Integrating Multivariate and Univariate Statistical Models to Investigate Genotype–Environment Interaction of Advanced Fragrant Rice Genotypes under Rainfed Condition

Abstract

Specialty fragrant rice is sold at a premium price in both local and international trade because of its superior grain qualities. In this research, 40 advanced fragrant rice accessions were evaluated in different environments. The primary objective was to identify genotypes with high grain yield and high stability using multivariate (GGE biplot) and univariate analysis (regression slope, deviation from regression, Shukla’s stability variance, Wricke’s ecovalence, and Kang’s stability statistic). The field experiment trials were laid in a randomized complete block design in three replications. The analysis of variance showed highly significant differences among genotypes, locations, seasons, and the interactions between genotype, locations, and seasons. The environment significantly explained about 43.32% (37.01 and 6.31% for locations and seasons) of the total sum of squares. Based on average ranking generated from multivariate and univariate stability measured, rice accessions were classified into three major categories, viz., genotypes having high trait performance, and high stability as category 1. The second category consists of genotypes that exhibit high mean performance but low stability, while the third category includes genotypes with high stability but low trait performance. Our results showed that breeding for yield performance was possible, and the identified genotypes could be recommended for commercial cultivation.