Unveiling Phenotypic and Environmental Dynamics: Exploring Genetic Stability and Adaptability of Faba Bean Cultivars in Norwegian Climates
Bibliographic record
Abstract
ABSTRACT This study evaluated 22 spring‐type faba bean cultivars in the main areas for cultivation of faba bean in Norway to assess the variation of 14 faba bean traits due to cultivar (G), environment (E), and their interaction (G × E), and to assess their stability across environments by using the additive main effects and multiplicative interaction (AMMI) analysis and coefficient of variation (CV). Significant G, E, and G × E effects were found for most traits, with environment accounting for much of the variance in yield and the growing degree days (GDD) to different developmental stages. Yield was highly correlated with thousand kernel weight (TKW) and GDD to BBCH 89 (maturation). The stability of the cultivars was studied for yield, TKW, and GDD to BBCH 89. Stability analysis using the AMMI stability value, yield stability index, CV, and the average sum of ranks identified Birgit, Stella, Bobas, and Macho as the most stable high‐yielding cultivars across environments, achieving a mean yield of 6–6.4 tons ha−1. Bobas, Macho, Stella, and Yukon had the most stable TKW (612–699 g) and Bobas, Capri, Trumpet, and Vertigo were the most stable regarding GDD to BBCH 89 (1257°C days, with a base temperature of 5°C). These stable cultivars can be utilized in breeding programs to achieve high and stable faba bean yield in the main growing areas of Norway and other Nordic‐Baltic countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".