z P, Gao J N. 2014. Global sensitivity analyses of DSSAT-CERSE-Wheat model using Morris and EFAST method. Transactions of the Chinese Society of Agricultural Machinery, 45, 124–131. Steduto P, Hsiao T C, Raes D, Elias F. 2009. AquaCrop-The FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agronomy Journal, 101, 426–437.Stella T, Frasso N, Negrini G, Bregaglio S, Cappelli G, Acutis M. 2014. Model simplification and development via reuse, sensitivity analysis and composition: A case study in crop modelling. Environmental Modelling & Software, 59, 44–58.Stricevic R, Cosic M, Djurovic N, Pejic B, Maksimovic L. 2011. Assessment of the FAO Aquacrop model in the simulation of rainfed and supplementally irrigated maize, sugar beet and sunflower. Agricultural Water Management, 98, 1615–1621.Vanuytrecht E, Raes D, Willems P. 2014. Global sensitivity analysis of yield output from the water productivity model. Environmental Modelling & Software, 51, 323–332.Vazquez-Cruz M A, Guzman-Cruz R, Lopez-Cruz I L, Cornejo-Perez O, Torres-Pacheco I, Guevara-Gonzalez R G. 2014. Global sensitivity analysis by means of EFAST and Sobol’ methods and calibration of reduced state-variable TOMGRO model using genetic algorithms. Computers & Electronics in Agriculture, 100, 1–12.Wang J, Li X, Lu L, Fang F. 2013. Parameter sensitivity analysis of crop growth models based on the extended Fourier Amplitude Sensitivity Test method. Environmental Modelling & Software, 48, 171–182.Wu J, Yu F S, Chen Z X, Chen J. 2009. Global sensitivity analysis of growth simulation parameters of winter wheat based on epic model. Transactions of the Chinese Society of Agricultural Engineering, 25, 136–142.Wu L F, Zhang F C, Fan J L, Zhou H, Xing Y, Qiang S. 2015. Sensitivity and uncertainty analysis for CROPGRO-cotton model at different irrigation levels. Transactions of the Chinese Society of Agricultural Engineering, 31, 55–64.Xie Y, Kiniry J R, Williams J R. 2003. The ALMANAC model’s sensitivity to input variables. Agricultural Systems, 78, 1–16. Xing H M, Xiang S Y, Xu X G, Feng H K, Yang G J, Chen Z X. 2017. Global sensitivity analysis of AquaCrop crop model parameters based on EFAST method. Scientia Agricultura Sinica, 50, 64–76. (in Chinese)Xing H M, Xu X G, Yang F Q, Feng H K, Yang G J. 2016. Global sensitivity analysis of winter wheat yield and process-based variable with AquaCrop model. In: IGARSS 2016–2016 IEEE International Geoscience and Remote Sensing Symposium. IEEE (Institute of Electrical and Electronics Engineers), Beijing. pp. 5929–5932.Zhang J X, Wei S U. 2012. Sensitivity analysis of CERES-Wheat model parameters based on EFAST method. Journal of China Agricultural University, 17, 149–154.Zhao G, Bryan B A, Song X. 2014. Sensitivity and uncertainty analysis of the APSIM-wheat model: Interactions between cultivar, environmental, and management parameters. Ecological Modelling, 279, 1–11.Zhu X F, Li Y Z, Pan Y Z, Shi P J, 2014. A review on the research and application of AquaCrop model. Chinese Agricultural Science Bulletin, 30, 270–278. (in Chinese) |
| No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
