Response of rice's hydraulic transport and photosynthetic capacity to drought-flood abrupt alternation

Knowledge of the potential interactive effects of drought and flooding on the maximum carboxylation rate at 25°C (Vmax25) and maximum hydraulic conductance (Kmax) is essential for the precise modeling of crop growth, water-carbon cycling, and crop yield formation. However, the lack of data on drought–flood abrupt alternation (DF) experiments and appropriate models to calibrate parameters without the need to specify photosynthetic and hydraulic transport capacity a priori make it difficult to further our understanding of the potential interaction effects on Vmax25 and Kmax. Hence, this study aimed to investigate the potential effects of interactions between the preceding drought and the subsequent flooding on Vmax25 and Kmax. We propose a nested optimization model for calibrating photosynthetic and hydraulic conductance capacity while simultaneously modeling carbon assimilation rate and stomatal conductance. A two-year DF experiment for rice from 2017 to 2018 was conducted to validate the new framework at the Key Laboratory of Water Resources and Hydropower of Anhui Province, Bengbu, China. The results show that reasonable Kmax and Vmax25 from gas exchange data can be extracted with the proposed nested model framework. We find two distinct interactions between the prior drought and the subsequent flooding on Vmax25 and Kmax: (1) the antagonistic effect of the preceding mild drought on the subsequent-flood-induced reduction of hydraulic transport and photosynthetic capacity, and (2) the synergistic effect of the subsequent flooding on the preceding drought-induced reduction in hydraulic transport and photosynthetic capacity. Revealing the interaction of drought and flooding on Kmax and Vmax25 of rice under DF events helps to understand rice’s response to compound water stress on multiple timescales and the stomatal and non-stomatal co-limitations, and these findings can be used as valuable guidelines for accurately predicting the impact of future extreme weather events on agricultural production.