Path Analyses of Population Density Effects on Short‐Season Soybean Yield
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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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".