Evaluation of method to model stomatal conductance and its use to assess biomass increase in poplar trees

9 páginas.- 7 figuras.- referencias.- Supplementary data associated with this article can be found in the online version at doi:10.1016/j.agwat.2021.107228

Stomatal conductance (gs) is the main limiting factor for photosynthesis and is sensitive to plant water status. Accurately assessing the behavior of gs under water deficit stress is essential to model plants carbon and water flux, which govern vegetation biomass production and dynamics. However, direct measurement of gs with gas exchange analyzer can be time-consuming and laborious, especially under field conditions, thus constraining the data availability for validating the modeling outcome. This difficulty can be solved if measurement of gs is automated. Here, we report on dynamics of gs and the maximum (gsmax) of Populus tomentosa, derived from automatically recorded meteorological variables and sap flux density (Js) and turgor pressure sensors outputs (Z) measured in three P. tomentosa trees from a short-rotation plantation subjected to different water stress levels along a whole growing season. The simulated gsmax was related to aboveground (ABM) and underground biomass (UBM) increase by leaf area. Js and Z were continuously measured using sap flow and ZIM sensors. Our results showed that the sensitivity of Js to air vapor deficit (D) (i.e. Js/D) correlated well with gs, and the sensitivity of Z to D (i.e. dZ/dD) was well coupled with gsmax. In addition, the ABM increase was linearly aligned with simulated gsmax multiplied by leaf area (LA) (R2 > 0.7). Also, increment in UBM was significantly correlated with simulated gsmax * LA across all observed trees, being the best described by a logistic function (R2 > 0.7). We conclude that gs can be well simulated through automatic monitoring of Js and Z for different meteorological and soil water content conditions. Moreover, the simulated gsmax was also closely related to biomass production both above and underground, which opens the possibility for using it to manage irrigation in smart agriculture and forestry in the future.

This research was jointly supported by “National Natural Science Foundation of China” (32001304, 31872702).

Peer reviewed