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Record W4229033760 · doi:10.3168/jds.2022-21813

Public perceptions of potential adaptations for mitigating heat stress on Australian dairy farms

2022· article· en· W4229033760 on OpenAlexafffund
Jillian Hendricks, Katelyn E. Mills, Lara V. Sirovica, Louise Sundermann, Sarah E. Bolton, M.A.G. von Keyserlingk

Bibliographic record

VenueJournal of Dairy Science · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEffects of Environmental Stressors on Livestock
Canadian institutionsUniversity of VictoriaUniversity of British Columbia
FundersDairy AustraliaHans-Sigrist-StiftungGenome Canada
KeywordsAnimal welfareDairy cattleLikert scaleWelfareLivestockBusinessAgricultural scienceBarnPastureHeat stressAgricultural economicsGeographyEnvironmental scienceAnimal scienceBiologyEconomicsPsychologyAgronomyEcology

Abstract

fetched live from OpenAlex

Temperature variability resulting from climate change poses challenges around the world for livestock production and the welfare of the animals in these systems. As animal industries attempt to combat these challenges, it is vital to understand how potential changes implemented by farmers resonate with societal values. The aims of this study were to determine how different proposed changes to mitigate heat stress in dairy cattle influence public perceptions toward Australian dairy farm systems, including perceptions of (1) cow welfare, (2) confidence in the industry, and (3) trust in farmers. Participants were presented with 1 of 4 treatments representing a potential solution to mitigate heat stress in dairy cattle: (1) indoor system (a fully indoor barn), (2) choice system (cows have agency to choose to be indoors or outdoors), (3) gene edition + pasture (cows are genetically modified to become more resilient to heat stress), and (4) pasture (outdoor system that is currently used in Australia, but the farmer plants more trees). Participants were then asked to respond to questions on a 7-point Likert scale. Questions were about cow welfare (3 questions), confidence in dairy industry (4 questions), and trust in farmers (9 questions), with each section followed by an open-ended question for participants to explain their answers. Participants perceived cow welfare to be the lowest in the indoor system (2.80 ± 0.10), followed by gene edition + pasture (4.48 ± 0.11), with choice and pasture systems being the highest but not different from each other (5.41 ± 0.11 and 5.32 ± 0.11, respectively). Confidence in the dairy industry was lower among participants in the indoor (4.78 ± 0.08) compared with participants assigned to the choice (5.28 ± 0.08) or pasture (5.25 ± 0.08) systems. Confidence was also lower among participants in the gene edition (4.95 ± 0.08) compared with the choice system. Trust in farmers was similar across all treatments. Our results provide the first evidence that the Australian public may be reluctant to accept heat stress mitigation strategies that either do not allow cows to have access to pasture or those that include gene-editing technologies.

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)0.001
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 score0.961
Threshold uncertainty score0.710

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.036
GPT teacher head0.260
Teacher spread0.224 · 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

Citations26
Published2022
Admission routes2
Has abstractyes

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