Implications of Precipitation, Warming, and Clipping for Grazing Resources in Canadian Prairies
Bibliographic record
Abstract
Climate change, in terms of both warming and altered precipitation, has the potential to affect grassland systems, with subsequent ramifications for grazing resources. Although grazing is the dominant land use in grasslands, little research has assessed how changes in climate might affect herbage quantity and quality, and how grazing intensity might influence these responses. We performed a fully controlled and factorial 3‐yr, multisite experiment simulating climate change and grazing (via clipping). This experiment was conducted at three northern temperate grassland sites across the Canadian prairies. We increased air temperature by 2 to 4°C, reduced precipitation by 60%, and clipped plants at low and high intensities. At one site, we also applied added (+60%) precipitation. We monitored changes in herbage quantity (regrowth and accumulated herbage) and one aspect of herbage quality (protein content) for both graminoid and forb components. Both climatic factors (i.e., warming and reduced precipitation) and clipping decreased season‐long accumulated herbage, with similar magnitudes of response to precipitation and clipping and smaller responses to warming. Regrowth biomass following clipping declined with reduced precipitation but had a limited decline with warming. Reduced precipitation and warming both decreased herbage quality, and clipping increased quality. These results indicate that the potential for losses in herbage production under drought and warming may be exacerbated by decreased herbage quality. We also saw evidence that graminoids, rather than forbs, will be more sensitive to climate changes. Our results further support the idea that planning for altered grazing resources under future climate conditions will be necessary.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".