Opportunities for future Australian dairy systems: a review
Bibliographic record
Abstract
During the last decade, Australian dairy farmers have been challenged to increase total factor productivity (the ratio between the rate of increase in total output and the rate of increase in the use of all inputs) in order to attenuate the negative effects of a steady decline in the terms of trade over the same period of time. Overall, the increase in total factor productivity has been low (1.5%) and farmers are questioning the most appropriate production system for the future. In an attempt to address this central question, we first identified the nature of the key pressures dairy farmers in Australia are likely to face in the future, namely labour and feed related issues. We then discuss major opportunities for developing new dairy production systems based on increased efficiency in the use of land and cows and on increasing the efficiency of labour management and lifestyle. We do not attempt to provide the best futuristic option for dairy systems in Australia. Instead, this review discusses key areas of the production system with potential to impact positively on any or all the physical, economic and labour-related aspects of modern dairy farming. By so doing, this review highlights the research questions that need to be addressed now in order to provide Australian dairy farmers with improved tools to manage their production systems in the future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".