Incorporating cultivar-specific stomatal traits into stomatal conductance models improves the estimation of evapotranspiration enchancing greenhouse climate management

Oliver Körner*, Dimitrios Fanourakis, Michael Chung-Rung Hwang, Benita Nordentoft Hyldgaard, Georgios Tsaniklidis, Nikolaos Nikoloudakis, Dorthe Horn Larsen, Carl-Otto Ottosen, Eva Rosenqvist

*Corresponding author for this work

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Abstract

The effect of considering cultivar differences in stomatal conductance (g s) on relative air humidity (RH)-related energy demand was addressed. We conducted six experiments in order to study the variation in evapotranspiration (ET c) of six pot rose cultivars, investigate the underlying processes and parameterise a g s-based ET c model. Several levels of crop ET c were realised by adjusting the growth environment. The commonly applied Ball–Woodrow–Berry g s-sub-model (BWB-model) in ET c models was validated under greenhouse conditions, and showed a close agreement between simulated and measured ET c. The validated model was incorporated into a greenhouse simulator. A scenario simulation study showed that selecting low-g s 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 (D s) or pore area. Although the modified BWB-Liu-model (considering D s) allowed higher accuracy (R 2 = 0.59), as compared to the basic version (R 2 = 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 g s sub-model for dynamic climate conditions under low RH using model-based control systems.

Original languageEnglish
JournalBiosystems Engineering
Volume208
Pages (from-to)131-151
Number of pages21
ISSN1537-5110
DOIs
Publication statusPublished - Aug 2021

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