Corn Yield Response to Pyraclostrobin with Foliar Fertilizers
Bibliographic record
Abstract
Strobilurin fungicides, including pyraclostrobin, protect many crops from several fungal pathogens and create opportunities to increase plant health and yields. However, corn (Zea mays L.) and many other plants’ physiological responses to pyraclostrobin include increases in processes that require nutrients. By applying foliar fertilizers, growers can adjust their nutrient-management strategy based on the plant’s reaction to pyraclostrobin. Identifying plants’ increased nutrient demands and meeting them with a foliar fertilizer at the time of fungicide application (tasselling) could increase yields. This study evaluated effects of foliar-applied pyraclostrobin at 0.11 kg ha-1 a.i. with or without 13 commonly available foliar fertilizers on yield, tissue macro- and micronutrient concentrations, severity of disease, and grain quality. Field research occurred at three University of Missouri research centers from 2008-2009. One foliar fertilizer, 0-0-30-0, caused up to 20% crop injury. Diseases affected plants in all six site-years, but overall severity was low (≤ 2%) and likely did not impact crop performance. Pyraclostrobin increased ear leaf B and Cu concentrations over all site-years seven days after treatment, and decreased N concentrations at one site-year. Grain yields increased 5% at two research sites with pyraclostrobin, and one location had increased grain moisture and grain oil at harvest. One foliar fertilizer, 30-0-0-0, increased grain yields by 10% at two sites compared to the non-treated control. However, foliar fertilizers showed no observable effects on grain quality characteristics, and none of the foliar fertilizers negatively impacted grain yield.
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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 | 0.000 |
| 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 | 0.000 | 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".