ArticleData in brief2025
Dataset for unrevealing the application of multi-trait genotype-ideotype distance index and multi-trait index based on factor analysis and ideotype-design models in the identification of high-yielding and stable barley genotypes.
Article in Data in brief, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Dissecting the genotype-by-environment interaction (GEI) effects in multi-environmental trials (METs) is a critical step in any breeding program before introducing new commercial varieties for cultivation in specific regions or across diverse environments. This dataset explores the application of two novel selection models: the multi-trait genotype-ideotype distance index (MGIDI) and the multi-trait index based on factor analysis and ideotype-design (FAI-BLUP). These models incorporate comprehensive stability parameters to identify high-yielding and stable barley genotypes across varying environmental conditions. In both models, the first three factors (FAs) with eigenvalues greater than 1 accounted for 92.3% of the total variation. The BLUP-based parameters, along with grain yield (GY) and the mean variance component (Ɵ), showed a positive selection deferential (SD) and correlated with the second factor (FA2). Notably, these models identified G3, G10, and G14 as the most stable genotypes. In conclusion, this dataset underscores the utility of comprehensive stability parameters and advanced selection models in identifying high-yielding, stable genotypes within the framework of METs.
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