Topography and Management of Nitrogen and Fungicide Affects Diseases and Productivity of Canola
Bibliographic record
Abstract
Successful application of precision agriculture technology requires information on crop response to many factors including fertilization and disease management. Field experiments were conducted on a hummocky landscape in the northern prairies to determine effects of slope (SL) position, N fertilization, and fungicide (FU) application on disease incidence, biomass yield, and seed yield, quality, N uptake, and recovery of applied fertilizer N for canola (Brassica napus L.). As N rate was increased, blackleg [Leptosphaeria maculans (Desmaz.) Ces. & De Not] disease incidence, biomass yield, and seed yield, protein content, N uptake, and percentage green increased while emergence, thousand‐seed weight, and seed oil content and recovery of fertilizer N declined. The response of seed yield to N fertilization was relatively greater at upper than at the lower SL position, indicating the fertilizer N requirement for optimum seed yield was less at lower (71 kg N ha−1) than upper (88 kg N ha−1) SL. The upper SL had higher blackleg incidence and seed oil content than the lower SL. Therefore, FU application to control blackleg tended to be more beneficial for high N rates at the upper SL position. Sclerotinia stem rot [Sclerotinia sclerotiorum (Lib.) de Bary] did not appear to vary between management units. The results indicate some potential to use precision agriculture based on topography to guide disease control and N fertilizer strategies although each disease must be considered individually and with consideration for other management practices and environmental conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".