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<p>During variety improvement, unfavorable correlations between key traits pose a significant challenge for breeders. Additionally, genotype-environment interaction (GEI), which critically influences plant performance, is recognized as another major challenge. Understanding the nature of this interaction is essential for developing effective breeding programs that enhance crop production. This study investigated the associations between key traits such as kernel yield (KY), number of kernel row (KR), number of kernels in a row (KIR), 1000 kernel weight (KW), kernel moisture (KM), plant height (PH), and ear height (EH), as well as the stability of KY in nine maize genotypes and a control variety. The experiment was conducted at seven locations using a randomized complete block design with three replications over two consecutive years. The results revealed a very high positive correlation between KY and the KR, as well as between KY and PH. A very weak negative correlation was observed between KY and the KIR, and between KY and KM. Genotype 7 was identified as the best genotype for the combinations of KY × KIR and KY × EH. For the combinations of KY × KR, KY × PH, KY × KW, and KY/KM, genotypes 1, 10, and 3 were recognized as the best performers. Based on the multi-trait stability index (MTSI), genotypes 4, 2, and 3 were identified as the most ideal genotypes across all traits. The additive effects analysis of the additive main effects and multiplicative interaction (AMMI) model indicated that the effects of genotype, environment, and GEI were significant for KY. The multiplicative effect analysis of the AMMI, decomposed into principal components (PCs), showed that four significant PCs explained 87.90% of the variation in the GEI. According to the biplot of mean yield versus the weighted average absolute scores (WAAS), genotypes 4 and 3 were identified as stable genotypes with favorable yield. Using the GGE biplot method, genotypes 10 and 3 were identified as the best stable genotypes in the MHD2022, MGN2023, KRJ2022, MGN2022, SYZ2022, and SYZ2023. Genotype 7 was the best in the KRJ2023, KER2022, HDM2022, and IFN2023, while genotype 1 was the best in the KER2023, HDM2023, IFN2022, and MHD2023. The likelihood ratio test (LRT) results showed that the effects of genotype and GEI were significant for KY. Genotype 1 had high mean values of best linear unbiased predictions (BLUP), making it a suitable genotype for KY. Among the experimental genotypes, genotypes 4 and 3 had higher WAASBY compared to others, indicating that they were stable genotypes with high KY. Among the various analytical models, the WAASBY index proved particularly effective for the simultaneous selection of yield and stability. Overall, genotypes 4 and 3 consistently emerged as the most desirable candidates, showcasing a superior combination of high KY and broad stability, making them valuable assets for future maize breeding programs.</p>

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Integrated analysis of genotype by yield trait and genotype by environment interactions for selecting superior maize genotypes

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月21日

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  • Afsaneh Shirzad,
  • Ali Asghari,
  • Sajjad Moharramnejad,
  • Mohammadreza Shiri,
  • Asghar Ebadi

摘要

During variety improvement, unfavorable correlations between key traits pose a significant challenge for breeders. Additionally, genotype-environment interaction (GEI), which critically influences plant performance, is recognized as another major challenge. Understanding the nature of this interaction is essential for developing effective breeding programs that enhance crop production. This study investigated the associations between key traits such as kernel yield (KY), number of kernel row (KR), number of kernels in a row (KIR), 1000 kernel weight (KW), kernel moisture (KM), plant height (PH), and ear height (EH), as well as the stability of KY in nine maize genotypes and a control variety. The experiment was conducted at seven locations using a randomized complete block design with three replications over two consecutive years. The results revealed a very high positive correlation between KY and the KR, as well as between KY and PH. A very weak negative correlation was observed between KY and the KIR, and between KY and KM. Genotype 7 was identified as the best genotype for the combinations of KY × KIR and KY × EH. For the combinations of KY × KR, KY × PH, KY × KW, and KY/KM, genotypes 1, 10, and 3 were recognized as the best performers. Based on the multi-trait stability index (MTSI), genotypes 4, 2, and 3 were identified as the most ideal genotypes across all traits. The additive effects analysis of the additive main effects and multiplicative interaction (AMMI) model indicated that the effects of genotype, environment, and GEI were significant for KY. The multiplicative effect analysis of the AMMI, decomposed into principal components (PCs), showed that four significant PCs explained 87.90% of the variation in the GEI. According to the biplot of mean yield versus the weighted average absolute scores (WAAS), genotypes 4 and 3 were identified as stable genotypes with favorable yield. Using the GGE biplot method, genotypes 10 and 3 were identified as the best stable genotypes in the MHD2022, MGN2023, KRJ2022, MGN2022, SYZ2022, and SYZ2023. Genotype 7 was the best in the KRJ2023, KER2022, HDM2022, and IFN2023, while genotype 1 was the best in the KER2023, HDM2023, IFN2022, and MHD2023. The likelihood ratio test (LRT) results showed that the effects of genotype and GEI were significant for KY. Genotype 1 had high mean values of best linear unbiased predictions (BLUP), making it a suitable genotype for KY. Among the experimental genotypes, genotypes 4 and 3 had higher WAASBY compared to others, indicating that they were stable genotypes with high KY. Among the various analytical models, the WAASBY index proved particularly effective for the simultaneous selection of yield and stability. Overall, genotypes 4 and 3 consistently emerged as the most desirable candidates, showcasing a superior combination of high KY and broad stability, making them valuable assets for future maize breeding programs.