Influence of Grazing Management Strategies on Forage Quality/Production and Animal Performance in an Ontario Cow Calf System
Bibliographic record
Abstract
These studies sought to develop best grazing management practices for optimizing forage growth/production and cattle performance. Cow-calf pairs grazed from May through September to evaluate the effectiveness of set stocking, rotational, strip, and continuous grazing management regimes on animal performance and forage growth. While different grazing methods did not increase forage biomass, sward height or animal performance, intensive grazing management (strip, rotational) was found to increase grazing days and dry matter intake as a percentage of body weight. A second study evaluated fall stockpile grazing using yearling heifers to evaluate grasses, alfalfa or birdsfoot trefoil effectiveness in an extended grazing system. The study found harvesting forages more than once prior to grazing may decrease available pasture during the fall and decrease grazing days. Heifer performance was not affected by pasture forage species. Considerations to forage/grazing management can directly benefit producers by lengthening the grazing season in both Spring and Fall.
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 | 0.001 | 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".