Multivariate approach in the selection of superior soybean progeny which carry the RR gene

Abstract

-Efficiency in the use of genetic variability, whether existing or created, increases when properly explored and analysed. Incorporation of biotechnology into breeding programs has been the general practice. The challenge for the researcher is the constant development of new and improved cultivars. The aim of this experiment was to select progenies with superior characteristics, whether or not carriers of the RR gene, derived from bi-parental crosses in the soybean, with the help of multivariate techniques. The experiment was carried out in a family-type experimental design, including controls, during the agricultural year 2010/2011 and 2011/2012 in Jaboticabal in the Brazilian State of São Paulo. From the F 3 generation, phenotypically superior plants were selected, which were evaluated for the following traits: number of days to flowering; number of days to maturity; height of first pod insertion; plant height at maturity; lodging; agronomic value; number of branches; number of pods per plant; 100-seed weight; number of seeds per plant; grain yield per plant. Given the results, it appears possible to select superior progeny by principal component analysis. Cluster analysis using the K-means method links progeny according to the most important characteristics in each group and identifies, by the Ward method and by means of a dendrogram, the structure of similarity and divergence between selected progeny. Both methods are effective in aiding progeny selection.

Multivariate Exploratory Approach and Influence of Six Agronomic Traits on Soybean Genotypes Selection

“…The range of the ellipse highlighted in red on the X axis and Y axis comprises values from 2 to -2 and this is in line with Dallastra et al (2014).…”

“…The combination of the linear relationship between the traits and the distribution of genotypes according to the projection of the vectors of the traits in the same analysis can be considered comprehensive and thorough analysis. Dallastra et al (2014), in a study on the selection of soybean genotypes carriers of the RR gene, and Pitta et al (2010), in a study on the selection of genotypes of groundnut resistant to Anticarsia gemmatalis, noted that principal componente analysis was efficient in the process of selection, leading to genetic gains for agronomic traits of interest, which is an advantageous and appropriate situation to identify superior genotypes. DS = differential of selection; MSG = Average of selected genotypes; MG 0 = overall average of genotypes; PHF = plant height at flowering (cm); PHM = plant height at maturity (cm); HIP = height at insertion of the first pod (cm); GP = grain productivity (PG) in kg ha -1 ; NN = number of nodes; NP = number of pods per plant.…”

“…Genotypes located outside the highlighted range are specific and differ significantly from the others, precisely because they have highly specific traits (DALLASTRA et al, 2014;VIANNA et al, 2013). After selection of genotypes through principal component analysis, the differential selection was estimated using the Eq.…”

“…Several multivariate methods can be applied to selection of superior genotypes, allowing also to observe the influence of agronomic traits in the selection of genotypes and the relationships between them. We emphasize the use of principal component analysis can be used in experimental data based on the average of genotypes for each trait, as used by Dallastra et al (2014); Viana et al (2013); Pitta et al (2010).…”

Non-hierarchical clustering ofManihot esculentaCrantz germplasm based on quantitative traits

“…, respectively), constituting promising groups to select genotypes for commercial use per se or to be used in breeding programs. K-means clustering were also used in soybean breeding programs to select F3 progenies by their ability to link progeny according to the most important characteristics E. J. Oliveira et al (DALLASTRA et al, 2014).…”