Gu Shenghao, Wen Weiliang, Xu Tianjun, Lu Xianju, Yu Zetao, Guo Xinyu, Zhao Chunjiang
Information Technology Research Center, Beijing Academy of Agriculture Forestry Sciences, Beijing, China.
Beijing Key Laboratory of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China.
Front Plant Sci. 2022 Aug 18;13:735981. doi: 10.3389/fpls.2022.735981. eCollection 2022.
Canopy photosynthesis integrates leaf functional and structural traits in space and time and correlates positively with yield formation. Many models with different levels of architectural details ranging from zero-dimensional (0D) to three-dimensional (3D) have been developed to simulate canopy light interception and photosynthesis. Based on these models, a crop growth model can be used to assess crop yield in response to genetic improvement, optimized practices, and environmental change. However, to what extent do architectural details influence light interception, photosynthetic production, and grain yield remains unknown. Here, we show that a crop growth model with high-resolution upscaling approach in space reduces the departure of predicted yield from actual yield and refines the simulation of canopy photosynthetic production. We found crop yield predictions decreased by 12.0-48.5% with increasing the resolution of light simulation, suggesting that a crop growth model without architectural details may result in a considerable departure from the actual photosynthetic production. A dramatic difference in light interception and photosynthetic production of canopy between cultivars was captured by the proposed 3D model rather than the 0D, 1D, and 2D models. Furthermore, we found that the overestimation of crop yield by the 0D model is caused by the overestimation of canopy photosynthetically active radiation (PAR) interception and the RUE and that by the 1D and 2D model is caused by the overestimated canopy photosynthesis rate that is possibly related to higher predicted PAR and fraction of sunlit leaves. Overall, this study confirms the necessity of taking detailed architecture traits into consideration when evaluating the strategies of genetic improvement and canopy configuration in improving crop yield by crop modeling.
冠层光合作用在空间和时间上整合了叶片功能和结构特征,并与产量形成呈正相关。已经开发了许多具有不同架构细节水平(从零维(0D)到三维(3D))的模型来模拟冠层光截获和光合作用。基于这些模型,作物生长模型可用于评估作物产量对遗传改良、优化措施和环境变化的响应。然而,架构细节在多大程度上影响光截获、光合生产和谷物产量仍不清楚。在这里,我们表明,采用空间高分辨率向上扩展方法的作物生长模型减少了预测产量与实际产量的偏差,并改进了冠层光合生产的模拟。我们发现,随着光模拟分辨率的提高,作物产量预测下降了12.0 - 48.5%,这表明没有架构细节的作物生长模型可能会导致与实际光合生产有相当大的偏差。所提出的3D模型而非0D、1D和二维模型捕捉到了不同品种间冠层光截获和光合生产的显著差异。此外,我们发现0D模型对作物产量的高估是由于对冠层光合有效辐射(PAR)截获和辐射利用效率(RUE)的高估,而1D和二维模型的高估是由于冠层光合速率的高估,这可能与更高的预测PAR和受光叶片比例有关。总体而言,本研究证实了在通过作物建模评估遗传改良策略和冠层配置以提高作物产量时,考虑详细架构特征的必要性。