Review: Redesigning Canadian prairie cropping systems for profitability, sustainability, and resilience
Bibliographic record
Abstract
Thiessen Martens, J. R., Entz, M. H. and Wonneck, M. D. 2015. Review: Redesigning Canadian prairie cropping systems for profitability, sustainability, and resilience. Can. J. Plant Sci. 95: 1049–1072. Redesign of agricultural systems according to ecological principles has been proposed for the development of sustainable systems. We review a wide variety of ecologically based crop production practices, including crop varieties and genetic diversity, crop selection and rotation, cover crops, annual polyculture, perennial forages, perennial grains, agroforestry systems, reducing tillage, use of animal manures and green manures, soil biological fertility, organic production systems, integrated crop–livestock systems, and purposeful design of farm landscapes (farmscaping), and discuss their potential role in enhancing the profitability, environmental sustainability, and resilience of Canadian prairie cropping systems. Farming systems that most closely mimic natural systems through appropriate integration of diverse components, within a context of supportive social and economic structures, appear to offer the greatest potential benefits, while creating a framework in which to place all other farming practices. Our understanding of ecological relationships within agricultural systems is currently lacking, and a major shift in research, education, and policy will be required to purposefully and proactively redesign Canadian prairie agricultural systems for long-term sustainability.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".