Long-term field trials assess temporal trends and seasonal weather on soybean seed yield, nutrient composition and nitrogen dynamics
Bibliographic record
Abstract
Soybean is vital for global food security, and understanding its response to environmental changes is crucial. We examined the interannual variation in seed traits of 14 short-season soybean cultivars from seven decades (1932-1992) in Eastern Canada, using field trials data from 1993 to 2016. Impacts of growing season weather variables—precipitation, mean maximum temperature (MTemp), and mean maximum vapor pressure deficit (MVPD), as well as historical atmospheric CO 2 —on seed yield, protein and oil percentages, carbon isotope discrimination (Δ 13 C), nitrogen isotopic composition (δ 15 N) were assessed. Seed yield and Δ 13 C increased with precipitation but decreased with MTemp and MVPD. Seed carbon percentage and Δ 13 C increased with atmospheric CO 2 , while seed protein and oil percentages, and δ 15 N decreased. Hierarchical partitioning highlighted vulnerability of soybean yield during the early reproductive stages (R1-R3, July) as well as the protein yield during the pod-formation and seed-filling period (R4-R6, August). Historical cultivar selection favored seed and oil yields, but not protein yield, Δ 13 C, and δ 15 N. Correlations between Δ 13 C, δ 15 N, and seed yield suggest selecting for higher yield may indirectly reduce water-use efficiency (indicated by higher D 13 C) and enhanced biological nitrogen fixation (reflected by lower d 15 N).
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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 | 0.001 | 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 | 0.000 | 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".