Shade Avoidance in Soybean Reduces Branching and Increases Plant-to-Plant Variability in Biomass and Yield Per Plant
Bibliographic record
Abstract
Recent studies have suggested that soybeans express shade avoidance in response to low red : far-red (R : FR) light reflected from neighboring plants and that this response may determine the onset and outcome of crop–weed competition. We tested the hypothesis that the low R : FR ratio would trigger characteristic shade avoidance responses in soybean and that the subsequent phenotype would experience reproductive costs under non–resource-limiting conditions. Soybeans were grown in a fertigation system in field trials conducted in 2007 and 2008 under two light quality treatments: (1) high R : FR ratio (i.e., weed-free) i.e., upward reflected light from a baked clay medium (Turface MVP®), or (2) low R : FR ratio (i.e., weedy) of upward reflected light, from commercial turfgrass. Results of this study indicated that a reduction in the R : FR ratio of light reflected from the surface of turfgrass increased soybean internode elongation, reduced branching, and decreased yield per plant. Shade avoidance also increased the plant-to-plant variability in biomass and yield per plant. Per plant yield losses were, however, more closely associated with reductions in biomass accumulation than population variability as the expression of a shade avoidance response did not influence harvest index. While these results suggest that weed induced shade avoidance decreases soybean per plant yield by reducing branching, it is possible the productivity of a soybean stand as a whole may be buffered against these reduction by a similar, but opposite, expression of plasticity in branching.
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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".