Impacts of COVID-19 on Canadian Beekeeping: Survey Results and a Profitability Analysis
Bibliographic record
Abstract
To gauge the impact of COVID-19 on the Canadian beekeeping sector, we conducted a survey of over 200 beekeepers in the fall of 2020. Our survey results show Canadian beekeepers faced two major challenges: 1) disrupted importation of honey bees (Hymenoptera: Apidae) (queen and bulk bees) that maintain populations; and 2) disrupted arrival of temporary foreign workers (TFWs). Disruptions in the arrival of bees and labor resulted in fewer colonies and less colony management, culminating in higher costs and lower productivity. Using the survey data, we develop a profitability analysis to estimate the impact of these disruptions on colony profit. Our results suggest that a disruption in either foreign worker or bee arrival allows beekeepers to compensate and while colony profits are lower, they remain positive. When both honey bee and foreign workers arrivals are disrupted for a beekeeper, even when the beekeeper experiences less significant colony health and cost impacts, a colony with a single pollination contract is no longer profitable, and a colony with two pollination contracts has significantly reduced profitability. As COVID-19 disruptions from 2020 and into 2021 become more significant to long-term colony health and more costly to a beekeeping operation, economic losses could threaten the industry's viability as well as the sustainability of pollination-dependent crop sectors across the country. The economic and agricultural impacts from the COVID-19 pandemic have exposed a vulnerability within Canada's beekeeping industry stemming from its dependency on imported labor and bees. Travel disruptions and border closures pose an ongoing threat to Canadian agriculture and apiculture in 2021 and highlight the need for Canada's beekeeping industry to strengthen domestic supply chains to minimize future risks.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".