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Record W2073853808 · doi:10.4141/cjas09119

Review: Grazing preferences in sheep and cattle: Implications for production, the environment and animal welfare

2010· article· en· W2073853808 on OpenAlexvenueno aff
S. M. Rutter

Bibliographic record

VenueCanadian Journal of Animal Science · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsnot available
Fundersnot available
KeywordsGrazingLivestockMonoculturePreferenceAnimal welfareBiologyAnimal husbandryDairy cattleWelfareRuminantNutrientAnimal scienceAgricultureAgronomyEcologyPastureEconomics

Abstract

fetched live from OpenAlex

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

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.881
Threshold uncertainty score0.209

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.247
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations75
Published2010
Admission routes1
Has abstractyes

Explore more

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