Performance prediction of crosses using estimated breeding values for regions of soybean production in Brazil

Abstract

ABSTRACT The aim of this study was use the performance prediction of crosses in a group of conventional soybean genotypes to obtain the breeding value (BV), and to evaluate the correlation between the prediction and the actual productive potential of the progeny generated by this method in experimental tests for different seasons and environments, and determine whether the methodology is efficient in generating progeny of high productive potential for the soybean macro-regions (SMR) and soil and climate regions (SCR) of Brazil. A total of 481 conventional elite genotypes were selected as parents, the BV were generated, and crosses were predicted using the restricted maximum likelihood/best linear unbiased prediction mixed-model procedure (REML/BLUP). In 2019, the predicted crosses and advancement of the F1 and F2 segregating populations were carried and sent to the breeding programs of a private company in Passo Fundo-RS, Cambé-PR, Rio Verde-GO, Lucas do Rio Verde-MT and Porto Nacional-TO, where they were sown during the 2019/2020 crop season. During the 2020/2021 season, 1868 progeny were selected and tested in experimental trials at these locations. The progeny were again tested during the 2021/2022 season in experimental trials in 50 environments in SCR throughout Brazil. The result of the analysis showed a very weak to moderate correlation, indicating little efficiency for the prediction model used in this study. It is suggested that the prediction model be revised to include a greater number of variables, such as the kinship matrix, so that the BV of the genotypes can be more assertively estimated, especially when the aim is to select progeny in early generations with a high degree of heterozygosity.

Keywords

• Glycine max
• Breeding value
• Correlation
• Genetic improvement
• Hybridization