Trends and prospects in the global food grain sector: opportunities for Australia's food grain industry
Bibliographic record
Abstract
In the context of the broad background of the food security issue, this thesis identifies the recent trends in the global food grain sector (e.g. production, consumption, trade and prices) and analyses the main factors (e.g. population and income growth, urbanisation etc.) causing those trends, outlining Australia's leading position in this sector. The joint value of global exports of wheat and rice, the two food grains which are the focus of this study, has increased sixfold between the late 1960s and the late 1990s .to reach around US$24 billion, with Australia providing around 10% of this amount. <br><br>Through the use of various descriptive statistical techniques, the study emphasises that the long-term price trends have generally been unfavourable (a downward trend) to most grain exporters, including those in Australia. This situation was mainly due to the oversupply artificially generated by the support and protection policies III major grain producing and exporting areas such as the United States and the European Union. <br><br>At the same time, the study reviews major international studies and projections in order to assess the prospects for the global food grain sector and more specifically for the Australian wheat and rice industries to the end of the current decade. It appears that the global food grain sector has good prospects for a balanced growth to 2010 and beyond, albeit at a lower rate than in the last three decades. Australia's food grain sector appears to be well placed, geographically and technologically, to continue its growth and leading position as an exporter of wheat and rice. Australia is projected to be the world's second largest exporter of wheat, in close competition with Canada, and the world's leading exporter of medium grain rice by 2010. Australia's geographic export opportunities appear to be concentrated in East Asia and the Middle East. In addition Australia has good prospects for exports of organic food grains. At the same time, the Australian food grain industry may find new opportunities in direct investment in the food grain sector of transition economies in Central and Eastern Europe, which would complement its traditional export opportunities. <br><br>It is anticipated that the research will represent an useful reference for Australian organizations involved in export marketing and planning, international market research, trade policy analysis and export strategy development related to the food grain sector.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".