Honey bee colony losses and causes during the active beekeeping season 2022/2023 in nine Sub-Saharan African countries
Bibliographic record
Abstract
This study reports for the first-time a multi-country survey of managed honey bee colony loss rates and associated risk factors during the active beekeeping season 2022/2023 in nine Sub-Saharan African countries, namely Kenya, Ethiopia, Rwanda, Uganda, Benin, Liberia, Nigeria, Cameroon and Democratic Republic of the Congo. It also evaluates the sustainability of bee swarm catches as a primary source for expanding apiary size by African beekeepers. In this survey, the 1,786 interviewed beekeepers across these countries collectively managing 41,761 colonies registered an overall loss rate of 21.3%, which varied significantly among countries (from 9.7 to 45.3%) and hive types (from 10.6% in hives with movable frames to 17.9% in frameless hives). The perceived causes of losses in order of significance were issues beyond the beekeeper's control (mostly theft, drought, and bushfire), absconding and pests (mostly wax moth, small and large hive beetles, ants and Varroa destructor mite), but this pattern varied greatly across countries. Among the management practices and characteristics, migratory beekeepers and professional beekeepers experienced lower losses than beekeepers practicing stationary beekeeping and semi-professionals and hobby beekeepers, respectively. Insights into the number of bee swarms caught revealed a significant decrease in swarm availability over the past three years in Kenya, while some regions in Ethiopia showed the opposite trend, requiring further investigation. Overall, this comprehensive survey highlights the complexities and challenges faced by beekeepers in Sub-Saharan Africa, underscoring the need for targeted interventions and sustained research to support the resilience and growth of the apicultural sector.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".