Revealing determinants that affects garlic production in Ethiopia using PRISMA methodology
Bibliographic record
Abstract
Today, garlic productivity in Ethiopia is below its potential which is ranked 15 in the world. This is due to numerous determinants that affect garlic production. Therefore, the study’s main objective was to reveal and sum up determinants affecting garlic production in Ethiopia using PRISMA Method. Preferred reporting items for systematic review and meta-analysis checklist s1 table were employed. Universally accredited and indexed high-quality databases and university libraries; specifically, Scopus, PubMed, Science Direct, University of Toronto Library, and Google Scholar were used for retrieving published data (2000–2022). The titles “Garlic” and Ethiopia” were used to retrieve articles. “Microsoft Excel” was used to summarize results. The present study indicated that a total of 12,000 publications from Google scholar, and 51 publications from the University of Toronto library search engine published on the web of science and Scopus were used. A total of 51 publications were chosen as being the most suitable for this paper. Among the 51 publications, 86%, 6%, 6%, and 2% were articles, Newsletter articles, Text resources, and conference proceedings, respectively. Accordingly, a study revealed that the low production of this crop is due to information obtained via a systematic review discussed and categorized as institutional determinants (15.69%), farmers’ characteristics (9.8%), production and management practice (68.63%), and postharvest (5.88%). Institutional; production and management practices are of the major determinants negatively affecting garlic production in Ethiopia. Moreover, research limitations also were revealed. The recommendation of this paper is therefore not general, specifically, each factor has been recommended.
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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 | 0.082 | 0.149 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.018 | 0.047 |
| Bibliometrics | 0.034 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.002 |
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".