Abstract：
Radiation use efficiency (RUE) is a key crop adaptation trait that quantifies the potential amount of aboveground biomass produced by the crop per unit of solar energy intercepted. But it is unclear why elite maize and grain sorghum hybrids differ in their RUE at the crop level. Here, we used a non-traditional top-down approach via canopy photosynthesis modelling to identify leaf-level photosynthetic traits that are key to differences in crop-level RUE. A novel photosynthetic response measurement was developed and coupled with use of a Bayesian model fitting procedure, incorporating a C4 leaf photosynthesis model, to infer cohesive sets of photosynthetic parameters by simultaneously fitting responses to CO2 , light, and temperature. Statistically significant differences between leaf photosynthetic parameters of elite maize and grain sorghum hybrids were found across a range of leaf temperatures, in particular for effects on the quantum yield of photosynthesis, but also for the maximum enzymatic activity of Rubisco and PEPc. Simulation of diurnal canopy photosynthesis predicted that the leaf-level photosynthetic low-light response and its temperature dependency are key drivers of the performance of crop-level RUE, generating testable hypotheses for further physiological analysis and bioengineering applications.
摘要：
辐射利用效率（RUE）是一种关键的作物适应性状，它可以量化每单位截获的太阳能作物产生的地上生物量的潜在数量。但目前尚不清楚为什么优良玉米和谷物高粱杂种在作物水平上的RUE不同。这里,我们通过冠层光合作用建模使用了非传统的自上而下的方法来识别叶片水平的光合性状，这些性状是作物水平RUE差异的关键。开发了一种新颖的光合响应测量方法，并结合了贝叶斯模型拟合程序，结合C4叶片光合作用模型，通过同时拟合对CO2的响应来推断光合参数的粘性集，光,和温度。在一系列叶片温度下，发现优良玉米和谷物高粱杂种的叶片光合参数之间存在统计学上的显着差异，特别是对光合作用的量子产率的影响，而且还用于Rubisco和PEPc的最大酶活性。昼夜冠层光合作用的模拟预测，叶片水平光合弱光响应及其温度依赖性是作物水平RUE表现的关键驱动因素，为进一步的生理分析和生物工程应用生成稳定的假设。
