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A coupled model of leaf photosynthesis, stomatal conductance, and leaf energy balance for chrysanthemum (Dendranthema grandiflora)

机译:菊花叶片光合作用,气孔导度和叶片能量平衡的耦合模型

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While dynamic greenhouse climatic regimes are often applied to achieve energy efficiency, dynamic mechanistic models can assist in climate control decisions, and to elucidate plant stress under extreme microclimatic conditions. The present study developed a couple model with three integrated sub models to predict net leaf photosynthesis (P-n1), stomatal conductance (g(s)), and leaf temperature under different microclimatic conditions: (1) a C-3 photosynthesis biochemical model; (2) a stomatal conductance model; and (3) a leaf energy balance model. Leaf photochemical efficiency and maximum gross photosynthesis using a negative exponential light response curve were modelled with different leaf temperatures, light levels, and CO2 concentrations. The stomatal conductance and leaf energy balance models were calibrated independently. P-n1, g(s), and leaf temperature model predictions were validated with independent measurements and climate input data. Model performance was evaluated by a linear regression of predicted values relative to observed values. The coupled model estimated P-nl with a 2-12% mean difference between the observed and the model, and a 1.82 degrees C maximum leaf temperature difference between the observed and the model. An additional stomatal model was implemented for comparison, and tested against the model system. Our model showed a better fit to P-n1, leaf temperature, and stomatal conductance validation data. The coupled model was therefore a good predictor for crop growth and microclimate. We suggest a multi-model approach with self-selective sub-models to assist in decisions optimising light, temperature, and CO2 for maximum photosynthetic rates for climatic conditions applied in the model (i.e. high light, temperature, and CO2 concentration). Furthermore, the model leaf temperature prediction could be used for leaf temperature monitoring under unfavorable microclimatic conditions. (C) 2016 Elsevier B.V. All rights reserved.
机译:虽然通常采用动态温室气候制度来实现能源效率,但是动态机械模型可以帮助做出气候控制决策,并阐明极端微气候条件下的植物胁迫。本研究开发了具有三个集成子模型的夫妇模型,以预测不同微气候条件下的净叶片光合作用(P-n1),气孔导度(g(s))和叶片温度:(1)C-3光合作用生化模型; (2)气孔导度模型; (3)叶片能量平衡模型。使用不同的叶片温度,光照水平和CO2浓度对使用负指数光响应曲线的叶片光化学效率和最大总光合作用进行建模。气孔导度和叶片能量平衡模型是独立校准的。 P-n1,g(s)和叶片温度模型的预测已通过独立的测量和气候输入数据进行了验证。通过将预测值相对于观察值进行线性回归来评估模型性能。耦合模型估计P-nl,观察到的模型之间的平均差为2-12%,观察到的模型之间的最大叶温差为1.82摄氏度。实施了另一个气孔模型进行比较,并针对模型系统进行了测试。我们的模型显示出更适合P-n1,叶片温度和气孔电导率验证数据。因此,耦合模型是作物生长和小气候的良好预测指标。我们建议采用具有自选子模型的多模型方法,以协助决策优化光,温度和CO2,以使模型中所应用的气候条件(即高光,温度和CO2浓度)达到最大光合速率。此外,模型叶片温度预测可用于不利的微气候条件下的叶片温度监测。 (C)2016 Elsevier B.V.保留所有权利。

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