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Record W4386102880 · doi:10.3168/jds.2023-23344

Outdoor access practices in the Canadian dairy industry

2023· article· en· W4386102880 on OpenAlexafffundabout
Anne-Marieke C. Smid, Vanessa Boone, Melanie Jarbeau, Jason E. Lombard, Herman W. Barkema

Bibliographic record

VenueJournal of Dairy Science · 2023
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of Calgary
FundersMerck Sharp and DohmeUniversität ZürichNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaUniversity of Calgary
KeywordsPastureHerdDairy cattleFree accessAnimal scienceGrazingAgricultural scienceGeographyBusinessBiologyAgronomyForestry

Abstract

fetched live from OpenAlex

Dairy cows are highly motivated to access pasture and have a partial preference for alternative forms of outdoor access (e.g., deep-bedded outdoor sand or wood-chip packs). In addition, Canadians value the provision of outdoor access to dairy cows as they perceive it as important for good cow welfare. In contrast to Europe, Oceania, and the United States, little data exist on the use of outdoor access on Canadian dairy farms. Therefore, our objective was to assess current outdoor access practices for dairy cows in Canada. An online questionnaire was used to determine housing and outdoor access practices for lactating cows, dry cows, pregnant heifers, and weaned, nonpregnant heifers on Canadian dairy farms. The questionnaire was distributed by the 10 provincial milk boards between November 2020 and August 2021, resulting in an 8.9% response rate (n = 903 completed questionnaires). In total, 75% (n = 675) of respondents provided some form of outdoor access to at least 1 cattle class on their farm. Pasture was the most frequently used form of outdoor access for all cattle classes. Based on a weighted average, a total of 29% and 48% of Canadian dairy farms provided lactating and dry cows, respectively, access to pasture; for youngstock, these numbers were 48% and 27% for pregnant heifers and weaned, nonpregnant heifers, respectively. Herd size (for each cow class), indoor housing system, and region were all associated with the provision of pasture. Farms with larger lactating herds less often provided access to pasture; larger herd sizes in terms of weaned, nonpregnant heifers, pregnant heifers, and dry cows were also associated with a lower likelihood of access to pasture. Farms using indoor bedded pack housing for their lactating cows more often provided pasture to this cattle class than farms with freestall or tiestall housing; this likelihood did not differ between farms with tie or freestall housing for this cattle class. Dry cows or pregnant heifers housed in a tiestall were more often provided pasture than freestall-housed dry cows or pregnant heifers. Housing type for weaned, nonpregnant heifers was not associated with the likelihood of pasture provision. Farms in British Columbia or on Canada's East Coast (i.e., Nova Scotia, New Brunswick, and Prince Edward Island) more often provided lactating cows pasture compared with farms in other regions. For the other 3 cattle classes, farms on the East Coast of Canada more often provided pasture than farms in other parts of Canada. These results will inform future decisions regarding outdoor access for Canadian dairy cattle and may also aid in identifying future areas of research. For example, our results may aid in designing housing systems that facilitate outdoor access in larger herds and in areas that are subject to more extreme weather conditions.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.200

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.233
GPT teacher head0.452
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2023
Admission routes3
Has abstractyes

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