Soil N2O emissions and functional genes in response to grazing grassland with livestock: A meta-analysis
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.015 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".