Scholarlink

<p>Developing rice cultivars with stable yield and high quality under environmental stresses requires efficient multi-trait selection tools. Conventional indices are limited by multicollinearity and subjective economic weights. This study aimed to assess the effectiveness of the multi-trait genotype–ideotype distance index (MGIDI) for identifying superior rice genotypes under water-limited environments. Fifty-two rice genotypes, including international aerobic rice lines, Iranian improved cultivars, and native landraces, were evaluated from 2014 to 2018. Five trait groups were considered: germination vigor and kinetics under osmotic stress, agronomic performance under field drought, physicochemical and cooking quality, micronutrient content (Iron [Fe], Zinc [Zn], Manganese [Mn], and protein), and blast disease resistance measured by the area under the disease progress curve (AUDPC). Factor analysis was used to manage multicollinearity, and MGIDI ranking was applied with a 15% selection intensity. Ten independent factors explained 81.97% of the total phenotypic variation. MGIDI identified eight superior genotypes, including the native cultivar ‘Alikazemi’ and elite IRRI-derived lines, showing balanced performance in yield stability, drought tolerance, grain quality, and disease resistance. Predicted selection differentials showed substantial gains in grain yield under drought stress (up to 26.1%), protein content (40.3%), zinc (15.7%), and iron (7.6%), while the AUDPC decreased by 27.5%. The strongest response to selection occurred at early growth stages under severe osmotic stress, with gains exceeding 100%. MGIDI effectively integrates complex trait relationships by eliminating subjective weighting and controlling multicollinearity. The index provides a robust ideotype-based framework for simultaneous improvement of yield, nutritional quality, and stress resilience, supporting sustainable rice breeding under water-limited conditions.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Ideotype-based Selection of Superior Rice Genotypes using the MGIDI Framework for Germination, Agronomic Performance, Blast Resistance, Drought Tolerance, and Nutritional Quality

  • 期刊论文

  • 发表日期：

    2026年6月18日

查看全文
  • Atefeh Sabouri,
  • Zahra Esmaeilpour,
  • Tayebeh Raiesi,
  • Reza Afshari,
  • Fatemeh Alinezhad,
  • Haniyeh Babaei-Raouf,
  • Elham Nasiri,
  • Masoud Esfahani,
  • Sedigheh Mousanejad,
  • Akbar Forghani,
  • Arvind Kumar

摘要

Developing rice cultivars with stable yield and high quality under environmental stresses requires efficient multi-trait selection tools. Conventional indices are limited by multicollinearity and subjective economic weights. This study aimed to assess the effectiveness of the multi-trait genotype–ideotype distance index (MGIDI) for identifying superior rice genotypes under water-limited environments. Fifty-two rice genotypes, including international aerobic rice lines, Iranian improved cultivars, and native landraces, were evaluated from 2014 to 2018. Five trait groups were considered: germination vigor and kinetics under osmotic stress, agronomic performance under field drought, physicochemical and cooking quality, micronutrient content (Iron [Fe], Zinc [Zn], Manganese [Mn], and protein), and blast disease resistance measured by the area under the disease progress curve (AUDPC). Factor analysis was used to manage multicollinearity, and MGIDI ranking was applied with a 15% selection intensity. Ten independent factors explained 81.97% of the total phenotypic variation. MGIDI identified eight superior genotypes, including the native cultivar ‘Alikazemi’ and elite IRRI-derived lines, showing balanced performance in yield stability, drought tolerance, grain quality, and disease resistance. Predicted selection differentials showed substantial gains in grain yield under drought stress (up to 26.1%), protein content (40.3%), zinc (15.7%), and iron (7.6%), while the AUDPC decreased by 27.5%. The strongest response to selection occurred at early growth stages under severe osmotic stress, with gains exceeding 100%. MGIDI effectively integrates complex trait relationships by eliminating subjective weighting and controlling multicollinearity. The index provides a robust ideotype-based framework for simultaneous improvement of yield, nutritional quality, and stress resilience, supporting sustainable rice breeding under water-limited conditions.