Pour-Aboughadareh Alireza, Jadidi Omid, Jamshidi Bita, Bocianowski Jan, Niemann Janetta
Seed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj 3183964653, Iran.
Department of Plant Breeding and Biotechnology, Science and Research Branch, Islamic Azad University, Tehran 14778-93855, Iran.
Data Brief. 2025 Feb 11;59:111383. doi: 10.1016/j.dib.2025.111383. eCollection 2025 Apr.
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.
在多环境试验（METs）中剖析基因型与环境互作（GEI）效应，是任何育种计划在引入新商业品种于特定区域或不同环境中种植之前的关键步骤。该数据集探讨了两种新型选择模型的应用：多性状基因型 - 理想型距离指数（MGIDI）和基于因子分析与理想型设计的多性状指数（FAI - BLUP）。这些模型纳入了综合稳定性参数，以识别在不同环境条件下高产且稳定的大麦基因型。在这两种模型中，特征值大于1的前三个因子（FAs）占总变异的92.3%。基于最佳线性无偏预测（BLUP）的参数，连同籽粒产量（GY）和平均方差分量（Ɵ），显示出正选择差异（SD）并与第二个因子（FA2）相关。值得注意的是，这些模型将G3、G10和G14鉴定为最稳定的基因型。总之，该数据集强调了综合稳定性参数和先进选择模型在多环境试验框架内识别高产、稳定基因型方面的实用性。
