In current rice breeding programs, morphological parameters such as plant height, leaf length and width, leaf angle, panicle architecture, and tiller number during the grain filling stage are used as major selection targets. However, so far, there is no robust approach to quantitatively define the optimal combinations of parameters that can lead to increased canopy radiation use efficiency (RUE). Here we report the development of a three-dimensional canopy photosynthesis model (3dCAP), which effectively combines three-dimensional canopy architecture, canopy vertical nitrogen distribution, a ray-tracing algorithm, and a leaf photosynthesis model. Concurrently, we developed an efficient workflow for the parameterization of 3dCAP. 3dCAP predicted daily canopy RUE for different nitrogen treatments of a given rice cultivar under different weather conditions. Using 3dCAP, we explored the influence of three canopy architectural parameters-tiller number, tiller angle and leaf angle-on canopy RUE. Under different weather conditions and different nitrogen treatments, canopy architecture optimized by manipulating these parameters can increase daily net canopy photosynthetic CO2 uptake by 10-52%. Generally, a smaller tiller angle was predicted for most elite rice canopy architectures, especially under scattered light conditions. Results further show that similar canopy RUE can be obtained by multiple different parameter combinations; these combinations share two common features of high light absorption by leaves in the canopy and a high level of coordination between the nitrogen concentration and the light absorbed by each leaf within the canopy. Overall, this new model has potential to be used in rice ideotype design for improved canopy RUE.
A three-dimensional canopy photosynthesis model in rice with a complete description of the canopy architecture, leaf physiology, and mechanical properties.
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作者:Chang Tian-Gen, Zhao Honglong, Wang Ning, Song Qing-Feng, Xiao Yi, Qu Mingnan, Zhu Xin-Guang
| 期刊: | Journal of Experimental Botany | 影响因子: | 5.700 |
| 时间: | 2019 | 起止号: | 2019 Apr 29; 70(9):2479-2490 |
| doi: | 10.1093/jxb/ery430 | ||
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