Effects of Nitrogen Application Rate and Plant Density on Severity of Tar Spot of Corn
Bibliographic record
Abstract
Tar spot of corn, caused by the fungus Phyllachora maydis, is an economically important foliar disease recently reported in the United States and Canada. Due to the recent introduction of Phyllachora maydis, the impacts of cultural management practices on disease development are still unknown. Separate field studies were conducted to determine the effects of nitrogen (N) application rate and plant density on disease development. Field trials were conducted across six site years in Michigan with two corn hybrids of differing disease susceptibility. The relative area under the disease progress curve was used to compare disease development between N application rates and plant densities. Nitrogen application rate had no significant effect on disease at any location. Plant density and disease had a significant ( P < 0.05) inverse relationship at five of six site years, with an average 41% decrease in the relative area under the disease progress curve for every 1,000 plants per hectare increase. The economically optimal planting density ranged from 73 to 77 thousand plants per hectare for US$150 to 300 per metric ton corn prices, demonstrating that relatively low planting densities were more profitable despite greater disease. Therefore, other disease management practices including hybrid selection may be more effective at protecting yield than increasing plant density.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 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".