Genetic Diversity and Selection of High Performing Cultivars for Grain Yield and Yield Components Improvement in Rice

Abstract

Background: Understanding the genetic diversity of the available rice germplasm could lead to the use of the best donors for the development of improved varieties with large genetic background. Aim: This study aimed to estimate the genetic diversity for grain yield and yield components among one hundred rice genotypes and select the best performing for grain yield and grain characteristics improvement. Study Design: The experiment was conducted in a 10 × 10 \(\alpha\)-lattice design in three replications under lowland field conditions. Place and Duration of Study: This experiment was conducted at the Council for Scientific and Industrial Research-Crops Research Institute (CSRI-CRI), Fumesua-Kumasi, Ghana, during the major planting season from March to September 2018. Methodology: Data were collected on grain yield (GY), tiller number (Ti), days to flowering (DTF), plant height (PH), panicle length (PL), kernel length (KL), and kernel length-to-width ratio (KLW). One hundred rice genotypes were used to estimate the genetic variability, including genetic coefficient of variation (GCV) and phenotypic coefficient of variation (PCV), broad-sense heritability and genetic advance among the traits under study. The correlation, principal components, cluster and regression analysis were conducted to quantify the relationships among the traits. Data were analysed following a mixed model with genotypes as random effects and repetitions as fixed effects. Genotypes were selected using the multi-trait genotype-ideotype distance index (MGIDI). Results: The GCV ranged from 4.3% for PL to 17.9% for GY. High heritability and moderate genetic advances were observed for DTF, PH, KL, and KLW. In general, the magnitudes of genetic correlations were higher than phenotypic correlations. GY showed a positive association with DTF and PL at both genetic and phenotypic levels, and with PH and KW at the genetic level only. The genotypes clustered into three groups and the first three principal components explained about 70.3% of the total variation with KLW, DTF, KL, PL, KL and GY being the principal discriminatory characters in descending order. Ten high-performing genotypes (CRI-AgraRice, T-MARSHAL, NERICA-L 19, CRI-AMANKWATIA, ART58-66-1-1-B-B, ART108-2-1-1-B-B, ART112-74-1-1-B-B, CRI-1-11-15-21, FAROX 508-3-10-F43-1-1 and Local_Sokwai) were identified through MGIDI index. Conclusion: There was adequate genetic variability in the germplasm to support breeding for improved grain yield and indirect selection for high yield can be done in early generations using DTF, PH, and PL.