Optical sensors have potential for determining nitrogen fertilizer topdressing requirements of canola in Saskatchewan
Bibliographic record
Abstract
An important challenge in N management is matching fertilizer inputs to crop requirements for specific environmental conditions. Field experiments were completed over 3 yr at two locations in Saskatchewan to evaluate the feasibility of using optical sensors and high-N reference plots along with topdressed liquid urea ammonium-nitrate (UAN) to arrive at more optimal N rates for canola (Brassica napus L.). Treatments included N management strategies where the timing and methods of application were varied along with the total quantities of N applied. Sensor-based N management was compared with the predominant practice of banding predetermined amounts of N at seeding. On average, sensor-based N management resulted in a 34 kg N ha -1 reduction in fertilizer use with no effect on seed yields except at Indian Head in 2006 where dry conditions resulted in yield reductions of 370 to 454 kg ha -1 compared with applying canola's entire N requirements at seeding. Sensor-based N management or split-N applications never increased yields relative to applying all N at seeding. Adopting this technology in western Canada will more likely result in reduced N inputs without reducing yield than increased seed yield. While sensor-based N management did not reduce post-harvest residual soil NO 3 -N levels, agronomic N use efficiency (ANUE) was increased 33% of the time and was never lessened. When significant, the increase in ANUE ranged from 4.8 to 7.8 kg seed kg N applied -1 . Overall, optical sensors have potential as a tool for managing N fertility more efficiently in canola production. Key words: Brassica napus, normalized difference vegetation index, urea ammonium-nitrate, nitrogen use efficiency, agriculture, precision
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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 | 0.001 | 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 | 0.001 | 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".