Physiological assumptions incorporated in crop models need improvement to more realistically represent responses to heat stress, rising CO_2, and genetic diversity. Coupling of leaf-level photosynthesis and stomatal conductance sub-models within an energy balance has been proposed to improve gas exchange predictions underlying many of these responses. The purpose of this study was to calibrate model subcomponents, assess individual- and coupled- sub-model performance, integrate this methodology with the ORYZA rice model, and evaluate the ability of the original and modified model to accurately simulate responses at canopy level using soil–plant-atmosphere research (SPAR) chamber data, with respect to plant age, CO₂ concentration, and short-term high heat exposure. Individual and coupled sub-models were more accurate for leaf rice photosynthesis (average R² of 0.81) than stomatal conductance or transpiration (average R² of 0.46 and 0.77 respectively) when averaged across all treatments and measurement dates. Photosynthetic parameter V_cmac25 linearly decreased over the growing season, while J_max25 and T_p were relatively constant over time and between CO₂ levels, until about 50% heading when declines between 25% and 45% were observed through the beginning of grain filling. Diurnal and daily canopy net photosynthesis estimates were closer to observed values (ambient CO₂. R² = 0.71, elevated CO₂. R² = 0.70, P < 0.001) using this coupled approach compared with the original ORYZA model (ambient CO₂. R² = 0.263, elevated CO₂. R² = 0.420, P < 0.01). Canopy transpiration results were similar between the two approaches at 28 °C, but the energy balance method showed more realistic responses to elevated CO₂ and warming temperature. These results indicated the use of a coupled energy balance model can more accurately predict rice gas exchange processes compared to uncoupled photosynthesis/transpiration methods when simulating responses to different CO₂ and temperature conditions, and thus may provide more realistic assessments of current and future climate impacts on rice production.

作物模型中包含的生理假设需要改进，以便更真实地表示对热胁迫，CO ₂升高和遗传多样性的反应。已经提出在能量平衡内将叶级光合作用和气孔导度子模型耦合，以改善许多此类响应下的气体交换预测。这项研究的目的是校准模型子组件，评估单个模型和耦合子模型的性能，将此方法与ORYZA水稻模型整合，以及评估原始模型和改良模型使用土壤准确模拟冠层水平响应的能力–关于植物年龄，CO _2的植物大气研究（SPAR）室数据集中，以及短期高热量暴露。当对所有处理和测量日期进行平均时，单个子模型和耦合子模型的叶水稻光合作用（平均R ²为0.81）比气孔导度或蒸腾量（平均R ²分别为0.46和0.77）更准确。光合参数V _cmac25在整个生长期呈线性下降，而J _max25和T _p在一段时间内以及在CO ₂之间相对恒定直到灌浆开始时观察到下降幅度在25％到45％之间为止，直到大约50％的抽穗。日变化和每日冠层净光合估计是更接近观测值（环境CO ₂：R ²  = 0.71，升高CO ₂的。R ² 使用此耦合方法与原始ORYZA模型（环境CO相比= 0.70，P <0.001）₂。 [R ²  = 0.263，升高CO ₂：R ²  = 0.420，P <0.01）。两种方法在28°C下的冠层蒸腾结果相似，但是能量平衡方法显示出对CO ₂升高的更现实的响应和升温温度。这些结果表明，在模拟对不同CO ₂和温度条件的响应时，与非耦合光合作用/蒸腾方法相比，使用耦合能量平衡模型可以更准确地预测稻米气体交换过程，因此可以提供对当前和未来气候影响的更实际评估。在水稻生产上。