Seeding Rate and Planting Arrangement Effects on Growth and Weed Suppression of a Legume‐Oat Cover Crop for Organic Vegetable Systems
Bibliographic record
Abstract
Winter cover crops can add soil organic matter, improve nutrient cycling, and suppress weeds in organic vegetable systems. A 2‐yr study was conducted on organic farms in Salinas and Hollister, CA, to evaluate the effect of seeding rate (SR) and planting arrangement on cover crop density, ground cover, and cover crop and weed dry matter (DM) with a mixed cover crop. The mix contained legumes (35% Vicia faba L., bell bean; 15% Vicia dasycarpa Ten., woolypod vetch; 15% Vicia benghalensis L., purple vetch; and 25% Pisum sativum L., pea) and 10% oat (Avena sativa L.) by seed weight. Three SRs (112, 224, and 336 kg ha−1) and two planting arrangements (one‐way versus grid pattern) were evaluated. Planting arrangement had no effect on the variables measured. When weeds were abundant, weed DM declined linearly with increasing SR from approximately 300 kg ha−1 at the low SR to <100 kg ha−1 at the high SR. Increasing SR increased oat and legume DM early in the season, but did not affect final cover crop DM that ranged from 7 to 12 Mg ha−1. Year affected final cover crop DM production at both sites. The legume DM portion of the total cover crop declined through the season but varied between sites and year, probably due to soil and climatic differences. Higher SRs may be cost effective because weed control is expensive and cover crop seed is a relatively small component of cover cropping costs in this region.
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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".