ABSTRACT:The nitrogen use efficiency (NUE) is defined as the capacity of a given genotype in take advantage of the applied nitrogen (N) and transform it in biomass and grains. The objective of this study was to evaluate 12 wheat cultivars as to the NUE and its components. The experiment was conducted in a controlled environment, in a randomized block design with three replications.

RESEARCHT he presence of genotype-by-environment interaction (GEI) is common in multi-environment trials in wheat (Triticum aestivum L.) and other crops; it leads to changes in the performance of cultivars in different environments. A GEI demands that trials be conducted at multiple locations for several years to obtain reliable data for the possible release of a new cultivar.Trials conducted across several years and locations increase the possibility of obtaining datasets with unbalanced or incomplete data. This can occur because of planned or unplanned actions by the breeder. The planned actions might be the removal of genotypes that did not perform well in a given year, introducing new genotypes developed by the breeding program and the introduction of newly released cultivars. On the other hand, unplanned actions, which are unrelated to the breeder, might be attributable to human or environmental causes (Yan, 2013).There are three primary strategies for analyzing unbalanced or incomplete datasets. The first strategy consists in the removal of genotypes or environments with missing values. The second ABSTRACT Multi-environment trials often yield unbalanced datasets, thus necessitating the estimation of missing values. It is unknown whether this estimation affects the graphic characteristics of genotype plus genotype-by-environment interaction (GGE) biplots. Therefore, our objectives were to investigate the effects of different percentages of missing values on the number of significant principal components (pCs) and on mega environments, "winner" (highest-performing) genotypes, and the amount of variation explained by the pCs. Two complete sets of two-way data from wheat (Triticum aestivum L.) were used. The first set consisted of the original data (Data1, from which we created scenarios with 0, 30, and 60% missing data. For the second dataset (Data2), we removed 50% data from the original dataset, estimated missing values to make it a new complete dataset, and created scenarios like those for Data1. Missing values were estimated via expectation-maximization-GGE (EM-GGE) and EM-additive main effects and multiplicative interaction (EM-AMMI) methods. The percentage of variation explained by the pCs was affected by the percentage of missing data; a large percentage of missing values considerably increased the amount of variation explained by pC 1 and pC 2 and reduced the complexity of the genotype-by-environment interaction because two pCs accounted for more than 80% of the variation, instead of the three pCs that were required to explain the variation in the original dataset. The EM-GGE estimation method was able to maintain the original conformation of the 'which-won-where' biplot when £30% of estimated data were used. The EM-GGE was superior to the EM-AMMI method for estimating missing data. The estimation of more than 30% of the data should be avoided because it can lead to significant changes in mega environment conformation and the identification of "winner" genotypes.

-The aim of this study was to verify whether using the Papadakis method improves model assumptions and experimental

O objetivo deste estudo foi selecionar linhagens de trigo para qualidade de panificação e para rendimento de grãos (RG), em ensaios multiambientes, e identificar ambientes de teste ideais para a seleção. Foram avaliados 39 genótipos de trigo, sendo 18 linhagens e nove cultivares testemunhas, em 2010, e 11 linhagens e quatro cultivares testemunhas, em 2011, no Estado do Paraná. Algumas linhagens e locais não foram repetidos entre os dois anos. O delineamento experimental utilizado foi o de blocos casualizados, com três repetições. A metodologia, em gráfico biplot GGE (Genotype and Genotype-by-Environment), foi utilizada para a avaliação de genótipos e ambientes de teste. Na safra 2010, os locais de Nova Fátima, Pitangueiras e Ventania foram discriminantes (maximizaram a expressão do RG e diferenciação entre genótipos) e representativos do conjunto de ambientes avaliados. Em 2011, Rolândia e Astorga destacaram-se como ambientes ideais para seleção simultânea para RG e qualidade de panificação. As linhagens BIO-08528 e BIO-08228 foram classificadas como genótipos ideais para o RG e a concentração proteica dos grãos (CPG), respectivamente. Na safra 2011, as linhagens BIO-10161 e BIO-10141 foram superiores para RG e para qualidade de panificação, respectivamente. O teste de sedimentação SDS e a CPG correlacionaram-se entre si (r = 0,61**) e foram moderadamente associados com a força de glúten (r = 0,49** e 0,74**), indicando que podem ser utilizados na seleção indireta para qualidade de panificação.