Nitrogen cycling, profit margins and sweet corn yield under fall cover crop systems
Bibliographic record
Abstract
In order to improve N best management practices in southwestern Ontario vegetable farming, the effect of cover crops on N dynamics in the fall and spring prior to sweet corn planting and during sweet corn season was assessed. The experiment was a split plot design in a fresh green pea cover crop sweet corn rotation that took place over 2 site-years at Bothwell and Ridgetown in 2006 2007 and 2007 2008, respectively. The main plot factor was fall cover crop type with five treatments including oat (Avena sativa L.), cereal rye (Secale cereale L.), oilseed radish (OSR; Raphanus sativus L. var. oleoferus Metzg Stokes), mixture OSR plus cereal rye (OSR&rye) and a no cover crop control. Compared with no cover crop, sweet corn profit margins were higher by $450 ha- 1 for oat at Bothwell and $1300 and $760 ha- 1 for OSR and OSR&rye, respectively, at Ridgetown. By comparing plant available N over the cover crop season, the cover crops tested were more effective at preventing N loss at Bothwell than at Ridgetown likely due to higher precipitation and sandier soil at Bothwell. Despite differences in site characteristics, cover crops did not result in increased plant available N compared with no-cover during the sweet corn season at either site, indicating that these cover crops will not provide an N credit to the following crop and growers should not modify N fertilizer applications based on cover crops.
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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 | 0.000 | 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) | 0.001 | 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".