Envisioning prairie agroecology: Farmer visual constructions of place-based ecological agriculture in Canada
Bibliographic record
Abstract
Climate change is driving new challenges for farmers around the world, provoking a sense of urgency in the search for on-farm solutions and pathways to sustainable agricultural systems. While agroecology has gained recognition as a promising option for climate resilience, it remains nascent in the Canadian Prairies—a significant agricultural region dominated by large-scale conventional agriculture. Amid the sprawling fields of grains and oilseeds primarily destined for export markets, growing farm consolidation, and increased land concentration, farmers committed to ecological principles are building a prefigurative foundation for prairie agroecology. Far from cohering into a unified definition, this article captures what agroecology on the Prairies looks like. Through a community-engaged research design and in consultation with the National Farmers Union of Canada, we explore farmers’ reflections on agroecology and provide a participatory platform for farmer-led constructions of agroecology. Through dialogue with 19 farmers of different farm sizes and production types that began in the 2021 farming season—one marked by drought and extreme heat—we use photovoice, visual elicitation interviews, and participatory video to document and mobilize visual constructions of an emerging prairie agroecology. Despite a shared vision among the farmer participants about the need for a sustainability transition through diversified farming practices, three key questions emerged about the future of prairie agroecology: (1) Can prairie agroecology scale up and out, and if so, under what conditions? (2) To what extent is prairie agroecology possible without dense, place-based social networks? and (3) What are the possibilities for prairie agroecology when the future of farming in the region is uncertain, especially given the changing farmer demographics and Canada’s settler colonial context? We explore these questions and provide reflections on key issues confronting the future of prairie agroecology.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.037 | 0.018 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".