Soil carbon increases with long‐term cattle stocking in northern temperate grasslands
Bibliographic record
Abstract
Abstract Grassland management aimed at enhancing carbon (C) in soil is an important tool in mitigation of rising atmospheric CO 2 , yet little is known of how grassland soil C changes with livestock stocking rate ( SR ). We relate soil organic and inorganic C mass (t ha −1 to 60 cm depth) with cattle stocking over periods of 7–27 year for 32 paddocks distributed across nine community pastures in the mixed‐grass prairie of Saskatchewan, Canada. Initial analysis comparing Akaike information criterion models showed that cattle SR explained a greater proportion of variance in soil C, particularly soil organic C, than rainfall. Soil organic C mass increased with cattle SR ( R 2 = .293; p = .001), even when the latter was normalized to account for differences in vegetation composition and growing conditions among pastures. Normalized SR varied from 0.49 to 2.30 times recommended levels, over which SOC increased from 24.7 to 57.4 t ha −1 . Increases in soil organic C under greater stocking coincided with increased abundance of introduced vegetation, particularly the rhizomatous grass Poa pratensis . Inorganic soil C accounted for 34.6% of total soil C, being particularly large below 30 cm soil depth, but did not vary with stocking rate. These findings indicate that both organic and inorganic C are important pools of C in northern temperate grassland soils, with soil organic C positively associated with long‐term cattle SR . Further studies are recommended to understand more fully the mechanisms regulating grazing impacts on soil C mass in northern temperate grasslands.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".