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 search 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. Mahboobe Ghobadi & Mahdi Gheysari & Mohammad Shayannejad & Hamze Dokoohaki, 2023. "Analyzing the Effects of Planting Date on the Uncertainty of CERES-Maize and Its Potential to Reduce Yield Gap in Arid and Mediterranean Climates," Agriculture, MDPI, vol. 13(8), pages 1-17, July.
    4. Bohan, David & Schmucki, Reto & Abay, Abrha & Termansen, Mette & Bane, Miranda & Charalabiis, Alice & Cong, Rong-Gang & Derocles, Stephane & Dorner, Zita & Forster, Matthieu & Gibert, Caroline & Harro, 2020. "Designing farmer-acceptable rotations that assure ecosystem service provision inthe face of climate change," MPRA Paper 112313, University Library of Munich, Germany.
    5. Wang, Weishu & Rong, Yao & Zhang, Chenglong & Wang, Chaozi & Huo, Zailin, 2024. "Data assimilation of soil moisture and leaf area index effectively improves the simulation accuracy of water and carbon fluxes in coupled farmland hydrological model," Agricultural Water Management, Elsevier, vol. 291(C).
    6. Lu, Yang & Chibarabada, Tendai P. & Ziliani, Matteo G. & Onema, Jean-Marie Kileshye & McCabe, Matthew F. & Sheffield, Justin, 2021. "Assimilation of soil moisture and canopy cover data improves maize simulation using an under-calibrated crop model," Agricultural Water Management, Elsevier, vol. 252(C).
    7. Heinen, Marius & Mulder, Martin & van Dam, Jos & Bartholomeus, Ruud & de Jong van Lier, Quirijn & de Wit, Janine & de Wit, Allard & Hack - ten Broeke, Mirjam, 2024. "SWAP 50 years: Advances in modelling soil-water-atmosphere-plant interactions," Agricultural Water Management, Elsevier, vol. 298(C).
    8. 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).
    9. Cai, Liping & Wang, Hui & Liu, Yanxu & Fan, Donglin & Li, Xiaoxiao, 2022. "Is potential cultivated land expanding or shrinking in the dryland of China? Spatiotemporal evaluation based on remote sensing and SVM," Land Use Policy, Elsevier, vol. 112(C).
    10. Serra, J. & Paredes, P. & Cordovil, CMdS & Cruz, S. & Hutchings, NJ & Cameira, MR, 2023. "Is irrigation water an overlooked source of nitrogen in agriculture?," Agricultural Water Management, Elsevier, vol. 278(C).
    11. Pagani, Valentina & Guarneri, Tommaso & Busetto, Lorenzo & Ranghetti, Luigi & Boschetti, Mirco & Movedi, Ermes & Campos-Taberner, Manuel & Garcia-Haro, Francisco Javier & Katsantonis, Dimitrios & Stav, 2019. "A high-resolution, integrated system for rice yield forecasting at district level," Agricultural Systems, Elsevier, vol. 168(C), pages 181-190.
    12. Mabhaudhi, Tafadzwanashe & Dirwai, Tinashe Lindel & Taguta, Cuthbert & Sikka, Alok & Lautze, Jonathan, 2023. "Mapping Decision Support Tools (DSTs) on agricultural water productivity: A global systematic scoping review," Agricultural Water Management, Elsevier, vol. 290(C).
    13. Lee, Sangchul & Qi, Junyu & McCarty, Gregory W. & Anderson, Martha & Yang, Yun & Zhang, Xuesong & Moglen, Glenn E. & Kwak, Dooahn & Kim, Hyunglok & Lakshmi, Venkataraman & Kim, Seongyun, 2022. "Combined use of crop yield statistics and remotely sensed products for enhanced simulations of evapotranspiration within an agricultural watershed," Agricultural Water Management, Elsevier, vol. 264(C).
    14. Brombacher, Joost & Silva, Isadora Rezende de Oliveira & Degen, Jelle & Pelgrum, Henk, 2022. "A novel evapotranspiration based irrigation quantification method using the hydrological similar pixels algorithm," Agricultural Water Management, Elsevier, vol. 267(C).
    15. Bai, Tiecheng & Zhang, Nannan & Wang, Tao & Wang, Desheng & Yu, Caili & Meng, Wenbo & Fei, Hao & Chen, Rengu & Li, Yanhui & Zhou, Baoping, 2021. "Simulating on the effects of irrigation on jujube tree growth, evapotranspiration and water use based on crop growth model," Agricultural Water Management, Elsevier, vol. 243(C).
    16. 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).
    17. 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.
    18. Xiulu Sun & Yizan Li & Marius Heinen & Henk Ritzema & Petra Hellegers & Jos van Dam, 2022. "Fertigation Strategies to Improve Water and Nitrogen Use Efficiency in Surface Irrigation System in the North China Plain," Agriculture, MDPI, vol. 13(1), pages 1-23, December.
    19. Kakoulaki, G. & Gonzalez Sanchez, R. & Gracia Amillo, A. & Szabo, S. & De Felice, M. & Farinosi, F. & De Felice, L. & Bisselink, B. & Seliger, R. & Kougias, I. & Jaeger-Waldau, A., 2023. "Benefits of pairing floating solar photovoltaics with hydropower reservoirs in Europe," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).
    20. 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).

    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.