Value chain analysis of community-based chickpea seed the case of selected districts of Gurage Zone, southern Ethiopia
Bibliographic record
Abstract
The study was aimed at analysing seed value chain of community-based chickpea in Selected Districts of Gurage zone. The objectives of the study was identifying chickpea seed value chain actors and defining their roles, analysing the market margin of actors, determinant factors and identifying constraints in the chain. A multi-stage sampling technique was implemented. The data were collected from both primary and secondary sources. Descriptive statistics, value chain and econometric analysis were employed to analyse data. Primary actors in the study were input suppliers, seed producers, collectors, wholesalers, South Seed Enterprise and final-use. The producer’s share is highest in channel-IV, which is 83.3% and net market margin is 53.7% when producers sell their seed to South Seed Enterprise. The result of the multiple linear regression model indicates that market supply was significantly affected by level of education; quantity of seed produced, frequency of extension contact, district. Shortages of improved seed, climate change, and weak extension contact were main constraints in production. The major marketing constraints were weak market linkage, low price at harvesting time, insufficient handling and poor quality seed and lack of modern storage centres in the production area. published by the Journal of Biodiversity and Environmental Sciences (JBES)
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.003 | 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".