Spatial variability in evaluation experiments of corn genotypes in the state of Mato Grosso do Sul, Brazil

• Autor(a) principal: Yamamotto, Euriann Lopes Marques
• Data de Publicação: 2022
• Outros Autores: Gonçalves, Manoel Carlos, Davide, Livia Maria Chamma, Rossoni, Diogo Francisco, Santos, Adriano dos
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Acta Scientiarum. Agronomy (Online)
• Texto Completo: http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/55972
• topic: Zea mays L.; nitrogen; autoregressive models; analysis of variance.; Zea mays L.; nitrogen; autoregressive models; analysis of variance.

Analysis of variance (ANOVA) is the most used procedure for comparing means between different groups. However, in some cases, disregarding the assumptions of ANOVA can lead to spatial dependence. In such cases, to ensure greater experimental precision, it is necessary to consider the study of spatial dependence. This study was carried out to compare the estimates of experimental precision of the traditional analysis of variance with those of the analysis of variance using an autoregressive (ANOVA-AR) model in corn experiments under different N conditions when evaluating grain yield. Data were obtained from 14 experiments using lattice designs conducted in 2012, 2014, and 2015 in the following counties in the Brazilian state of Mato Grosso do Sul: Caarapó, Dourados, Glória de Dourados, and Laguna Carapã. Of the 14 experiments, 7 were performed with N fertilization (ideal) and 7 experiments were performed under stressful conditions (zero or low). Both analyses were compared by considering estimates of reduction of the error mean square, coefficient of determination, F-value, and selective accuracy as well as the difference in the order of 25% of the genotypes of each experiment (from 13 to 56 genotypes, considering the size of the experiment). Differences in the error mean square and genotype mean square were slightly more evident in 1, 2, 3, 4, 5, 6, and 11 experiments but the use of ANOVA-AR did not promote major changes. The analysis of variance with an autoregressive model provided parameter values of experimental precision similar to those expressed by traditional analysis of variance. There was no difference in terms of correlated errors in experiments under different N conditions.