Genetic Improvement in Short Season Soybeans: I. Dry Matter Accumulation, Partitioning, and Leaf Area Duration
Why this work is in the frame
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Bibliographic record
Abstract
Genetic improvement of short‐season soybean [ Glycine max (L.) Merr.] cultivars has resulted in a 0.5% annual gain in yield. Although yield is the product of dry matter (DM) accumulation and partitioning, the relative contributions of these two components of yield to genetic improvement has not been documented. Furthermore, the mechanism by which higher DM accumulation or harvest index (HI) is accomplished in the newer cultivars is unclear. The objective of the current study was to characterize DM accumulation and partitioning in cultivars which differ in yield potential, and determine the role of these traits in yield improvement. Two older (low yield potential) and two newer (higher yield potential) soybean cultivars of similar maturity were grown in side‐by‐side trials in 1996 and 1997. Plant samples were taken during each growing season and separated into leaves, stems + petioles, roots, and seeds. Dry matter accumulation and leaf area indices were measured. Seed yield of the new cultivars was 30% greater than their older counterparts. Increased DM accumulation contributed 78% and increased HI contributed 22% towards the genetic gain in yield. Total plant dry weight increased to a maximum around R4/R5 and subsequently declined during the seed‐filling period (SFP) as pod development increased and leaf senescence began. This decline in dry weight during the SFP was greater for the old than for the new cultivars. The newer cultivars maintained leaf area further into the SFP than the old cultivars enabling continued dry matter accumulation. The results of this experiment indicate that genetic yield improvement in the short‐season soybean cultivars examined was mainly associated with longer leaf area duration and the subsequently greater DM accumulation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it