Legume Cover Crops with Winter Cereals in Southern Manitoba
Bibliographic record
Abstract
The opportunity to include late‐season cover crops in northern cropping systems has been enhanced with the adoption of winter cereal production; however, cover crop feasibility has not been evaluated in these regions. Field experiments were conducted at two sites in Manitoba in 1998 and 1999 to (i) assess establishment and dry matter (DM) production of legume cover crops that were relay‐cropped [alfalfa (Medicago sativa L.) and red clover (Trifolium pratense L.)] or double‐cropped [chickling vetch (Lathyrus sativus L.) and black lentil (Lens culinaris Medik. subsp. culinaris)] with winter cereals [winter wheat (Triticum aestivum L.) and fall rye (Secale cereale L.)], (ii) assess the effect of relay cover crops on cereal grain yield, and (iii) characterize the effects of a red clover cover crop on the microclimate after winter wheat harvest. Establishment and midseason DM of the relay crops were not affected consistently by cereal crop type. Legume DM at freeze‐up was similar in winter wheat and fall rye systems and ranged from 190 to 1800 kg ha−1, with moisture availability being the critical factor. Across all site‐years, final DM for red clover, alfalfa, chickling vetch, and lentil averaged 1157, 690, 746, and 634 kg ha−1. Relay crops did not affect main‐crop grain yield but did significantly reduce main‐crop DM production in some cases. The red clover cover crop created a moderating effect on late summer and fall surface (5‐cm height) air temperatures and decreased soil moisture availability. Including relay and double crops in winter cereal‐based cropping systems appears feasible in southern Manitoba.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".