Summary: Through enviromics, precision breeding leverages innovative geotechnologies to customize crop varieties to specific environments, potentially improving both crop yield and genetic selection gains. In Brazil's four southernmost states, data from 183 distinct geographic field trials (also accounting for 2017–2021) covered information on 164 genotypes: 79 phenotyped maize hybrid genotypes for grain yield and their 85 nonphenotyped parents. Additionally, 1342 envirotypic covariates from weather, soil, sensor‐based, and satellite sources were collected to engineer 10 K synthetic enviromic markers via machine learning. Soil, radiation light, and surface temperature variations remarkably affect differential genotype yield, hinting at ecophysiological adjustments including evapotranspiration and photosynthesis. The enviromic ensemble‐based random regression model showcases superior predictive performance and efficiency compared to the baseline and kernel models, matching the best genotypes to specific geographic coordinates. Clustering analysis has identified regions that minimize genotype‐environment (G × E) interactions. These findings underscore the potential of enviromics in crafting specific parental combinations to breed new, higher‐yielding hybrid crops. The adequate use of envirotypic information can enhance the precision and efficiency of maize breeding by providing important inputs about the environmental factors that affect the average crop performance. Generating enviromic markers associated with grain yield can enable a better selection of hybrids for specific environments.

The efficiency of recurrent selection depends, among other factors, on the estimation of parameters that guide the breeder to obtain genetic gains for multiple traits. The aim of this research was to estimate genetic and phenotypic parameters of maize populations during the third cycle of recurrent selection for traits of interest. The populations CRE-01 and CRE-02, potential for resistance to corn stunt, were described using genetic and phenotypic parameters of variance components, heritability, variation indices and correlation between 16 agronomic traits. Direct and indirect selection gains were estimated for each trait and considering the Smith and Hazel, Mulamba and Mock and Z index. Both populations have genetic variability for the traits. High heritability estimates were obtained for most of the traits, which associated with the genetic variability, indicate that these populations may show continuous gains with recurrent selection. All selection indexes provided satisfactory genetic gains, but the Z index was the most promising considering the results of both populations.