Assessment of diverse cicer milkvetch (<i>Astragalus cicer</i> L.) germplasm for agro-morphological traits under a stockpiling system
Bibliographic record
Abstract
Cicer milkvetch ( Astragalus cicer L.) is a non-bloating perennial forage legume suitable for stockpiled grazing in the fall because of its rapid regrowth and high nutritive value. Genetically diverse germplasm are needed for the development of improved cicer milkvetch cultivars that can provide consistent production across variable climatic conditions. The objective of this research was to assess the diversity and relationship of 27 cicer milkvetch populations to inform the selection of populations for future cultivars that have superior agro-morphological traits during summer and fall growth. A completely randomized field trial was established in 2019 near Clavet, Saskatchewan. In 2020 and 2021, forage dry matter yield (DMY), maximum stem length, leaf number per stem, rhizome spread, and stem density were recorded on 27 populations of cicer milkvetch in late June at a first harvest and mid-October at a stockpile harvest. All five traits were different ( p < 0.05) among the populations at both harvests except for leaf number per stem in late June. Principal component analysis identified that the first three principal components described 89% of the variation in agro-morphological traits at the first and stockpile harvests. Of the agro-morphological traits, maximum stem length had the greatest correlation with forage DMY at the first harvest ( r = 0.69) and stockpile harvest ( r = 0.6). Our research demonstrates that there is a high morphological diversity among cicer milkvetch populations, and plant introductions, PI 362266, PI 576963, PI 440143, and PI 362254 could be used as novel genetic resources for the development of climate-resilient 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".