Historical agroeconomic analysis of the relationship between commercial pollinator use and <i>Vaccinium angustifolium</i> (Ericales: Ericaceae) yield in Quebec, Canada (2015–2021)
Bibliographic record
Abstract
Lowbush blueberry (Vaccinium angustifolium Ait.) production has the highest export value among all fruit crops in Canada. As blueberry flowers require insect pollination, blueberry farms rely on commercial pollinators to ensure productivity of their fields. Our study aimed to assess the impact of commercial pollinator density and diversity on lowbush blueberry yield using historical data, from 2015 to 2021, across various production contexts. The analysis included 178 blueberry fields, representing approximately 3,000 ha and 11% of Québec's total crop production area. Data on field size, berry yield, commercial pollinator density/diversity (honey bees; Apis mellifera L. [Hymenoptera: Apidae]), bumble bees; Bombus impatiens Cresson [Hymenoptera: Apidae], and alfalfa leafcutter bees; Megachile rotundata Fabr. [Hymenoptera: Megachilidae]), establishment year, and management system were collected for each field. Additionally, we examined data on honey bee hive strength, landscape structure, and meteorological factors influencing yield. Results showed significant yield increase at certain densities of honey bee hives and frames, bumble bee hives and alfalfa leafcutter bee gallons. The diversity of commercial pollinators used in the fields also increased crop yield. Other key factors influencing lowbush blueberry yield included the year, snow cover during winter, field age, and frost events during pollination. This study clarifies the relationship between commercial pollinators and lowbush blueberry productivity while accounting for several other agroeconomic variables, and demonstrates that increasing managed pollinator density and diversity lead to higher yields for lowbush blueberry growers.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".