Multi trait genotype- ideotype distance index (MGIDI) for early seedling vigour and yield related traits to identify elite lines in rice (Oryza sativa L.)

Abstract

Rice (Oryza sativa L.) stands as a important cereal sustaining over half of the world's population. This study delves into the challenges confronting breeders in the realm of crop improvement, specifically focusing on the intricate task of designing an ideotype-a genotype amalgamating diverse attributes for optimal performance. Traditional methodologies, exemplified by the Smith-Hazel (SH) index, grapple with issues such as multicollinearity and the complexities of economic weighting decisions. In response to these challenges, the Multi-Trait Genotype-Ideotype Distance Index (MGIDI), conceptualized by Olivoto and Nardino (2021), emerges as a ground breaking approach. Principal Component Analysis (PCA) aids in the reduction of trait dimensionality, revealing four key factors that collectively contribute to 79.444% of total variability. The Scree plot guides factor selection, ensuring a targeted analysis. The MGIDI index computation yields a total genetic gain of 273.025%, with specific traits like spikelet fertility and seedling dry weight exhibiting significant gains. Six high-performing rice accessions-SM227, NLR33892, MTU3626, 239(3), SMB3, and 405C3 were identified through MGIDI. These identified genotypes serve as valuable resources for developing recombinant populations, aligning with sustainable and effective crop improvement strategies. Additionally, these promising varieties exhibit strengths across various traits, offering potential for simultaneous trait improvement in future breeding programmes. The efficiency of MGIDI is highlighted through its innovative application in simultaneous trait selection, underscoring its significance across a wide range of crops.