Farmer-Led Learning Innovative Best Management Practices for potato production in Ontario
Bibliographic record
Abstract
Best management practices (BMPs) are practical, affordable alternatives to conventional production systems, which improve agricultural production systems’ ability to address social, economic and ecological challenges. BMPs enhance the viability and sustainability of agriculture when successfully applied, but in systems where intensive industrial agriculture predominates their use is limited. Working with diverse potato producers in Southern Ontario, this project applied a Systems Thinking approach to understand motivating drivers and structural, institutional and organizational barriers impacting the adoption of BMPs for potato cultivation. Using a mixed-methods approach, this study collected quantitative and qualitative data using a farm-level survey, focus groups, workshops, and participant observation. Data was collected regarding demographics, management approaches, social networking and perceived challenges with BMP uptake. Analysis of our data revealed that family and future generations, ecosystem, soil and human health, community and social relationships, and efficiency and profitability were motivating drivers (based on beliefs and values) influencing management decisions. However, structural, institutional and organizational barriers (including market access, regulation, production efficiencies and competition), mediate producers’ abilities to act according to these motivations. Small-scale, medium-scale and large-scale producers are impacted by these barriers differently. In understanding the decision-making factors which drive BMP uptake in Ontario’s potato sector, policy and program design can leverage drivers and reduce barriers.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".