Winter Cover Crop Seeding Rate and Variety Affects during Eight Years of Organic Vegetables: II. Cover Crop Nitrogen Accumulation
Bibliographic record
Abstract
Winter cover crops (CC) can improve nutrient use efficiency by scavenging residual soil N. Shoot nitrogen accumulation (NA) of rye ( Secale cereale L.), legume–rye, and mustard was determined in December to February or March during the first 8 yr of the Salinas Organic Cropping Systems (SOCS) trial focused on high‐value crops in Salinas, CA. By seed weight, legume–rye included 10% rye, 35% faba bean ( Vicia faba L.), 25% pea ( Pisum sativum L.), 15% common vetch ( V. sativa L.), and 15% purple vetch ( V. benghalensis L.); mustard included 61% Sinapis alba L., and 39% Brassica juncea Czern. Cover crops were fall planted at 1x and 3x seeding rates (SR); 1x SR were 90 (rye), 11 (mustard), and 140 (legume–rye) kg ha −1 . Vegetables followed CC annually. Early‐season NA was greatest in mustard. Nitrogen accumulation increased more gradually through the season in legume–rye than in other CC. Final NA (kg ha −1 ) was lower in rye (110) and mustard (114), than legume–rye (151), and varied by year. During December, SR increased NA in legume–rye by 41% but not for the other CC. Legumes contributed 36% of final NA in legume–rye, presumably from N scavenging and biological fixation. Nitrogen accumulation was highly correlated with shoot dry matter of legume–rye but not of rye or mustard. Seed costs per kg of NA were more than two times higher for legume–rye than rye and mustard. We conclude that high SR are necessary to hasten early season NA and minimize N leaching potential in legume–rye mixtures.
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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".