Cropping system typologies perform differently under climate stress in Manitoba, Canada: multi-criteria assessment
Bibliographic record
Abstract
Information is required to understand how novel biodiversity strategies can improve cropping system resilience to climate change. A “business as usual” (BAU) rotation (wheat–canola–wheat–soybean) was compared with a “warm-season crop” (WS) rotation (corn–sunflower–dry bean–canola); a “biodiverse” (BD) rotation (fall rye with cover crop– corn/soybean intercrop—pea/canola intercrop–green fallow mixture); a “perennial grain” (Kernza) rotation (Kernza grain intermediate wheatgrass); and an organic (ORG) rotation (millet-green fallow mixture-wheat). Drought conditions prevailed in both study years. The BAU rotation had the lowest average yield (1821 kg ha −1 compared with 2533 and 3083 kg ha −1 for the BD and WS rotations, respectively), less post-harvest residual biomass, and a lower net return than the WS rotation. The WS rotation was limited by herbicide-resistant weeds in dry beans. Kernza seed yield was five times lower than for spring wheat. ORG wheat yielded the same as other wheat and had a higher net return. Live roots days, a measure of soil health potential, were 95 for the BAU rotation and 174 and 113 for the BD and WS rotations, respectively; the most were recorded for Kernza (365). Seasonal crop growth duration was increased by including Kernza and fall rye for early season growth, and corn, sunflower, a corn–soybean intercrop, a cover crop, and fall-seeded rye for late season growth. WS and BD rotations outperformed the BAU rotation and the BD system accomplished this with half the N fertilizer. Results demonstrate the potential of biodiverse rotations; barriers to their adoption should be addressed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".