The net greenhouse gas balance of an intensively managed forage crop in the Lower Fraser Valley in British Columbia, Canada
Bibliographic record
Abstract
Intensively managed grasslands have been found to be either net greenhouse gas (GHG) sources or sinks depending on management and climate, where the uptake of carbon dioxide (CO2) is balanced by respiration, crop harvest, and the emission of potent, non-CO2 GHGs. This study reports eddy-covariance measurements of carbon dioxide (CO2), nitrous oxide (N2O), and methane (CH4) combined with non-gaseous imports and exports of carbon to determine the net greenhouse gas balance (NGB) of a conventionally managed forage field on a dairy farm in Agassiz, British Columbia, Canada. The forage crop (ryegrass and tall fescue) was intensively managed via ‘cut and carry’, where the crop was harvested and removed from the field up to 6 times a year. The field received multiple applications of dairy manure slurry and additionally fertilized with inorganic nitrogen. A previous study (Pow et al., 2024) determined that the field was a weak or moderate source of C in terms of the net ecosystem carbon balance (NECB); this study additionally reports that the NGB of the field was 2038 ± 890 and 901 ± 920 g CO2e m-2 y-1 (± indicates the uncertainty range) and a moderate GHG source during 2020 and 2021, respectively, with the large range attributed to interannual variation in the NECB relative to the non-CO2 GHG emissions. Elevated N2O emissions were observed after dairy manure slurry applications and N-fertilizer application, and the magnitude and duration of these post-management N2O fluxes were associated with variations in near-surface soil volumetric water content. Multiple soil freezing events were associated with elevated N2O fluxes, with the magnitude of fluxes associated with freezing intensity, and were determined to be a substantial proportion of annual N2O emissions when growing season conditions were not favourable for enhanced N2O emissions.
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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 | 0.002 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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".