Strategic positioning of soybean based on the agronomic ideotype and on fixed and mixed multivariate models

Abstract

The objective of this work was to decompose the variations of the genotype × environment interaction through fixed multivariate models, as well as to understand the genetic variations through mixed models, for the estimation and prediction of the genetic value of soybean (Glycine max) genotypes in the state of Rio Grande do Sul, Brazil. Tests were carried out during the 2016/2017, 2017/2018, and 2018/2019 crop seasons in different municipalities in six regions of the state, using the additive main effects and multiplicative interaction (AMMI) and genotype main effects + genotype-by-environment interaction (GGE) models. The genotypes were also evaluated using an index that allows weighting between mean performance and stability (WAASBY) and by the restricted maximum likelihood (REML) and the best linear unbiased prediction (BLUP) models. The used experimental design was randomized complete blocks (18 environments x 12 genotypes), with three replicates. The best performing genotypes in favorable environments are: 'BMX Valente RR', 'BMX Alvo RR', 'NS 5959 IPRO', 'DM 5958RSF IPRO', and 'BMX Ativa RR'. The favorable environments are the 2017/2018 season in the municipality of Bagé and the 2016/2017 season in the municipalities of São Luiz Gonzaga and Cachoeira do Sul, where higher grain yields were obtained. The genotypes that show excellent performance in unfavorable environments are cultivars BMX Ativa RR, DM 5958RSF IPRO, NS 5959 IPRO, and TMG 7262 RR. The 2016/2017 season is considered unfavorable in the municipalities of São Luiz Gonzaga and Cachoeira do Sul. The AMMI, GGE, and WAASBY or BLUP models for genotype selection must be used simultaneously.

Soybean adaptability and stability analyzes to the organic system through AMMI, GGE Biplot and mixed models methodologies

“…Some studies portray the importance of the simultaneous use of adaptability and stability methods to obtain greater reliability in the selection of genotypes. HONGYU et al (2015), SINGH et al (2021) and KEHL et al (2022 showed that the AMMI and GGE analysis used simultaneously is a strategy that can increase the assertiveness of genotype positioning. Thus, the Genotype and Genotype-by-Environment (GGE) method was also applied.…”

Early selection of resilient progenies to seed yield in soybean populations

“…For that, the REML/BLUP methodology has helped to select genotypes in crops such as soybeans (PRADEBON et al, 2023;KEHL et al, 2022;KNEBEL et al, 2021), wheat (LORO et al, 2023), white oats (SCHMIDT et al, 2023) and corn (CARVALHO et al, 2017). This methodology makes it possible to estimate variance components and genetic parameters that indicate the proportion of the phenotype that is determined by the genetic makeup.…”