Policy Recommendations for BMP adoption in Ontario's Potato Sector
Bibliographic record
Abstract
Potato producers in Ontario face individual and structural challenges impacting their operations. Adopting Best Management Practices (BMPs) can improve productivity, profitability, and sustainability of producers in Ontario’s potato sector, but depending on their size and market participation, they have different capacity and willingness to adopt certain BMPs. To support the increased use and adoption of BMPs by farmers throughout the sector this study aimed to understand key drivers and barriers to BMP adoption, and construct a Theory of Change outlining pathways to increase adoption for diverse producers in the sector. Quantitative survey data was triangulated with qualitative data from semi-structured interviews, participant observation and Focus Group Discussion to understand the context, key challenges, and key differences facing farmers. A systems thinking approach was then used to understand key drivers and barriers of BMP use. A Theory of Change workshop was conducted with policy-makers, researchers and practitioners to collaboratively identify the main changes that need to take place to increase the adoption of BMPs in the sector. This study identified that small, medium and large-scale farmers experience different drivers and barriers impacting their operations and BMP use. Different access to resources, market participation, production styles and social networks require that different approaches are required when considering their BMP adoption. This study found that willingness to adopt BMPs largely depends on whether farmers have the capacity and the incentives to adopt and that distinct approaches are required to address the different barriers experienced by small, medium and large-scale producers.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".