Stubble management effects on canola performance across different climatic regions of western Canada
Bibliographic record
Abstract
Cardillo, M. J., Bullock, P., Gulden, R., Glenn, A. and Cutforth, H. 2015. Stubble management effects on canola performance across different climatic regions of western Canada. Can. J. Plant Sci. 95: 149–159. Previous research in the most arid region of the Canadian prairies has shown that wheat stubble cut tall the previous year can improve performance of the following canola crop. This study aimed to determine if tall stubble could benefit canola across the climatic conditions typically experienced in western Canada. Tall stubble impacts on canola were monitored over 11 site-years located throughout the prairies. At each site, tall stubble (50 cm) was compared with short stubble (20 cm). At some sites the stubble lodged allowing an unintended comparison between stubble that remained intact and stubble that was flattened. The comparison of snow water equivalent showed tall stubble caught more snow than short stubble but the benefit of additional spring soil moisture was masked by heavy spring precipitation in both 2011 and 2012. Canola biomass and yield were significantly lower in damaged versus intact stubble, either short or tall. In both years, wet spring conditions were followed by hotter and drier weather in the mid to late growing season. Soil under the damaged stubble (short or tall) likely warmed and dried more slowly in the spring, limiting early-season growth, biomass and yield. At sites where both tall and short stubble remained intact, there was a significant yield advantage with tall stubble. The intact tall stubble may have slowed evaporation and soil drying compared with intact short stubble, which reduced moisture stress later in the growing season, imparting a yield advantage.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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".