Enhanced efficiency urea fertilizers and timing effects on N<sub>2</sub>O emissions from spring wheat production in Manitoba
Bibliographic record
Abstract
Abstract Opportunities exist to reduce nitrous oxide (N2O) emissions from nitrogen (N) fertilizers using enhanced efficiency fertilizers (EEFs) and managing application timing. This study examined (1) application timing (fall/spring) and (2) fertilizer N source on N2O emissions, yield, and N uptake of Canadian hard red spring wheat (Triticum aestivum L.) in Southern Manitoba. Fertilizer N sources included granular urea and four EEF products: (1) polymer‐coated urea (environmentally smart nitrogen [ESN]); (2) urea plus nitrification inhibitor (eNtrench); (3) urea plus urease inhibitor (Limus); and (4) urea plus nitrification and urease inhibitor (SuperU). Nitrification‐inhibited products most consistently reduced N2O emissions while maintaining productivity. Compared to urea alone, urea + eNtrench was most effective in reducing cumulative N2O emissions by 47%–64% at four of six site‐years. SuperU reduced N2O emissions by 37%–57% at three of six site‐years. ESN and urea + Limus did not affect emissions in most years. Wheat yield, protein, and N uptake were unaffected by N source in five of six site‐years. Compared to spring, fall application gave greater N2O emissions by 33%–67% at three of six site‐years due to spring‐thaw emissions. Fall was inferior to spring application in wetter site years with lower yield, protein, and N uptake. Overall, nitrification‐inhibited products—either alone or with a urease inhibitor—are a promising tool to reduce N2O emissions while maintaining wheat productivity in Manitoba. However, given that there were few consistent increases in yield or protein, the additional cost of the inhibitors will be a barrier to adoption.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".