Bridging Photosynthesis and Crop Yield Formation with a Mechanistic Model of Whole Plant Carbon-Nitrogen Interaction

IF 2.6 Q1 AGRONOMY
Tianxin Chang, Zhongwei Wei, Zai Shi, Yi Xiao, Honglong Zhao, Shuoqi Chang, Mingnan Qu, Qingfeng Song, Faming Chen, Fenfen Miao, Xinguang Zhu
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Abstract

Crop yield is determined by potential harvest organ size, source organ photosynthesis, and carbohydrate partitioning. Filling the harvest organ efficiently remains a challenge. Here, we developed a kinetic model of rice grain filling, which scales from the primary biochemical and biophysical processes of photosynthesis to whole-plant carbon and nitrogen dynamics. The model reproduces the rice yield formation process under different environmental and genetic perturbations. In silico screening identified a range of post-anthesis targets—both established and novel—that can be manipulated to enhance rice yield. Remarkably, we pinpointed the stability of grain filling rate from flowering to harvest as a critical factor for maximizing grain yield. This finding was further validated in two independent super-high yielding rice cultivars, each yielding approximately 21 t ha -1 of rough rice at 14% moisture content. Furthermore, we revealed that stabilizing the grain filling rate could lead to a potential yield increase of around 30-40% in an elite rice cultivar. Notably, the cumulative grain filling rates around 15- and 38-days post-flowering significantly influence grain yield, and we introduced an innovative in situ approach using ear respiratory rates for precise quantification of these rates. We finally derived an equation to predict maximum dried brown rice yield (Y, t ha -1) of a cultivar based on its potential gross photosynthetic accumulation from flowering to harvest (Apc, t CO2 ha -1): Y = 0.74 * Apc + 1.9. Overall, this work establishes a framework for quantitatively dissecting crop physiology and designing high-yielding ideotypes.
利用全植物碳氮相互作用的机理模型桥接光合作用和作物产量形成
作物产量由潜在收获器官大小、源器官光合作用和碳水化合物分配决定。有效地填充收获器官仍然是一个挑战。在这里,我们开发了一个水稻籽粒灌浆的动力学模型,该模型从光合作用的主要生化和生物物理过程扩展到整个植物的碳和氮动力学。该模型再现了不同环境和遗传扰动下的水稻产量形成过程。计算机筛选确定了一系列花后靶标,既有已建立的靶标,也有新的靶标,这些靶标可以用来提高水稻产量。值得注意的是,我们指出,从开花到收获,灌浆速率的稳定性是最大限度提高粮食产量的关键因素。这一发现在两个独立的超高产水稻品种中得到了进一步验证,每个品种在14%的水分含量下都能生产约21吨公顷的糙米。此外,我们发现,稳定灌浆速率可以使优质水稻品种的潜在产量增加约30-40%。值得注意的是,开花后15天和38天左右的累积灌浆速率显著影响粮食产量,我们引入了一种创新的原位方法,使用穗呼吸速率来精确量化这些速率。最后,我们根据一个品种从开花到收获的潜在总光合积累(Apc,tCO2 ha-1),推导出了一个预测其最大糙米干产量(Y,t ha-1)的方程:Y=0.74*Apc+1.9。总的来说,这项工作为定量剖析作物生理学和设计高产理想型建立了一个框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
in silico Plants
in silico Plants Agricultural and Biological Sciences-Agronomy and Crop Science
CiteScore
4.70
自引率
9.70%
发文量
21
审稿时长
10 weeks
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