Soil N2O emissions and functional genes in response to grazing grassland with livestock: A meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Livestock grazing affects nitrous oxide (N2O) emissions from grassland ecosystems by altering soil physical, chemical and biological properties. However, how soil N2O emissions related to nitrogen process rates and functional genes are unexplored. We compiled 83 published studies of soil N2O emissions, potential nitrification and denitrification rates, and the abundance of nitrogen functional genes to uncover their associations with varying intensities of livestock grazing. Compared to ungrazed condition, heavy and moderate grazing reduced N2O emissions by 22–25%, nitrification rate by 23–37%, and denitrification rate by 44–48%, respectively, while light grazing had no effect. Furthermore, moderate to heavy grazing intensities decreased the abundances of ammonia-oxidizing bacteria ammonia monooxygenase (AOB amoA) by 40–47%. Heavy grazing also simultaneously decreased ammonia-oxidizing archaea (AOA amoA) by 43%. Additionally, grazing significantly decreased the abundance of nitrate reductase (narG) and nitrite reductase (nirS) and by 28% and 35%, respectively, but did not affect the abundance of nitrous oxide reductase (nosZ). Overall, potential nitrification rate was positively correlated with AOB amoA and AOA amoA abundances. This global-scale assessment demonstrates that moderate to heavy livestock grazing can reduce grassland N2O emissions, and such reductions were linked to decreased abundances of amoA genes with decreasing soil moisture and inorganic N (NO3– and NH4+) availabilities. Considering that heavy grazing may increase the risk of grassland degradation, we recommend that livestock grazing at an appropriately moderate intensity is important for sustaining livestock production while contributing to greenhouse gas mitigation.
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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 | 0.002 |
| 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 it