COMPETITIVENESS AND DETERMINANTS OF LENTIL (LENS CULINARIS MEDIK.) EXPORT FROM NEPAL: A PORTER’S FIVE FORCES FRAMEWORK APPROACH
Bibliographic record
Abstract
Lentil (Lens culinaris Medik.) is a vital pulse crop for Nepal, holding significant economic value in domestic agriculture and exports. Despite being a major producer, Nepal’s lentil export competitiveness is limited by inconsistent quality, minimal value addition, and a fragmented supply chain. This study investigates the determinants of Nepalese lentil export competitiveness, focusing on the Dhaka market, using Porter’s Five Forces framework and secondary data from sources like FAOSTAT. Analytical tools such as descriptive statistics, multiple OLS regression, the Augmented Dickey-Fuller test, Johansen co-integration, competitive matrix, and Rapid Market Appraisal are employed. Findings indicate that Nepalese lentils are favored for their taste and cooking quality, yet face stiff competition from Canada, Australia, and India. Key determinants affecting competitiveness include market share, consumer demand, quality attributes, and pricing. OLS regression shows Nepal’s exports are significantly influenced by exchange rate (β = 2.93, p = 0.011), Bangladesh’s domestic production (t = -3.43, p = 0.004), and international prices (β = -1.0689, t = -2.51, p = 0.025). Within study among markets and traders in Dhaka, Nepal contributes around 16% share and this share found declined in recent years, with rising imports from Australia and Canada. Exporters and importers emphasize market demand, taste, and cooking quality as trade drivers. The study concludes with strategic recommendations including export credit and efficient production to improve Nepal’s competitive position. Strengthening value chains, quality assurance, and market linkages is essential to ensure sustainable growth and global market integration for Nepalese lentils.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".