Long‐Term Grazing Alters Soil Trace Gas Fluxes from Grasslands in the Foothills of the Rocky Mountains, Canada
Bibliographic record
Abstract
Abstract Long‐term cattle grazing may degrade grassland soils, but how soil CO2, CH4 and N2O fluxes respond to long‐term cattle grazing is poorly understood. Therefore, we quantified soil CO2, CH4 and N2O fluxes in response to four levels (none, light, heavy, very heavy) of long‐term (>65 years) cattle grazing on a rough fescue grassland in the foothills of the Rocky Mountains, Canada over three grazing seasons. The grazed grassland soils emitted 37 to 51% more CO2 than non‐grazed soils. Grazed grassland soils were small CH4 sinks and small N2O sources each season, and their cumulative fluxes were significantly affected by a cattle stocking rate × year interaction, indicating the grazing effect was influenced by environmental conditions. Soil CH4 uptake was negatively correlated with soil moisture (r = −0·59). The 2013 grazing season had about 41% greater precipitation than average and grazing significantly decreased CH4 uptake 31 to 38% compared with non‐grazed soils. The N2O emissions were 122 to 179% greater with heavy and very heavy grazing than none in the wet season, unaffected by grazing in the normal precipitation season and 72% lower with light grazing than none in the dry season. Predicting trace gas fluxes from grazed grassland soils across space and time is difficult because of interactions among weather conditions, edaphic properties and grazing intensity. However, long‐term cattle grazing increased soil CO2 fluxes, while the grazing effect on CH4 uptake depended on precipitation and the soil N2O flux responded as a function of grazing intensity and precipitation. © 2016 Her Majesty the Queen in Right of Canada. Land Degradation & Development Published by John Wiley & Sons Ltd.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".