Crop rotations compared with continuous canola and wheat for crop production and fertilizer use over 6 yr
Bibliographic record
Abstract
Local economic, logistic, soil, and weather conditions have made appropriate rotations vary from one area to another. Seed yield and fertilizer use in rotations were compared with continuous cropping for 6 yr in the southeast Peace region of Alberta, Canada. Canola (C) (Brassica napus L.), wheat (W) (Triticum aestivum L.), pea (P) (Pisum sativum L.), barley (B) (Hordeum vulgare L.), and flax (F) (Linum usitatissimum L.) were grown in 12 treatments [i.e., continuous canola (CC) and wheat (WW) and rotations of W–C, P–W–W, C–W–W, C–C–W, P–C–W, C–P–W, W–B–C, B–W–C, F–W–C, and F–C–W]. Canola yield increased with 1 or 2 yr breaks from canola. Within rotations, there was a trend to lower canola yield on canola stubble compared with other crop stubbles. Wheat yield was generally lower in WW than in rotations. Among rotations, wheat yield tended to be greater on pea stubble compared with canola, wheat, barley, and flax stubbles. Averaged over 2010–2015, the canola yield benefit from rotations was 0.632 Mg ha−1 (19.4%) over CC and the wheat yield benefit from rotations was 0.313 Mg ha−1 (7.2%) over WW. Nitrogen saving was observed when pea was included in 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 | 0.000 | 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) | 0.004 | 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".