Short‐Season Soybean Yield Compensation in Response to Population and Water Regime
Bibliographic record
Abstract
Short‐season soybean [Glycine max (L) Merr.] production systems, such as double cropping and late sowing, require high populations to optimize yield, but effects of high populations on seed number and seed mass are unknown. We evaluated plant population effects on yield compensation, stability of harvest index, assimilate partitioning for seed number, and seed‐filling characteristics for 2 yr near Keiser, AR. The study had two cultivars, two levels of irrigation, and three row spacings that each had five levels of population ranging from 6 to 134 plants m−2 Increasing population reduced yield per plant but increased yield per unit area. Harvest index was relatively constant across populations for a given year and irrigation regime, and yield was closely associated with biomass at maturity. At high populations, plants maintained individual seed mass by reducing the proportion of shell mass per pod. Final individual seed mass, seed growth rate (SGR), and the length of effective filling period did not change with increasing population for irrigated or nonirrigated treatments. Reductions in yield caused by low population density were due to low seed number. Seed number per square meter was directly proportional to the ratio of crop growth rate (CGR) to SGR. For short‐season production, high populations ensured early canopy coverage and maximized light interception, CGR, and crop biomass, resulting in increased seed number and yield potential.
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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".