Inference of population effect and progeny selection via a multi-trait index in soybean breeding

Abstract

The selection of superior genotypes of soybean entails a simultaneous evaluation of a number of favorable traits that provide a comparatively superior yield. Disregarding the population effect in the statistical model may compromise the estimate of variance components and the prediction of genetic values. The present study was undertaken to investigate the importance of including population effect in the statistical model and to determine the effectiveness of the index based on factor analysis and ideotype design via best linear unbiased prediction (FAI-BLUP) in the selection of erect, early, and high-yielding soybean progenies. To attain these objectives, 204 soybean progenies originating from three populations were examined for various traits of agronomic interest. The inclusion of the population effect in the statistical model was relevant in the genetic evaluation of soybean progenies. To quantify the effectiveness of the FAI-BLUP index, genetic gains were predicted and compared with those obtained by the Smith-Hazel and Additive Genetic indices. The FAI-BLUP index was effective in the selection of progenies with balanced, desirable genetic gains for all traits simultaneously. Therefore, the FAI-BLUP index is an adequate tool for the simultaneous selection of important traits in soybean breeding.

Multitrait selection in seedless grape hybrids in semiarid regions of Brazil

“…Similar results were found by Volpato et al. (2021), who obtained desired gains for all traits evaluated for soybean using the FAI‐BLUP selection index. When using other indices, gains in the undesirable direction were obtained for some variables.…”

Strategies for Multi-trait Selection of Sweet Sorghum Progenies

“…By the FAI/BLUP index, under single-and multi-trait approaches, all traits were explained by only one factor, which might be associated with the high correlations (Rocha et al 2017). In the literature, the FAI/BLUP index has been used to select superior progenies of elephant grass (Rocha et al 2017), common bean (Rocha et al 2019), soybean (Woyann et al 2019, Volpato et al 2021, and biomass sorghum (Silva et al 2018). Our use of the FAI/BLUP index with multi-trait BLUPs proved to be an interesting alternative, since it provided balanced gains for almost all traits, and its estimate for TBH was very close to that obtained with direct selection and by the additive index (Figure 2).…”

“…Working with sweet corn, Entringer et al (2016) observed that the use of the additive index combined with the multi-trait approach provided higher gain estimates and was more efficient in selecting progenies than the sum of rank index. In soybean crop, the additive and FAI/BLUP indices were efficient in selecting productive progenies associated with upright architecture (Volpato et al 2021).…”

Estratégias de seleção em estágios precoces do melhoramento de soja com o uso de modelos mistos

“…A herdabilidade encontrada para a seleção de progênies de soja foi de 0,48 e 0,57, para os ensaios de Sorriso e Capinópolis, respectivamente e estão de acordo com o relata a literatura (VIOTTO DEL CONTE et al, 2020;VOLPATO et al, 2019;. Isto indica que a produtividade medida das progênies sofreu menor influência dos efeitos ambienta is quando comparado ao ensaio de Sorriso.…”

“…Para o ensaio de Capinópolis as herdabilidades estimadas para progênies, grupo 1 e grupo 2 de genitores foram de 0,43, 0,18 e 0,01, respectivamente. Os valores de herdabilidade encontrados para a avaliação da produtividade de grãos em soja estão de acordo com os encontrados na literatura (VIOTTO DEL CONTE et al, 2020;Volpato et al, 2019;.…”