Anatomy of Competition in the Global Agricultural Market: A Cross-Country Comparative Analysis
Bibliographic record
Abstract
With the concept of sustainability that emerged in the twentieth century, discussions on agricultural production methods continue. Agriculture maintains its position of strategic importance for all countries in general. The fact that it is directly related to people’s nutrition is the key factor that makes the sector important. The significance of agriculture has required countries to compete. The volume of global agricultural production exceeded 5 trillion US dollars on average for 2021-2022, increasing by about 19% over the last decade. This study analyses the competitiveness of the top ten leading countries and Türkiye and Uzbekistan in global agricultural trade. Vollrath’s Relative Export Advantage Index (RXA) was calculated using competitiveness analysis. The data of 24 food and agricultural product groups for 2013-2022 were used in the index calculations. The analysis showed that China and Germany had a comparative disadvantage in exports of agricultural products, while the US, the Netherlands, Brazil, France, Spain, Canada, Italy, Belgium, Türkiye, and Uzbekistan had a comparative advantage. Brazil, the Netherlands, and Spain had the highest competitive advantage. Increasing efficiency, productivity, and quality as well as reducing costs are considered important issues in enhancing the competitiveness of countries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".