MALT BARLEY VALUE CHAIN ANALYSIS IN GUMER WOREDA, GURAGHE ZONE, ETHIOPIA
Bibliographic record
Abstract
The study was aimed to identify actors, their role, benefit share, the existing challenges and opportunities of malt barley value chain. The primary data was collected from 146 malt barley producer farmers, 18 traders, 8 consumers and key informants; using structured questioner and scheduled interview. The collected data was analyzed using descriptive statistics, value chain analysis methodology and multiple linear regressions. The value chain analysis result indicated direct actors of malt barley value chain in the study area was input suppliers, producers, multipurpose farmers cooperatives, rural collectors, wholesalers, retailers, local Kolo processors and consumers. In the study area there was no coordinated market linkage and governance system. From total marketing margin the highest share was took by local Kolo processors followed by producers and the least marketing margin was took by rural collectors.The finding of the study also indicated, productive labour size of the household, land area allotted for malt barley, access to credit, frequency of extension visit and number of production technology used were variables that significantly affect quantity supply of malt barley. Therefore the study recommended improving financial institutions service, increasing frequency of extension visit, support farmer through training about production technology adoption. In addition, to solve the identified challenges like lack of improved seed, shortage of chemicals for weed control and emerging disease the study suggests strengthening input supplier institutions such as Woreda Agriculture office and multipurpose farmers cooperatives' for proper supply of inputs.
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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 | 0.002 | 0.004 |
| 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".