Leaf cell membrane stability‐based mechanisms of zinc nutrition in mitigating salinity stress in rice
Bibliographic record
Abstract
Abstract Excess salt affects about 955 million ha of arable land worldwide, and 49% of agricultural land is Zn‐deficient. Soil salinity and zinc deficiency can intensify plant abiotic stress. The mechanisms by which Zn can mitigate salinity effects on plant functions are not well understood. We conducted an experiment to determine how Zn and salinity effects on rice plant retention of Zn, K+ and the salt ion Na+ affect chlorophyll formation, leaf cell membrane stability and grain yield. We examined the mechanisms of Zn nutrition in mitigating salinity stress by examining plant physiology and nutrition. We used native Zn‐deficient soils (control), four salinity (EC) and Zn treatments – Zn 10 mg·kg−1 (Zn10), EC 5 dS·m−1 (EC5), Zn10+EC5 and Zn15+EC5, a coarse rice (KS‐282) and a fine rice (Basmati‐515) in the study. Our results showed that Zn alone (Zn10) significantly increased rice tolerance to salinity stress by promoting Zn/K+ retention, inhibiting plant Na+ uptake and enhancing leaf cell membrane stability and chlorophyll formation in both rice cultivars in native alkaline, Zn‐deficient soils (P < 0.05). Further, under the salinity treatment (EC5), Zn inputs (10–15 mg·kg−1) could also significantly promote rice plant Zn/K+ retention and reduce plant Na+ uptake, and thus increased leaf cell membrane stability and grain yield. Coarse rice was more salinity‐tolerant than fine rice, having significantly higher Zn/K+ nutrient retention. The mechanistic basis of Zn nutrition in mitigating salinity impacts was through promoting plant Zn/K+ uptake and inhibiting plant Na+ uptake, which could result in increased plant physiological vigour, leaf cell membrane stability and rice productivity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".