Effect of Seeding Rate and Planting Arrangement on Rye Cover Crop and Weed Growth
Bibliographic record
Abstract
Weed growth in winter cover crops in warm climates may contribute to weed management costs in subsequent crops. A 2‐yr experiment was conducted on an organic vegetable farm in Salinas, California, to determine the impact of seeding rate and planting arrangement on rye (Secale cereale L. ‘Merced’) cover crop growth and weed suppression. Each year, rye was planted in October at three rates (90, 180, and 270 kg ha−1) and two planting arrangements (one‐way versus grid pattern). Averaged across years, rye population densities were 322, 572, and 857 plants m−2 at the 90, 180, and 270 kg ha−1 seeding rates, respectively. Early season rye ground cover increased with seeding rate and was higher in the grid than one‐way arrangement in Year 1; however, rye ground cover was not affected by rate and was higher in the one‐way arrangement in Year 2. Aboveground dry matter (DM) of rye increased with seeding rate at the first two harvests but not at the final one. Planting arrangement did not affect rye aboveground DM in Year 1, but rye DM was higher in the grid pattern at the first and final harvests in Year 2. Weed emergence was not affected by seeding rate or planting arrangement. Weed biomass decreased with increased seeding rate and was also lower in the grid than in the one‐way arrangement in Year 2. A grid planting pattern provided no consistent benefit but planting rye at higher seeding rates maximizes early season rye DM production and minimizes weed growth.
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 | 0.000 | 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".