Enhancement of total lipid production in vegetative tissues of alfalfa and sainfoin using chemical mutagenesis
Bibliographic record
Abstract
Abstract Alfalfa (Medicago sativa L.) and sainfoin (Onobrychis viciifoila Scop.) are two key forage legumes for the western Canadian cattle industry. Despite the high protein content, drawbacks to their use exist, including inefficient protein digestibility and energy use efficiency in the ruminant system, leading to economic losses and negative environmental impacts. Increasing the proportion of lipids in the diet of cattle is known to mitigate greenhouse gas emissions; however, the above two forage legumes possess only trace quantities of lipids in the shoot tissues used by the ruminants. In the current study, chemical mutagenesis was used as a conventional breeding approach to enhance lipid levels in the vegetative tissues of alfalfa and sainfoin. The mutagenesis procedures for these two forages need to be firmly established. We developed protocols for ethyl methanesulfonate (EMS)‐mediated mutagenesis by optimizing mutagen concentration and seed soaking duration. The EMS‐treated populations were assessed for morphological variants and total shoot lipid content (TSLC). Fatty acid composition was examined in a subset of plants with increased TSLC. Within 24 mo, the screening process identified mutagenized plants with significant increases in TSLC (3–5% on a dry weight basis), and a subset of these also displayed alterations in fatty acid composition in both species. These genotypes provide a novel source of germplasm for the future improvement of these two forage species.
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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".