Genotype × Environment Interactions, Stability, and Agronomic Performance of Soybean with Altered Fatty Acid Profiles
Bibliographic record
Abstract
There has been a major effort to produce soybean [Glycine max (L.) Merr.] lines with modified fatty acid profiles in order to improve quality and develop new uses for soybean oil. Utilization of the lines depends on their agronomic traits and stability of the fatty acid profiles in diverse environments. The objectives of this study were to (i) evaluate the influence of years and locations on the fatty acid composition of soybean genotypes with unique fatty acid profiles, (ii) determine which fatty acids and fatty acid profiles are the most stable, and (iii) evaluate agronomic and seed quality traits of mutant soybean lines. Genotypes were evaluated over three years (1996, 1997, and 1998) at four locations in Southern Ontario, Canada. Year effects had the largest impact on all fatty acid levels. Location effects were significant only for oleic and linolenic acids. Genotype x year interaction effect was significant for all fatty acids whereas genotype x location and genotype x year x location interaction effects were significant only for oleic, linoleic, and linolenic acids. Mutants with reduced or elevated palmitic, elevated oleic, or reduced linolenic acid concentrations exhibited average or higher stability than lines with normal levels of these fatty acids. Therefore, these lines may be suitable for growing in a wide range of environments. Maturity, plant height, lodging, seed size, and seed quality were significantly different between mutants and cultivars. Seed yield was significantly reduced in mutants compared to 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 | 0.000 |
| 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".