Exploiting Morphophysiological Traits for Yield Improvement in Upland Cotton under Salt Stress
Bibliographic record
Abstract
The increased land salinization threatens land productivity, food security, and economic losses. The study used a comprehensive set of morpho-physiological, biochemical, and fiber quality parameters to examine the genetic variability of 24 cotton genotypes against 15 dSm-1 salt stress. The general linear model (GLM) effect revealed significant effects of salinity for studied accessions except for lint percentage and fiber strength. The genotype × treatment effects were also significant for all studied traits, while non-significant effects were observed for seed number per boll (SNB), potassium to sodium ratio (K+/Na+), K+, peroxidase (POD), and catalase (CAT). A notable reduction for all traits was observed except for fiber fineness, superoxide (SOD), CAT, POD, carotenoid contents, and hydrogen peroxide (H2O2), which were increased under saline conditions. Based on multivariate analyses, hybrids viz. MS-71× KAHKASHAN, followed by MS-71× CRS-2007 and NS-131× CRS-2007, performed well under both normal stressed conditions. Moreover, biochemical and agronomical traits in PCA validate that the MS-71× KAHKASHAN is the most desirable genotype under both conditions. Better hybrid performance under normal and 15 dSm-1 salt stress conditions supports the hybrid adaptability under salinity stress environments. The outcome would assist breeders in developing salt-tolerant cotton varieties under climate change scenarios.
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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) | 0.000 | 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 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".