Effect of Grazing Intensities on Soil N2O Emissions from an Alpine Meadow of Zoige Plateau in China
Bibliographic record
Abstract
The alpine meadow of Zoige Plateau plays a key role in local livestock production of cattle and sheep. However, it remains unclear how animal grazing or its intensity affect nitrous oxide (N2O) emissions, and the main driving factors. A grazing experiment including four grazing intensities (G0, G0.7, G1.2, G1.6 yak ha−1) was conducted between January 2013 and December 2014 to evaluate the soil nitrous oxide (N2O) fluxes under different grazing intensities in an alpine meadow on the eastern Qinghai–Tibet Plateau of China. The N2O fluxes were examined with gas collected by the static chamber method and by chromatographic concentration analysis. N2O emissions in the growing seasons (from May to September) were lower than that in non-growing seasons (from October to April) in 2013, 1.94 ± 0.30 to 3.37 ± 0.56 kg N2O ha−1 yr−1. Annual mean N2O emission rates were calculated as 1.17 ± 0.50 kg N2O ha−1 yr−1 in non-grazing land (G0) and 1.94 ± 0.23 kg N2O ha−1 yr−1 in the grazing land (G0.7, G1.2, and G1.6). The annual mean N2O flux showed no significant differences between grazing treatments in 2013. However, there were significantly greater fluxes from the G0.7 treatment than from the G1.6 treatment in 2014, especially in the growing season. Over the two years, the soil N2O emission rate was significantly negatively correlated with soil water-filled pore space (WFPS) and dissolved organic carbon (DOC) content as well as positively correlated with soil available phosphorus (P). No relationship was observed between soil N2O emission rate and temperature or rainfall. Our results showed that the meadow soils acted as a source of N2O for most periods and turned into a weak sink of N2O later during the sampling period. Our results highlight the importance of proper grazing intensity in reducing N2O emissions from alpine meadow. The interaction between grazing intensity and N2O emissions should be of more concern during future management of pastures in Zoige Plateau.
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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".