Global sensitivity analysis of wheat grain yield and quality and the related process variables from the DSSAT-CERES model based on the extended Fourier Amplitude Sensitivity Test method

doi:10.1016/S2095-3119(18)62046-5

Journal of Integrative Agriculture

2019, Vol. 18

Issue (7): 1547-1561 DOI: 10.1016/S2095-3119(18)62046-5

Crop Science

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Global sensitivity analysis of wheat grain yield and quality and the related process variables from the DSSAT-CERES model based on the extended Fourier Amplitude Sensitivity Test method

LI Zhen-hai^{1, 2}, JIN Xiu-liang³, LIU Hai-long⁴, XU Xin-gang², WANG Ji-hua⁵

¹ Key Laboratory of Agri-Informatics, Ministry of Agriculture and Rural Affairs, Beijing 100097, P.R.China
² Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, P.R.China
³ UMR EMMAH, INRA, NAPV, Avignon 84914, France
⁴ Agricultural Information Institute, Chinese Academy of Agricultural Sciences/Key Laboratory of Agri-Information Service Technology, Ministry of Agriculture and Rural Affairs, Beijing 100081, P.R.China
⁵ Beijing Research Center for Agri-Food Testing and Farmland Monitoring, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, P.R.China

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Abstract A crop growth model, integrating genotype, environment, and management factor, was developed to serve as an analytical tool to study the influence of these factors on crop growth, production, and agricultural planning. A major challenge of model application is the optimization and calibration of a considerable number of parameters. Sensitivity analysis (SA) has become an effective method to identify the importance of various parameters. In this study, the extended Fourier Amplitude Sensitivity Test (EFAST) approach was used to evaluate the sensitivity of the DSSAT-CERES model output responses of interest to 39 crop genotype parameters and six soil parameters. The outputs for the SA included grain yield and quality (take grain protein content (GPC) as an indicator) at maturity stage, as well as leaf area index, aboveground biomass, and aboveground nitrogen accumulation at the critical process variables. The key results showed that: (1) the influence of parameter bounds on the sensitivity results was slight and less than the impacts from the significance of the parameters themselves; (2) the sensitivity parameters of grain yield and GPC were different, and the sensitivity of the interactions between parameters to GPC was greater than those between the parameters to grain yield; and (3) the sensitivity analyses of some process variables, including leaf area index, aboveground biomass, and aboveground nitrogen accumulation, should be performed differently. Finally, some parameters, which improve the model’s structure and the accuracy of the process simulation, should not be ignored when maturity output as an objective variable is studied.

Keywords: global sensitivity analysis DSSAT EFAST wheat yield grain protein content

Received: 23 February 2018 Online: 29 June 2018 Accepted:

Fund: The work was supported by the National Natural Science Foundation of China (41701375, 41601369, and 41471285), and the European Space Agency (ESA) and Ministry of Science and Technology of China (MOST) Dragon 4 Cooperation Programme (32275-1).

Corresponding Authors: Correspondence XU Xin-gang, Tel: +86-10-51503215, E-mail: xuxg@nercita.org.cn; WANG Ji-hua, Tel: +86-10-51503215, E-mail: wangjh@nercita.org.cn

About author: LI Zhen-hai, Tel: +86-10-51503215, E-mail: lizh323@126.com;

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LI Zhen-hai, JIN Xiu-liang, LIU Hai-long, XU Xin-gang, WANG Ji-hua. 2019. Global sensitivity analysis of wheat grain yield and quality and the related process variables from the DSSAT-CERES model based on the extended Fourier Amplitude Sensitivity Test method. Journal of Integrative Agriculture, 18(7): 1547-1561.

Allen R G, Pereira L S, Raes D, Smith M. 1998. Crop Evapotranspiration-Guidelines for Computing Crop Water Requirements-Fao Irrigation and Drainage Paper 56. FAO, Rome.
Angulo C, Rotter R, Lock R, Enders A, Fronzek S, Ewert F. 2013. Implication of crop model calibration strategies for assessing regional impacts of climate change in Europe. Agricultural and Forest Meteorology, 170, 32–46.
Araya A, Habtu S, Hadgu K M, Kebede A, Dejene T. 2010. Test of AquaCrop model in simulating biomass and yield of water deficient and irrigated barley (Hordeum vulgare). Agricultural Water Management, 97, 1838–1846.
Asseng S, Bar-Tal A, Bowden J W, Keating B A, Van Herwaarden A, Palta J A, Huth N I, Probert M E. 2002. Simulation of grain protein content with APSIM-Nwheat. European Journal of Agronomy, 16, 25–42.
Asseng S, McIntosh P C, Wang G M, Khimashia N. 2012. Optimal N fertiliser management based on a seasonal forecast. European Journal of Agronomy, 38, 66–73.
Asseng S, Milroy S P. 2006. Simulation of environmental and genetic effects on grain protein concentration in wheat. European Journal of Agronomy, 25, 119–128.
Bacour C, Baret F, Jacquemoud S. 2002. Information content of HyMap hyperspectral imagery. Proceedings of the 1st International Symposium on Recent Advances in Quantitative Remote Sensing, Valencia (Spain), 16, 503–508.
Balaghi R, Tychon B, Eerens H, Jlibene M. 2008. Empirical regression models using NDVI, rainfall and temperature data for the early prediction of wheat grain yields in Morocco. International Journal of Applied Earth Observation and Geoinformation, 10, 438–452.
Booltink H, van Alphen B J, Batchelor W D, Paz J O, Stoorvogel J J, Vargas R. 2001. Tools for optimizing management of spatially-variable fields. Agricultural Systems, 70, 445–476.
Boote K J, Kropff M J, Bindraban P S. 2001. Physiology and modelling of traits in crop plants: Implications for genetic improvement. Agricultural Systems, 70, 395–420.
Brun F, Wallach D, Makowski D, Jones J W. 2006. Working With Dynamic Crop Models: Evaluation, Analysis, Parameterization, and Applications. Elsevier, Amsterdam.
Confalonieri R, Acutis M, Bellocchi G, Donatelli M. 2009. Multi-metric evaluation of the models WARM, CropSyst, and WOFOST for rice. Ecological Modelling, 220, 1395–1410.
Confalonieri R, Bellocchi G, Bregaglio S, Donatelli M, Acutis M. 2010. Comparison of sensitivity analysis techniques: A case study with the rice model WARM. Ecological Modelling, 221, 1897–1906.
Dente L, Satalino G, Mattia F, Rinaldi M. 2008. Assimilation of leaf area index derived from ASAR and MERIS data into CERES-Wheat model to map wheat yield. Remote Sensing of Environment, 112, 1395–1407.
Dzotsi K A, Basso B, Jones J W. 2013. Development, uncertainty and sensitivity analysis of the simple SALUS crop model in DSSAT. Ecological Modelling, 260, 62–76.
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.
Fan M, Shibata H. 2014. Spatial and temporal analysis of hydrological provision ecosystem services for watershed conservation planning of water resources. Water Resources Management, 28, 3619–3636.
Hanks J, Ritchie J T. 1991. Wheat phasic development. In: Modeling Plant and Soil Systems. American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, Madison, Wisconsin.
He J Q, Cai H J, Bai J P. 2013. Irrigation scheduling based on CERES-Wheat model for spring wheat production in the Minqin Oasis in Northwest China. Agricultural Water Management, 128, 19–31.
He J Q, Dukes M D, Hochmuth G J, Jones J W, Graham W D. 2012. Identifying irrigation and nitrogen best management practices for sweet corn production on sandy soils using CERES-Maize model. Agricultural Water Management, 109, 61–70.
Helton J C. 1993. Uncertainty and sensitivity analysis techniques for use in performance assessment for radioactive waste disposal. Reliability Engineering & System Safety, 42, 327–367.
Hoogenboom G, Jones J W, Porter C H, Wilkens P W, Boote K J, Hunt L A, Tsuji G Y. 2010. Volume 1: Overview, Decision Support System for Agrotechnology Transfer Version 4.5. University of Hawaii, Hawaii.
Hoogenboom G, Jones J W, Traore P C, Boote K J. 2012. Experiments and Data for Model Evaluation and Application. Improving Soil Fertility Recommendations in Africa using the Decision Support System for Agrotechnology Transfer (DSSAT). Springer, The Netherlands. pp. 9–18.
Hoogenboom G, White J W, Messina C D. 2004. From genome to crop: Integration through simulation modeling. Field Crops Research, 90, 145–163.
Jeuffroy M H, Casadebaig P, Debaeke P, Loyce C, Meynard J M. 2014. Agronomic model uses to predict cultivar performance in various environments and cropping systems. A review. Agronomy for Sustainable Development, 34, 121–137.
Jiang Z W, Chen Z X, Zhou Q B, Ren Z Q. 2011. Global sensitivity analysis of CERES-Wheat model parameters. Transactions of the CSAE, 27, 236–242.
Jones J W, Hoogenboom G, Porter C H, Boote K J, Batchelor W D, Hunt L A, Wilkens P W, Singh U, Gijsman A J, Ritchie J T. 2003. The DSSAT cropping system model. European Journal of Agronomy, 18, 235–265.
Jones J W, Naab J, Fatondji D, Dzotsi K, Adiku S, He J. 2012. Uncertainties in simulating crop performance in degraded soils and low input production systems. In: Improving Soil Fertility Recommendations in Africa using the Decision Support System for Agrotechnology Transfer (DSSAT). Springer, The Netherlands. pp. 43–59.
De Jonge K C, Ascough J C, 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.
Lamboni M, Makowski D, Lehuger S, Gabrielle B, Monod H. 2009. Multivariate global sensitivity analysis for dynamic crop models. Field Crops Research, 113, 312–320.
Letcher R A, Croke B, Jakeman A J, Merritt W S. 2006. An integrated modelling toolbox for water resources assessment and management in highland catchments: Model description. Agricultural Systems, 89, 106–131.
Li Z, Wang J, Xu X, Zhao C, Jin X, Yang G, Feng H. 2015. Assimilation of two variables derived from hyperspectral data into the DSSAT-CERES model for grain yield and quality estimation. Remote Sensing, 7, 12400–12418.
Liu H L, Yang J Y, Drury C A, Reynolds W D, Tan C S, Bai Y L, He P, Jin J, Hoogenboom G. 2011. Using the DSSAT-CERES-Maize model to simulate crop yield and nitrogen cycling in fields under long-term continuous maize production. Nutrient Cycling in Agroecosystems, 89, 313–328.
Liu L Y, Wang J J, Bao Y S, Huang W J, Ma Z H, Zhao C J. 2006. Predicting winter wheat condition, grain yield and protein content using multi-temporal EnviSat-ASAR and Landsat TM satellite images. International Journal of Remote Sensing, 27, 737–753.
Manache G, Melching C S. 2008. Identification of reliable regression- and correlation-based sensitivity measures for importance ranking of water-quality model parameters. Environmental Modelling & Software, 23, 549–562.
Miao Z W, Lathrop R G, Xu M, La Puma I P, Clark K L, Hom J, Skowronski N, Van Tuyl S. 2011. Simulation and sensitivity analysis of carbon storage and fluxes in the New Jersey Pine lands. Environmental Modelling & Software, 26, 1112–1122.
Palosuo T, Kersebaum K C, Angulo C, Hlavinka P, Moriondo M, Olesen J E, Patil R H, Ruget F, Rumbaur C, Taká? J. 2011. Simulation of winter wheat yield and its variability in different climates of Europe: A comparison of eight crop growth models. European Journal of Agronomy, 35, 103–114.
Paux E, Sourdille P, Mackay I, Feuillet C. 2012. Sequence-based marker development in wheat: Advances and applications to breeding. Biotechnology Advances, 30, 1071–1088.
Reynolds M, Foulkes J, Furbank R, Griffiths S, King J, Murchie E, Parry M, Slafer G. 2012. Achieving yield gains in wheat. Plant Cell and Environment, 35, 1799–1823.
Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J, Gatelli D, Saisana M, Tarantola S. 2008. Global Sensitivity Analysis: The Primer. John Wiley & Sons, Italy.
Saltelli A, Tarantola S, Chan K. 1999. A quantitative model-independent method for global sensitivity analysis of model output. Technometrics, 41, 39–56.
Senaviratne G, Udawatta R P, Baffaut C, Anderson S H. 2014. Evaluation of a stepwise, multiobjective, multivariable parameter optimization method for the APEX model. Journal of Environmental Quality, 43, 1381–1391.
Smith G P, Gooding M J. 1999. Models of wheat grain quality considering climate, cultivar and nitrogen effects. Agricultural and Forest Meteorology, 94, 159–170.
Song M, Feng H, Li Z, Gao J. 2014. Global sensitivity analyses of dssat-ceres-wheat model using morris and efast methods. Transactions of the Chinese Society of Agricultural Machinery, 45, 124–131, 166.
Spiessl S M, Becker D A, Rubel A. 2012. EFAST analysis applied to a PA model for a generic HLW repository in clay. Reliability Engineering & System Safety, 107, 190–204.
Tarantola S. 2005. SimLab 2.2 Reference Manual. Institute for Systems, Informatics and Safety, European Commission, Joint Research Center, Ispra, Italy.
Thorp K R, Batchelor W D, Paz J O, Steward B L, Caragea P C. 2006. Methodology to link production and environmental risks of precision nitrogen management strategies in corn. Agricultural Systems, 89, 272–298.
Thorp K R, DeJonge K C, Kaleita A L, Batchelor W D, Paz J O. 2008. Methodology for the use of DSSAT models for precision agriculture decision support. Computers and Electronics in Agriculture, 64, 276–285.
Thorp K R, Hunsaker D J, French A N, White J W, Clarke T R, Pinter Jr P J. 2010. Evaluation of the CSM-CROPSIM-CERES-Wheat model as a tool for crop water management. Transactions of the ASAE (American Society of Agricultural Engineers), 53, 87.
Thorp K R, Wang G, West A L, Moran M S, Bronso K F, White J W, Mon J. 2012. Estimating crop biophysical properties from remote sensing data by inverting linked radiative transfer and ecophysiological models. Remote Sensing of Environment, 124, 224–233.
Valade A, Vuichard N, Ciais P, Ruget F, Viovy N, Gabrielle B, Huth N, Martine J F. 2014. ORCHIDEE-STICS, a process-based model of sugarcane biomass production: Calibration of model parameters governing phenology. Global Change Biology Bioenergy, 6, 606–620.
Vanuytrecht E, Raes D, Willems P. 2014. Global sensitivity analysis of yield output from the water productivity model. Environmental Modelling & Software, 51, 323–332.
Varella H, Guerif M, Buis S. 2010. Global sensitivity analysis measures the quality of parameter estimation: The case of soil parameters and a crop model. Environmental Modelling & Software, 25, 310–319.
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 and Electronics in Agriculture, 100, 1–12.
Wang E, Engel T. 1998. Simulation of phenological development of wheat crops. Agricultural Systems, 58, 1–24.
Wang F G, Mladenoff D J, Forrester J A, Keough C, Parton W J. 2013. Global sensitivity analysis of a modified CENTURY model for simulating impacts of harvesting fine woody biomass for bioenergy. Ecological Modelling, 259, 16–23.
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.
de Wit A M, van Diepen C A. 2007. Crop model data assimilation with the Ensemble Kalman filter for improving regional crop yield forecasts. Agricultural and Forest Meteorology, 146, 38–56.
Xiao Y F, Zhao W J, Zhou D M, Gong H L. 2014. Sensitivity analysis of vegetation reflectance to biochemical and biophysical variables at leaf, canopy, and regional scales. IEEE Transactions on Geoscience and Remote Sensing, 52, 4014–4024.
Xing H M, Xu X G, Li Z H. 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, 2444–2458.
Yang J. 2011. Convergence and uncertainty analyses in Monte-Carlo based sensitivity analysis. Environmental Modelling & Software, 26, 444–457.
Yu Z W. 2003. Crop Cultivation. China Agriculture Press, Beijing. (in Chinese)

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