Understanding How Canadian Producers Use Information Sources to Inform Their Adoption of Beneficial Management Practices
Bibliographic record
Abstract
The adoption of beneficial management practices (BMPs) by producers can support both environmental and financial sustainability in the sector. Current literature shows that behavioural factors shape BMP adoption decisions within economic constraints, making behavioural approaches crucial to advancing BMP adoption. However, there is limited literature available in the Canadian context about the role of specific actors, and the behaviours and attitudes of producers. Research was conducted to help fill this gap. The purpose of the research was to identify producers’ attitudes related to adopting new practices; How producers seek out, trust, and use information sources to support their adoption of new practices; How producers gain information from agrologists and other professional experts. A quantitative phone survey of 1,015 Canadian producers was fielded from March-May 2024. Responses were weighted by region and farm revenue. A pre-analysis plan was established, although additional exploratory analyses were also undertaken. The analysis revealed a number of confirmatory and novel findings, such as greater interest in adapting to extreme weather is associated with greater adoption of BMPs after controlling for other factors. Producers who gather information from in-person events and peers are more likely to adopt BMPs compared to farmers who rely more on vendors and newsletters. Producers recall agrologists working independently or for non-profit organizations providing more information about a new practice’s day-to-day impacts and risks than agrologists working for input suppliers or governments. These findings offer a range of policy implications, such as better aligning communications with producer goals and producer learning and relying more on interactive modes of knowledge transfer from trusted sources as a means of support BMP adoption.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".