Scholarlink

<p><?tk 4?>Rice is a staple crop providing over half of global caloric intake, yet its productivity is increasingly constrained by climate variability and stagnating yields. This study evaluated six genotypes, including four BINA-developed mutants and two checks (Binadhan-16 and Binadhan-17), by integrating physiological and agronomic traits across multi-environment trials. Pot experiments measured SPAD value, photosynthetic rate, and nitrate reductase activity at four critical growth stages, while multi-location field trials across three environments evaluated morphological traits, biomass partitioning, and yield components. Correlation analysis revealed strong positive associations between SPAD value and leaf and root dry weights, highlighting the importance of early-stage physiological efficiency for structural growth. Linear mixed model analysis showed location as the major source of variation (0.826; <i>p</i> &lt; 0.001). Trait-specific variance analysis indicated that grain- and biomass-related traits, such as total shoot weight (H<sup>2</sup> = 0.87) and grain weight (H<sup>2</sup> = 0.82), are strongly genetically controlled, whereas plant height (H<sup>2</sup> = 0.15) and effective tillers (H<sup>2</sup> = 0.25) are primarily environment-driven. Principal component biplot analysis revealed PC1 (53.25%) and PC2 (24.70%), jointly explaining 77.95% of the variations. Multi-location evaluation revealed significant genotype × environment interactions, and path and GGE biplot analyses identified straw weight and harvest index as key direct contributors to grain yield, highlighting genotype-specific stability patterns. Combined ranking based on mean yield, stability, and BLUP identified M3 as the best-performing genotype, followed by Binadhan-16 and Binadhan-17. The study demonstrates that integrating physiological traits with structural and yield components provides a practical framework to select high-performing rice genotypes with potential stress-adaptive traits.</p> Graphical Abstract <p></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

Exploiting physiological efficiency and genetic insights in rice through integrating trait–yield dynamics across multi-environment trials

  • 期刊论文

  • 发表日期：

    2026年2月5日

查看全文
  • Sakina Khanam,
  • Masud Perves,
  • Nahid Afridi

摘要

Rice is a staple crop providing over half of global caloric intake, yet its productivity is increasingly constrained by climate variability and stagnating yields. This study evaluated six genotypes, including four BINA-developed mutants and two checks (Binadhan-16 and Binadhan-17), by integrating physiological and agronomic traits across multi-environment trials. Pot experiments measured SPAD value, photosynthetic rate, and nitrate reductase activity at four critical growth stages, while multi-location field trials across three environments evaluated morphological traits, biomass partitioning, and yield components. Correlation analysis revealed strong positive associations between SPAD value and leaf and root dry weights, highlighting the importance of early-stage physiological efficiency for structural growth. Linear mixed model analysis showed location as the major source of variation (0.826; p < 0.001). Trait-specific variance analysis indicated that grain- and biomass-related traits, such as total shoot weight (H2 = 0.87) and grain weight (H2 = 0.82), are strongly genetically controlled, whereas plant height (H2 = 0.15) and effective tillers (H2 = 0.25) are primarily environment-driven. Principal component biplot analysis revealed PC1 (53.25%) and PC2 (24.70%), jointly explaining 77.95% of the variations. Multi-location evaluation revealed significant genotype × environment interactions, and path and GGE biplot analyses identified straw weight and harvest index as key direct contributors to grain yield, highlighting genotype-specific stability patterns. Combined ranking based on mean yield, stability, and BLUP identified M3 as the best-performing genotype, followed by Binadhan-16 and Binadhan-17. The study demonstrates that integrating physiological traits with structural and yield components provides a practical framework to select high-performing rice genotypes with potential stress-adaptive traits.

Graphical Abstract