About the Journal
In Memory of Professor Jerzy Radecki
Ownership and Management Statement
Editorial Office
Editorial Board
Scope
Impact Factor
Indexed by
Journal Impact Factor contributing items
Publication Frequency
Revenue Sources & Business Model
Contact
Subscription
Current issue
Online first
Instructions for Authors
Copyright & Licensing
Publication Fees
Editorial policies
Publication Policy Overview
Peer Review Policy
Research Ethics and Malpractice Policy
Plagiarism Policy
Conflict of Interest Policy
Open Access Policy
Instructions for Reviewers
Generative AI and AI-Assisted Technologies Policy
Advertising Policy
Direct Marketing & Manuscript Solicitation
Digital Archiving and Preservation Policy
CrossMark, Correction, and Retraction Policy
Archive
Publication Fees
Copyright & Licensing
Editorial policies
Publication Policy Overview
Peer Review Policy
Research Ethics and Malpractice Policy
Plagiarism Policy
Conflict of Interest Policy
Open Access Policy
Instructions for Reviewers
Generative AI and AI-Assisted Technologies Policy
Advertising Policy
Direct Marketing & Manuscript Solicitation
Digital Archiving and Preservation Policy
CrossMark, Correction, and Retraction Policy
eISSN: 2083-5906
ISSN: 1230-1485
ISSN: 1230-1485
ORIGINAL RESEARCH
Sensitivity Analysis of AquaCrop Model
Parameters for Winter Wheat under
Different Meteorological Conditions
Based on the EFAST Method
1
College of Surveying and Planning, Shangqiu Normal University, Shangqiu, 476000, China
2
Henan Agricultural Remote Sensing Big Data Development and Innovation Laboratory,
Shangqiu Normal University, Shangqiu, 476000, China
3
Engineering Technology Research Center of Remote Sensing Big Data and Smart Agriculture,
Shangqiu Normal University, Shangqiu, 476000, China
4
Henan Engineering Technology Research Center of Ecological Protection and Management of the Old Course
of Yellow River, Shangqiu Normal University, Shangqiu, 476000, China
5
Information Technology Research Center, Beijing Academy of Agriculture and Forest Sciences, Beijing 100097, China
Submission date: 2023-10-12
Final revision date: 2024-01-16
Acceptance date: 2024-03-14
Online publication date: 2024-07-23
Publication date: 2025-01-02
Corresponding author
Pol. J. Environ. Stud. 2025;34(1):329-345
KEYWORDS
TOPICS
ABSTRACT
To analyze the global sensitivity of winter wheat parameters using the AquaCrop model on a global
scale, the extended Fourier amplitude sensitivity test (EFAST) was utilized to identify parameter
sensitivity differences in different regions and meteorological conditions represented by eight stations
in Henan Province, including Zhengzhou, Anyang, Shangqiu, Luanchuan, Nanyang, Xuchang,
Zhumadian, and Xinyang. The results showed that: (1) the sensitivity of crop parameters is little
affected by meteorological conditions for biomass, and the sensitivity parameters of the eight regions
were consistent; there were minimum growing degrees required for total biomass production (stbio),
normalized water productivity (wp), maximum canopy cover in fraction soil cover (mcc), crop coefficient
when the canopy was complete but prior to senescence (kcb), Growing degree-days (GDD)-from sowing
to emergence (eme), and GGD-increase in canopy cover (cgc); (2) for canopy cover, the most sensitive
parameters were mcc, cgc, soil surface covered by an individual seedling at 90% emergence (ccs), and
other parameters were more sensitive in early growth stage of winter wheat; (3) for yield, GDD-from
sowing to flowering (flo) was the most sensitive parameter. The results of this study will provide support
for the use of the AquaCrop model to investigate crop management at the local level.
CONFLICT OF INTEREST
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
REFERENCES (49)
1.
AKUMAGA U., GAO F., ANDERSON M., DULANEY W.P., HOUBORG R., RUSS A., HIVELY W.D. Integration of remote sensing and field observations in evaluating DSSAT model for estimating maize and soybean growth and yield in Maryland, USA. Agronomy, 13 (6), 1540, 2023. https://doi.org/10.3390/agrono....
2.
LI Z., JIN X., LIU H., XU X., WANG J. 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, 2019. https://doi.org/10.1016/S2095-....
3.
ZHOU H., GENG G., YANG J., HU H., SHENG L., LOU W. Improving Soil Moisture Estimation via Assimilation of Remote Sensing Product into the DSSAT Crop Model and Its Effect on Agricultural Drought Monitoring. Remote Sensing, 14 (13), 3187, 2022. https://doi.org/10.3390/rs1413....
4.
SINGH S., NEGM L., JEONG H., COOKE R., BHATTARAI R. Comparison of simulated nitrogen management strategies using DRAINMOD-DSSAT and RZWQM. Agricultural Water Management, 266, 107597, 2022. https://doi.org/10.1016/j.agwa....
5.
AHMADPOUR A., FARHADI B., AZARI A. Proposing a combined method for the estimation of spatial and temporal variation of crop water productivity under deficit irrigation scenarios based on the AquaCrop model. Applied Water Science, 12 (7), 154, 2022. https://doi.org/10.1007/s13201....
6.
HSIAO T.C., HENG L., STEDUTO P., ROJAS‐LARA B., RAES D., FERERES E. AquaCrop - The FAO crop model to simulate yield response to water: III. Parameterization and testing for maize. Agronomy Journal, 101 (3), 448, 2009. https://doi.org/10.2134/agronj....
7.
RAES D., STEDUTO P., HSIAO T.C., FERERES E. AquaCrop - The FAO crop model to simulate yield response to water: II. Main algorithms and software description. Agronomy Journal, 101 (3), 438, 2009. https://doi.org/10.2134/agronj... PMCid:PMC8374466.
8.
STEDUTO P., HSIAO T.C., RAES D., FERERES E. AquaCrop - The FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agronomy Journal, 101 (3), 426, 2009. https://doi.org/10.2134/agronj....
9.
ROSA S.L.K., SOUZA J.L.M.D., TSUKAHARA R.Y., KOCHINSKI E.G. Sensitivity analysis of the AquaCrop model for wheat crop in Campos Gerais region, Paraná. Revista Ceres, 70 (1), 32, 2023. https://doi.org/10.1590/0034-7....
10.
JIN X., KUMAR L., LI Z., FENG H., XU X., YANG G., WANG J. A review of data assimilation of remote sensing and crop models. European Journal of Agronomy, 92, 141, 2018. https://doi.org/10.1016/j.eja.....
11.
NIE T., TANG Y., JIAO Y., LI N., WANG T., DU C., ZHANG Z., CHEN P., LI T., SUN Z., ZHU S. Effects of irrigation schedules on maize yield and water use efficiency under future climate scenarios in Heilongjiang province based on the AquaCrop model. Agronomy, 12 (4), 810, 2022. https://doi.org/10.3390/agrono....
12.
PIRMORADIAN N., SAADATI Z., REZAEI M., KHALEDIAN M.R. Simulating water productivity of paddy rice under irrigation regimes using AquaCrop model in humid and semiarid regions of Iran. Applied Water Science, 10 (7), 1, 2020. https://doi.org/10.1007/s13201....
13.
WU H., YUE Q., GUO P., XU X., HUANG X. Improving the AquaCrop model to achieve direct simulation of evapotranspiration under nitrogen stress and joint simulation-optimization of irrigation and fertilizer schedules. Agricultural Water Management, 266, 107599, 2022. https://doi.org/10.1016/j.agwa....
14.
XING H., XU X., LI Z., CHEN Y., FENG H., YANG G., CHEN Z. 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, 2017. https://doi.org/10.1016/S2095-....
15.
LU Y., CHIBARABADA T.P., MCCABE M.F., DE LANNOY G.J.M., SHEFFIELD J. Global sensitivity analysis of crop yield and transpiration from the FAO-AquaCrop model for dryland environments. Field Crops Research, 269, 108182, 2021. https://doi.org/10.1016/j.fcr.....
16.
ZHANG C., XIE Z., WANG Q., TANG M., FENG S., CAI H. AquaCrop modeling to explore optimal irrigation of winter wheat for improving grain yield and water productivity. Agricultural Water Management, 266, 107580, 2022. https://doi.org/10.1016/j.agwa....
17.
CARCEDO A.J., JUNIOR N.V., MARZIOTTE L., CORRENDO A.A., ARAYA A., PRASAD P.V., MIN D., STEWART Z.P., FAYE A., CIAMPITTI I.A. The urgency for investment on local data for advancing food assessments in Africa: A review case study for APSIM crop modeling. Environmental Modelling & Software, 105633, 2023. https://doi.org/10.1016/j.envs....
18.
EBRAHIMI-MOLLABASHI E., HUTH N.I., HOLZWOTH D.P., ORDÓÑEZ R.A., HATFIELD J.L., HUBER I., CASTELLANO M.J., ARCHONTOULIS S.V. Enhancing APSIM to simulate excessive moisture effects on root growth. Field Crops Research, 236, 58, 2019. https://doi.org/10.1016/j.fcr.....
19.
DE W.A., BOOGAARD H., FUMAGALLI D., JANSSEN S., KNAPEN R., VAN K.D., SUPIT I., VAN DER W.R., VAN DIEPEN K. 25 years of the WOFOST cropping systems model. Agricultural Systems, 168, 154, 2019. https://doi.org/10.1016/j.agsy....
20.
ZHUO W., FANG S., GAO X., WANG L., WU D., FU S., WU Q., HUANG J. Crop yield prediction using MODIS LAI, TIGGE weather forecasts and WOFOST model: A case study for winter wheat in Hebei, China during 2009-2013. International Journal of Applied Earth Observation and Geoinformation, 106, 102668, 2022. https://doi.org/10.1016/j.jag.....
21.
CEGLAR A., VAN DER W.R., DE WIT A., LECERF R., BOOGAARD H., SEGUINI L., VAN DEN B.M., TORETI A., ZAMPIERI M., FUMAGALLI M., BARUTH B. Improving WOFOST model to simulate winter wheat phenology in Europe: Evaluation and effects on yield. Agricultural Systems, 168, 168, 2019. https://doi.org/10.1016/j.agsy....
22.
ZHUO W., HUANG J., XIAO X., HUANG H., BAJGAIN R., WU X., GAO X., WANG J., LI X., WAGLE P. Assimilating remote sensing-based VPM GPP into the WOFOST model for improving regional winter wheat yield estimation. European Journal of Agronomy, 139, 126556, 2022. https://doi.org/10.1016/j.eja.....
23.
AL-ADHAILEH M.H., ALDHYANI T.H. Artificial Intelligence Framework for Modeling and Predicting Crop Yield to Enhance Food Security in Saudi Arabia. PeerJ Computer Science, 8, e1104, 2022. https://doi.org/10.7717/peerj-... PMid:36262130 PMCid:PMC9575863.
24.
DIVYA K.L., MHATRE P.H., VENKATASALAM E.P., SUDHA R. Crop simulation models as decision-supporting tools for sustainable potato production: a review. Journal of Agrometeorology, 23 (1), 1, 2021.
25.
MULLA S., SINGH S.K., SINGH K.K., PRAVEEN B. Climate change and agriculture: a review of crop models. Global Climate Change and Environmental Policy: Agriculture Perspectives, 423, 2020. https://doi.org/10.1007/978-98....
26.
PENG B., GUAN K., TANG J., AINSWORTH E.A., ASSENG S., BERNACCHI C.J., COOPER M., DELUCIA E.H., ELLIOTT J.W., EWERT F., GRANT R.F., GUSTAFSON D.I., HAMMER G.L., JIN Z., JONES J.W., KIMM H., LAWRENCE D.M., LI Y., LOMBARDOZZI D.L., MARSHALL-COLON A., MESSINA C.D., ORT D.R., SCHNABLE J.C., VALLEJOS C.E., WU A., YIN X., ZHOU W. Towards a multiscale crop modelling framework for climate change adaptation assessment. Nature Plants, 6 (4), 338, 2020. https://doi.org/10.1038/s41477... PMid:32296143.
27.
AHMED M., AHMAD S., RAZA M.A., KUMAR U., ANSAR M., SHAH G.A., PARSONS D., HOOGENBOOM G., PALOSUO T., SEIDEL S. Models calibration and evaluation. Systems Modeling, 151, 2020. https://doi.org/10.1007/978-98....
28.
CHENU K., PORTER J.R., MARTRE P., BASSO B., CHAPMAN S., EWERT F., BINDI M., ASSENG S. Contribution of crop models to adaptation in wheat. Trends in Plant Science, 22 (6), 472, 2017. https://doi.org/10.1016/j.tpla... PMid:28389147.
29.
SALTELLI A., ALEKSANKINA K., BECKER W., FENNELL P., FERRETTI F., HOLST N., LI S., WU Q. Why so many published sensitivity analyses are false: A systematic review of sensitivity analysis practices. Environmental Modelling & Software, 114, 29, 2019. https://doi.org/10.1016/j.envs... PMCid:PMC8081243.
30.
RAPADAMNABA R., RIBATET M., MOHAMMADI B. Global Sensitivity Analysis for Assessing the Parameters Importance and Setting a Stopping Criterion in a Biomedical Inverse Problem. International Journal for Numerical Methods in Biomedical Engineering, 37 (6), e3458, 2021. https://doi.org/10.1002/cnm.34... PMid:33759369.
31.
HERRERA P.A., MARAZUELA M.A., HOFMANN T. Parameter estimation and uncertainty analysis in hydrological modeling. Wiley Interdisciplinary Reviews: Water, 9 (1), e1569, 2022. https://doi.org/10.1002/wat2.1....
32.
CONFALONIERI R., BELLOCCHI G., BREGAGLIO S., DONATELLI M., ACUTIS M. Comparison of sensitivity analysis techniques: A case study with the rice model WARM. Ecological Modelling, 221, 1897, 2010. https://doi.org/10.1016/j.ecol....
33.
TOSIN M., CÔRTES A.M., CUNHA A. A Tutorial on Sobol' Global Sensitivity Analysis Applied to Biological Models. Networks in Systems Biology: Applications for Disease Modeling, 93, 2020. https://doi.org/10.1007/978-3-....
34.
WANG J., LI X., LU L., FANG F. Parameter Sensitivity Analysis of Crop Growth Models Based on the Extended Fourier Amplitude Sensitivity Test Method. Environmental Modelling & Software, 48, 171, 2013. https://doi.org/10.1016/j.envs....
35.
LIU J., LIU Z., ZHU A., SHEN F., LEI Q., DUAN Z. Global sensitivity analysis of the APSIM-Oryza rice growth model under different environmental conditions. Science of the Total Environment, 651, 953, 2019. https://doi.org/10.1016/j.scit... PMid:30257234.
36.
LI P., REN L. Evaluating the effects of limited irrigation on crop water productivity and reducing deep groundwater exploitation in the North China Plain using an agrohydrological model: I. Parameter sensitivity analysis, calibration and model validation. Journal of Hydrology, 574, 497, 2019. https://doi.org/10.1016/j.jhyd....
37.
MA H., WANG J., LIU T., GUO Y., ZHOU Y., YANG T., ZHANG W., SUN C. Time series global sensitivity analysis of genetic parameters of CERES-maize model under water stresses at different growth stages. Agricultural Water Management, 275, 108027, 2023. https://doi.org/10.1016/j.agwa....
38.
YU Q., CUI Y., LIU L. Assessment of the parameter sensitivity for the ORYZA model at the regional scale - A case study in the Yangtze River Basin. Environmental Modelling & Software, 159, 105575, 2023. https://doi.org/10.1016/j.envs....
39.
SALTELLI A., TARANTOLA S., CHAN K.S. A quantitative model-independent method for global sensitivity analysis of model output. Technometrics, 41, 39, 1999. https://doi.org/10.1080/004017....
40.
SALTELLI A., RATTO M., TARANTOLA S., CAMPOLONGO F. Update 1 of: Sensitivity analysis for chemical models. Chemical Reviews, 112, PR1-21, 2012. https://doi.org/10.1021/cr2003... PMid:22515330.
41.
TARANTOLA S., GATELLI D., MARA T.A. Random balance designs for the estimation of first order global sensitivity indices. Reliability Engineering & System Safety, 91, 717, 2006. https://doi.org/10.1016/j.ress....
42.
SOBOL' I. Sensitivity estimates for nonlinear mathematical models. Mathematical Modelling and Computational Experiments, 2 (1), 112, 1990.
43.
VANROLLEGHEM P.A., MANNINA G., COSENZA A., NEUMANN M.B. Global sensitivity analysis for urban water quality modelling: terminology, convergence and comparison of different methods. Journal of Hydrology, 522, 339, 2015. https://doi.org/10.1016/j.jhyd....
44.
MA J., LI Y., CHEN Y., DU K., ZHENG F., ZHANG L., SUN Z. Estimating above ground biomass of winter wheat at early growth stages using digital images and deep convolutional neural network. European Journal of Agronomy, 103, 117, 2019. https://doi.org/10.1016/j.eja.....
45.
JAVADINEJAD S., ESLAMIAN S., OSTAD-ALIASKARI K. The analysis of the most important climatic parameters affecting performance of crop variability in a changing climate. International Journal of Hydrology Science and Technology, 11 (1), 1, 2021. https://doi.org/10.1504/IJHST.....
46.
KERSEBAUM K.C., WALLOR E. Process-Based Modelling of Soil-Crop Interactions for Site-Specific Decision Support in Crop Management. In Precision Agriculture: Modelling, 25, 2023. https://doi.org/10.1007/978-3-....
47.
VANUYTRECHT E., RAES D., WILLEMS P. Global sensitivity analysis of yield output from the water productivity model. Environmental Modelling & Software, 51, 323, 2014. https://doi.org/10.1016/j.envs....
48.
ZHU X., XU K., LIU Y., GUO R., CHEN L. Assessing the vulnerability and risk of maize to drought in China based on the AquaCrop model. Agricultural Systems, 189, 103040, 2021. https://doi.org/10.1016/j.agsy....
49.
ORLOVA Y., LINKER R. Data assimilation with sensitivity-based particle filter: A simulation study with AquaCrop. Computers and Electronics in Agriculture, 204, 107538, 2023. https://doi.org/10.1016/j.comp....
CITATIONS (1):
1.
Spatial-temporal patterns of wheat growth model parameter sensitivity and strategies for region-adaptive calibration and management
Xinlong Li, Zhiduo Dong, Jia Gao, Boyue Zhang, Fengli Jiao, Panpan Guo, Shichao Chen, Taisheng Du, Shaozhong Kang, Risheng Ding
Agricultural Water Management
Xinlong Li, Zhiduo Dong, Jia Gao, Boyue Zhang, Fengli Jiao, Panpan Guo, Shichao Chen, Taisheng Du, Shaozhong Kang, Risheng Ding
Agricultural Water Management
Share
RELATED ARTICLE
| eISSN: | 2083-5906 |
| ISSN: | 1230-1485 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
