Assessing the usefulness of GGE biplot as a statistical tool for plant breeders and agronomists

Abstract

Genotype main effect plus genotype-by-environment interaction (GGE) biplot produces a graphical display of results that facilitates a better understanding of complex genotype-by-environment interaction in multi-environment trials of breeding and agronomic experiments. However, the full potential and weaknesses of this powerful tool are not fully understood by breeders, agronomists, entomologists and pathologists. The objective of this paper was to review the usefulness of this statistical tool and enumerate some of its weaknesses. Its main application has so far been in the analysis of multi-environment data. It has been used to analyze the performance of crop cultivars under multiple stress environments, from which ideal cultivars, mega-environments, and core testing sites were identified. More recently, GGE biplot has been employed in genetic analysis of diallel data to estimate the combining abilities and identify heterotic groups among inbred parents. Genotype-by-trait biplot has also been utilized in trait profile analysis, and in identification of traits that are reliable for indirect selection of a target primary trait. Two major shortcomings of this tool are (i) failure to identify more than two distinct, contrasting groups in diallel studies and (ii) lack of statistical tests for most of its graphical displays. Other aspects of GGE biplot that need further study and development are (i) estimation of genetic variances, covariances, and heritability, including the analysis of data generated from North Carolina Designs I, II, and III as well as other genetic designs, considering their importance in plant breeding programs; (ii) analysis of Quantitative Trait Loci (QTL) data for proper understanding of the genetic constitution of each individual plant or line; and (iii) analysis of Genotype-by-pathogen or insect strain interaction data. Nevertheless, GGE biplot has helped greatly in the accurate analysis and interpretation of data from breeding and agronomic field evaluation experiments.

Grouping sunflower genotypes for yield, oil content, and reaction to Alternaria leaf spot using GGE biplot

“…The second component represents the contribution of disease severity and yield to compare sunflower genotypes. Akinwale et al (2014) state that no studies have been carried out to specify when the proportion of variation explained by a biplot becomes too small to make a valid conclusion; however, it is generally assumed that any proportion below 40% is too small. This shows the importance of PC1, as reported by Ullah et al (2007), regarding that ideal sunflower cultivars should have a large PC1 score and a small (absolute) PC2 score.…”

“…GGE biplot has greatly helped in the accurate analysis and data interpretation from breeding and agronomic field experiments (Akinwale et al, 2014), and offered the opportunity to identify adapted sunflower genotypes for resistance to Alternaria leaf spot that can be used in sunflower breeding programs.…”

“…Biplot analysis was first developed by Gabriel (1971), and it can interpret multiple variables in function of different treatments in the same graphic (Akinwale et al, 2014). GGE biplot has been used to show the performance of sunflower hybrids in different testing environments or sowing dates (Ullah et al, 2007;Balalic et al, 2012;Brankovic et al, 2012).…”

Selection of superior genotype of sweet-potato in Indonesia based on stability and adaptability

“…This shows that the genotypes may still be improved if the numbers of tested locations are added. Biplot analysis is an important multivariate tool that displays results in a graph that is easy to understand (Akinwale et al, 2014). Biplot also show the level of cultivar and environmental stability (Kivuva et al, 2014;Tumuhimbise et al, 2014).…”

EVALUATION OF YIELD AND SOME QUALITY CHARACTERS OF WINTER BARLEY (Hordeum vulgare L.) GENOTYPES USING BIPLOT ANALYSIS

“…Ngozi (2011), genotype evaluation and selection of parents for traits are facilitated by GT biplot. The genotype with both high mean performance and high stability for all of the traits was called an ideal genotype (Akinwale et al, 2014). Therefore the center of the concentric circles (i.e., ideal genotype) was the AEA in the positive direction.…”