Grazing impacts on litter and roots: Perennial versus annual grasses
Bibliographic record
Abstract
Soil carbon (C) and nitrogen (N) storage in grasslands is a function of litter and root mass production. Research on how annual grasses compare with perennials for above ground and below ground mass production, and contributions to the soil C pool under pasture management is scarce. The objective of this research was to evaluate grazing intensity effects on litter and root mass, C and N pools of perennial grasses, smooth bromegrass (Bromus inermis L.) and meadow bromegrass (Bromus riparius Rhem.), and the annual grass, winter triticale (X Triticosecale Wittmack). Litter mass and C pool for the perennial grasses were greater than those for triticale. Litter C and N pools generally decreased with increased grazing intensity. Root mass was greater for the perennial grasses than for triticale at all grazing intensities. Meadow bromegrass generally produced more root mass than smooth bromegrass. Root C and N pools for triticale were 31 and 27%, respectively, of that for the perennial grasses. Estimated total C contribution (roots and litter) to the resistant soil organic C pool was 1.5 times greater for light compared to heavy grazing. Total C (litter + root) contribution for perennial grasses was 2.7 times greater than that for triticale. Perennial grasses provided a larger litter base and root system that promote greater storage of C in the soil compared with triticale.
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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 | 0.000 |
| 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) | 0.001 | 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".