Seeding rate affects the performance of oat and black oat
Bibliographic record
Abstract
Abstract Basic management practices, including ideal seeding rates, are still lacking for black oat (Avena strigosa Schreb.) in the southeastern United States. This study evaluated the performance of five seeding rates (15, 30, 60, 120, and 240 lb acre−1) on ‘Legend 567’ oat (Avena sativa L.) and ‘UF‐10’ black oat at three harvest dates (early, mid‐season, and late) per year. Seeding rates of 60, 120, and 240 lb acre−1 tended (P = .07) to increase total herbage accumulation [4,760 lb dry matter (DM) acre−1] compared with 15 lb acre−1 (3,945 lb DM acre−1). Tiller density was usually greater for black oat than for oat. Seeding rate was positively associated with tiller density and explained 84 to 98% and 96 to 98% of the variation for black oat and oat, respectively. Tiller mass was greater for oat (0.045 oz tiller−1) than black oat (0.034 oz tiller−1), and greater for the mid‐season harvest (0.043 oz tiller−1), compared with the early and late harvests (0.037 oz tiller−1 on average). There was a negative effect (P < .001) of seeding rate on tiller mass. Increasing the seeding rate had a negative effect on leaf length at the early and middle harvests (P < .05), and decreased leaf width from 0.56 to 0.39 inches in oat, and from 0.32 to 0.29 inches in black oat when changing from 15 to 240 lb acre−1. To maximize herbage accumulation at a lower seeding rate, we recommend 60 lb acre−1 for both oat and black oat for multiple harvests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 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".