LEPSE, Université Montpellier, INRA, Montpellier SupAgro, Montpellier, France.
The New Zealand Institute for Plant & Food Research Limited, Private Bag, Christchurch, New Zealand.
J Exp Bot. 2019 Apr 29;70(9):2449-2462. doi: 10.1093/jxb/erz012.
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.
准确预测生理阶段的时间和发育速率对于预测田间条件下的作物表现至关重要。植物的发育受叶出现率(LAR)的控制,而我们对 LAR 如何响应环境因素的理解仍然有限。在这里,我们检验了这样一个假设,即碳供应可能解释光照、光周期、大气 CO2 浓度和个体发育对 LAR 的影响。我们在生长室中进行了三项实验,以量化和区分冬小麦和春小麦品种在不同环境场景下的这些影响。使用光热商来量化碳的供应/需求比,可以很好地解释 LAR 在环境场景之间观察到的变化。因此,我们开发了一种基于光热商的生理生态模型,该模型考虑了温度、光照、光周期和个体发育对 LAR 的影响。将我们模型的模拟结果与观察到的叶片阶段和 LAR 与线性热时间模型和修正播种日期的分段线性热时间模型的模拟结果进行比较表明,我们的模型可以用最低的误差模拟田间观察到的 LAR 变化。我们的研究结果表明,一种假设驱动的方法,将更多的生理学纳入作物模型的特定过程中,可以提高其在多变环境下的预测能力。