Nitrous Oxide Emissions from a Clay Soil Receiving Granular Urea Formulations and Dairy Manure
Bibliographic record
Abstract
Soil N2O emissions vary with N source. A study was undertaken on a clay soil in the Red River Valley, Manitoba, Canada, to determine the effect of granular N fertilizers and dairy manure on N2O emissions from a field cropped to rapeseed (Brassica napus L.) in 2009 and spring wheat (Triticum aestivum L.) in 2010. Treatments included an unamended control, granular urea, controlled‐release urea (ESN), stabilized urea (SuperU), and solid dairy manure added at rates to achieve a total of 140 kg available N ha−1 (product plus soil N test). The N fertilizers were broadcast and shallowly incorporated each spring before planting; the manure was broadcast incorporated the previous fall. Nitrous oxide emissions were monitored from planting to freeze in fall and during spring thaw in 2011 using static‐vented chambers. In both years, N2O emissions occurred within 4 to 5 wk of planting but not in fall after manure application. Area‐scale cumulative N2O emissions (∑N2O, kg N ha−1) from planting to freeze were control < ESN = manure < urea = SuperU. Nitrous oxide emission factors were 0.017 kg N2O‐N kg−1 available N added for urea and SuperU and 0.007 kg N2O‐N kg−1 available N for ESN. Seventy‐eight percent of the variation in ∑N2O could be explained by NO3− intensity, an integration of soil NO3− concentrations during the study periods. Greater ∑N2O were also associated with higher yields. These findings suggest that N release rates, as indicated by NO3− intensity and yield, determined N2O emissions. The results highlight the challenge of meeting crop demand yet reducing N2O emissions by selection of an N source.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".