Ali Reza Askari kalestani; Seyed Mahmoud Tabib Ghaffary; Hassan Zali; Mohsen Esmaeilzadeh Moghadam
Abstract
Introduction
Wheat (Triticum aestivum L.) is a socioeconomically important crop in Iran. Achieving genetic gain in quantitative traits through selection is essential for successful ...
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Introduction
Wheat (Triticum aestivum L.) is a socioeconomically important crop in Iran. Achieving genetic gain in quantitative traits through selection is essential for successful breeding programs. Identification of high-yielding genotypes with high desirable growth traits is a primary objective in wheat breeding. This study aimed to identify superior bread wheat genotypes based on grain yield and morpho-phenological traits while comparing different selection indices, including the Selection Index of Ideal Genotype (SIIG), Multi-Trait Genotype-Ideotype Distance Index (MGIDI), and ideotype design using the Best Linear Unbiased Prediction (FAI-BLUP).
Materials and Methods
The study was conducted during the 2022-2023 cropping season to identify superior bread wheat genotypes for warm and dry conditions in southern Iran (Darab in Fars province and Safiabad-Dezful). The experiment followed an augment design with six incomplete blocks and three control varieties: Sarang, Mehrgan, and Barat. Traits measured included plant height (PLH), days to heading (DHE), days to maturity (DMA), thousand kernel weight (TKW), seed filling rate (SFR), seed filling period (SFP), and grain yield (YLD). Genetic parameters were estimated using the Restricted Maximum Likelihood (REML) method. Statistical analyses were subsequently computed using SAS and R software.
Results and Discussion
The likelihood ratio test (LRT) revealed that genotype effects were significant at the 1% probability level for PLH, DHE, DMA, SFR, SFP, and YLD (excluding TKW). Heritability estimates varied, with the highest heritability observed for PLH (0.879) and the lowest for SFR (0.124). These findings, supported by the heat map of traits, highlighted substantial genetic diversity among the wheat genotypes. Heritability estimates across traits ranged from 0.124 (SFP) to 0.879 (PLH). For the Darab region, the SIIG index was the most effective, while in Dezful and across both regions, the FAI-BLUP index outperformed the other indices. The FAI-BLUP index demonstrated superior performance in selecting genotypes with multiple desirable traits and exhibited significant correlations with a larger number of traits compared to the other indices. In Darab, the SIIG index identified G5, G36 and G42 as superior genotypes. In Dezful, the FAI-BLUP index identified G26, G30 and G37 as the best genotypes, and for the combined regions, G30, G35 and G36 genotypes were identified as superior genotypes using the FAI-BLUP index.
Conclusion
Overall, the FAI-BLUP index emerged as the most effective selection tool for identifying superior bread wheat genotypes under the conditions of this study. A two-dimensional analysis of selection indices and grain yield highlighted G50, G52, and G60 as promising genotypes for preliminary trials in Darab and Dezful regions.