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 NO3-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 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".