Export Dynamics of Vinegar Preserved Gherkins from India: Markov Chain Approach
Bibliographic record
Abstract
The study examines the factors driving the export success of gherkins from India, emphasizing its performance in global markets. India’s horticultural sector, particularly gherkins production and exports, has grown significantly, establishing its key role in international trade. The study aimed to analyze the growth and instability in export, direction of trade and projection of export from India of prepared/preserved in vinegar gherkins from 2011-12 to 2022-23. The Compound Annual Growth Rate (CAGR) was used to assess export growth, while the Coefficient of Variation and Cuddy-Della Valle Index measured export instability. Markov Chain was employed to analyse the direction of trade and demand projection. The study found a negative CAGR of -3.77 per cent for preserved gherkins, with an instability index of 17.53 per cent, indicating moderate market fluctuations. Although demand from the USA and Canada remains steady, European markets, especially France and the Netherlands, show a decline, with France experiencing the highest instability (54.44%). Markov Chain analysis revealed decreasing market loyalty in Europe, while North America demonstrated stronger retention. To counter negative growth, the sector must explore emerging markets, innovate and emphasize sustainability. Enhancing quality, addressing processing inefficiencies and implementing contract farming models to empower farmers are key to improving competitiveness and fostering rural economic growth.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".