Effect of varying seeding date on crop development, yield and yield components in canaryseed
Bibliographic record
Abstract
The effects of varying seeding date on crop development, yield and yield components in canaryseed (Phalaris canariensis L.) have not been previously reported. In 1996 and 1997, a seeding date study was conducted at Swift Current, SK, which included barley (Hordeum vulgare L.), canaryseed and wheat (Triticum aestivum L.) sown at three dates in separate tilled fallow and untilled wheat stubble sites. Terminal summer drought occurred in both years of this experiment. Cumulative degree days (DD0) to reach maturity did not differ significantly among seeding dates for barley, or for wheat in 1997, while cumulative degree days to reach maturity decreased by 60 DD0 with delayed seeding for wheat in 1996. In contrast, cumulative degree days to reach maturity in canaryseed increased by 70 DD0 in 1996 and by 90 DD0 in 1997 with delayed seeding. Delaying seeding from the early to the late date decreased canaryseed yield by 29%, while barley and wheat yields decreased only 14 and 11%, respectively. Panicle density in canaryseed was reduced 24% between the early and late seeding dates, while barley and wheat spike densities were reduced only 2 and 6%, respectively. The large yield reduction in canaryseed was likely due to slowed crop development with delayed seeding, which intensified late-season drought stress. The slowed crop development with delay in seeding date in canaryseed may be due to vernalization requirement in this crop. In the semiarid prairie region, canaryseed should be seeded early to maintain a rapid crop development rate to minimize yield loss due to drought stress. Key words: Canaryseed, Phalaris canariensis L., seeding date, drought stress
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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".