Farmer-Led Learning: Innovative Best Management Practices in Ontario
Bibliographic record
Abstract
As rising fuel prices and climate pressures threaten the profitability, productivity and longevity of Ontario’s potato sector, diverse actors (public, private, academic, farmer) are discovering innovative, sustainable Best Management Practices to address these challenges. Limited research examining the multi-dimensional motivations and behavioural drivers impacting farmers’ ability and willingness to apply these BMPs has limited their uptake, contributing to the continued use of conventional methods. Examining transition pathways for Ontario’s potato sector, this study works with potato producers (smallholder, organic, conventional) in Southern Ontario to identify innovative BMPs, and the factors which motivate farmers to apply them. Conceptualizing ‘BMPs’ only as in-field practices, research ignores how wider systemic actions across the value chain enable and support farmers’ uptake of alternative methods. Survey results captured baseline socio-demographic information and provided data to understand challenges, approaches, and limitations farmers face when exploring alternative production methods. Findings show farmers are most concerned about changing climate conditions and rising costs, and feel most limited by encroachment of government regulations. Triangulating survey results with observations from initial field-visits, semi-structured interviews, and participant observation, our research illustrates the wide diversity of approaches applied by farmers to address issues of sustainability, while also showing how complex social and structural constraints guide, shape or limit both the approaches available to individuals, and their desire to apply alternative practices. This research exposes structural and social factors limiting sustainable transitions in Ontario’s potato sector, highlighting potential areas for future research, policy support and farmer-led action.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".