Review: Grazing preferences in sheep and cattle: Implications for production, the environment and animal welfare
Bibliographic record
Abstract
The evolutionary and domestic ancestors of sheep and cattle will have evolved diet selection behaviours that enabled them to select a diet that met their individual nutrient requirements whilst minimising the risk of being killed through predation or by eating toxins. Modern intensive farming generally involves grazing monocultures or feeding total mixed rations and these restrict the ability of livestock to select their own diet. Research has shown that grazing sheep and cattle have a partial preference of approximately 70% for clover (when offered as a monoculture sward alongside grass), and they show a consistent diurnal pattern of preference. Dairy cattle and sheep that are given the ability to select their own diet show higher levels of production than animals grazing mixed swards. There is some evidence that animals that can select their own diet are optimising their own efficiency of nutrient capture, and this potential environmental benefit warrants further research. Further research is also needed to establish if dairy cattle “need” to graze or whether they prefer to eat prepared rations indoors. Preventing animals from expressing their innate diet preferences by feeding them mixed rations may cause frustration and so compromise animal welfare, although this hypothesis requires further research.Key words: Grazing, ruminant, preference, choice, behaviour, welfare
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".