Crop Genotype and Plant Population Density Impact Flowering Phenology and Synchrony between Cropped and Volunteer Spring Wheat
Bibliographic record
Abstract
Wheat (Triticum aestivum L.) is one of many crops into which novel traits have been incorporated using recombinant DNA technology, and thus may require segregation from nontransgenic wheat. Volunteer wheat populations, which cannot be selectively removed from wheat crops, pose a challenge to segregation because they may serve to facilitate trait movement. However, diverse flowering phenologies among wheat genotypes planted at various densities may result in flowering asynchrony, thus minimizing pollen‐mediated gene flow (PMGF). We tested this theory with a comparative analysis that examined the influence of crop plant population density, genotype, and height on flowering phenology and synchrony between volunteer and cropped wheat populations. We found that time from crop sowing to first flower, peak flowering, and flowering cessation varied significantly among genotypes. Increasing crop plant population density resulted in accelerated crop flowering for all genotypes, but had little effect on flowering synchrony. Although not always significant, the time interval from sowing to 5, 50, and 95% flowering, as well as the flowering duration of the volunteer population, were also greater at low crop plant population densities. Synchronicity of flowering varied among genotypes, with tall genotypes consistently exhibiting more flowering synchrony with volunteer wheat than short genotypes. However, the response of flowering synchrony to height may have been a product of the genotypes tested and further study to confirm these results is needed. The results of this study suggest that, despite a short flowering period, there is considerable potential for flowering synchrony between cropped and volunteer wheat populations. Nevertheless, opportunity exists to reduce flowering synchrony between cropped and volunteer wheat populations by utilizing genotypic variation for flowering phenology to minimize flowering overlap, provided that an adequate crop plant population density is attained.
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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 | 0.001 |
| 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 | 0.001 |
| 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".