Genetic Improvement Estimates, from Cultivar × Crop Management Trials, Are Larger in High‐Yield Cropping Environments
Bibliographic record
Abstract
ABSTRACT Soybean [Glycine max (L.) Merr.] genetic improvement studies conducted under different stresses have provided conflicting results while maize (Zea mays L.) studies have shown that new cultivars were tolerant to a wide range of stresses. Using 25 genetic improvement studies from the literature, the objectives of this study were to compare estimates of genetic improvement under a range of yield capacities and also to determine the Finlay‐Wilkinson adaptability of old to newer cultivars. The 25 genetic improvement studies were performed under a range of yield capacities due to varying density, fertility, irrigation, fungicide treatments, or year‐to‐year variation. Genetic improvement rates for each experiment were estimated from linear regression of cultivar yield on year of release. A cultivar's adaptability was estimated from regression of cultivar yield on site yield. In general, higher estimates of genetic gain were found in high‐yielding environments regardless of whether the higher yields were provided by reducing stress (controlling weeds or diseases, increasing nitrogen or water) or increasing stress using higher plant densities. Newer cultivars had higher adaptability values, indicating they are better adapted to high‐yield environments. Older cultivars appear to have little utility for current use in any of the cropping systems in these studies. Plant breeders need to consider the possibility of lower genetic progress if using lower‐yield‐potential testing environments such as reduced fertility or weed control. The use of high‐yield sites may maximize genetic progress even if it does not reflect current producer environments.
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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 | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".