Graphical techniques have become important tools to show results of maize (Zea mays L.) breeding experiments in current literature. The present study compared different graphical techniques to determine the best parental lines and cross combinations for yield and kernel quality traits in maize breeding experiments. We measured single plant yield, protein content, oil content, carotenoid content, oleic acid, and linoleic acid in a 5 x 2 line x tester design. Genotype + genotype x environment (GE) biplot, principal component analysis (PCA) biplot, and polar plot were used to analyze data and compare them with conventional line x tester analysis. In the conventional analysis, parents with high means and positive general combining ability (GCA) values were A680 and HYA for single plant yield, IHP for protein content, IHO and HYA for oil content, A680 and Q2 for carotenoid content, IHP for oleic acid content, and A680 for linoleic acid content. The B73 tester exhibited positive GCA values for most investigated traits. The HYA x B73 combination was the best cross in terms of single plant yield, protein, and oil contents. Results showed that biplot methods had both advantages and disadvantages. The PCA biplots can be used alone while the GGE biplot and polar plots are both useful for combining ability, heterosis, and gene action analysis in a line x tester design. Overall, graphical analysis results were very similar to conventional analysis. Consequently, it was assumed that the graphical methods used could be useful to analyze/present data from maize breeding experiments carried out with a line x tester design.

Considera-se que a qualidade está relacionada principalmente com a concentração de proteínas e de óleos na semente de milho. Grandes esforços foram feitos para elevar as concentrações de óleos e de proteínas; no entanto, os estudos para realçar a qualidade da composição do óleo têm sido limitados. Os genótipos de milho com composição especial de ácidos graxo podem conferir diferentes utilizações finais e maior valor. Foram investigados oito linhagens de milho quanto à qualidade da semente e à composição de ácidos graxos. As amostras de sementes de um ensaio de dois anos foram analisadas quanto aos teores de óleos, proteínas, umidade e cinza, assim como diversos ácidos, incluindo oleico, linoleico e linolênico. Os resultados indicaram diferenças entre os genótipos para todas as linhagens, com exceção do ácido behênico. O A680 pareceu ser promissor para elevar os níveis de ácido oleico, enquanto o N194 e o RSSSC foram as linhagens com os níveis mais elevados de óleos e de proteínas. Uma nova abordagem estatística, a Análise Discriminante Canônica com a técnica de Hipótese-Erro, foi utilizada para avaliar os dados. Esta indicou bom nível de concordância com os testes lineares, e mostra resultados semelhantes aos do teste de comparação múltipla. Os resultados gráficos são fáceis de interpretar e constitui boa alternativa para análise dos dados.

Quality is considered to be mainly related to protein and oil concentration in maize kernel. Great effort has been made through the elevation of oil and protein concentrations; however, studies to enhance the composition quality of oil have been limited. Maize genotypes with special fatty acid composition may confer different end uses and added value. We investigated eight maize inbreds for kernel quality and fatty acid composition. Kernel samples from a two-year experiment were analyzed for oil, protein, moisture and ash, as well as several fatty acids, including oleic, linoleic, and linolenic acids. Results indicated differences among the genotypes for all traits, except for behenic acid. A680 appeared to be a promising parent to elevate oleic acid, while N194 and RSSSC were the genotypes with the highest oil and protein levels, respectively. A new statistical approach, Canonical Discriminant Analysis with Hypothesis-Error plot technique was utilized to evaluate the data. It showed a good level of agreement with linear tests, and show similar results with those of multi comparison test. With the easily understandable graphical outputs that can interpret the interaction of variables and cases, it offers a good alternative way in analyzing data.