Meta-analysis of 4R nitrogen management on direct nitrous oxide emissions from cropland in the Canadian Prairies and Northern Great Plain USA
Bibliographic record
Abstract
Agricultural soils are a major source of direct and indirect emissions of the greenhouse gas nitrous oxide (N2O). The 4R nutrient stewardship framework, involving using the right source at the right time, rate, and place, can significantly reduce N2O emissions. Here a meta-analysis of reported studies conducted in Western Canada and Northern Great Plains of USA with similar climatic conditions (Köppen Dfb, warm summer humid continental climate) compared total emissions of N2O (ΣN2O, kg N2O-N- ha-1) for fertilizer sources (conventional fertilizers versus Enhanced Efficiency Fertilizers- EEFs), placement (broadcast versus band), timing of application (spring versus fall), and application rate. The results show that compared to conventional granular urea, the use of nitrification inhibitors, dual inhibitors (Super-U), and polymer-coated urea (PCU) significantly reduced ΣN2O by 35%, 48%, and 16%, respectively. Urease inhibitors had no effect on ΣN2O. Products containing nitrification inhibitors with UAN significantly reduced ΣN2O by 17%. Overall, the use of nitrification inhibitors reduced the fertilizer-induced N2O emissions by 67% from urea alone and by 30% from UAN alone. Compared to broadcast-incorporation, banding generally tended to decrease ΣN2O by 4%. Among banding depths, deep banding (> 6 cm) tended to decrease ΣN2O by 16% whereas shallow banding (< 6 cm) tended to increase ΣN2O by 5%. In case of banding positions, side banding and midrow banding tended to decrease ΣN2O by 2% and 4% respectively. Whereas compared to surface application, banding resulted in an increase of ΣN2O by 14%. Across banding depths, deep and shallow banding tended to increase ΣN2O by 3% and 20%, respectively. Among banding positions, side banding tended to decrease ΣN2O by 11% whereas midrow banding showed a considerate increase in ΣN2O by 40%. The fall application of fertilizer significantly reduced ΣN2O compared with spring application under specific soil conditions (i.e., pH < 7, and silt soil) but the overall impact was not statistically significant. Total and fertilizer-induced N2O emissions exhibited increasing trend with the increase in nitrogen (N) input. Based on the analysis we conclude that adopting the 4Rs can greatly reduce N2O emissions among the reviewed and sorted datasets.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.028 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".