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Response of stomatal conductance to plant water stress in buffalograss seed production: Observation with UAV thermal infrared imagery

Author

Listed:
  • Wang, Chu
  • Zhu, Kai
  • Bai, YanYan
  • Li, ChenYan
  • Li, Maona
  • Sun, Yan

Abstract

Stomatal conductance (gs) is an indicator that allows for direct evaluation of plant water status, but it is challenging to achieve rapid monitoring in large-scale fields due to limitations in observation methods. Here this study was conducted to identify the thresholds of gs with different target yields and develop a gs-based water stress diagnostic model for buffalograss (Buchloe dactyloides (Nutt.) Engelm.) using UAV thermal infrared imagery for buffalograss in 2022 and 2023. The results of the field experiment demonstrated that the gs rapidly response to changes in the water stress status of buffalograss. The thresholds of gs were 403 and 385 mmol m−2 s−1 for the vegetative and reproductive growth stages, respectively, with the target seed yield of 1224 kg ha−1. The gs values were classified into three levels for the vegetative growth and four levels for the in reproductive growth stage of buffalograss, respectively. The canopy temperature depression response to water stress is consistent with the gs. Based on this relationship, this study developed a gs-based diagnostic model with a random forest algorithm for buffalograss. Furthermore, a spital map of gs was created using UAV thermal infrared imagery. The modification test results indicated that the model made a good estimation of gs were good with normalized root mean square errors of 15% in the vegetative stage and 11% in the reproductive stage, respectively. Therefore, it is feasible to use thermal infrared imagery for monitoring gs and evaluating the water stress of plants in buffalograss fields.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:agiwat:v:292:y:2024:i:c:s0378377423005267
    DOI: 10.1016/j.agwat.2023.108661
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