Grass and Legume Cover Crop Effects on Dry Matter and Nitrogen Accumulation
Bibliographic record
Abstract
Careful cover crop management during the spring growth period may allow farmers to maximize dry matter (DM) yield and N accumulation for the subsequent crop. A 2‐yr study was conducted to determine the effect of grass and legume cover crops on spring DM production and N accumulation. Each year, cover crops were planted in late August and late September on a loamy, mixed, mesic Humaquept in the Fraser River Delta. Wheat ( Triticum aestivum L.), rye ( Secale cereale L.), and ryegrass ( Lolium multiflorum L.) were planted in monoculture and in mixtures with crimson clover ( Trifolium incarnatum L.). Other treatments included pure stand of crimson clover and wheat–hairy vetch ( Vicia villosa Roth.) mixture. Cover crop biomass was sampled three times in 1995 and four times in 1996 during the spring growth period. Dry matter accumulation of early planted cover crops increased by 26 to 269% during the spring growth period, ranging between 0.6 Mg ha −1 for clover and 10 Mg ha −1 for wheat, wheat–clover, and wheat–vetch treatments. Late‐planted cover crops produced between 15 and 75% lower DM yield compared with early planted cover crops. Nitrogen accumulation increased by 3 to 74 kg ha −1 for early planted crops and by 3 to 47 kg ha −1 for late‐planted crops. Nitrogen accumulation at final spring sampling ranged from 44 to 144 kg ha −1 for early planted crops and from 10 to 99 kg ha −1 for late‐planted crops. The low C/N ratio of wheat–vetch treatment compared with wheat monoculture at final sampling indicated the potential for vetch to increase the N content of the mixture.
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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 | 0.000 | 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) | 0.000 | 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".