Investigating the effects of seed treatments on the economically optimal seeding rate of conventional soybean in Atlantic Canada
Bibliographic record
Abstract
Soybean seeding rates in the cool growing environment of Atlantic Canada are much higher than other regions impacting economic return. Recent studies across North America have suggested that soybean seeding rates can be lowered to maximize profitability. Seed treatments have been shown to improve abiotic stress tolerance and may be another mechanism to reduce seeding rates. Therefore, the objectives of the present study were to ( i) determine the economically optimal seeding rate (EOSR) for conventional soybean in Atlantic Canada and ( ii) determine if fungicide seed treatments can reduce this rate. Field studies were conducted in 2020 and 2021 to evaluate the effects of four seeding rates and four fungicide seed treatments on soybean stand establishment, growth, resource use efficiency, yield, and profitability. Price received had a dramatic effect on producer return and the EOSR which ranged from 243 000 seeds ha−1 under a low price received scenario ($0.45 kg−1) up to 613 000 seeds ha−1 under a high price received scenario ($0.82 kg−1). In contrast, seed and pesticide costs had a minimal impact on expected returns. Soybean resource use efficiency was not impacted by seeding rate or by seed treatments. Further seed treatments did not impact soybean stand establishment or profitability. Soybean yield increased with seeding rate and plateaued at a seeding rate of 741 000 seeds ha−1, whereas individual plant yield dramatically declined as seeding rate increased. Results of this study suggest that soybean producers in Atlantic Canada should base their seeding rates on contracted or expected price received to maximize profitability.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".