IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v473y2022ics0304380022002472.html
   My bibliography  Save this article

Yield estimation of Lycium barbarum L. based on the WOFOST model

Author

Listed:
  • Shi, Yinfang
  • Wang, Zhaoyang
  • Hou, Cheng
  • Zhang, Puhan

Abstract

Previous research about crop growth simulation and yield prediction mainly focused on annual crops with very few such studies relating to perennial fruit tree crops. In this study, field experiments were conducted in different growth periods of Lycium barbarum L. in Gansu province, China to determine and improve the initial WOFOST model parameters with combination of the time-series leaf area index (LAI) obtained from Sentinel-2 satellite and predict yields for L. barbarum shrubs at a field scale for summer fruit (SF) and autumn fruit (AF). The results showed that the performance of initial parameters established by previous research and partial measured data was relatively poor, with a relative error (RE) of 20.95% for annual yield. The simulation after model parameter calibration significantly improved model prediction accuracy. The predicted SF was 2588 kg/ha and AF was 601 kg/ha with a RE of -5.51%. This study provided a strategy to improve modelled yield for fruit tree crops through utilization of remote sensed and field measured data in model parameter calibration, which on the other hand provides useful information for regional development planning and crops marketing.

Suggested Citation

  • Shi, Yinfang & Wang, Zhaoyang & Hou, Cheng & Zhang, Puhan, 2022. "Yield estimation of Lycium barbarum L. based on the WOFOST model," Ecological Modelling, Elsevier, vol. 473(C).
    • Handle: RePEc:eee:ecomod:v:473:y:2022:i:c:s0304380022002472
    DOI: 10.1016/j.ecolmodel.2022.110146
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380022002472
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2022.110146?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Li, Yan & Zhou, Qingguo & Zhou, Jian & Zhang, Gaofeng & Chen, Chong & Wang, Jing, 2014. "Assimilating remote sensing information into a coupled hydrology-crop growth model to estimate regional maize yield in arid regions," Ecological Modelling, Elsevier, vol. 291(C), pages 15-27.
    2. Zhao, Yanxia & Chen, Sining & Shen, Shuanghe, 2013. "Assimilating remote sensing information with crop model using Ensemble Kalman Filter for improving LAI monitoring and yield estimation," Ecological Modelling, Elsevier, vol. 270(C), pages 30-42.
    3. de Wit, Allard & Boogaard, Hendrik & Fumagalli, Davide & Janssen, Sander & Knapen, Rob & van Kraalingen, Daniel & Supit, Iwan & van der Wijngaart, Raymond & van Diepen, Kees, 2019. "25 years of the WOFOST cropping systems model," Agricultural Systems, Elsevier, vol. 168(C), pages 154-167.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Zhaoyang & Shi, Yinfang & Hou, Cheng & Zhang, Puhan, 2024. "Sensitivity analysis of simulated Lycium barbarum L. yield in the WOFOST model under different climate conditions," Ecological Modelling, Elsevier, vol. 488(C).

    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. Zhang, Yuliang & Wu, Zhiyong & Singh, Vijay P. & He, Hai & He, Jian & Yin, Hao & Zhang, Yaxin, 2021. "Coupled hydrology-crop growth model incorporating an improved evapotranspiration module," Agricultural Water Management, Elsevier, vol. 246(C).
    2. Ji, Zhonglin & Pan, Yaozhong & Li, Nan, 2021. "Integrating the temperature vegetation dryness index and meteorology parameters to dynamically predict crop yield with fixed date intervals using an integral regression model," Ecological Modelling, Elsevier, vol. 455(C).
    3. Chevuru, Sneha & Lamsal, Gambhir & (Rens) van Beek, L.P.H. & van Vliet, Michelle T.H. & Marston, Landon & Bierkens, Marc F.P., 2025. "Comparing crop growth models across the contiguous USA with a focus on dry and warm spells," Agricultural Water Management, Elsevier, vol. 311(C).
    4. Lu, Yang & Wei, Chunzhu & McCabe, Matthew F. & Sheffield, Justin, 2022. "Multi-variable assimilation into a modified AquaCrop model for improved maize simulation without management or crop phenology information," Agricultural Water Management, Elsevier, vol. 266(C).
    5. Mulders, Puck J.A.M. & van Zutphen, Menno J.T.C. & Ravensbergen, Arie P.P. & Cobbenhagen, A.T.J.R. & van den Heuvel, Edwin R. & van de Molengraft, M.J.G. & Reidsma, Pytrik & Antunes, Duarte Guerreiro , 2025. "Spatial and temporal optimization of potato planting based on on-farm collected data and field experiments," Agricultural Systems, Elsevier, vol. 225(C).
    6. Shen Tan & Shengchao Qiao & Han Wang & Sheng Chang, 2024. "Predicting Wheat Potential Yield in China Based on Eco-Evolutionary Optimality Principles," Agriculture, MDPI, vol. 14(11), pages 1-15, November.
    7. Feng, Dingrui & Li, Guangyong & Wang, Dan & Wulazibieke, Mierguli & Cai, Mingkun & Kang, Jing & Yuan, Zicheng & Xu, Houcheng, 2022. "Evaluation of AquaCrop model performance under mulched drip irrigation for maize in Northeast China," Agricultural Water Management, Elsevier, vol. 261(C).
    8. Mary Ollenburger & Page Kyle & Xin Zhang, 2022. "Uncertainties in estimating global potential yields and their impacts for long-term modeling," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 14(5), pages 1177-1190, October.
    9. Zainali, Sebastian & Lu, Silvia Ma & Fernández-Solas, Álvaro & Cruz-Escabias, Alejandro & Fernández, Eduardo F. & Zidane, Tekai Eddine Khalil & Honningdalsnes, Erlend Hustad & Nygård, Magnus Moe & Lel, 2025. "Modelling, simulation, and optimisation of agrivoltaic systems: a comprehensive review," Applied Energy, Elsevier, vol. 386(C).
    10. Zhang, Yuxi & Walker, Jeffrey P. & Pauwels, Valentijn R.N., 2022. "Assimilation of wheat and soil states for improved yield prediction: The APSIM-EnKF framework," Agricultural Systems, Elsevier, vol. 201(C).
    11. Melpomeni Nikou & Theodoros Mavromatis, 2023. "Demonstrating the Use of the Yield-Gap Concept on Crop Model Calibration in Data-Poor Regions: An Application to CERES-Wheat Crop Model in Greece," Land, MDPI, vol. 12(7), pages 1-19, July.
    12. Rubo, Samantha & Zinkernagel, Jana, 2025. "Enhancing the prediction of irrigation demand for open field vegetable crops in Germany through neural networks, transfer learning, and ensemble models," Agricultural Water Management, Elsevier, vol. 312(C).
    13. Muhammad Alkaff & Abdullah Basuhail & Yuslena Sari, 2025. "Optimizing Water Use in Maize Irrigation with Reinforcement Learning," Mathematics, MDPI, vol. 13(4), pages 1-21, February.
    14. Dhakar, Rajkumar & Sehgal, Vinay Kumar & Chakraborty, Debasish & Sahoo, Rabi Narayan & Mukherjee, Joydeep & Ines, Amor V.M. & Kumar, Soora Naresh & Shirsath, Paresh B. & Roy, Somnath Baidya, 2022. "Field scale spatial wheat yield forecasting system under limited field data availability by integrating crop simulation model with weather forecast and satellite remote sensing," Agricultural Systems, Elsevier, vol. 195(C).
    15. Batista, Fabiana de Souza & Duku, Confidence & Hein, Lars, 2023. "Deforestation-induced changes in rainfall decrease soybean-maize yields in Brazil," Ecological Modelling, Elsevier, vol. 486(C).
    16. Gardner, A.S. & Gaston, K.J. & Maclean, I.M.D., 2021. "Combining qualitative and quantitative methodology to assess prospects for novel crops in a warming climate," Agricultural Systems, Elsevier, vol. 190(C).
    17. Dhouib, M. & Zitouna-Chebbi, R. & Prévot, L. & Molénat, J. & Mekki, I. & Jacob, F., 2022. "Multicriteria evaluation of the AquaCrop crop model in a hilly rainfed Mediterranean agrosystem," Agricultural Water Management, Elsevier, vol. 273(C).
    18. Hamze, Mohamad & Cheviron, Bruno & Baghdadi, Nicolas & Lo, Madiop & Courault, Dominique & Zribi, Mehrez, 2023. "Detection of irrigation dates and amounts on maize plots from the integration of Sentinel-2 derived Leaf Area Index values in the Optirrig crop model," Agricultural Water Management, Elsevier, vol. 283(C).
    19. Mohamed Sallah, Abdoul-Hamid & Tychon, Bernard & Piccard, Isabelle & Gobin, Anne & Van Hoolst, Roel & Djaby, Bakary & Wellens, Joost, 2019. "Batch-processing of AquaCrop plug-in for rainfed maize using satellite derived Fractional Vegetation Cover data," Agricultural Water Management, Elsevier, vol. 217(C), pages 346-355.
    20. Linker, Raphael, 2021. "Stochastic model-based optimization of irrigation scheduling," Agricultural Water Management, Elsevier, vol. 243(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:ecomod:v:473:y:2022:i:c:s0304380022002472. 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.journals.elsevier.com/ecological-modelling .

    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.