ABSTRACT. 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.

ABSTRACT This study aimed to estimate and compare parameters of adaptability and stability for maize grain yield in a variety of environments by different projection methods. Data from experiments on 36 maize genotypes, in simple lattice 6x6, in 2012/13 season performed at nine growing locations in central Brazil were used. Adaptability and stability analyses were performed using the methods of Lin & Binns (1988) with decomposition, MHPRVG through REML/BLUP, AMMI-Biplot, and GGE-Biplot analysis. These methods have similarities in terms of genotype ordering but differ in precision and amount of information provided on genotype-environment (GxE) interactions. When compared to GGE-Biplot, AMMI method retained a good percentage of the total square sum, based on pattern of GxE interaction. The method of Lin & Binns with decomposition is similar to MHPRVG, but these one is more accurate, practice and informative. MHPRVG and GGE-Biplot methods should be used together to select the most promising genotypes. The genotypes G5 and G8 can be recommended for cultivation in central Brazil due to their adaptability, stability, and yield.