Two decades of micrometeorological measurements show annual trends in N2O emissions from an agricultural field: the role of non-growing season soil freezing and fertilization in eastern Canada
Bibliographic record
Abstract
Emissions of nitrous oxide (N2O) from agricultural soils over the winter and spring, also referred to as the non-growing season (NGS), have been shown to contribute significantly to annual N2O budgets. However, such conclusions are based on limited observations of N2O flux during the NGS. Multi-year datasets that include the NGS are needed to improve understanding of what drives N2O emissions. This study uses twenty years of quasi-continuous N2O observations from a crop field in Nepean, Ontario, that has been gap filled using the DeNitrification and DeComposition model (DNDCv.CAN) output data (d = 0.75 across all years of annual total emissions). Across twenty years, two key emission periods occur: the first from March 9 to April 24 associated with snowmelt, and the second from May 11 to June 21 associated with fertilization. I evaluated known drivers of soil N2O emissions, including variability in weather and field management, to determine their relationship to these peaks. Consistent with the literature, fertilizer rate proved to be a key driver of annual emissions, particularly the May 11 to June 21 peak. To determine drivers of the March to April emission period (which is encompassed by the NGS), I expanded upon an empirical model proposed by Wagner-Riddle et al. (2017), which uses a relationship between NGS soil freezing degree days (FDD) and N2O emissions. Nepean data supports the relationship that NGS N2O emissions are proportional to soil freezing. The addition of Nepean data to this model resulted in higher emissions under fewer FDD. This revision would likely lead to higher estimates of N2O emissions when used to make global estimates. This change introduced by a single additional site emphasizes that more observations need to be included to make it more generally applicable. Results from this study improve understanding of key emission periods that occur post-snow melt, driven by over winter soil freezing, and post-fertilization, driven by fertilizer application rate. Considering soil freezing and fertilizer application as key drivers, management methods may be revised to reduce emissions, with emphasis on right timing and rate of fertilizer application to limit NGS and post-fertilization loss.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".