Residue management and overwinter survival in winter canola (<i>Brassica napus</i> L.)
Bibliographic record
Abstract
Winter canola production in eastern Canada is a growing segment of the Canadian canola industry. Crop establishment and survival overwinter have been identified as two of the major challenges associated with the production of winter canola. Environmental conditions can interact with agronomic practices, including seeding date, plant population density, fertility, and residue management, to influence stand establishment in autumn and overwinter survival. The objective of the current research was to evaluate the impact and interaction of preceding crop residue and tillage practices on the establishment, overwinter survival, and yield of winter canola in southern Ontario. Winter canola was seeded following either winter wheat or soybean and using conventional, no-till, or strip-till practices. Over the course of the 3 years of study, the annual decline in winter canola plant population density was influenced by the type of tillage practice used but not by the preceding crop. The overwinter decrease in plant population density was largest in no-till, followed by strip-till, and finally conventional tillage. At physiological maturity, winter canola yields in reduced tillage practices (i.e., no-till and strip-till) were equivalent to those achieved utilizing conventional tillage practices. When grain yields were standardized as a function of the spring plant population density, the highest and lowest yield per plant coincided with the lowest and highest plant population densities and were observed in strip-till (14.2 g plant−1) and no-till (9.1 g plant−1), respectively. Results of this research have established that winter canola can be successfully produced in Ontario utilizing a range of tillage practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".