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Maize plants can be N-use efficient or N-stress tolerant. The first have high yields in favorable environments but is drastically affected under stress conditions; whereas the second show satisfactory yields in stressful environments but only moderate ones under optimal conditions. In this context, our aim was to assess the possibility of selecting tropical maize lines

The design of the training set is a key factor in the success of the genomic selection approach. The nature of line inclusion in soybean [Sorghum bicolor (L.) Moench.] breeding programs is highly dynamic, so generating a training set that endures across the years and regions is challenging. Therefore, we aimed to define the best strategies for building training sets to apply genomic selection in segregating soybean populations for traits with different genetic architectures. We used two datasets for grain yield (GY) and maturity group (MG) from two different soybean breeding regions in Brazil. Five training set schemes were tested. In addition, we included a training set formed by an optimization algorithm based on the predicted error variance. The predictions achieved good values for both traits, reaching 0.5 in some scenarios. The best scenario changed according to the trait. Although the best performance was achieved with the use of full‐sibs in the MG dataset, for GY, full‐sibs and a set of advanced lines were equivalent. For both traits, no improvement in predictive ability resulted from training set optimization. Furthermore, the use of advanced lines from the same breeding program is recommended as a training set for GY, so the training set is continually renewed and closely related to the breeding populations, and no additional phenotyping is needed. On the other hand, to improve prediction accuracies for MG, it is necessary to use training sets with less genetic variability but with more segregation resolution.

In soybean [Glycine max (L.) Merr.], new commercial lines are commonly obtained from biparental crosses, and the selection is performed as homozygosity increases. However, it is difficult to select for quantitative traits in the early steps of breeding, due to the high heterozygosity level and a vast number of new progenies, which sometimes lead breeders to randomly select for these traits in this phase. Therefore, we aimed to assess the impact of genomic selection in early generations of a soybean breeding program. Working on germplasm derived from two different maturity regions in Brazil, genotyped in F2 and phenotyped in F2:4 for grain yield, plant height, maturity rating, and days to maturity, we compared the composition of different training populations, models with and without the genotype × environment (G × E) interaction effect, and two types of relationship measurements (genetic similarity and Euclidian distance). Results showed superior performance of the Euclidian distance kernel over the standard VanRaden kernel in major scenarios tested. In general, G × E models did not obtain superior performance compared with mean principal models, and the training population composed only of the nearest progenies had the highest prediction ability. The best models achieved prediction abilities between 0.40 and 0.56, thereby enabling application of a low‐intensity selection in F2. As a result, half of the progenies could be discarded without missing a great part of the good ones. Our results show that through genomic prediction, it is possible to select for quantitative traits in the early steps of breeding, which might increase the efficiency of the program in the advanced phases.