Enhanced Efficiency Foliar Nitrogen and Pyraclostrobin Applications for High Yielding Corn
Bibliographic record
Abstract
Combining a foliar fertilizer and fungicide in a single application could complement soil-applied nitrogen (N) and reduce application costs. Limited research has evaluated such combinations for high-yield corn (Zea mays L.) production systems. This research evaluated the effect of mixing order of enhanced-efficiency foliar N (30-0-0-0, Nitamin) rates (0, 9, and 28 L ha-1) with pyraclostrobin under different crop-yield environments (soil applied N at 84, 169, and 337 kg ha-1) on crop injury, disease severity, chlorophyll content, grain quality, and yield at Novelty and Albany, Missouri, in 2010 and 2011. There was no effect of 30-0-0-0 at 9 or 28 L ha-1 on corn yields in low- (soil applied N at 84 kg ha-1) or medium-yield environments (soil applied N at 169 kg ha-1). In a high-yield environment (soil applied N at 337 kg ha-1 at Novelty and Albany), 30-0-0-0 at 9 L ha-1 increased grain yields 0.38 Mg ha-1 (3.7%) compared to the non-treated control, but 30-0-0-0 at 28 L ha-1 did not increase yield due to 3-4% crop injury. In a high-yield environment (> 9.4 Mg ha-1), pyraclostrobin increased yields 3.9 to 7.1% (0.38 to 0.7 Mg ha-1) compared to the non-treated plants. This study found no significant effect of mixing order on corn yield response when 30-0-0-0 was applied with pyraclostrobin at 0.055 kg ai ha-1. The severity of diseases (Cercospora zea-maydis, Puccinia sorghi, and Exserohilum turcicum), which was less than 12% depending on the treatment, was affected by soil-applied N rate, 30-0-0-0 rate, and pyraclostrobin, depending on the site-year. Pyraclostrobin at 0.11 kg ai ha-1 (7.1%) and 30-0-0-0 at 9 L ha-1 (3.7%) were the highest-yielding treatments compared to the non-treated control with good crop safety.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".