A Large‐Effect QTL for Grain Yield under Reproductive‐Stage Drought Stress in Upland Rice
Bibliographic record
Abstract
ABSTRACT Genetic control of yield under reproductive‐stage drought stress was studied in a population of 436 random F 3 –derived lines from a cross between the upland rice ( Oryza sativa L.) cultivars Vandana and Way Rarem. Screening was conducted under upland conditions at IRRI during the dry seasons of 2005 and 2006. Lines were evaluated in drought stress and nonstress trials in both years to identify QTL contributing to drought resistance. For QTL detection, a set of random lines and the highest and lowest‐yielding lines under both stress and nonstress conditions were genotyped by 126 SSR markers. A QTL ( qtl12.1 ) with a large effect on grain yield under stress was detected on Chromosome 12 in both years. The whole population was genotyped for additional markers on Chromosome 12, allowing QTL localization to a 10.2 cM region between SSR markers RM28048 and RM511. Under stress conditions, the locus also increased harvest index, biomass yield, and plant height while reducing the number of days to flowering. Under nonstress conditions, qtl12.1 did not significantly affect any trait. The additive effect of this QTL on grain yield under stress was 172 kg ha −1 per year over the 2 yr of testing, representing 47% of the average yield under stress and explaining 51% of the genetic variance. The yield‐increasing allele was derived from the susceptible parent, Way Rarem, suggesting an epistatic effect. This is the first QTL reported in rice having a large and repeatable effect on grain yield under severe drought stress in the field.
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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 | 0.001 | 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".