IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v307y2025ics0378377424005717.html
   My bibliography  Save this article

Nonlinear water stress response functions can improve the performance of the DSSAT-CERES-Wheat model under water deficit conditions

Author

Listed:
  • Yao, Ning
  • Wei, Yingnan
  • Jiang, Kunhao
  • Liu, Jian
  • Li, Yi
  • Ran, Hui
  • Javed, Tehseen
  • Feng, Hao
  • Yu, Qiang
  • He, Jianqiang

Abstract

Crop growth simulation models are valuable for understanding and managing agro-ecological systems, especially in arid regions. The DSSAT-CERES-Wheat model is widely used to simulate wheat growth and development, but its accuracy diminishes under water stress conditions. This study evaluated the effects of different types of water stress response functions on the CERES-Wheat model, focusing on unit grain weight, biomass, and yield to improve the model's accuracy under water stress conditions. Our findings have disclosed that the calibration process demonstrated average Relative Mean Absolute Error (RMAE) and Relative Root Mean Square Error (RRMSE) values hovering at 5 %, whereas the evaluation process values surpassed 15 %. It was found that water stress occurring before the jointing stage significantly influenced model simulations of biomass and grain yield. This study assesses the performance of modified water stress response functions stepwise. Optimal parameters for these functions were identified, with the smallest RMAE and RRMSE observed at specific parameter values for different curve types. Comparative analyses using the modified CERES-Wheat model with six water stress response curves (WSRF) demonstrated improved WSRF 1, 2, 3, and 6 performance over the default function. The Penman-Monteith method for estimating potential evapotranspiration (E0) outperformed the Priestley-Taylor method, particularly in simulating unit grain weight, aboveground biomass, and grain yield. Convex functions (WSRF 1–3) were superior to concave functions (WSRF 4–6), enhancing overall simulation accuracy by 13.7 %. Furthermore, time-series output variables, including soil water and biomass dynamics, were evaluated. The original model underestimated early soil moisture and overestimated soil water stress, leading to significant biomass simulation errors. The modified model showed substantial improvements, particularly under low irrigation. However, simulation errors persisted under severe early-stage water stress. Validation results using DSSAT v4.7 databases confirmed the enhanced performance of the modified model under water stress conditions. The finding of this study proved that the improved model for the vast arid regions of China and similar environments globally can offer valuable insights and improve the accuracy of simulations, aiding in more effective agricultural management and planning under water-limited conditions.

Suggested Citation

  • Yao, Ning & Wei, Yingnan & Jiang, Kunhao & Liu, Jian & Li, Yi & Ran, Hui & Javed, Tehseen & Feng, Hao & Yu, Qiang & He, Jianqiang, 2025. "Nonlinear water stress response functions can improve the performance of the DSSAT-CERES-Wheat model under water deficit conditions," Agricultural Water Management, Elsevier, vol. 307(C).
    • Handle: RePEc:eee:agiwat:v:307:y:2025:i:c:s0378377424005717
    DOI: 10.1016/j.agwat.2024.109235
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377424005717
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2024.109235?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. He, Jianqiang & Jones, James W. & Graham, Wendy D. & Dukes, Michael D., 2010. "Influence of likelihood function choice for estimating crop model parameters using the generalized likelihood uncertainty estimation method," Agricultural Systems, Elsevier, vol. 103(5), pages 256-264, June.
    2. Yingnan Wei & Han Ru & Xiaolan Leng & Zhijian He & Olusola O. Ayantobo & Tehseen Javed & Ning Yao, 2022. "Better Performance of the Modified CERES-Wheat Model in Simulating Evapotranspiration and Wheat Growth under Water Stress Conditions," Agriculture, MDPI, vol. 12(11), pages 1-15, November.
    3. Aftab Wajid & Khalid Hussain & Ayesha Ilyas & Muhammad Habib-ur-Rahman & Qamar Shakil & Gerrit Hoogenboom, 2021. "Crop Models: Important Tools in Decision Support System to Manage Wheat Production under Vulnerable Environments," Agriculture, MDPI, vol. 11(11), pages 1-22, November.
    4. Schwartz, Robert C. & Domínguez, Alfonso & Pardo, José J. & Colaizzi, Paul D. & Baumhardt, R. Louis & Bell, Jourdan M., 2020. "A crop coefficient –based water use model with non-uniform root distribution," Agricultural Water Management, Elsevier, vol. 228(C).
    5. Hafiza, Barira Shoukat & Ishaque, Wajid & Osman, Raheel & Aziz, Marjan & Ata-Ul-Karim, Syed Tahir, 2022. "Simulation of wheat yield using CERES-Wheat under rainfed and supplemental irrigation conditions in a semi-arid environment," Agricultural Water Management, Elsevier, vol. 264(C).
    6. Yao, Ning & Li, Yi & Xu, Fang & Liu, Jian & Chen, Shang & Ma, Haijiao & Wai Chau, Henry & Liu, De Li & Li, Meng & Feng, Hao & Yu, Qiang & He, Jianqiang, 2020. "Permanent wilting point plays an important role in simulating winter wheat growth under water deficit conditions," Agricultural Water Management, Elsevier, vol. 229(C).
    7. Wang, Tianshu & Xu, Yanqi & Zuo, Qiang & Shi, Jianchu & Wu, Xun & Liu, Lining & Sheng, Jiandong & Jiang, Pingan & Ben-Gal, Alon, 2023. "Evaluating and improving soil water and salinity stress response functions for root water uptake," Agricultural Water Management, Elsevier, vol. 287(C).
    8. McCown, R. L. & Hammer, G. L. & Hargreaves, J. N. G. & Holzworth, D. P. & Freebairn, D. M., 1996. "APSIM: a novel software system for model development, model testing and simulation in agricultural systems research," Agricultural Systems, Elsevier, vol. 50(3), pages 255-271.
    9. Singh, Anil Kumar & Tripathy, Rojalin & Chopra, Usha Kiran, 2008. "Evaluation of CERES-Wheat and CropSyst models for water-nitrogen interactions in wheat crop," Agricultural Water Management, Elsevier, vol. 95(7), pages 776-786, July.
    10. Yan, Ling & Jin, Jiming & Wu, Pute, 2020. "Impact of parameter uncertainty and water stress parameterization on wheat growth simulations using CERES-Wheat with GLUE," Agricultural Systems, Elsevier, vol. 181(C).
    11. Homaee, M. & Feddes, R. A. & Dirksen, C., 2002. "Simulation of root water uptake: II. Non-uniform transient water stress using different reduction functions," Agricultural Water Management, Elsevier, vol. 57(2), pages 111-126, October.
    12. Si, Zhuanyun & Zain, Muhammad & Li, Shuang & Liu, Junming & Liang, Yueping & Gao, Yang & Duan, Aiwang, 2021. "Optimizing nitrogen application for drip-irrigated winter wheat using the DSSAT-CERES-Wheat model," Agricultural Water Management, Elsevier, vol. 244(C).
    13. Rallo, Giovanni & Provenzano, Giuseppe, 2013. "Modelling eco-physiological response of table olive trees (Olea europaea L.) to soil water deficit conditions," Agricultural Water Management, Elsevier, vol. 120(C), pages 79-88.
    14. Ahmed, Mukhtar & Bilal, Muhammad & Ahmad, Shakeel, 2024. "Simulation of source sink partitioning in wheat under varying nitrogen regimes using DSSAT-CERES-wheat model," Agricultural Water Management, Elsevier, vol. 303(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yingnan Wei & Han Ru & Xiaolan Leng & Zhijian He & Olusola O. Ayantobo & Tehseen Javed & Ning Yao, 2022. "Better Performance of the Modified CERES-Wheat Model in Simulating Evapotranspiration and Wheat Growth under Water Stress Conditions," Agriculture, MDPI, vol. 12(11), pages 1-15, November.
    2. Chen, Shang & He, Liang & Cao, Yinxuan & Wang, Runhong & Wu, Lianhai & Wang, Zhao & Zou, Yufeng & Siddique, Kadambot H.M. & Xiong, Wei & Liu, Manshuang & Feng, Hao & Yu, Qiang & Wang, Xiaoming & He, J, 2021. "Comparisons among four different upscaling strategies for cultivar genetic parameters in rainfed spring wheat phenology simulations with the DSSAT-CERES-Wheat model," Agricultural Water Management, Elsevier, vol. 258(C).
    3. Attia, Ahmed & El-Hendawy, Salah & Al-Suhaibani, Nasser & Alotaibi, Majed & Tahir, Muhammad Usman & Kamal, Khaled Y., 2021. "Evaluating deficit irrigation scheduling strategies to improve yield and water productivity of maize in arid environment using simulation," Agricultural Water Management, Elsevier, vol. 249(C).
    4. Yang, Cuiping & Liu, Changhong & Liu, Yanxin & Gao, Yunhe & Xing, Xuguang & Ma, Xiaoyi, 2024. "Prediction of drought trigger thresholds for future winter wheat yield losses in China based on the DSSAT-CERES-Wheat model and Copula conditional probabilities," Agricultural Water Management, Elsevier, vol. 299(C).
    5. Shen, Hongzheng & Wang, Yue & Jiang, Kongtao & Li, Shilei & Huang, Donghua & Wu, Jiujiang & Wang, Yongqiang & Wang, Yangren & Ma, Xiaoyi, 2022. "Simulation modeling for effective management of irrigation water for winter wheat," Agricultural Water Management, Elsevier, vol. 269(C).
    6. Md Rafique Ahasan Chawdhery & Murtuza Al-Mueed & Md Abdul Wazed & Shah-Al Emran & Md Abeed Hossain Chowdhury & Sk Ghulam Hussain, 2022. "Climate Change Impacts Assessment Using Crop Simulation Model Intercomparison Approach in Northern Indo-Gangetic Basin of Bangladesh," IJERPH, MDPI, vol. 19(23), pages 1-20, November.
    7. Abi Saab, Marie Therese & Todorovic, Mladen & Albrizio, Rossella, 2015. "Comparing AquaCrop and CropSyst models in simulating barley growth and yield under different water and nitrogen regimes. Does calibration year influence the performance of crop growth models?," Agricultural Water Management, Elsevier, vol. 147(C), pages 21-33.
    8. Dahri, Shahzad Hussain & Shaikh, Irfan Ahmed & Talpur, Mashooque Ali & Mangrio, Munir Ahmed & Dahri, Zakir Hussain & Hoogenboom, Gerrit & Knox, Jerry W., 2024. "Modelling the impacts of climate change on the sustainability of rainfed and irrigated maize in Pakistan," Agricultural Water Management, Elsevier, vol. 296(C).
    9. You, Yang & Wang, Yakun & Fan, Xiaodong & Dai, Qin & Yang, Guang & Wang, Wene & Chen, Dianyu & Hu, Xiaotao, 2024. "Progress in joint application of crop models and hydrological models," Agricultural Water Management, Elsevier, vol. 295(C).
    10. Negm, L.M. & Youssef, M.A. & Skaggs, R.W. & Chescheir, G.M. & Jones, J., 2014. "DRAINMOD–DSSAT model for simulating hydrology, soil carbon and nitrogen dynamics, and crop growth for drained crop land," Agricultural Water Management, Elsevier, vol. 137(C), pages 30-45.
    11. Rosa, R.D. & Ramos, T.B. & Pereira, L.S., 2016. "The dual Kc approach to assess maize and sweet sorghum transpiration and soil evaporation under saline conditions: Application of the SIMDualKc model," Agricultural Water Management, Elsevier, vol. 177(C), pages 77-94.
    12. L. Brilli & E. Lugato & M. Moriondo & B. Gioli & P. Toscano & A. Zaldei & L. Leolini & C. Cantini & G. Caruso & R. Gucci & P. Merante & C. Dibari & R. Ferrise & M. Bindi & S. Costafreda-Aumedes, 2019. "Carbon sequestration capacity and productivity responses of Mediterranean olive groves under future climates and management options," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 24(3), pages 467-491, March.
    13. Wang, Lichun & Shi, Jianchu & Zuo, Qiang & Zheng, Wenjuan & Zhu, Xiangming, 2012. "Optimizing parameters of salinity stress reduction function using the relationship between root-water-uptake and root nitrogen mass of winter wheat," Agricultural Water Management, Elsevier, vol. 104(C), pages 142-152.
    14. Jing Wang & Feng Fang & Qiang Zhang & Jinsong Wang & Yubi Yao & Wei Wang, 2016. "Risk evaluation of agricultural disaster impacts on food production in southern China by probability density method," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 83(3), pages 1605-1634, September.
    15. Unknown, 1997. "A New Soil Conservation Methodology and Application to Cropping Systems in Tropical Steeplands: A comparative synthesis of results obtained in ACIAR Project PN 9201," Technical Reports 113906, Australian Centre for International Agricultural Research.
    16. Anshuman Gunawat & Devesh Sharma & Aditya Sharma & Swatantra Kumar Dubey, 2022. "Assessment of climate change impact and potential adaptation measures on wheat yield using the DSSAT model in the semi-arid environment," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(2), pages 2077-2096, March.
    17. Probert, M. E. & Dimes, J. P. & Keating, B. A. & Dalal, R. C. & Strong, W. M., 1998. "APSIM's water and nitrogen modules and simulation of the dynamics of water and nitrogen in fallow systems," Agricultural Systems, Elsevier, vol. 56(1), pages 1-28, January.
    18. Cabelguenne, M. & Debaeke, P. & Bouniols, A., 1999. "EPICphase, a version of the EPIC model simulating the effects of water and nitrogen stress on biomass and yield, taking account of developmental stages: validation on maize, sunflower, sorghum, soybea," Agricultural Systems, Elsevier, vol. 60(3), pages 175-196, June.
    19. Enliang Guo & Jiquan Zhang & Yongfang Wang & Ha Si & Feng Zhang, 2016. "Dynamic risk assessment of waterlogging disaster for maize based on CERES-Maize model in Midwest of Jilin Province, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 83(3), pages 1747-1761, September.
    20. Mavromatis, T., 2016. "Spatial resolution effects on crop yield forecasts: An application to rainfed wheat yield in north Greece with CERES-Wheat," Agricultural Systems, Elsevier, vol. 143(C), pages 38-48.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:agiwat:v:307:y:2025:i:c:s0378377424005717. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.