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Applicability of stomatal conductance models comparison for persistent water stress processes of spring maize in water resources limited environmental zone

Author

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  • Qi, Yue
  • Zhang, Qiang
  • Hu, Shujuan
  • Wang, Runyuan
  • Wang, Heling
  • Zhang, Kai
  • Zhao, Hong
  • Zhao, Funian
  • Chen, Fei
  • Yang, Yang
  • Tang, Guoying
  • Hu, Yanbin

Abstract

Through the Stomata of plants, water (H2O) and carbon dioxide (CO2) are transferred between leaves and the atmosphere. The intakes of CO2 during photosynthesis and water loss through transpiration are facilitated by stomata. To effectively model plant transpiration, and study the mass, energy transfer between plants, and the atmosphere, stomatal conductance of plant leaves requires accurate modelling. Abnormal changes in soil moisture result from frequent droughts in water-strapped environmental regions in a warming context. This has an impact on the stomatal conductance models's applicability and, in turn, the precision of the carbon and water cycles in agro-ecosystems. Four commonly used stomatal conductance models-Jarvis, Ball-Woodrow-Berry (BWB), Ball-Berry-Leuning (BBL) and unified stomatal optimization (OS) were investigated in the simulation of spring maize during persistent water stress to determine the impact of introducing a soil moisture response function on the simulation effect of the stomatal conductance model. The results showed that the BWB model was the best model for spring maize during persistent water stress, followed by the OS and BBL models, and the Jarvis model was the worst model. The OS and BBL models' simulation effects were improved by the addition of the soil moisture response function, while the Jarvis and BWB models' simulation effects were diminished. The OS-θ model was the best, followed by the BBL-θ and BWB-θ models, and the Jarvis-θ model was the worst, according to the model simulation effect. The 95 % confidence intervals of BWB-θ and OS-θ models were simulated with the addition of the soil moisture response function. The addition of soil moisture function improved the model's applicability, allowing it to be used for a variety of relative soil moisture contents, including 13∼68 %, 13∼89 %, 13∼78 % and 13∼89 %forthe Jarvis, BWB, BBL and USO models. With or without the addition of a moisture response function, the OS model performs optimally and is appropriate for various soil moisture conditions. The study's finding may serve as a foundation for choosing an appropriate stomatal conductance model for effective simulation of carbon and water cycles of agricultural ecosystems under drought conditions in water-limited environmental regions. This may support effective use and evaluation of agricultural water resources.

Suggested Citation

  • Qi, Yue & Zhang, Qiang & Hu, Shujuan & Wang, Runyuan & Wang, Heling & Zhang, Kai & Zhao, Hong & Zhao, Funian & Chen, Fei & Yang, Yang & Tang, Guoying & Hu, Yanbin, 2023. "Applicability of stomatal conductance models comparison for persistent water stress processes of spring maize in water resources limited environmental zone," Agricultural Water Management, Elsevier, vol. 277(C).
  • Handle: RePEc:eee:agiwat:v:277:y:2023:i:c:s0378377422006370
    DOI: 10.1016/j.agwat.2022.108090
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    References listed on IDEAS

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    1. Yan-Shih Lin & Belinda E. Medlyn & Remko A. Duursma & I. Colin Prentice & Han Wang & Sofia Baig & Derek Eamus & Victor Resco de Dios & Patrick Mitchell & David S. Ellsworth & Maarten Op de Beeck & Gör, 2015. "Optimal stomatal behaviour around the world," Nature Climate Change, Nature, vol. 5(5), pages 459-464, May.
    2. Yue Qi & Qiang Zhang & Shujuan Hu & Runyuan Wang & Heling Wang & Kai Zhang & Hong Zhao & Sanxue Ren & Yang Yang & Funian Zhao & Fei Chen & Yang Yang, 2022. "Effects of High Temperature and Drought Stresses on Growth and Yield of Summer Maize during Grain Filling in North China," Agriculture, MDPI, vol. 12(11), pages 1-14, November.
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    Cited by:

    1. Wang, Chu & Zhu, Kai & Bai, YanYan & Li, ChenYan & Li, Maona & Sun, Yan, 2024. "Response of stomatal conductance to plant water stress in buffalograss seed production: Observation with UAV thermal infrared imagery," Agricultural Water Management, Elsevier, vol. 292(C).
    2. Ruifeng Sun & Juanjuan Ma & Xihuan Sun & Shijian Bai & Lijian Zheng & Jiachang Guo, 2023. "Study on a Stomatal Conductance Model of Grape Leaves in Extremely Arid Areas," Sustainability, MDPI, vol. 15(10), pages 1-13, May.

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