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首页> 外文期刊>Journal of Experimental Botany >Experimental and modeling evidence of carbon limitation of leaf appearance rate for spring and winter wheat
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Experimental and modeling evidence of carbon limitation of leaf appearance rate for spring and winter wheat

机译：春冬小麦叶片外观率碳限制的实验和建模证据

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摘要

Accurate predictions of the timing of physiological stages and the development rate are crucial for predicting crop performance under field conditions. Plant development is controlled by the leaf appearance rate (LAR) and our understanding of how LAR responds to environmental factors is still limited. Here, we tested the hypothesis that carbon availability may account for the effects of irradiance, photoperiod, atmospheric CO2 concentration, and ontogeny on LAR. We conducted three experiments in growth chambers to quantify and disentangle these effects for both winter and spring wheat cultivars. Variations of LAR observed between environmental scenarios were well explained by the supply/demand ratio for carbon, quantified using the photothermal quotient. We therefore developed an ecophysiological model based on the photothermal quotient that accounts for the effects of temperature, irradiance, photoperiod, and ontogeny on LAR. Comparisons of observed leaf stages and LAR with simulations from our model, from a linear thermal-time model, and from a segmented linear thermal-time model corrected for sowing date showed that our model can simulate the observed changes in LAR in the field with the lowest error. Our findings demonstrate that a hypothesis-driven approach that incorporates more physiology in specific processes of crop models can increase their predictive power under variable environments.
机译：精确预测生理阶段的时序和开发速度对于预测现场条件下的作物性能至关重要。植物开发由叶片外观率（LAR）控制，我们对响应环境因素的理解仍然有限。在这里，我们测试了碳可用性可能考虑辐照度，光周期，大气二氧化碳二氧化碳浓度和组来的影响的假设。我们在增长室进行了三次实验，以量化和解散冬季和春小麦品种的影响。通过使用光热源量化的碳的供应/需求比率，在环境情景之间观察到的LAR的变化很好地解释。因此，我们基于光热商开发了一种生态学模型，其考虑了温度，辐照度，光周期和组来的影响。观察到的叶片阶段和来自我们模型的模拟的比较，从线性热时间模型以及从播种日期进行校正的分段线性热时间模型表明，我们的模型可以模拟现场中的观察到的变化最低错误。我们的研究结果表明，在作物模型的特定过程中包含更多生理学的假设驱动方法可以在可变环境下增加其预测力。

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