Positive attitudes, positive outcomes: The relationship between farmer attitudes, management behaviour and sheep welfare
Bibliographic record
Abstract
This study examined the relationships between the attitudes and the management behaviour of the farmer and the on-farm welfare of their ewes. To our knowledge, this is the first study investigating these relationships in extensive sheep farming systems. Thirty-two sheep farmers and 6200 ewes were sampled across Victoria, Australia. Questionnaire interviews and on-farm animal welfare assessments were conducted. The ewes were assessed at two-time points, mid-pregnancy and weaning. To examine relationships between farmer and ewe variables, categorical principal component analyses, correlations and logistic regressions were used. The main findings of this study indicate relationships between farmer attitudes and management behaviour, consistent with findings from other more intensive livestock industries. Farmers were more likely to check the body condition of their ewes (Odds ratio = 2.37, P = 0.03), perform ultrasound pregnancy diagnosis (Odds ratio = 1.16, P = 0.02) and test for egg count before deworming sheep (Odds ratio = 2.88, P = 0.01) if they perceived these activities were important/valuable. In addition, farmers that performed these activities had a more active management style, and ewes in better welfare: fewer lame ewes at mid-pregnancy (r = -0.38 P = 0.04), and fewer ewes in need of further care at mid-pregnancy and weaning respectively (r = -0.47, P = 0.01; r = -0.50, P = 0.01). When combining the qualitative and quantitative analyses, behavioural attitudes (attitudes towards specific management behaviours) and perceived behavioural control (perceived barriers to performing the behaviour) emerged as the two main drivers underpinning farmer management behaviour. The results of this study indicate that the way farmers manage their ewes influences welfare outcomes, and management decisions are influenced by attitudes towards management practices. These findings demonstrate the opportunity to create change in farmer management behaviour and improve sheep welfare via targeted education programs.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".