Honey bee queen production: Canadian costing case study and profitability analysis
Bibliographic record
Abstract
Abstract The recent decline in honey bee (Hymenoptera: Apidae) colony health worldwide has had a significant impact on the beekeeping industry as well as on pollination-dependent crop sectors in North America and Europe. The pollinator crisis has been attributed to many environmental and anthropological factors including less nutrient rich agricultural monocultures, pesticide exposure, new parasite and pathogen infestations as well as beekeeper management and weather. Canadian beekeepers have indicated that issues with honey bee queens are the most significant factor affecting their colony health. In Canada, beekeepers manage colony losses by relying on the importation of foreign bees, particularly queens from warmer climates, to lead new replacement colonies. Unfortunately, the risks associated with imported queens include the introduction of new and potentially resistant pests and diseases, undesirable genetics including bees with limited adaptations to Canada’s unique climate and bees negatively affected by transportation. Importing a large proportion of our queens each year also creates an unsustainable dependency on foreign bee sources, putting our beekeeping and pollination sectors at an even greater risk in the case of border closures and restrictions. Increasing the domestic supply of queens is one mitigation strategy that could provide Canadian beekeepers, farmers and consumers with a greater level of agricultural stability through locally bred, healthier queens. Our study is the first rigorous analysis of the economic feasibility of Canadian queen production. We present the costs of queen production for three case study operations across Canada over two years as well as the profitability implications. Our results show that for a small to medium sized queen production operation in Canada, producing queen cells and mated queens can be profitable. Using a mated queen market price ranging from $30 to $50, a producer selling mated queens could earn a profit of between $2 and $40 per queen depending on price and the cost structure of his operation. If the producer chose to rear queens for his own operation, the cost savings would also be significant as imported queen prices continue to rise. Our case studies reveal that there is potential for both skilled labour acquisition over time in queen production as well as cost savings from economies of scale. Our queen producers also reduced their production costs by re-using materials year to year. Domestic queen production could be one viable strategy to help address the current pollinator crisis in Canada.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".