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

IF 7.6 1区 农林科学 Q1 AGRONOMY Plant Phenomics Pub Date : 2024-09-17 eCollection Date: 2024-01-01 DOI:10.34133/plantphenomics.0253
Shiwei Ruan, Hong Cao, Shangrong Wu, Yujing Ma, Wenjuan Li, Yong Jin, Hui Deng, Guipeng Chen, Wenbin Wu, Peng Yang
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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.

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基于作物生长模型的考虑非叶面绿色器官的油菜产量估算
为解决传统基于作物模型的禾本科作物产量模拟方法对油菜产量的低估问题,本研究利用WOFOST作物模型,基于2年的田间实测数据,考虑非叶面绿色器官硅油的光合作用,对湘南主产区的油菜产量进行了估算。首先,提出了考虑硅藻光合作用的总光合面积指数(TPAI)代替叶面积指数(LAI)作为模型的标定变量。随后提出了两种考虑硅藻光合作用的参数校准方法:基于TPAI耦合特定荚果面积的TPAI- spa法和基于TPAI和曲线拟合的TPAI- curve法。最后,通过2年的油菜观测数据对所提出的2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方法可以提高油菜产量估算的精度。TPAI-SPA法和TPAI-Curve法对贮藏器官总重(TWSO)和地上生物量(TAGP)的估计精度(r2)分别提高了9.68%和49.86%和14.04%和42.94%。因此,本文提出的两种校正方法对于提高油菜产量模拟的准确性具有重要的现实意义。本研究为利用作物生长模型估算油料作物产量提供了一种新的技术途径。
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来源期刊
Plant Phenomics
Plant Phenomics Multiple-
CiteScore
8.60
自引率
9.20%
发文量
26
审稿时长
14 weeks
期刊介绍: Plant Phenomics is an Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and published by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. The mission of Plant Phenomics is to publish novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data analytics. Plant Phenomics aims also to connect phenomics to other science domains, such as genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics, and computer sciences. Plant Phenomics should thus contribute to advance plant sciences and agriculture/forestry/horticulture by addressing key scientific challenges in the area of plant phenomics. The scope of the journal covers the latest technologies in plant phenotyping for data acquisition, data management, data interpretation, modeling, and their practical applications for crop cultivation, plant breeding, forestry, horticulture, ecology, and other plant-related domains.
期刊最新文献
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