Economic evaluation of variable rate nitrogen management of canola for zones based on historical yield maps and soil test recommendations
Bibliographic record
Abstract
Abstract Canola (Brassica napus L.) is a highly valuable crop for Canada’s economy, making the efficient management of canola a priority. A field-scale study was conducted at ten sites between 2014 and 2016 to evaluate the viability of site specific nitrogen (N) management zones (MZ) based on analysis of historical yield maps and soil test recommendations to improve canola productivity and profitability in western Canada. Treatments included factorial combinations of three canola yield zones (low, average, high) by four N rates, replicated four times at each site. The canola yield function had a quadratic form in each field but the effects of MZ varied between fields with positive effects in only a few fields. When ten site-years data were combined, MZ had positive effects on canola performance. On average, MZ of N fertilizer over ten fields generated between $28 to $65 ha−1 more net revenue (NR) relative to average yield management. Site-years, which reflect farm management and other farm characteristics had significant effects on yield and NR ranging from − $91 to $352 ha−1 compared to a baseline. Nitrogen application under MZs was only reduced by 8% compared to uniform rates. The potential for MZ does exist; however, its effectiveness is highly variable.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".