Market Chain Analysis of HighValue Fruits in Bench Maji Zone, Southwest Ethiopia
Bibliographic record
Abstract
This research aimed at assessing the market chain of banana, avocado and mango fruits in Bench Maji zone. Both primary and secondary data were collected from 2 purposively selected fruits producing districts namely North Bench and South Bench districts. Primary data were collected through semi-structured questionnaire and focus group discussion. A total of 150 households were selected by using systematic random sampling technique. In addition, 40 traders were selected by using simple random sampling technique. Market structure – conduct – performance analysis model was used to assess the performance of the fruits market. The result revealed that the participants in the fruits market were identified as primary actors and secondary actors. Primary actors in the fruits market chain were producers, brokers/ agents, farmer traders, collectors, and wholesalers. Whereas, local tax authority, local police, transporters, and district Trade and Industry office were identified as secondary actors. Fruits market in the area was characterized by non-competitive nature with concentration ratio ranging from 42 to 91.10% indicating the existence of oligopoly market structure. Entrance and exit in the fruits market was blocked by licensing and access to channel. A channel that links producers to local wholesalers through brokers was more efficient in terms of large volumes of sales. However, performance of the fruits market was affected by seasonality, the existence of few big traders, limited access to information, absence of organized market center and brokers’ interference. Therefore, attention has to be given to alleviate the problems so as to improve the performance of the fruits market.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".