Effects of maturity group and stem growth habit on the branching plasticity of soybean cultivars grown at various planting densities
Bibliographic record
Abstract
To elucidate the effects of maturity and the stem growth habit on the planting density-dependent branching plasticity of soybean cultivars, we studied the branch traits of 12 cultivars or lines planted at different densities (8.3, 16.7, and 22.2 plants m−2) in Sapporo (2012) and Ebetsu (2013). The 12 cultivars and lines consisted of three determinate cultivars from Hokkaido, three indeterminate cultivars from the northern US, and near-isogenic lines with the backgrounds of Canadian, US, and Japanese cultivars exhibiting diverse stem growth habits. We investigated the relationship between the maturity or stem growth habit and branching plasticity, which was calculated based on the ratios of different branch traits under sparse and dense planting conditions. The use of the ratios of the total branch length and the number of nodes per branch under sparse and dense planting conditions as a measure of branching plasticity revealed varietal differences across years. For the determinate and indeterminate cultivars in both years, branching plasticity was positively correlated with the number of days until stage R5 (onset of seed filling), which is when branches cease to elongate. Comparisons of Japanese and US cultivars and near-isogenic lines for the Dt1 gene (mediating the stem growth habit) indicated that the branching plasticity of indeterminate cultivars and lines is greater than that of determinate cultivars, with a large variation among backgrounds and cultivars. The results of this study imply that branching plasticity is greater in late-maturing soybean cultivars. Moreover, the indeterminate growth habit substantially enhances branching plasticity.
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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 | 0.001 |
| 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".