Genetic Improvement of U.S. Soybean in Maturity Groups II, III, and IV
Bibliographic record
Abstract
ABSTRACT Soybean improvement via plant breeding has been critical for the success of the crop. The objective of this study was to quantify genetic change in yield and other traits that occurred during the past 80 yr of North American soybean breeding in Maturity Groups (MGs) II, III, and IV. Historic sets of 60 MG II, 59 MG III, and 49 MG IV soybean cultivars, released from 1923 to 2008, were evaluated in field trials conducted in 17 U.S. states and one Canadian province during 2010 to 2011. Averaged over 27 MG II and MG IV and 26 MG III site‐years of data, the estimated rates of yield improvement during the 80 yr were 23 kg ha –1 yr –1 for MGs II and III, and 20 kg ha –1 yr –1 for MG IV cultivars. However, a two‐segment linear regression model provided a better fit to the data and indicated that the average current rate of genetic yield gain across MGs is 29 kg ha –1 yr –1 . Modern cultivars yielded more than old cultivars in all environments, but particularly in high‐yielding environments. New cultivars in the historic sets used in this study are shorter in height, mature later, lodge less, and have seeds with less protein and greater oil concentration. Given that on‐farm soybean yields in the United States are also increasing at a rate of 29 kg ha –1 yr –1 , it can be inferred that continual release of greater‐yielding cultivars has been a substantive driver of the U.S. on‐farm realized yield increases.
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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) | 0.000 | 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 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".