Winter Cover Crop Seeding Rate and Variety Effects during Eight Years of Organic Vegetables: III. Cover Crop Residue Quality and Nitrogen Mineralization
Bibliographic record
Abstract
Winter cover crops (CCs) can improve nutrient‐use efficiency in tillage‐intensive cropping systems. Shoot residue quality and soil mineral N following incorporation of rye ( Secale cereale L.), legume–rye, and mustard CCs was determined in December to February or March during the first 8 yr of the Salinas Organic Cropping Systems trial in Salinas, CA. Legume–rye included Vicia faba L., V. sativa L., V. benghalensis L., Pisum sativum L., and rye; mustard included Sinapis alba L. and Brassica juncea Czern. Cover crops were planted in the fall at standard and three times higher seeding rates (SRs) before vegetables annually. Significant CC × year interactions occurred for C and N concentrations and C/N ratios of CC shoots. In general, C concentrations were higher in rye and legume–rye, N concentrations were higher in mustard and legume–rye, and C/N ratios were higher in rye. During the season, C concentrations and C/N ratios tended to increase, whereas N concentrations decreased. Compared with rye and mustard, legume–rye residue quality changed least during each season. Increasing the SR reduced N concentrations and increased C/N ratios; however, the effect varied with time and by residue. Following CC incorporation, soil mineral N varied between years and CC and was typically highest following legume–rye or mustard and lowest without CC. Rainfall after CC incorporation reduced soil N one year, suggesting that leaching occurred. We conclude that mustard and legume–rye produce higher quality residue that will decompose more rapidly and minimize tillage challenges for subsequent vegetables but may be more prone to post‐incorporation N leaching.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".