Economic Effects of Preceding Crops and Nitrogen Application on Canola and Subsequent Barley
Bibliographic record
Abstract
The rising cost of N in western Canada has created interest in alternative sources of N fertilizer. Legumes have the ability to fix N supply for subsequent crops, but knowledge of the effects of legumes on subsequent canola or barley is limited. A multi‐location study was conducted from 2009 to 2011 in western Canada to evaluate the economic effects of various preceding crops (P) and N rate on subsequent canola and barley in a P–canola–barley rotation. Six preceding crops (field pea, lentil, faba bean, canola, wheat, and green manure [GRM] legume [faba bean]) were grown in factorial combination with five N rates (0, 30, 60, 90, and 120 kg ha−1) at seven sites in Alberta, Saskatchewan, and Manitoba. When the preceding crop was GRM, the net revenue (NR) of canola or canola–barley was highest but insufficient to compensate for negative NR during the GRM year (2009). Canola as a preceding crop yielded the least NR for the canola and canola–barley phases of the rotation. The quadratic responses of NR for canola and barley to optimal N indicated that N applied could be reduced below 120 kg ha−1 without diminishing yield at some locations in western Canada. Over the entire 3‐yr crop sequence, legume preceding crops (lentil or field pea) grown for seed provided the greatest returns. The GRM improved the yield of the following crops considerably but the increased canola and barley yields were not able to alleviate the lost NR during the preceding crop phase.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".