Right source and right time: reducing nitrous oxide emissions with enhanced efficiency nitrogen fertilizers
Bibliographic record
Abstract
Fall applications of synthetic nitrogen (N) fertilizers to cropland induces significant nitrous oxide (N2O) emissions during spring-thaw and throughout the growing season; impeding N2O gas reductions for the Canadian agricultural sector. A possible means to reduce N2O is with enhanced efficiency fertilizers (EEF) which are designed to delay the activity of urea hydrolysis and/or nitrification, or control the availability of plant inorganic N. The objective of this thesis was to quantify and compare cumulative N2O (ΣN2O kg N ha-1) and agronomic measurements of Canadian hard red spring wheat (Triticum aestivum L.) with EEF and non-EEF sources, and application timing (spring versus fall) in southern Manitoba. Treatments included fall and spring applications of granular urea with and without EEF containing urease inhibitor product- LIMUS, nitrification inhibitor product- eNtrench, double inhibitor product- SuperU, polymer coated urea- ESN, and anhydrous ammonia (AA) with and without a nitrification inhibitor- N-Serve. Using the static-vented chamber technique, treatments were examined in two replicated plot trials in each of three years from 2015 to 2017. Among five out of six sites which had an N source effect, eNtrench and SuperU reduced ΣN2O by 54 and 43%, respectively. Combining urea sources, the fall-applied ΣN2O was similar to spring at 4 of 6 sites. Precipitation events induced largest daily emissions from both fall and spring applications. LIMUS protein levels were 0.6 and 0.5% lower compared to eNtrench and ESN. No site observed a significant grain yield response to EEF sources. Grain yields and protein from fall applications were generally similar or lower than spring. eNtrench and SuperU were the most optimal products to reduce N2O and maintain agronomic variables. Future studies should investigate whether reduced EEF rates produce similar agronomic and environmental measurements as to this current study.
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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".