Changes in Forage Quantity and Quality With Continued Late-Summer Cattle Grazing a Riparian Pasture in Eastern Oregon of United States
Bibliographic record
Abstract
A pasture (45 ha) in northeastern Oregon was grazed with 30 yearlings (419 kg, Body Condition Score [BCS] = 5.05) and 30 mature cows with calves (499 kg, BCS = 4.65) during August of 2001 and 2002. Sampling dates were d 0, d 10, d 20, and d 30. Forage availability before grazing was 1,039.0 kg·ha-1 and declined to 332.6 kg·ha-1 after grazing (p < 0.10). Grasses dominated the pasture (44.5%), followed by forbs (30.7%), grasslikes (15.9%), and shrubs (8.9%). Due to grazing quackgrass (Agropyron repens (L.) Beauv.), western fescue (Festuca occidentalis Walt.), California brome (Bromus carinatus Hook.), and redtop (Agrostis alba L.) exhibited the greatest decline in quantity. Shrub utilization was high from d 20 to d 30 (49 to 58% for willow [Salix rigida {Hook.} Cronq.] and 58 to 74 % for alder [Alnus incana {L.} Moench.]). Forbs decreased (p < 0.10) in moisture late in the grazing period, while shrubs were (p > 0.10) still succulent (63%). Forbs and shrubs were higher (p < 0.10) than grasses in crude protein (11, 14, and 6%, respectively) and digestibility (59, 50, and 42%, respectively). In summary, our results suggest that cattle grazing late-summer riparian pastures will switch to intensive shrub utilization when grasses decline in quantity and quality, and forbs decline in quantity. Land managers need to know the effect of their management on vegetation and if a goal is to protect riparian woody vegetation, our data suggest that late-summer grazing should be light, or avoided when grasses have senesced.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".