Soybean Yield and Seed Composition Changes in Response to Increasing Atmospheric CO2 Concentration in Short-Season Canada
Bibliographic record
Abstract
From 1993, we have conducted trials with the same set of old to newer soybean cultivars to determine the impact of plant breeding on seed yield, physiological and agronomic characteristics, and seed composition. Since 1993, global atmospheric [CO2] increased by 47 ppm. The objective of our current analysis with this data set was to determine if there were changes in soybean seed yield, quality or phenology attributable to elevated atmospheric CO2 concentration (eCO2), temperature or precipitation. Additionally, we estimated genetic gain annually. Over 23 years, there was a significant increase in atmospheric [CO2] but not in-season average maximum or minimum temperatures, or average in-season precipitation. Seed yield was increased significantly by eCO2, higher precipitation and higher minimum temperatures during flowering and podding. Yield decreased with higher minimum temperatures during vegetative growth and seed filling. Seed oil and also seed protein plus oil concentrations were both reduced with eCO2. Phenology has also changed, with soybean cultivars spending less time in vegetative growth, while time to maturity remained constant. Over the 23 years of the study, genetic improvement rates decreased as [CO2] increased. Newer cultivars are not better adapted to eCO2 and soybean breeders may need to intentionally select for favourable responses to eCO2 in the future.
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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 | 0.000 | 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".