Factor Analysis and Gge Biplot for Environmental and Genotypic Evaluation in Sunflower Trials

Abstract

The definition of mega-environments is of critical relevance for a more accurate cultivar recommendation. This study aimed to verify the potentialities of the GGE biplot and factor analysis for environmental evaluation, to investigate possible mega-environment, and to evaluate the adaptability and stability of sunflower genotypes. A factor analysis and GGE biplot were used for evaluating the individual effects of genotypes, environments, interactions and mega-environment discrimination based on the data from 16 sunflower genotypes evaluated in 14 environments. The factor analysis was able to identify mega-environment inconsistency and, consequently, excluded a mega-environment for recommendation. The genotype BRS 387 showed wide adaptability and high stability in the mega-environment to which it belonged, indicative of its efficiency for the region to which it is being tested. Although the GGE biplot has many interpretation possibilities, extra care is needed when making decisions because important phenomena may be left unidentified in this analysis.

Performance and Stability of Pearl Millet Varieties for Grain Yield and Micronutrients in Arid and Semi-Arid Regions of India

“…In barley MET data, environment accounted for as high as 76.7% (Jalata, 2011). Similar trends are observed in proso millet with environment contributing up to 85% (Pan-pan et al, 2016) and up to 82% in sunflower (Santos et al, 2019). However, Tefera (2018) reported a moderate 51.6% of variation being explained by environment in soybean MET data, and Krishnamurthy et al (2017) reported 40.5% in rice MET data.…”

“…The grain yield stability of OPVs and suitability of test environments is tested using the GGE biplot based on the following model proposed by Santos et al (2019).…”