Use of Good Agricultural Practices and Attitudes toward On-Farm Food Safety among Niche-Market Producers in Ontario, Canada: A Mixed-Methods Study
Bibliographic record
Abstract
Major agri-food commodities in Canada use on-farm food safety (OFFS) programs that include good agricultural practices (GAPs), but niche-market (e.g. organic and small-scale) producers might have limited awareness of these programs or barriers to implementing them. We used a mixed-methods approach to study the reported use of recommended GAPs and factors related to the potential adoption of an OFFS program among niche-market producers in Ontario, Canada. Questionnaires were administered and 23 semi-structured interviews were conducted during 2008–2009. In total, 575 questionnaires were collected. The most commonly-produced commodities among respondents were vegetables (54.4%), fruits (36.7%) and beef cattle (31.1%). Disinfection of food animal drinking water and of post-harvest produce wash water was reported by 19.0% and 39.4% of respondents, respectively. Organic (26.4%) and OFFS program participation status (24.7%) were associated with the use of GAPs. Primary themes identified through interviews included concerns about the food safety of imported products, suggestions that OFFS programs be tailored by farm scale and be user-friendly and cost-recoverable, and the importance of producer education and government support. Future outreach with niche-market producers should focus on water disinfection (where appropriate), and they should be engaged in reviewing OFFS programs directed toward them to ensure their suitability and 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".