Soybean seed protein content is lower but protein quality is higher in Western Canada compared with Eastern Canada
Bibliographic record
Abstract
Low seed protein content in soybeans [ Glycine max (L.) Merr.] grown in Western Canada can result in soybean meal that does not meet the 48% protein standard. The objectives of this study were to quantify seed composition, agronomic differences between Eastern and Western Canada-grown soybeans, and to determine the yield cost of raising Western soybean protein. Twenty high-to-low protein, including one non-nodulating, genotypes were grown at two locations in Eastern Canada, and eight locations in Western Canada from 2018 to 2021 to determine seed protein, seed composition, and agronomic traits. Over all environments, genotype seed protein ranged from 36.8% to 46.9% with 35.0% for the non-nodulating line. Average seed protein was significantly higher in Eastern Canada (41.6%) compared with Eastern Prairie (39.3%) and Prairie sites (39.7%). There are not separate east–west mega-environments for seed protein in Canada; a high protein genotype is high protein across Canada. With an increase of seed protein by 1%, seed yield dropped by 45.3 kg ha −1 in Eastern Canada, 53.1 kg ha −1 in the Eastern Prairie, and 78.4 kg ha −1 in Prairie sites. In Western Canada, plants were taller but lower yielding with fewer and smaller seeds, and produced lower fixed nitrogen protein yield compared with Eastern Canada. Seed protein quality, quantified with the 11S:7S ratio, was higher in Western Canada compared with Eastern Canada. Plant breeders and growers may need to select higher protein genotypes at the cost of lower yield, if the soybean industry is unable to exploit the protein quality advantage in Western Canada.
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 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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".