Path Analyses of Population Density Effects on Short‐Season Soybean Yield
Why this work is in the frame
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Bibliographic record
Abstract
Yield component analysis provides a framework for identifying potentially useful traits for yield improvement. Consideration of how population density affects other yield components has not been addressed specifically for short‐season soybean [ Glycine max (L.) Merr.] production. We assessed the direct and indirect contributions of population density for short‐season soybean yield and its components over a wide range of population densities (6–134 plants m −2 ) using path‐coefficient analysis. Data were from field tests conducted in 1997, 1998, and 1999 at Keiser, AR. Although population density had a large inverse association with pods plant −1 , the large direct effect of population density on yield was greater than its negative indirect effect via pods plant −1 . The direct effects of pod number plant −1 and seeds pod −1 on yield were positive, whereas mass seed −1 had a negligible effect. Pods fertile‐node −1 differed between cultivars, and it was reduced by increasing population density. For early sowing, the contribution of population density to yield was less because pods m −2 could be achieved at low population densities by a large number of fertile‐nodes plant −1 and pods fertile‐node −1 . In contrast, at late sowing, the decreased potential for fertile‐nodes plant −1 was compensated by increasing plant population density. In short seasons, maximizing nodes m −2 and pods m −2 can be achieved by high population densities and early canopy closure, rather than the conventional system of larger plants with greater numbers of pods plant −1 and pods fertile‐node −1
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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