Ruan Shiwei, Cao Hong, Wu Shangrong, Ma Yujing, Li Wenjuan, Jin Yong, Deng Hui, Chen Guipeng, Wu Wenbin, Yang Peng
School of Information and Communication Engineering, North University of China, Taiyuan 030051, China.
State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China (the Institute of Agricultural Resources and Regional Planning), Chinese Academy of Agricultural Sciences, Beijing 100081, China.
Plant Phenomics. 2024 Sep 17;6:0253. doi: 10.34133/plantphenomics.0253. eCollection 2024.
To address the underestimation of rape yield by traditional gramineous crop yield simulation methods based on crop models, this study used the WOFOST crop model to estimate rape yield in the main producing areas of southern Hunan based on 2 years of field-measured data, with consideration given to the photosynthesis of siliques, which are non-foliar green organs. First, the total photosynthetic area index (TPAI), which considers the photosynthesis of siliques, was proposed as a substitute for the leaf area index (LAI) as the calibration variable in the model. Two parameter calibration methods were subsequently proposed, both of which consider photosynthesis by siliques: the TPAI-SPA method, which is based on the TPAI coupled with a specific pod area, and the TPAI-Curve method, which is based on the TPAI and curve fitting. Finally, the 2 proposed parameter calibration methods were validated via 2 years of observed rape data. The results indicate that compared with traditional LAI-based crop model calibration methods, the TPAI-SPA and TPAI-Curve methods can improve the accuracy of rape yield estimation. The estimation accuracy ( ) for the total weight of storage organs (TWSO) and above-ground biomass (TAGP) increased by 9.68% and 49.86%, respectively, for the TPAI-SPA method and by 14.04% and 42.94%, respectively, for the TPAI-Curve method. Thus, the 2 calibration methods proposed in this study are of important practical importance for improving the accuracy of rape yield simulations. This study provides a novel technical approach for utilizing crop growth models in the yield estimation of oilseed crops.
为解决基于作物模型的传统禾本科作物产量模拟方法对油菜产量估计不足的问题，本研究基于两年的田间实测数据，利用WOFOST作物模型对湘南主要产区的油菜产量进行估计，同时考虑了角果（非叶绿色器官）的光合作用。首先，提出了考虑角果光合作用的总光合面积指数（TPAI），以替代叶面积指数（LAI）作为模型中的校准变量。随后提出了两种参数校准方法，均考虑了角果的光合作用：基于TPAI与特定荚果面积相结合的TPAI-SPA方法，以及基于TPAI和曲线拟合的TPAI-Curve方法。最后，通过两年的油菜实测数据对所提出的两种参数校准方法进行了验证。结果表明，与传统的基于LAI的作物模型校准方法相比，TPAI-SPA和TPAI-Curve方法能够提高油菜产量估计的准确性。TPAI-SPA方法对贮藏器官总重量（TWSO）和地上生物量（TAGP）的估计准确率（ ）分别提高了9.68%和49.86%，TPAI-Curve方法分别提高了14.04%和42.94%。因此，本研究提出的两种校准方法对于提高油菜产量模拟的准确性具有重要的实际意义。本研究为利用作物生长模型进行油料作物产量估计提供了一种新的技术途径。
