Estimating the economic optimal target density of hybrid canola based on data from a western Canadian meta-analysis
Bibliographic record
Abstract
Canola production in western Canada has expanded such that in many regions canola area rivals the area seeded to wheat. This change can be mainly attributed to adoption of herbicide resistant hybrid varieties and resulting higher relative profitability. Producers responded to higher seed prices by reducing seeding rates, leading to plant densities that were often lower than industry recommendations. The study objectives were to examine canola yield response to plant density and assess economically optimal density levels relative to industry recommendations and grower practices. A meta-analysis approach was applied to yield response and plant density data with herbicide resistant hybrid canola from multiple studies. Three alternative marginal yield–density functions were estimated and used to calculate economically optimal plant density. Sensitivity of results to key parameters was assessed. Resulting optimal density levels under recent average prices, seed size, and emergence were 62–73 plants·m−2, consistent with industry recommendations for western Canada. Recent field surveys report lower densities than this range, suggesting that producers are seeding at less than optimal rates; however, there is significant uncertainty associated with canola production and interaction of seeding rate with other factors. These would impact optimal plant density, although much of the uncertainty remains until well after seeding. The empirical results represent a foundation for development of more detailed models, highlight the significance of the relationship between plant density and economically optimal yield, and suggest the need to develop decision making tools to support producers in making canola planting decisions.
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 | 0.032 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.025 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".