Modelling soybean yield responses to seeding date under projected climate change scenarios
Bibliographic record
Abstract
Climate change is projected to increase growing season length and temperature in Canada but how soybean [Glycine max (L.) Merr.] will respond is uncertain. By modelling soybean responses to climate change scenarios, stakeholders can develop adaptation strategies. The CSM-CROPGRO-Soybean and STICS models were used to simulate soybean responses under baseline (1971–2000) and in near (2041–2070) and distant (2071–2100) future climate scenarios, including those resulting in altered seeding dates in eastern Canada. Field data collected in Ottawa were used to evaluate the models. The simulated seed yield using the CSM-CROPGRO-Soybean model showed an increase of about 14% (0.34 t ha−1) in the near future and a decrease in the distant future under RCP8.5 and the STICS model estimated a decrease in both the near and distant future. When the crop parameters determining the life cycle were increased by 30% and 40%, the simulated seed yield increased by more than 5%–10% and 10%–20% and by more than 20%–30% and 27%–40% if combined with current harvest index levels. Our simulations showed that soybean seed yield would not benefit from a prolonged growing season under the projected future climate in eastern Canada, unless harvest index is maintained.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".