Comparison of Rye and Legume–Rye Cover Crop Mixtures for Vegetable Production in California
Bibliographic record
Abstract
Rye (Secale cereale L.) is an important cover crop in high‐value vegetable production in California. A 2‐yr winter study on organic farms in Salinas and Hollister, CA evaluated cover crop population densities, ground cover, aboveground dry matter (DM), and N content of rye and five legume–rye mixtures. Mixtures had 60 or 90% legumes by seed weight and included two or more of the following legumes: faba bean (Vicia faba L.), vetches (V. benghalensis L., V. dasycarpa Ten., V. sativa L.), and pea (Pisum sativum L.). Seeding rates were 90 (rye) and 140 (mixtures) kg ha−1, and densities were 142 to 441 plants m−2 Early‐season ground cover was usually greater in monoculture rye and the 60% legume mixtures than the 90% legume mixtures. Total DM, and legume and rye DM in mixtures differed by year, site, harvest, and cover crop. Total DM was usually at least two times higher at season end than mid‐season. The 90% legume mixtures generally produced more legume DM than the 60% legume mixtures, but legume DM usually declined after mid‐season. Rye DM increased with rye density. Total cover crop N uptake was greater in Hollister than Salinas; however, legume DM and legume N uptake were greater in Salinas. Interactions between site, year, cover crop, and harvest illustrate the complex growth dynamics of legume–rye mixtures. The 90% legume mixtures appear most suitable for vegetable production in California because they had a better balance of legume and rye DM at season end.
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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 | 0.001 | 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".