Genetic and Environmental Effects on Fatty Acid Composition in Soybeans with Potential Use in the Automotive Industry
Bibliographic record
Abstract
ABSTRACT Environmental effects on quantitative traits such as seed oil fatty acid composition in soybean [ Glycine max (L.) Merrill] can significantly affect the performance of a given genotype when exposed to varying growing conditions. High linoleic acid oils have the potential to be used as raw materials for the production of polyols and polyurethane that can be used in the automotive industry. The objectives of this study were (i) to determine the sources of variation affecting seed oil fatty acids profiles and (ii) to evaluate stability of soybean genotypes with different fatty acids across different environments. Fifty‐six soybean genotypes with altered fatty acid composition selected from two recombinant inbred line populations segregating for saturated and linoleic acids were used along with commercial high yielding cultivars. All genotypes were evaluated in southwestern Ontario, Canada, at three locations in 2008 and two locations in 2009, and data was collected for fatty acid composition as well as other seed and agronomic traits. Combined analysis of variances showed significant location and genotype × location effects for stearic and oleic acids. Genotype × environment effect was significant for unsaturated fatty acids plus seed oil and protein concentrations. The effect of genotype × year was significant for unsaturated fatty acids. Stability analyses using Francis and Kannenberg's mean coefficient of variation stability, Shukla's stability variance statistic (σ 2 ), and Lin and Binns cultivar superiority measure identified genotypes E‐49 and E‐14 as being superior for high linoleic and low saturated fatty acids oil production for potential use in the automotive industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".