Blade Roller–Green Manure Interactions on Nitrogen Dynamics, Weeds, and Organic Wheat
Bibliographic record
Abstract
The blade roller offers new opportunities to reduce tillage, especially in organic farming. The objective of the study was to reduce tillage in the green manure phase of a green manure–wheat (Triticum aestivum L.) rotation by substituting tillage with blade rolling. A pea (Pisum sativum L.) and oat (Avena sativa L.) green manure was used for two site‐years at Carman, MB, while a pea monocrop was used for one site‐year at Oxbow, SK. At pea flowering, the green manure was terminated by rolling, tilling, or a combination of the two. Ammonia emissions were greater in the no‐till compared with the tilled green manure system, though total ammonia losses were low (<13 kg ha−1). Replacing tillage with rolling reduced soil nitrate N in autumn after green manure by 56 to 88 kg ha−1 in the 0‐ to 60‐cm soil depth. Reduced green manure tillage did not affect wheat establishment but delayed plant development in some instances. Fewer weeds were often observed in wheat in the no‐till compared with tilled plots. Total N supply in the green manure–wheat system was reduced in the no‐till system compared to the tilled only system at two out of three site‐years by an average of 44%. While reduced N supply in the reduced tillage system coincided with reduced wheat yield and protein, it was concluded that factors other than N also were involved. Using the blade roller instead of tillage in the green manure year provides soil conservation benefits and facilitates wheat production the following year.
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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 | 0.001 | 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".