From Wineries to Wedding Barns: Planning for On-Farm Diversification to Balance Agricultural Viability, Farmland Preservation, and Rural Economic Development
Bibliographic record
Abstract
With the loss of prime farmlands, small- to medium-sized farms, and challenges in maintaining agricultural viability, Ontario agriculture is changing. Many farmers are diversifying their land uses and business operations to include agricultural and non- agricultural related uses to earn a profitable livelihood in agriculture. Provincial policy and guidance, such as the OMAFRA Guidelines on Permitted Uses in Prime Agricultural Areas (2016), supports these uses. Still, clarity in policy, practice, and implementation at the municipal level is critical and far-ranging. How should the planning profession permit these uses in training and walk the fine line in balancing farmland preservation, agricultural viability, and rural economic opportunities? Based on a province-wide research study, this presentation explores trends in on-farm diversification, the impacts of on-farm diversification policy on family farmers, and the emerging approaches to support and responsibly plan for on-farm diversified uses in Ontario’s rural communities. Perspectives from planners at both the municipal and provincial levels in Ontario are presented and aim to build consensus around best practices for promoting on-farm diversification. This timely study is a step forward in identifying the ways the planning profession can support agriculture to maximize community benefit while protecting agri-food systems, promoting sustainable development, and supporting the entrepreneurial spirit of farmers. Funding: OMAFRA through the Ontario Agri-Food Innovation Alliance
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".