Shade Avoidance Restricts Soybean Breeding Progress and Increases Herbivore Susceptibility
Bibliographic record
Abstract
High planting densities expose field crops to competition for light, which typically induces shade avoidance responses such as stem elongation. While adaptive in natural environments, these responses can lower yield and increase susceptibility to stress in agricultural systems. We tested whether soybean breeding over the past century has altered shade avoidance and associated trade-offs. Twenty-one Canadian cultivars released between 1922 and 2018 were grown in pots under either control or shade-avoidance-inducing light conditions, achieved by altering reflected light spectrum without reducing photosynthetic radiation. Plants exposed to shade-avoidance-inducing light grew taller and suffered greater thrips damage, consistent with expectations of increased stem elongation and reduced defence. More recent cultivars showed higher susceptibility to thrips than older ones. Breeding progress in seed yield was driven largely by greater biomass allocation to seeds and reduced branching. However, under shade-inducing light, the yield improvements were smaller, pointing to shade avoidance as a limiting factor. Our results indicate that while soybean breeding has improved yield and shifted morphology towards ideotypes suited for high-density stands, persistent shade avoidance responses constrain breeding progress for yield and increase herbivore susceptibility. Breeding strategies that reduce sensitivity to neighbor cues may therefore improve soybean productivity and resilience.
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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 | 0.000 |
| 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".