Evaluating effectiveness of clonal plant selection of alfalfa (<scp><i>Medicago sativa</i></scp> L.) and sainfoin (<scp><i>Onobrychis viciifolia</i></scp> Scop.) in mixtures: Mean performance and stability in a multi‐environment trial
Bibliographic record
Abstract
Abstract Alfalfa ( Medicago sativa L.) ‐ grass or alfalfa ‐ sainfoin ( Onobrychis viciifolia Scop.) mixtures are commonly used for pastures in western Canada because of their high forage quality and their low risk of causing frothy bloat in grazing animals. However, the proportion of these two legumes declines in mixed forage stands over time. The objective of this study was to evaluate the effectiveness of selecting superior genotypes from clonally propagated alfalfa or sainfoin under plant competition in different growth environments. For this study, around 100 genotypes of each legume were cloned and transplanted into meadow bromegrass ( Bromus riparius Relm.) or alfalfa swards at Saskatoon, SK and Lethbridge, AB, Canada in 2017. Genotype‐environment (G x E) interactions of alfalfa and sainfoin genotypes were analysed by an additive main‐effects and multiplicative interaction (AMMI) model. Significant variations in plant height, spring vigour and total dry matter yield (TDM) were observed for both species. Among the measured traits, plant height was a more highly heritable trait (H 2 = .16 for alfalfa and H 2 = .18 for sainfoin), while TDM was the least heritable (H 2 = .08 for alfalfa and H 2 = .04 for sainfoin). In the AMMI Analysis of variance for TDM, the genotype explained <12% of the variation for both species, suggesting a direct selection of yield would result in a low genetic gain. The biplot mean performance (Y) x weighted average of absolute scores from the singular value decomposition of the matrix of BLUPs stability index (WAASB) identified several promising genotypes with superior performance and stability across different environments that were selected for producing synthetic lines. However, the synthetic lines of both alfalfa and sainfoin did not consistently exhibit superior performance in mixtures compared to their respective check cultivars.
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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 | 0.003 | 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".