Effect of seeding date, environment and cultivar on soybean seed yield, yield components, and seed quality in the Northern Great Plains
Bibliographic record
Abstract
Abstract Western Canada grows more than 25% of Canadian soybeans [Glycine max (L.) Merr.] and is the new northern extent of the North American soybean‐growing region. Canada is the seventh largest soybean‐exporting country, yet little information on yield and quality in modern cultivars exists for that region. The objective of this study was to determine the impact of delayed seeding on soybean seed yield, yield components, maturity, and seed quality in Manitoba, located in the eastern northern Great Plains, and provide the first characterization of the relative influence of environment, seeding date and cultivar on those variables. Field studies were conducted from 2015 to 2017 at three locations in southern Manitoba to evaluate the performance of three soybean cultivars at three seeding dates from 24 May to 24 June. Up to 90% of total variation in the response variables was explained by environment, seeding date, cultivar and their interactions, with environment often consuming the majority of total sums of squares. Among environments, seed yield ranged from 1610 to 3590 kg ha−1, seed number from 1719 to 3828 seeds m−2, seed weight from 125 to 169 g 1000 seeds−1, oil concentration from 16.1 to 18.7% and protein concentration from 32.8 to 35.3%. Overall, very late seeding reduced yield, seed weight, and oil but did not affect protein. This study demonstrates that environmental conditions in Manitoba have a large influence on soybean performance compared to seeding date or pedigree and that protein concentration varies at a finer geographical scale than previously reported.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".