Moderate alternate wetting and drying irrigation enhances drought-resistance abilities by improving structural mesophyll conductance of water-saving and drought-resistant rice under severe drought.

Water-saving and drought-resistant rice (WDR) coupled with alternate wetting and drying irrigation (AWDI) possesses a high photosynthetic potential due to higher mesophyll conductance (gm) under drought conditions. However, the physiological and structural contributions to the gm of leaves and their mechanisms in WDR under AWDI are still unclear. In this study, WDR (Hanyou 73) and drought-sensitive rice (Huiliangyou 898) were selected as materials. Three irrigation patterns were established from transplanting to the heading stage, including conventional flooding irrigation (W1), moderate AWDI (W2), and severe AWDI (W3). A severe drought with a soil water potential of -50 kPa was applied for a week at the heading stage across all treatments and cultivars. The results revealed that severe drought reduced gas exchange parameters and gm but enhanced antioxidant enzyme activities and malondialdehyde content in the three treatments and both cultivars. The maximal photosynthetic rate (Amax) of HY73 in the W2 treatment was greater than that in the other combinations of cultivars and irrigation patterns. The contribution of leaf structure (54%) to gm (gm-S, structural gm) was higher than that of leaf physiology (46%) to gm (gm-P, physiological gm) in the W2 treatment of Hanyou 73. Additionally, gm-S was significantly and linearly positively correlated with gm under severe drought. Moreover, both the initial and apparent quantum efficiencies were significantly and positively with gm in rice plants (p < 0.05). These results suggest that the improvements in photosynthesis and yield in the WDR combined with moderate AWDI can mainly be attributed to the enhancement of gm-S under severe drought conditions. Quantum efficiency may be a potential factor in regulating photosynthesis by cooperating with the gm of rice plants under severe drought conditions.

Improving the prediction performance of leaf water content by coupling multi-source data with machine learning in rice (Oryza sativa L.).

BACKGROUND: Leaf water content (LWC) significantly affects rice growth and development. Real-time monitoring of rice leaf water status is essential to obtain high yield and water use efficiency of rice plants with precise irrigation regimes in rice fields. Hyperspectral remote sensing technology is widely used in monitoring crop water status because of its rapid, nondestructive, and real-time characteristics. Recently, multi-source data have been attempted to integrate into a monitored model of crop water status based on spectral indices. However, there are fewer studies using spectral index model coupled with multi-source data for monitoring LWC in rice plants. Therefore, 2-year field experiments were conducted with three irrigation regimes using four rice cultivars in this study. The multi-source data, including canopy ecological factors and physiological parameters, were incorporated into the vegetation index to accurately predict LWC in rice plants. RESULTS: The results presented that the model accuracy of rice LWC estimation after combining data from multiple sources improved by 6-44% compared to the accuracy of a single spectral index normalized difference index (ND). Additionally, the optimal prediction accuracy of rice LWC was produced using a machine algorithm of gradient boosted decision tree (GBDT) based on the combination of ND(1287,1673) and crop water stress index (CWSI) (R2 = 0.86, RMSE = 0.01). CONCLUSIONS: The machine learning estimation model constructed based on multi-source data fully utilizes the spectral information and considers the environmental changes in the crop canopy after introducing multi-source data parameters, thus improving the performance of spectral technology for monitoring rice LWC. The findings may be helpful to the water status diagnosis and accurate irrigation management of rice plants.