Grazing improves C and N cycling in the Northern Great Plains: a meta-analysis
Bibliographic record
Abstract
Grazing potentially alters grassland ecosystem carbon (C) and nitrogen (N) storage and cycles, however, the overall direction and magnitude of such alterations are poorly understood on the Northern Great Plains (NGP). By synthesizing data from multiple studies on grazed NGP ecosystems, we quantified the response of 30 variables to C and N pools and fluxes to grazing using a comprehensive meta-analysis method. Results showed that grazing enhanced soil C (5.2 ± 4.6% relative) and N (11.3 ± 9.1%) pools in the top layer, stimulated litter decomposition (26.8 ± 18.4%) and soil N mineralization (22.3 ± 18.4%) and enhanced soil NH4(+) (51.5 ± 42.9%) and NO3(-) (47.5 ± 20.7%) concentrations. Our results indicate that the NGP grasslands have sequestered C and N in the past 70 to 80 years, recovering C and N lost during a period of widespread grassland deterioration that occurred in the first half of the 20(th) century. Sustainable grazing management employed after this deterioration has acted as a critical factor for C and N amelioration of degraded NGP grasslands and about 5.84 Mg C ha(-1) CO2-equivalent of anthropogenic CO2 emissions has been offset by these grassland soils.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".