Cultivating sustainable futures: multi-environment evaluation and seed yield stability of faba bean (Vicia faba L.) genotypes by using different stability parameters in Ethiopia
Bibliographic record
Abstract
Faba bean is an important legume crop with significant potential to contribute to sustainable agricultural systems and food security in Ethiopia. Despite its importance, the crop is prone to various biotic and abiotic constraints that can reduce seed yield and affect its stability and adaptability. To identify stable and adaptable genotypes, 10 faba bean genotypes were evaluated at three locations over two growing seasons using different stability parameters. Genotype-by-environment interaction (GGE biplot) and additive main effect and multiplicative interaction (AMMI) analyses are the statistical methods used to evaluate crop genotype performance across different environments to identify high-performing, stable, and adaptable genotypes and to highlight preferable environments for genotype differentiation. This study utilized cultivar superiority, regression coefficients, and deviations from regression parameters that provide valuable insights into genotype performance under varying environmental conditions. This approach helps to identify robust cultivars that can thrive across different agricultural settings and challenges, ultimately contributing to improved crop production and food security. The results revealed that G9, G8, and G7 are the three most stable and adaptable genotypes. These faba bean genotypes showed greater resilience to environmental changes and improved suitability for sustainable production, making them better options for local farmers. They also bolster resilience against climate variability and ultimately ensure agricultural sustainability. The AMMI model indicated that the genotype-environment interaction (GEI) significantly influences seed yield. These findings provide crucial insights into the genetic potential of faba bean genotypes that can help breeding programs to develop high-yielding, adaptable, and stable varieties for the region and other areas with similar agro-ecological conditions.
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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 | 0.001 | 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".