Optimizing Seeding Dates and Rates for Canola Production in the Humid Eastern Canadian Agroecosystems
Bibliographic record
Abstract
Optimum seeding date (OSD) and seeding rate is an important management practice to improve the performance of canola ( Brassica napus L.) production. A field study was conducted to investigate the influence of seeding date and rate on plant stand count, yield components, yield, and seed oil and protein concentrations, and to develop a location‐sensitive model for estimating OSD for maximizing canola yield. The factorial experiment of three seeding dates (early, intermediate, and late) and three seeding rates (2.5, 5.0, and 7.5 kg ha −1 ) was performed in 2011 and 2012 at seven locations across eastern Canada. An independent dataset from an additional 2‐yr field experiment at the Ottawa site was used for model verification. Our data showed that seed yield, seed oil, and pod number per plant were significantly affected by seeding date and seeding rate. The greatest yield and seed oil concentration were obtained with the early seeding in most site‐years. The OSD was a quadratic function of the long‐term (30 yr) average daily minimum air temperature ( T min ) in April and May with R 2 = 0.98, P < 0.01 and SE = 2.6 d. Increasing seeding rate from 2.5 to 5.0 kg ha −1 increased seed yield for early‐seeded canola in most site‐years but the yield did not increase with further increases in seeding rate. Early seeding at 5.0 kg ha −1 is therefore recommended as the optimum seeding rate across eastern Canada. Quantitative relationship between optimum seeding date and mean minimum temperature in April and May. A regression model to predict optimum seeding date for spring canola. A suitable seeding rate for canola production in eastern Canada. The impact of seeding date on canola seed oil and protein concentration. Canola yield and yield components as affected by seeding date and rate.
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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 | 0.000 | 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 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".