Response of annual canarygrass (<i>Phalaris canariensis</i> L.) to nitrogen fertilizer and fungicide applications
Bibliographic record
Abstract
Annual canarygrass (Phalaris canariensis L.) is a specialty crop grown in Canada and the harvested grain is primarily used to feed wild and domesticated bird species. A field experiment was conducted at 5 locations in both 2012 and 2013 to study the response of annual canarygrass development and grain yield to the combined effects of fungicide (propiconazole + trifloxystrobin) and nitrogen (N) fertilizer, and to determine the minimum number of site years required to detect these effects. The experimental design was a split plot with fungicide application (none or fungicide) as the main plot and N application rate as the sub plot (10, 20, 30, 50, 70, 90 kg N·ha−1). There was a linear increase of 14.5% in grain yield as the rate of N fertilizer increased. The fungicide application increased the grain yield 8.5% by increasing kernel density from 15 197 kernels m−2 to 16 288 kernels m−2. There was no interaction between the N rate and fungicide application. The application of a fungicide did not increase the responsiveness of annual canarygrass to N fertilizer. The lack of an interaction between N and fungicide application indicates that these two practices can be managed independently by annual canarygrass producers. To optimize grain yield producers should apply 50 kg N·ha−1 and apply a fungicide to increase grain yield especially in regions where septoria leaf mottle is prevalent. The number of site years needed to consistently detect the impact of N and fungicide on the grain yield were 4 and 5 site years, respectively.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".