Selection Index in the Study of Adaptability and Stability in Maize

• Published in: TheScientificWorld Vol. 2014; no. 2014; pp. 1 - 6
• Main Authors: Costa Melo, Wagner Mateus, Miranda Pires, Luiz Paulo, Furtado Ferreira, Daniel, Garcia Von Pinho, Renzo, Lunezzo de Oliveira, Rogério
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2014; Hindawi Limited; Wiley
• Subjects: Adaptation, Physiological - genetics; Agricultural production; Brazil; Chromosome Mapping - methods; Corn; Genetic aspects; Genome, Plant - genetics; Genomic Instability - genetics; Genotype; Grain; Identification and classification; Methods; Polymorphism, Single Nucleotide - genetics; Quantitative Trait Loci - genetics; Quantitative Trait, Heritable; Selection, Genetic - genetics; Studies; Brazil
• ISSN: 2356-6140; 1537-744X; 1537-744X
• DOI: 10.1155/2014/360570

This paper proposes an alternative method for evaluating the stability and adaptability of maize hybrids using a genotype-ideotype distance index (GIDI) for selection. Data from seven variables were used, obtained through evaluation of 25 maize hybrids at six sites in southern Brazil. The GIDI was estimated by means of the generalized Mahalanobis distance for each plot of the test. We then proceeded to GGE biplot analysis in order to compare the predictive accuracy of the GGE models and the grouping of environments and to select the best five hybrids. The G × E interaction was significant for both variables assessed. The GGE model with two principal components obtained a predictive accuracy (PRECORR) of 0.8913 for the GIDI and 0.8709 for yield (t ha−1). Two groups of environments were obtained upon analyzing the GIDI, whereas all the environments remained in the same group upon analyzing yield. Coincidence occurred in only two hybrids considering evaluation of the two features. The GIDI assessment provided for selection of hybrids that combine adaptability and stability in most of the variables assessed, making its use more highly recommended than analyzing each variable separately. Not all the higher-yielding hybrids were the best in the other variables assessed.