Rape Yield Estimation Considering Non-Foliar Green Organs Based on the General Crop Growth Model.

Abstract

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 ( R 2 ) 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.

Plant phenotyping relevance

非葉部器官の光合成を組み込んだTPAIを新たな校正変数として提案し、作物モデルによる収量・バイオマス推定法を開発・検証しているため、表現型推定手法が中心である。

Code and data availability

The paper's Data Availability statement explicitly deposits the authors' TPAI-SPA and TPAI-Curve calibration code/training scripts for the WOFOST rape yield estimation on GitHub at a public URL, which is an allowed URL and matches the paper's computational analysis.