Challenges Faced by Mango Exporters in India: A Case Study of GI registered Mango 'Malihabadi Dussehri'
Bibliographic record
Abstract
The present study assessed relative importance of the key challenges mango exporters face, particularly those involved in exporting Malihabadi Dussehri mangoes. Data were gathered from seventeen exporters, exporting this mango variety to various countries, including the United Arab Emirates, United Kingdom, United States of America, Kuwait, Qatar, Canada, Oman, Nepal, and Saudi Arabia. The study identifies high international freight charges and difficulties in tracing the growers of Malihabadi Dussehri mangoes as the most significant constraints. Other issues include competition from other mango-exporting countries, inadequate institutional credit support, price fluctuations, challenges finding reliable foreign distributors, and the complexities of quoting prices amid fluctuating exchange rates. Exporters also reported difficulties with certification processes, a lack of training and knowledge about foreign countries' export rules and policies, and hurdles in customs clearance. Furthermore, the lack of modern post-harvest technology and expertise in mango grading as sanitary and phytosanitary (SPS) concerns were also notable challenges. As India is the largest producer of mangoes and a major exporter of fresh mangoes and mango pulp, addressing these constraints is essential for enhancing export competitiveness.The study also provides valuable insights for government officials and agricultural policymakers,enabling them to design focused policies and initiatives that assist mango exporters.. KEYWORDS :Mango exporter, Geographical indication, Production, Export challenges.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".