Identifying high-yielding and stable durum wheat genotypes using G × E analysis in climate change context
Bibliographic record
Abstract
Assessing durum wheat genotypes in varied environments is essential for evaluating yield stability and adaptability. This research evaluated the performance of 25 different durum wheat genotypes over three years (2020–2023) across 16 environments in Morocco. One objective was to evaluate how genotypes interact with varying environmental conditions (GEI) and to identify genotypes that combine high yield with stability across specific mega-environments. This analysis was conducted using a randomized complete block design with three replicates. The primary criteria for selection included grain yield, the AMMI Stability Value (ASV) and the Genotype Selection Index (GSI). Analysis revealed highly significant differences (P<0.0001) among genotypes, environments and their interactions (GEI). According to the AMMI analysis of variance, the total variation in yield was attributed to the environment (77.91 %), genotype (0.80 %) and GEI (5.93 %). Principal components (PC) 1 and 2 accounted for 51.9 % of the observed variation. Similarly, the GGE biplot demonstrated that PC1 and PC2 contributed 31.51 % and 18.67 % of the yield variation, respectively. Based on yield and ASV, G4, G16, G10, G23 and G5 demonstrated high performance, while G4, G9, G11 and G14 exhibited stability. According to the GSI, genotypes G4, G5, G23, G14 and G17 were most desirable. The findings highlight the substantial impact of GEI on yield variability, with genotype G16 emerging as optimal and G4, G5 and G17 being identified as favourable candidates for cultivation across five identified mega-environments, each suited to specific genotype adaptation. The identified high-performing genotypes can be integrated into Moroccan breeding programs to enhance durum wheat productivity and resilience to climate variability.
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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 | 0.002 |
| 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".