Economic Impact of Residual Nitrogen and Preceding Crops on Wheat and Canola
Bibliographic record
Abstract
Core Ideas Study on the residual effects of preceding crops and past N management on wheat and canola. Initial positive impact of legume preceding crops on net revenue diminished over time. Residual N had positive effects on net revenue, especially at the highest N rate. Annual fertilization based on production capacity of the region gave the best economic results. Dry conditions allow excess N to remain in soil as residual N, reducing the economic risk of fertilizer over‐application. A 6‐yr study was conducted across western Canada to evaluate the residual effects of preceding crops (PCs) and past N rate management on the economics of subsequent wheat ( Triticum aestivum L.) and canola ( Brassica napus L.). Field pea ( Pisum sativum L.), lentil ( Lens culinaris Medik.), canola and wheat harvested for grain, and faba bean ( Vicia faba L.) grown and harvested for grain or as a green manure were direct seeded in 2009. Canola was seeded in 2010, barley in 2011, and canola again in 2012 with fertilizer N applied at varying rates for each crop. Spring wheat grown in 2013 and canola in 2014, both without N application, were used to determine residual PC and residual N effects. The positive benefit of legume PCs on the annual crop net revenue (NR) of wheat and canola crops diminished over time. Residual N from previously applied N had positive effects on annual wheat NR in 2013, but only the highest application rate contributed significantly to canola NR in 2014. The NR was greatest with an annual fertilization program based on regional production capacity, but under the dry conditions of the Canadian prairies excess N remaining in the soil after crop production could remain in the soil as residual N to be used by following crops. While generally insufficient to optimize crop production, residual N can reduce the economic risk from over‐application of fertilizer if N is not utilized by the crop due to adverse growing conditions in the year of fertilizer application.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".