Application of Nitrapyrin with Banded Urea, Urea Ammonium Nitrate, and Ammonia Delays Nitrification and Reduces Nitrogen Loss in Canadian Soils
Bibliographic record
Abstract
Core Ideas Nitrapyrin has been tested in fallow field trials in cereal and oilseed growing regions of Canada. Nitrapyrin maintained N in the ammonium form through critical loss periods. Nitrapyrin preserved more mineral N in the soil relative to non‐stabilized fertilizer. Soil N loss is a significant impediment to maximizing yield and profitability for farmers in Canada. Maintaining N in the stable and plant‐available NH4+ form via use of nitrification inhibitors limits the potential for soil N losses from denitrification or leaching. Between 2013 and 2015, twenty‐one research trials were established across the major cereal and oilseed growing regions of Canada to evaluate the efficacy of two commercially available formulated nitrapyrin products, eNtrenchTM and N‐Serve, at stabilizing soil N in the NH4+ form and protecting against N loss. Urea, urea ammonium nitrate (UAN) or NH3 fertilizer treatments were banded in the fall or spring on fallow land, and the soil was sampled to a depth of 60 cm at multiple time intervals after application. Fall applications of nitrapyrin resulted in 21–63% more NH4N, and 10–19% more total mineral N, at spring sampling after soil thaw relative to non‐stabilized fertilizer. Spring applications of nitrapyrin resulted in larger pools of NH4–N for at least 8 weeks after treatment, and they increased total mineral N by up to 25%, compared with non‐stabilized treatments. Results suggest that eNtrench and N‐Serve are useful tools for growers looking to protect their N investment, optimize crop yield potential, and enhance flexibility of their N application timing.
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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 | 0.001 | 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".