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

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  • 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.

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
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    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).

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