Simultaneous selection for early maturity, increased grain yield and elevated grain protein content in spring wheat
Bibliographic record
Abstract
Abstract High grain yield and grain protein content, and early maturity are important traits in global bread wheat (Triticum aestivum L.)‐breeding programmes. Improving these three traits simultaneously is difficult due to the negative association between grain yield and grain protein content and the positive association between maturity and grain yield. We investigated the genetic relationship between maturity, grain yield and grain protein content in a population of 130 early maturing spring wheat lines in a high latitude (52–53°N) wheat‐growing region of Canada. Grain protein content exhibited negative genetic correlation with maturity (−0.87), grain fill duration (−0.78), grain fill rate (−0.49), grain yield (−0.93) and harvest index (−0.71). Grain yield exhibited positive genetic correlation with maturity (0.69), rate (0.78) and duration (0.49) of grain fill, and harvest index (0.55). Despite the positive association between maturity and grain yield, and negative association between grain yield and grain protein content, higher yielding lines with medium maturity and higher grain protein content were identified. Broad‐sense heritabilities were low (<0.40) for rate and duration of grain fill, grain protein content, spike per m2, grains per spike, harvest index and grain yield, and medium to high (>0.40) for grain weight, days to anthesis and maturity, and plant height. Selection for longer preanthesis and shorter grain fill periods may help circumvent the negative association between grain yield and grain protein content. Selection for shorter grain fill periods and higher grain fill rate may be a useful strategy for developing early maturing cultivars with acceptable grain yields in northern wheat‐growing regions.
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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".