Toward net-zero farming systems using diversity, integration, and perenniality in the Black Soil Zone of the Canadian prairies: A co-design approach
Bibliographic record
Abstract
• Transdisciplinary approach links soil, crop, and social research pillars • Peri-perennial practices will enhance soil health, biodiversity, and climate resilience • Landscape studies assess trade-offs in productivity, well-being, and biodiversity • Modelling connects farm data and GHG emissions for future farm scenario planning • Multi-metric data will inform policy supporting long-term agricultural resiliency Agricultural production in Canada’s Black Soil Zone is highly productive but dependent on simplified monoculture systems that contribute to greenhouse gas (GHG) emissions, biodiversity loss, and soil degradation. The “Leveraging Ecosystems to transform Agriculture on the Prairies” (LEAP) Project addresses these challenges through an interdisciplinary, co-design approach focused on enhancing diversity, integration, and perenniality in cropping systems. LEAP integrates biophysical research with farmer and Indigenous community perspectives to evaluate both ecological and social dimensions of agricultural transformation. The project is organized around five interconnected Pillars: (1) farmer leadership, emphasizing co-learning and mental health in sustainable decision-making; (2) First Nations self-determined farming systems, elevating Indigenous knowledge and governance; (3) landscape analyses of farmer-led annual, peri-perennial and perennial practices; (4) experimental field studies testing novel integrations of cover crops, intercropping, pollinator habitats, and livestock; and (5) scenario modelling using the Holos platform to assess system-level GHG outcomes and trade-offs. Together, these Pillars aim to identify strategies that support climate resilience, soil health, biodiversity, and farmer well-being while addressing economic and policy realities. This multi-metric, co-design approach also includes program and policy development which is essential to impacting long-term resiliency in the Canadian Black Soil Zone and the greater agricultural landscape.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".