Assessing Türkiye’s Competitive Position in the Global Hazelnut Trade: Trends, Opportunities, and Strategic Outcomes
Bibliographic record
Abstract
Türkiye is the world’s leading producer and exporter of hazelnuts, accounting for approximately 70% of global supply and playing a central role in the trade of "Edible fruit and nuts; peel of citrus fruit or melons". Despite its dominance, recent years have seen growing competition from countries such as Italy, Spain, and the United States, threatening Türkiye's market share in key importing nations like Italy. This study examines Türkiye’s hazelnut export performance from 2019 to 2023, focusing on market share trends in the top 10 importing countries (Italy, Spain, Netherlands, Germany, France, USA, Belgium, Poland, Canada and UK) and identifying both opportunities for growth in underpenetrated markets like Spain and the Netherlands and threats in declining markets. Through trend analysis, comparative evaluations, and theoretical frameworks like comparative advantage and Porter’s Diamond Model, the study provides actionable insights for exporters and policymakers to strengthen Türkiye’s global competitiveness. Unlike previous research, which often emphasizes macroeconomic trends, this study offers a granular, market-specific analysis, bridging a critical gap in understanding competitive dynamics within the hazelnut trade. Key findings highlight a declining market share in traditional strongholds like Italy, while stable but low shares in Spain and the Netherlands present untapped growth potential. The research emphasizes strategic interventions, such as market diversification, yield optimization, and policy support, to sustain Türkiye’s leadership in the global hazelnut sector.
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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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".