Slope position regulates response of carbon and nitrogen stocks to cattle grazing on rough fescue grassland
Bibliographic record
Abstract
Grasslands play a crucial role in offsetting greenhouse gas emissions and mitigating climate change. A moderate change in grassland carbon (C) and nitrogen (N) stocks may substantially alter the global C and N cycle and thereby influence climate. But how grassland C and N stocks respond to grazing and slope position remains uncertain. This research investigates how C and N stocks respond to cattle grazing along a landscape slope. We studied a grassland that has been grazed by cattle at four cattle stocking rates (0, 1.2, 2.4, and 4.8 animal unit months (AUM) ha −1 ) since 1949, representing control (CK), light (L), heavy (H), and very heavy (VH) grazing intensities, respectively. Samples were taken from the top and bottom slope positions within each paddock (only the top position in CK); C and N stocks in soil, roots, litter, and standing crop were estimated. Soil C and N stocks were estimated based on equivalent mass (1500 Mg ha −1 ). Root C and N stocks were estimated to the depth of 15 cm. All parameters, except for litter N stock and standing crop C stock, significantly responded to the interaction of grazing intensity and slope position. In the bottom position, soil and standing crop C and N stocks as well as litter C stock were higher with the L treatment than with VH, while no significant differences were found among the three grazed treatments for root C and litter N stocks. In the top position, soil and root C and N stocks were higher with the VH treatment than with L, whereas litter C and N stocks and standing crop C stock were lower with VH than with L. Our results provide evidence that slope position plays an important role in regulating the response of C and N stocks to grazing and may need to be considered when developing optimal grazing management strategies.
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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".