Selection to high productivity and stink bugs resistance by multivariate data analyses in soybean

Abstract

Stink bugs that affect soybeans are responsible for significant losses in seed production, quality and germination potential, in addition to hindering the mechanized harvest. To develop insect resistant materials, the breeder can compile a selection index by factor analysis. Therefore, the objective of this work was to validate the use of factor analysis, by means of its estimated gains, for the selection of highly productive and stink bugs resistant genotypes in two soybean segregating populations. For this, the phenotypic evaluation was performed in the generation F 2:3 , in two distinct experiments, being the populations from the crosses between IAC-100 × PI 295952 and IAC-100 × PI 306712. The experiments were installed in an 18 × 9 alpha-lattice design, with three replicates for each population. Agronomic and resistance characters were evaluated. The factorial scores for each character were obtained for the creation of "supercharacters". These were designed to check if the selection in the new characters could provide satisfactory simultaneous gains in the original characters. Subsequently, the analysis of variance was performed for all factors, in both populations. The F test showed the presence of variability among genotypes, allowing the selection of superior genotypes. None of the factors selected progenies with all the characters favorably, and their use was not interesting for both populations. With this, complementary studies should be performed with other selection indices in these populations.

MGIDI: towards an effective multivariate selection in biological experiments

“…Finally, the distance from a given genotype to the ideotype could be computed using the Euclidean distance. So, in contrast to conclusions of recent studies (Bermudez & Pinheiro, 2020), we have shown here that factor analysis is an efficient technique to establish an index in which most of the traits are selected favorably, showing satisfactory gains for the application in breeding programs.…”

“…Even though the SH index is widely used as a multi-trait selection index in plant breeding trials, there has been evidence (Bhering et al, 2012) that the adoption of this index is not interesting for plant breeding. Since the combination of multivariate techniques is efficient to account for the multicollinearity issue in multi-trait indexes (Rocha, Machado, & Carneiro, 2018), there would seem to be value in an investigation to develop an index that covers the weaknesses of the SH index in which all the traits are selected favorably and with satisfactory gains for the application in breeding programs (Bermudez & Pinheiro, 2020).…”

Multivariate analyses reveal key traits in the identification of nitrogen‐use‐efficient cultivars of common beans

“…Factor analysis has already been used as a selection index in several studies (Barbosa et al., 2019; Bermudez & Pinheiro, 2020; Filipović et al., 2014). Based on this analysis, we aimed to clarify the specific factors that explained the set of analyzed variables.…”

“…Cluster analyses are used to classify genotypes into distinct groups, such that genotypes that constitute the same group have similar characteristics, whereas genotypes in different groups are distinct (Darkwa et al., 2020). Conversely, factor analysis is a powerful tool used to explain the relationship between numerous variables, reducing the trait complexity by defining a smaller set of dimensions, called factors, which can then be used as a selection index (Barbosa et al., 2019; Bermudez & Pinheiro, 2020; Filipović et al., 2014; Mair, 2018). In this sense, the objective of this study was to identify the key traits and genotypes of carioca common beans with high NUsE using multivariate statistical methods.…”

Utilização de imagens na seleção de genótipos de soja resistentes ao complexo de percevejos e com elevada qualidade fisiológica de sementes

“…Para o caráter TOL foi necessário realizar a análise individual dos experimentos devido à não homogeneidade dos resíduos entre os dois experimentos. Pode-se observar uma diferença no CV entre os experimentos, já que em 2019/20 foi de 13,40 e em 2020/21 foi de 3,27, o que é um indício de maior influência do ataque de percevejos no primeiro ano (BERMUDEZ; PINHEIRO, 2020;FERNANDES et al, 2019;ROCHA et al, 2014). A média também foi distinta para os dois anos agrícolas, sendo 68,51 na primeira e 90,87 na segunda e mesmo havendo essa diferença na expressão da TOL, houve diferença significativa para o efeito de genótipos em ambos os experimentos realizados.…”