The performance of spring wheat cultivar mixtures under conventional and organic management in Western Canada
Bibliographic record
Abstract
Abstract Wheat (Triticum aestivum L.) cultivar mixtures may stabilize yield across environments, control air‐borne diseases, and manage pest populations in both conventional and organically managed systems. The objective of this study was to evaluate agronomic and end‐use quality characteristics of wheat cultivar mixtures. Five Canada Western Red Spring wheat cultivars (‘Go Early’, ‘Carberry’, ‘Glenn’, ‘CDC Titanium’, and ‘Lillian’) differing in agronomic and quality traits were selected to compose 20 possible two‐way and three‐way combinations. Field experiments were conducted in four conventional and two organic environments in Alberta and Saskatchewan, Canada in 2016 and 2017. Wheat cultivar mixtures out‐yielded their mid‐component averages in both conventional and organic environments. Averaged across locations, two mixtures, Glenn–Lillian and Go Early–Glenn–Lillian, significantly out‐yielded their mid‐components. The overall yield increase ranged from 3.3 to 14.1%, with a mean of 0.18 Mg ha–1 over different environments. Grain yield was negatively correlated with protein content (r = –.53) and falling number was negatively correlated with sedimentation volume (r = –.66) in conventional systems. Protein content was positively correlated with falling number in both conventional (r = .60) and organic (r = .40) systems. Days to maturity was positively correlated with yield (r = .40) and sedimentation volume (r = .40), but negatively correlated with falling number (r = –.80) in organic system. Sole cultivars were more stable under conventional, and mixtures were more stable under organic management. Our results suggest that wheat cultivar mixtures may provide yield advantage under abiotic stresses in conventional, whereas superior yield and grain quality in organic management.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".