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Incorporating cultivar-specific stomatal traits into stomatal conductance models improves the estimation of evapotranspiration enhancing greenhouse climate management
Biosystems Engineering ( IF 4.4 ) Pub Date : 2021-06-09 , DOI: 10.1016/j.biosystemseng.2021.05.010
Oliver Körner , Dimitrios Fanourakis , Michael Chung-Rung Hwang , Benita Hyldgaard , Georgios Tsaniklidis , Nikolaos Nikoloudakis , Dorthe Horn Larsen , Carl-Otto Ottosen , Eva Rosenqvist

The effect of considering cultivar differences in stomatal conductance (gs) on relative air humidity (RH)-related energy demand was addressed. We conducted six experiments in order to study the variation in evapotranspiration (ETc) of six pot rose cultivars, investigate the underlying processes and parameterise a gs-based ETc model. Several levels of crop ETc were realised by adjusting the growth environment. The commonly applied Ball–Woodrow–Berry gs-sub-model (BWB-model) in ETc models was validated under greenhouse conditions, and showed a close agreement between simulated and measured ETc. The validated model was incorporated into a greenhouse simulator. A scenario simulation study showed that selecting low-gs cultivars reduces energy demand (≤5.75%), depending on the RH set point. However, the BWB-model showed poor prediction quality at RH lower than 60% and a good fit at higher RH. Therefore, an attempt was made to improve model prediction: the in situ-obtained data were employed to adapt and extend either the BWB-model, or the Liu-extension with substrate water potential (Ψ; BWB-Liu-model). Both models were extended with stomatal density (Ds) or pore area. Although the modified BWB-Liu-model (considering Ds) allowed higher accuracy (R2 = 0.59), as compared to the basic version (R2 = 0.31), the typical lack of Ψ prediction in greenhouse models may be problematic for implementation into real-time climate control. The current study lays the basis for the development of cultivar specific cultivation strategies as well as improving the gs sub-model for dynamic climate conditions under low RH using model-based control systems.



中文翻译:

将品种特定的气孔特征纳入气孔导度模型可改善蒸散量的估计,从而增强温室气候管理

解决了考虑气孔导度 (g s ) 的品种差异对相对空气湿度 (RH) 相关能量需求的影响。我们进行了六个实验以研究六个盆栽玫瑰栽培品种的蒸散量 (ET c )的变化,调查潜在过程并参数化基于ag s的 ET c模型。通过调整生长环境,实现了作物ET c 的多个水平。ET c模型中常用的 Ball-Woodrow-Berry g s子模型(BWB 模型)在温室条件下得到验证,模拟和测量的 ET c之间表现出密切的一致性. 经验证的模型被纳入温室模拟器。情景模拟研究表明,根据 RH 设定点,选择低 g s品种会降低能源需求(≤5.75%)。然而,BWB 模型在 RH 低于 60% 时显示出较差的预测质量,而在较高 RH 时表现良好。因此,尝试改进模型预测:采用原位获得的数据来适应和扩展 BWB 模型或具有底物水势的 Liu 扩展(Ψ;BWB-Liu 模型)。两种模型都扩展了气孔密度 (D s ) 或孔隙面积。尽管修改后的 BWB-Liu 模型(考虑 D s)允许更高的精度(R 2  = 0.59),但与基本版本(R2  = 0.31),温室模型中典型的 Ψ 预测缺乏可能会导致实时气候控制的实施存在问题。目前的研究为开发特定品种的栽培策略以及使用基于模型的控制系统改进低相对湿度下动态气候条件的 g s子模型奠定了基础。

更新日期:2021-06-10
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