Characteristics of Soil Profile Greenhouse Gas Concentrations and Fluxes of Alpine Grassland as Affected by Livestock Grazing
Bibliographic record
Abstract
Previous research has investigated the effects of different grazing intensities on soil surface greenhouse gas (GHG) emissions, whereas the dynamics of GHG production and consumption within the soil profile and their responses to different grazing intensities remain unclear. In this study, a field experiment was conducted in 2017 and 2018 to evaluate the influences of three grazing intensities (none, light, heavy) on both soil surface and subsurface (0–60 cm) GHG fluxes estimated using chamber-based and concentration gradient-based methods, respectively. Results showed that soil at lower depths (30–60 cm) had higher carbon dioxide (CO2) concentrations but lower methane (CH4) concentrations. In contrast, soil profile nitrous oxide (N2O) concentration did not vary with depth, possibly resulting from the relatively low soil moisture in the semiarid grassland, which increased air diffusivity across the soil profile. Grassland soil acted as a source of N2O and CO2 production but as a sink for CH4 uptake, which mainly attributed to the topsoil (0–5 cm for N2O, and 0–15 cm for CO2 and CH4). The estimated soil surface GHG flux rates based on the concentration gradient method did not align well with those directly measured using the chamber method. Furthermore, the cumulative N2O flux over the study period was significantly higher for the concentration gradient method than the chamber method, whereas a contrary result was observed for CO2 emission and CH4 uptake. This study confirms that the grassland soil serves as an important source of CO2 and N2O emissions and a weak sink for CH4 consumption, playing a crucial role in the annual carbon budget of livestock-grazed grassland ecosystems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".