Legume cover crop as a primary nitrogen source in an organic crop rotation in Ontario, Canada: impacts on corn, soybean and winter wheat yields
Bibliographic record
Abstract
Abstract This study presents results from the first 5 years of an organic cropping trial in Ontario, Canada, where legume cover crops were the primary nitrogen source in a soybean-winter wheat/cover crop-corn rotation. Treatments included cover crop termination using moldboard plow (MP) or chisel plow (CP), a no-cover crop control under conventional production (CK-C), and four cover crops including summer-seeded crimson clover (CC, Trifolium incarnatum L.), summer-seeded hairy vetch (HV, Vicia villosa L. Roth), summer-seeded red clover (RC ss , Trifolium pratense L.), and frost-seeded red clover (RC fs ). Summer-seeding occurred after wheat harvest (July–August), and frost-seeding occurred in early spring (March–April). At cover crop termination, average aboveground cover crop biomass ranged from 5.9 to 8.1 Mg ha −1 , while accumulated biomass nitrogen ranged from 155 to 193 kg ha −1 . Corn grain yields were 11.6 Mg ha −1 for MP and 10.2 Mg ha −1 for CP tillage-termination method; and 13.3 Mg ha −1 for CK-C, 10.9 Mg ha −1 for RC fs , 10.6 Mg ha −1 for HV, 10.2 Mg ha −1 for CC, and 9.5 Mg ha −1 for RC ss . Organic winter wheat yields were nitrogen-limited, averaging 27% lower than CK-C. Winter wheat yields were 10–15% lower in the RC fs than in other summer-seeded cover crop treatments. Soybean yields were largely unaffected by the treatments. It was concluded that summer-seeded legume cover crops are an effective primary nitrogen source for corn, but not as effective for the winter wheat phase of the soybean-winter wheat-corn rotation.
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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 | 0.001 | 0.001 |
| 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".