Participatory Systems Mapping. Drivers and Barriers identification in adopting BMP for potato producers in Southern Ontario using Gephi Visual
Bibliographic record
Abstract
Regional agricultural systems, such as the Ontario potato sector, are economically vital to Canada’s agri-food economy but increasingly challenged by climate change, market volatility, and rising production costs. Best Management Practices (BMPs) offer promising strategies for enhancing sustainability in the sector; however, adoption by producers remains inconsistent. Inconsistency is shaped by a complex interplay of social, economic, and environmental factors, yet how these dynamics operate across different farm scales (i.e. small, medium, and large) remains poorly understood. This critical knowledge gap is addressed by employing a participatory systems mapping approach, combined with network analysis using Gephi, to investigate the factors influencing BMP adoption among Ontario potato producers. Through Focus groups discussions and stakeholder engagement, the research identifies distinct patterns across farm scales: small-scale producers rely heavily on social networks, knowledge sharing, and crop’s diversification strategies; medium-scale producers face challenges related to market access and regulatory compliance; and large-scale producers are primarily influenced by economic efficiency and corporate’s buyer requirements. The findings underscore the limitations of one-size-fits-all policy frameworks, revealing the need of tailored, context specific interventions that account for the specific pressures and motivations of different producer typologies. By illuminating the scale-dependent dynamics shaping BMP adoption, this study contributes critical insights for policymakers, researchers, and industry stakeholders to advance sustainable agricultural practices in Canada’s potato sector and beyond.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".