Osvaldo Toshiyuki Hamawaki

Several genetic breeding programs contributed to the development of soybean cultivars with high yield and adapted to different Brazilian edaphoclimatic conditions. However, the continuous progress of genetic breeding of this specie depends on the genetic variability and application of genetic parameters informations which helps a more efficient selection process.There are many multivariated technical approaches to study the variability among soybean groups, such as dissimilarity measures, cluster analysis, principal components and canonical variables. The heritability estimation, genetic gain and genetic correlations are important parameters which permit the breeder to choose the best improvement strategy. Parâmetros genéticos e variabilidade em genótipos de sojaVários programas de melhoramento genético contribuíram para o desenvolvimento de cultivaresde soja com alto rendimento e adaptados às diferentes condições edafoclimáticas Brazileiras. Noentanto, o progresso contínuo de melhoramento genético desta espécie depende da variabilidadegenética e da aplicação de informações sobre parâmetros genéticos que corroborem com oprocesso. Há muitas abordagens técnicas multivariadas para estudar a variabilidade entre osgrupos de soja, tais como as medidas de dissimilaridade, análise de agrupamento, componentesprincipais e variáveis canônicas. A estimativa de herdabilidade, ganho genético e correlaçõesgenéticas são importantes parâmetros que permitem ao criador a escolhada melhor estratégia demelhoramento.

The goals of this research were to evaluate the phenotypic and genotypic correlations between agronomic traits, to perform path analysis, having as main character grain yield, and to identify indirect selection criteria for grain yield. The experiment was carried out in an experimental area located at Capim Branco farm, which belongs to Federal University of Uberlândia, during the growing season of 2015/2016.Twenty-four soybean genotypes were evaluated under randomized complete block design with three repetitions, of which agronomic traits and grain yield were measured. There was genetic variability for all traits at 5% probability level through the F-test. Thirty significant phenotypic correlations were also observed with values oscillating from 0.42 to 0.87, which indicated a high level of association between some evaluated traits. Additionally, we verified that phenotypic and genotypic correlations were essential of the same direction, being the genotypic ones of superior magnitudes. Plants with superior vegetative cycle had longer life cycles; this fact could be explained by the significant phenotypic correlations between the number of days to the blooming and number of days to maturity (0.76). Significantly positive phenotypic and genotypic correlations for the total number of pods per plant and grain yield per plant (0.84) were observed. Through the path analysis, the trait that contributed the most over grain yield was the number of pods with three seeds as it showed the highest direct effect on grain yield per plant, as well as a strong indirect effect on the total number of pods. Therefore, the phenotypic and genotypic correlations suggested high correlations between grain yield and number of branched nodes, the number of pods with two and three seeds, and the total number of pods. Also, the path analysis determined the number of pods with three seeds as having the highest favorable effect on grain yield, and thus, being useful for indirect selection toward productive soybean genotypes.

In the final phases of new soybean cultivar development, lines are cultivated in several locations across multiple seasons with the intention of identifying and selecting superior genotypes for quantitative traits. In this context, this study aimed to study the genotype-by-environment interaction for the trait grain yield (kg/ha), and to evaluate the adaptability and stability of early-cycle soybean genotypes using the additive main effects and multiplicative interaction (AMMI) analysis, genotype main effects and genotype x environment interaction (GGE) biplot, and factor analysis methods. Additionally, the efficiency of these methods was compared. The experiments were carried out in five cities in the State of Mato Grosso: Alto Taquari, Lucas do Rio Verde, Sinop, Querência, and Rondonópolis, in the 2011/2012 and 2012/2013 seasons. Twenty-seven early-cycle soybean genotypes were evaluated, consisting of 22 lines developed by Universidade Federal de Uberlândia (UFU) soybean breeding program, and five controls: UFUS Carajás, MSOY 6101, MSOY 7211, UFUS Guarani, and Riqueza. Significant and complex genotype-by-environment interactions were observed. The AMMI model presented greater efficiency by retaining most of the variation in the first two main components (61.46%), followed by the GGE biplot model (57.90%), and factor analysis (54.12%). Environmental clustering among the methodologies was similar, and was composed of one environmental group from one location but from different seasons. Genotype G5 presented an elevated grain yield, and high adaptability and stability as determined by the AMMI, factor analysis, and GGE biplot methodologies.

This study aimed to evaluate the clustering pattern consistency of soybean (Glycine max) lines, using seven different clustering methods. Our aim was to evaluate the best method for the identification of promising genotypes to obtain segregating populations. We used 51 generations F5 and F6 soybean lines originating from different hybridizations and backcrosses obtained from the soybean breeding program of Universidade Federal de Uberlândia in addition to three controls (Emgopa 302, BRSGO Luziânia, and MG/BR46 Conquista). We evaluated the following agronomic traits: number of days to flowering, number of days to maturity, height of the plant at maturity, insertion height of the first pod, grain yield, and weight of 100 seeds. The data was submitted to analyses of variance followed by average Euclidean distance matrix estimation used as measure of dissimilarity. Subsequently, clusters were formed using the Tocher method and dendrograms were constructed using the hierarchical methods simple connection (nearest neighbor), complete connection (most distant neighbor), Ward, median, average within cluster connection. The nearest neighbor method presented the largest number of genotypes in group I and showed the greatest similarity with the Tocher optimization method. The joint use of these two methodologies allows for differentiation of the most genetically distant genotypes that may constitute the optimal parents in a breeding program.

O conhecimento das relações existentes entre caracteres, tais como estimados pelas correlações, tem sido de grande relevância no melhoramento vegetal, pois fornece informações úteis ao melhorista no processo de seleção. Todavia, a quantificação e a interpretação da magnitude das correlações não implicam efeitos diretos e indiretos. Nesse contexto, a análise de trilha apresenta-se como uma alternativa viável. Os objetivos deste estudo foram avaliar as correlações fenotípicas e genotípicas entre caracteres agronômicos importantes no melhoramento genético da soja, realizar análise de trilha, tendo como caráter principal a produtividade de grãos e identificar critérios de seleção indireta para produtividade de grãos. Foram conduzidos dois experimentos em condições de casa de vegetação, semeados em fevereiro e em dezembro de 2007. Os tratamentos foram constituídos de 90 genótipos de soja incluindo linhagens e cultivares. Adotou-se o delineamento de blocos casualizados com três repetições. Cada unidade experimental foi constituída por três plantas cultivadas em substrato num vaso de 3dm3. Avaliaram-se os caracteres: número de dias para o florescimento e maturidade; altura da planta no florescimento e maturidade; número de nós na haste principal; altura da primeira vagem; número de vagens; produtividade de grãos; número médio de grãos por vagem; e o peso de 100 grãos. As correlações genotípicas tiveram, predominantemente, magnitude superior às correlações fenotípicas, sendo ambas de mesmo sinal nas duas épocas de semeadura. A partir das correlações fenotípicas, genotípicas e a análise de trilha identificaram-se o caráter número de vagens por planta, independentemente da época de semeadura, de maior efeito favorável sobre a produtividade de grãos.