Rearing goat kids away from their dams 2. Understanding farmers’ views on changing management practices
Bibliographic record
Abstract
Improving animal welfare is an important aim of livestock industries and is dependent on human management. Understanding attitudes to change and perceived barriers is therefore a key consideration for welfare scientists. A survey that aimed to investigate farmers’ attitudes towards changing goat kid-rearing practices was distributed. Likert scales examined willingness to change and the importance of factors in decision-making alongside open-text responses for further explanation. A total of 242 farmers (United States of America (USA) 72; United Kingdom (UK) 71; Australia 33; Canada 23; New Zealand 20; European Union 14; Other 9) rearing goat kids away from their dams responded. All respondents rated from one (highly unwilling) to seven (highly willing), how willing they would be to supply three enrichment types. Willingness to provide enrichments differed (χ2(2) = 190.114, P < 0.001), with farmers most likely to provide climbing or loose items rather than swinging items. The most common reasons cited for unwillingness to provide enrichment were related to safety (101 responses/76.5%). Those currently abruptly weaning were asked how willing they would be to use gradual weaning methods. Those abruptly weaning from ad libitum milk systems (n = 47) showed no difference in willingness to change to different gradual weaning methods; the median (Interquartile Range (IQR)) for the willingness to change to removing teats was 2 (1–4), reducing milk temperature 3 (1–5) and diluting milk 2 (1–5), with most concerns relating to feasibility. Those abruptly weaning from bottle feeding (n = 18) also showed no difference in willingness to change to gradual weaning methods. Median (IQR) score for willingness to change to reduced number of bottle feeds was 4 (1–7), reducing milk quantity 3 (1–6.25), and diluting milk 1 (1–5), respectively. Health concerns were the most common reason for not being willing to change. All 242 respondents were asked to rate how important different factors are when deciding to implement a new management practice. There was a significant difference in importance between factors (χ2(2) = 34.779, P < 0.001). Median (IQR) importance of the factors was labour/time 5 (4–7), cost 5 (4–7), evidence beneficial to welfare 6 (5–7), evidence beneficial to health 6 (5–7), and evidence beneficial to growth 6 (4–7). To our knowledge, this is the first study to examine goat farmers’ attitudes towards changing management practices and could help ensure that future research addresses farmer concerns and therefore has the best opportunity to be implemented on-farm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".