Sowing date affects yield components of canarygrass seed
Bibliographic record
Abstract
Canarygrass (Phalaris canariensis L.) crops are sown from June to mid-September in the southeastern area of the province of Buenos Aires, Argentina. Sowing dates in this range result in different growing temperatures and photoperiods that affect the duration of developmental stages, biomass production, and seed yield and its components. For Argentina, there are no reported studies that address these effects. This study on the effects of sowing date was conducted during four growing seasons (1996–1999) at the Instituto Nacional de Tecnologí a Agropecuaria (INTA) Experimental Station at Balcarce, Argentina, using a population provided by Dr. Jaime Lloveras, University of Leyda, Spain. Different seeding dates were chosen from June to mid-September. The experiment was a randomized complete block design with four blocks. When the sowing date was delayed, total dry matter (DM) decreased. For early sowing dates seed yield was constant, but after 10 August it was reduced by 1.5% for each day of delay. Earlier sowing increased the duration of pre-anthesis development with greater uniformity in panicle size and the number of seeds. Seed yield was related lin early to the number of seeds and plant dry matter yield (DMY). The rate of progress from emergence to anthesis (1/days from emergence to anthesis) was proportional to the mean photoperiod. Under the environmental conditions in Balcarce, the accumulated required thermal units for anthesis was reduced when sowing was delayed from June to September. This reduction was related to the photoperiod and was estimated as –189.3 growing degree-days per hour of photoperiod increment. Key words: Canarygrass, seed yield, sowing date, yield components
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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 | 0.000 |
| 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".