A Modeling Approach to Quantify the Effects of Stomatal Behavior and Mesophyll Conductance on Leaf Water Use Efficiency

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Moualeu-Ngangue, Dany P.; Chen, Tsu-Wei; Stützel, Hartmut: A Modeling Approach to Quantify the Effects of Stomatal Behavior and Mesophyll Conductance on Leaf Water Use Efficiency. In: Frontiers in Plant Science 7 (2016), 875. DOI: http://dx.doi.org/10.3389/fpls.2016.00875

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Water use efficiency (WUE) is considered as a determinant of yield under stress and a component of crop drought resistance. Stomatal behavior regulates both transpiration rate and net assimilation and has been suggested to be crucial for improving crop WUE. In this work, a dynamic model was used to examine the impact of dynamic properties of stomata on WUE. The model includes sub-models of stomatal conductance dynamics, solute accumulation in the mesophyll, mesophyll water content, and water flow to the mesophyll. Using the instantaneous value of stomatal conductance, photosynthesis, and transpiration rate were simulated using a biochemical model and Penman-Monteith equation, respectively. The model was parameterized for a cucumber leaf and model outputs were evaluated using climatic data. Our simulations revealed that WUE was higher on a cloudy than a sunny day. Fast stomatal reaction to light decreased WUE during the period of increasing light (e.g., in the morning) by up to 10.2% and increased WUE during the period of decreasing light (afternoon) by up to 6.25%. Sensitivity of daily WUE to stomatal parameters and mesophyll conductance to CO2 was tested for sunny and cloudy days. Increasing mesophyll conductance to CO2 was more likely to increase WUE for all climatic conditions (up to 5.5% on the sunny day) than modifications of stomatal reaction speed to light and maximum stomatal conductance.
License of this version: CC BY 4.0 Unported
Document Type: Article
Publishing status: publishedVersion
Issue Date: 2016
Appears in Collections:Naturwissenschaftliche Fakultät

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pos. country downloads
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1 image of flag of Germany Germany 83 69.17%
2 image of flag of United States United States 9 7.50%
3 image of flag of China China 7 5.83%
4 image of flag of Russian Federation Russian Federation 3 2.50%
5 image of flag of Thailand Thailand 2 1.67%
6 image of flag of Malaysia Malaysia 2 1.67%
7 image of flag of India India 2 1.67%
8 image of flag of United Kingdom United Kingdom 2 1.67%
9 image of flag of France France 2 1.67%
10 image of flag of Switzerland Switzerland 2 1.67%
    other countries 6 5.00%

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