Pronitridine Nitrification Inhibitor With Urea Ammonium Nitrate for Corn
Bibliographic record
Abstract
Nitrification inhibitors have been used to enhance the efficiency of nitrogen fertilizers. This research evaluated the effectiveness of nontreated urea ammonium nitrate (UAN) at 0, 67, 135, 202, and 270 kg N ha-1 as well as UAN treated with nitrification inhibitors (pronitridine at 9.4 and 18.8 L ha-1 or nitrapyrin at 0.5 kg a.i. ha-1) to enhance N uptake and increase yield of corn (Zea mays L.). The study took place from 2012-2014 in upstate Missouri on a claypan soil. During the experiments, environmental conditions (high, medium, and low yielding years) affected corn response to pronitridine and nitrapyrin. In general, UAN plus pronitridine at 9.4 L ha-1 had similar effects on corn compared pronitridine at a higher (18.7 L ha-1) rate. During a high-yielding year (2014), in order to produce yields equivalent to 67 kg N ha-1 plus pronitridine at 9.4 L ha-1 or nitrapyrin, UAN needed to be increased 14 to 19%. Similarly, the amount of nontreated UAN needed to be increased 8 to 11% for yields to be equivalent to UAN at 135 kg N ha-1 plus pronitridine at 9.4 L ha-1 or nitrapyrin. Grain N removal and agronomic efficiency was highest with pronitridine at 9.4 L ha-1 and nitrapyrin combined with 67 and 135 kg N ha-1, respectively. This research indicates that pronitridine was as effective as nitrapyrin when added to a pre-emergence application of UAN placed between the rows in a dribble band.
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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) | 0.001 | 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 | 0.001 |
| 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".