Improved nitrogen fertilizer management practices that reduce growing season nitrous oxide emissions may increase non-growing season emissions
Bibliographic record
Abstract
Field cropping practices in Canada include routine use of nitrogen (N) fertilizer, which produces substantial amounts of nitrous oxide (N2O) emissions. Adoption of improved N management practices may reduce both the amount of N applied and these N2O emissions. Using flux-tower field measurements, we investigated how dual inhibitors (urease and nitrification inhibitors with urea) reduced N fertilizer-induced N2O emissions, compared with urea only, in eastern Canada across 7 years. We also used meta-analysis (of static chamber studies) to examine how inhibitors and other enhanced efficiency fertilizers (EEFs), along with other improved N management techniques, affected fertilizer-induced N2O emissions from Canadian agricultural cropping systems. From the field study, the dual inhibitors reduced growing season N2O emissions by 22% and annual N2O emissions by 10% for high N application rates to corn (Zea mays), while N2O emissions from lower N applications to wheat (Triticum aestivum) showed no differences between the EEF and urea. Crop yields for both the corn and wheat were similar between the different N fertilizer treatments. Across Canada, the meta-analysis showed that EEFs (which include coated slow-release fertilizers and both nitrification and urease inhibitors combined and on their own), on average, reduced N2O emissions by 11%. Nitrification inhibitors (alone or in combination with urease inhibitors) averaged a 19% reduction in N2O emissions. Most of the studies used in the meta-analysis had minimal sampling through the non-growing season though, so the total annual N2O emission reductions were not evaluated and may actually be lower. The meta-analysis indicated that the most effective N management techniques for reducing N2O emissions were the use of EEFs, split application of N fertilizers and the use of organic fertilizers, with the effectiveness of these practices all strongly influenced by soil and weather conditions. The meta-analysis also found that reductions with EEFs from studies that included year-round measurements, tended to be less than studies that included only the growing season. This suggests that when improved N management practices use the same N application rates as the regular practice, more residual N may be available for non-growing season losses. As a result, when no yield benefit is noted, these improved practices should be combined with N rate reductions.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".