Photosynthetic Physiological Mechanisms of Leaf and Sheath Organs to Drought Stress in Water‐Saving and Drought‐Resistant Rice
Bibliographic record
Abstract
Water-saving and drought-resistant rice (WDR) achieves drought resistance and stable yield by maintaining high photosynthetic potential in leaves under severe drought. We speculated that the sheath organ serving as a photosynthetic source can contribute to a positive response to drought in WDR. However, the synergetic photosynthetic adaptation mechanisms of leaf and sheath organs to drought remain unknown. In this study, a pot experiment was conducted to investigate the WDR of Hanyou73 (HY73) and its parents, Hanhui3 (HH3) and Huhan7A (HH7A). All varieties were subjected to drought at heading with -100 kPa soil water potential. The results demonstrated that chlorophyll content, relative water content, photosynthesis-related parameters, and sugar contents of leaf and sheath organs were significantly reduced during drought across three varieties. However, the activities of catalase and peroxidase and the contents of proline, hydrogen peroxide, and abscisic acid increased during drought treatment in leaf and sheath organs of all varieties. The stomatal conductance changed less in the sheath organ than in the leaf organ under drought stress. Further analyses revealed that stomatal conductance in leaf and sheath organs was mainly regulated by large stomatal apertures among anatomical structural characteristics of stomata across varieties and treatments. For HY73, high drought resistance was associated with a high sucrose-supplying capacity, resulting from high photosynthetic potential in leaf and sheath organs, which was achieved by improving stomatal aperture compared to its parents. This study provides a theoretical basis for the mechanism of photosynthetic adaptation of leaf and sheath organs, synergistically improving drought resistance in rice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".