Evolution of wheat production systems in southern Australia
Bibliographic record
Abstract
Wheat has been grown in Australia since European settlement, initially to feed colonists, but soon as an export crop. Although total national production, 19 Mt (five-year average 2003–2008), remains small by world standards, the high proportion (60%) that is exported ranks Australia fourth, after the USA, Canada, and the EU, among wheat exporting countries. This chapter describes the continuing evolution of wheat-cropping systems in semi-arid southern Australia (annual rainfall 300–500 mm) using yield data for the State of Victoria from soon after inception of the industry c . 1800. The analysis reveals how a sequence of cropping systems has developed in response to technological innovation , economic incentives , and societal pressures . Economic pressure to compete on world markets has been, and will likely remain, a major driver of change in these cropping systems. Producers receive little subsidy to relieve competitive pressure. Among OECD countries, subsidies account for 25 and 40% of farm income in the USA and the EU, respectively, but only 6% in Australia. Driving forces for change may be further complicated by widely anticipated climate change. Producers, agronomists, and researchers now have access to new tools to meet the increasingly complex objectives that must account for variability in climatic and economic environments, and also address societal interests. The principles and range of strategies and tactics available to combat crop response to low and variable rainfall have been presented in Chapters 9 and 13.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".