Genetic Improvement Rates of Short‐Season Soybean Increase with Plant Population
Bibliographic record
Abstract
Studies with a series of historic soybean [Glycine max (L.) Merr.] cultivars have found genetic improvement rates for seed yield of about 0.5% per year since soybean was first cultivated in Canada. Work with maize (Zea mays L.) has shown seed yield differences between old and new hybrids are more pronounced under higher seeding rates. The objective of our research was to determine the effect of plant populations on the rate of genetic improvement of short‐season soybeans. Soybean cultivars released from1934 to 1996 were grown in 40‐cm‐wide rows at several seeding rates (Exp. 1, 42 cultivars grown at 25, 50, 75 seeds m−2 in 1998 and 1999; and Exp. 2, seven cultivars grown at 25, 50, 100, 150, 200 seeds m−2 in 1999 and 2000) in two fields for 2 yr at Ottawa, ON. Genetic improvement rates were based on cultivar year of release and were compared across plant populations. In Exp. 1, seed yields did not reach a plateau. In Exp. 2, a seed yield plateau was reached and genetic improvement rates generally increased with plant population. Maximum rates of genetic improvement occurred at plant populations three to four times greater than current commercial practice (40 plants m−2). A plateau for seed yield was reached at lower plant populations compared to plateaus for genetic improvement rates which were reached at higher plant populations (>80 plants m−2). Higher plant populations also resulted in earlier maturity, increased plant height, and increased lodging. Seed protein increased and seed oil decreased with higher plant populations. New soybean cultivars appear more tolerant to plant population stress than older cultivars.
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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 | 0.001 |
| 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".