Beekeeping practices and challenges in Algeria: a SWOT-based survey analysis
Bibliographic record
Abstract
This study aims to characterize and understand the beekeeping activity and its main challenges in Algeria, based on data collected through a comprehensive survey in 2021. A detailed, structured questionnaire was developed in collaboration with the MEDIBEES consortium, examining key aspects of beekeeping practices, including management, production, and challenges, beekeeper demographics, perceptions of honey bee characteristics, honey yields, and knowledge of pathogens and control measures. Data were collected from 200 beekeepers across 19 wilayas through both email and in-person distribution. A SWOT analysis was used to capture insights into strengths, weaknesses, opportunities, and threats in the beekeeping sector. Findings show that 73.5% of beekeepers believe local honey bee subspecies are endangered, and 69% do not practice queen rearing. Re-queening is mostly done once per year (40%), followed by twice (35%) and three times (25%). A majority (86.5%) reported declining honey yields over the last decade. Beekeepers were predominantly male (97.5%) and aged 41–50. Most practiced stationary beekeeping (69.5%) and kept Apis mellifera intermissa (95%). Statistical tools, including chi-square tests, multiple correspondence analysis (MCA), and Generalized Linear Models (GLMs), revealed significant associations, particularly between colony count and migratory behavior and between education level and queen rearing. Hierarchical cluster analysis (HCA) further identified three distinct beekeeper profiles based on practices and characteristics. Overall, this study provides crucial insights into Algerian beekeeping, revealing challenges such as low queen rearing, production declines, and threats to native bee populations. These findings underscore the need for targeted interventions to support beekeepers and safeguard Algeria’s apicultural heritage.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".