The ability to predict genotypes that have not yet been tested is always a target of plant breeders. Over the last twenty years, many studies presented genomic selection (GS) as a tool contributing to this goal. Currently, many research papers have shown encouraging results in the application of GS. However, there are few examples of long-term, successful applications of GS in plant breeding programs. Furthermore, for breeders and researchers considering the application of GS, there are a series of important considerations on how to adapt a breeding program to maximize the benefit of GS, aiming to reduce the costs and maximize the genetic gains. Under this perspective, we present a review with a general view about applied GS in maize breeding, future perspectives of this technique, and an applied study case of a breeding program using GS. We attempt to provide a brief review of the literature with recent developments, as well as a discussion involving the number of markers required to deploy GS, the different statistical approaches to create GS models, the different ways to define training populations, and the incorporation of non-additive effects and genotype by environment interaction. We end with general recommendations and conclusions about some critical points about adopting GS in maize breeding.

We provide the first record of Rhinella inopina in the state of Minas Gerais, southeastern Brazil, in municipalities of Bonito de Minas and Januária. It is also the species southernmost record, extending its known geographic distribution in about 170 and 210 km respectively southeastward its closest previously record, in municipality of Sítio d'Abadia, State of Goiás, Central Brazil.

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Myersiella microps (Duméril and Bibron, 1841) is considered data deficient (DD) in the State of Minas Gerais, Southeastern Brazil. Herein we provide new records and a geographic distribution map of this poorly known species. These data provide valuable information for a conservation status assessment of M. microps.

The performance of inbred lines in advanced endogamous generation is commonly evaluated in successive generations of testing and selection, which we defined as "multistage field trials" (MSFT). MSFT data routinely exhibit heterogeneity of (co)variances at several levels due to genetic and/or statistical imbalance. Nowadays, mixed models have been widely used to deal with unbalanced data. However, few studies on common bean have addressed the use of a mixed model approach with modeling of (co)variance structures for random effects in MSFT. Furthermore, factor analysis and genotype-ideotype distance (FAI-BLUP) selection index was originally proposed using best linear unbiased predictions from individual analysis. In this regard, we aimed to study the implications of modeling (co)variance structures for random effects in the estimation of genetic parameters and evaluate the accuracy and efficiency of inbred line selection by the modeled FAI-BLUP approach. A total of five trials were evaluated from 2018 to 2020. The results revealed that the unstructured covariance matrix fitted better for grain yield, whereas the matrix with uniform correlation and heterogeneity of variances fitted better for grain aspect and plant architecture. The modeled FAI-BLUP approach increased the values of selection accuracy and selection efficiency. Our results suggest that modeling the different structures of (co)variances and selecting the best-performing genotypes by modeled FAI-BLUP approach should be used in common bean assays involving unbalanced data.