British Columbia beekeeping revenues and costs: survey data and profit modeling
Bibliographic record
Abstract
British Columbia beekeepers, like many beekeepers around the world, are currently facing declines in honey bee health and high overwinter colony losses. To better understand the economics and the cycle of yearly colony loss and replacement of this critical agricultural industry, we collected and analyzed survey data on beekeeping costs and returns. Forty British Columbia beekeepers provided details about revenue sources, variable costs, capital costs, and investments. Ten surveyed beekeepers managed between 1 and 9 colonies, 10 managed between 10 and 39 colonies, 9 managed between 40 and 100 colonies, 5 managed between 101 and 299 colonies, 3 managed between 300 and 699 colonies, and 3 managed 700 colonies or more. The data was used to calculate beekeeping profit and to parameterize a model that explores the economic impact of colony loss rates and replacement strategies. Survey results show that when the data is aggregated, revenues exceed costs for beekeeping operations in British Columbia with a per colony profit of $56.92 or $0.87 per pound of honey produced. Surveyed operations with fewer than 100 colonies have negative profits, while operations with 100-299 colonies have positive profits. Surveyed operations in the Cariboo, North Coast, and Okanagan regions have the highest profits while surveyed operations in the Peace region have the lowest profits. Profit modeling shows that replacing losses with packages generates lower profit than replacing losses with split colonies. Our modeling shows that operations that diversify their revenue to include bee sales and commercial pollination accrue higher profits and can withstand higher winter loss rates.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".