Public perceptions of potential adaptations for mitigating heat stress on Australian dairy farms
Bibliographic record
Abstract
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.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".