Analysis of frankincense value chain in Northwest Ethiopia: Variance-weighted least-squares regression approach
Bibliographic record
Abstract
• Frankincense is the first and foremost important non-timber products for income generation in the Ethiopia. • The frankincense value chain is underdeveloped in the country due to different factors. • Understanding and reducing these factors plays a great role for its sustainable value chain development. • Variance-Weighted Least square regression is used to analyze the data. • Weak value chain governance and limited upgrading mechanisms among producers highlight the need for improved organization and market information access Frankincense is the first and foremost important non-timber product for income generation in the country. However, there is a gap in the current value chain and the frankincense value chain is underdeveloped. This study was therefore designed to analyze the frankincense value chain in the Western Amhara Region of Ethiopia. The data were collected from 288 randomly selected sample respondents using interviews, focus group discussion, and observation (as a method) and a semi-structured questionnaire (as a tool). Both descriptive and econometric analyses (Variance-Weighted Least squares regression (VWLS)) were used to analyze the data. The marketing margins of the frankincense producers, collectors, wholesalers, and retailers were 20.74%, 37.18%, 17.54% and 24.54%, respectively. The profit shares of the frankincense producers, collectors, wholesalers, and retailers were 9.12%, 24.56%, 27.21%, and 39.11%, respectively. The value chain governance system was ineffective for frankincense producers due to a lack of market information (price and consumers' demand preferences) (79.68%) and a weak organizational structure (99.33%) among the frankincense producers. The frankincense producers mainly (50.82%) used the functional upgrading. Lack of tapping experience or poor knowledge on the production of frankincense, poor infrastructure and market access, lack of processing industries, poor handling and storing practices, lack of value-added activities, lack of continues follow-ups and support of cooperatives, deforestation (for agricultural expansion, fuel and construction) were the main constraints of the frankincense value chain. The VWLS regression results revealed that the frankincense market's experiences, selling price, access to market information, sex, cooperative membership, livestock and crop production involvement were significant factors affecting the amount of frankincense supplied to the market. Therefore, designing policies towards improving production practices, markets access, and increasing consumer awareness should improve frankincense value chain.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".