Waste not, want not: Value chain stakeholder attitudes to surplus dairy calf management in Australia
Bibliographic record
Abstract
The management of surplus dairy calves in Australia has traditionally been influenced by the economic viability of different practices. When beef prices are favourable, more surplus calves are raised for beef, and when beef prices are low, more calves are killed in the first few days of life. Early life killing of surplus calves may however threaten the dairy industry's social licence to operate. The aim of this study was to describe the views of value chain stakeholders regarding the management of surplus calves. Representatives from seven post-farm gate organisations participated in semi-structured interviews and were asked about their views on current practices, alternatives to early life killing and how best to implement change. Responses were analysed using inductive thematic analysis and were organised into three themes: (1) ethics of surplus calf management; (2) economics of surplus calf management; and (3) moving towards solutions including approaches to affecting change. We conclude that stakeholders widely recognised early life killing of surplus calves as a threat to the industry's social licence. Whilst technical solutions such as beef on dairy breeding programmes were cited as important, participants emphasised that implementing sustainable solutions will require collaboration, leadership, and commitment by all stakeholders along the value chain.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".