Winter fluxes of greenhouse gases from snow‐covered agricultural soil:intra‐annual and interannual variations
Bibliographic record
Abstract
Despite the length of winter in cold temperate climates, few studies refer to greenhouse gas emissions from soils during the nongrowing season. In this study, N2O and CO2 fluxes from agricultural and forest soils in southeastern Quebec (Canada) were measured during winter and spring from 1994 to 1997, and the influences of climate, soil, and snow properties on the gaseous emissions were examined. N2O fluxes were far greater from the agricultural soil (2‐187 ng N2O m−2 s−1) than from the forest soil (< 3 ng N2O m−2 s−1), but CO2 fluxes were equivalent for both soil systems (2‐102 μ CO2 m−2 s−1). The higher N2O concentrations in the lower soil horizons could be explained by positive temperature gradients with depth and concomitant negative gas solubility gradients. However, the higher N2O concentrations could also be explained by variations in the expression of N2O reductase with depth, which can modify the N2/N2O ratios in relation to the availability of O2. Calculated N2O‐N fluxes showed that N losses by gaseous emissions from soils during winter and spring were comparable to, or exceeded, similar reported N losses during the growing season. The highest winter fluxes observed in 1997 were interpreted to be due to favorable meteorological conditions that prevailed for denitrification through high soil water content in summer and fall of 1996. Although interannual and nterseasonal variations of fluxes are important, this study shows that wintertime losses of N2O from agricultural soil can be up to 2 to 4 times greater than emissions measured during the growing season in similar agroecosystems.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".