The objectives of this study were to examine the implication of the genotype × environment interaction (G × E) in the identification of genetically superior soybean [Glycine max (L.) Merr.] progenies; to obtain estimates of genetic and phenotypic parameters for agronomic traits in soybean progenies; and to select genetically superior progenies to obtain lineages. Progenies F_3:4 were evaluated in the municipalities of Lavras‐MG and Itutinga‐MG, Brazil, in the agricultural year 2016–2017. Progenies F_3:5 were evaluated in the summer of 2017–2018 in the municipalities of Lavras‐MG, Itutinga‐MG, and Ijaci‐MG. The traits of days to flowering, days to full maturity, plant height, bottom pod height, lodging, and grain yield were evaluated. The genetic and phenotypic parameters, expected gain from selection, genetic correlation, correlated response, achieved heritability, and frequency distribution of the adjusted means were estimated. The estimates of the components of variance showed the existence of variability among the progenies, enabling the selection of superior genotypes. There was an effect of the G × E interaction for all traits, and most of the interaction was due to complex interaction. From the analysis of genotypic correlation, significant estimates were observed between the traits. The G × E interaction affected the estimates of genetic and phenotypic parameters in the soybean, and the achieved heritability is a tool to study this interaction.

The aim of this study was to verify the adaptability and stability of soybean cultivars with regards to yield and oil content. Data of soybean yield and oil content were used from experiments set up in six environments in the 2011/12 and 2012/13 crop seasons in the municipalities of Patos de Minas, Uberaba, Lavras, and Sao Gotardo, Minas Gerais, Brazil, testing 36 commercial soybean cultivars of both conventional and transgenic varieties. The Wricke method and GGE biplot analysis were used to evaluate adaptability and stability of these cultivars. Large variations were observed in grain yield in relation to the different environments studied, showing that these materials are adaptable. The cultivars exhibited significant differences in oil content. The cultivars BRSGO204 (Goiania) and BRSMG (Garantia) exhibited the greatest average grain yield in the different environments studied, and the cultivar BRSMG 760 SRR had the greatest oil content among the cultivars evaluated. Ecovalence was adopted to identify the most stable cultivars, and the estimates were nearly uniform both for grain yield and oil content, showing a variation of 0.07 and 0.01%, respectively. The GGE biplot was efficient at identifying cultivars with high adaptability and phenotype stability.

In breeding programs, the demand for high-throughput phenotyping is substantial as it serves as a crucial tool for enhancing technological sophistication and efficiency. This advanced approach to phenotyping enables the rapid and precise measurement of complex traits. Therefore, the objective of this study was to estimate the correlation between vegetation indices (VIs) and grain yield and to identify the optimal timing for accurately estimating yield. Furthermore, this study aims to employ photographic quantification to measure the characteristics of corn ears and establish their correlation with corn grain yield. Ten corn hybrids were evaluated in a Complete Randomized Block (CRB) design with three replications across three locations. Vegetation and green leaf area indices were estimated throughout the growing cycle using an unmanned aerial vehicle (UAV) and were subsequently correlated with grain yield. The experiments consistently exhibited high levels of experimental quality across different locations, characterized by both high accuracy and low coefficients of variation. The experimental quality was consistently significant across all sites, with accuracy ranging from 79.07% to 95.94%. UAV flights conducted at the beginning of the crop cycle revealed a positive correlation between grain yield and the evaluated vegetation indices. However, a positive correlation with yield was observed at the V5 vegetative growth stage in Lavras and Ijaci, as well as at the V8 stage in Nazareno. In terms of corn ear phenotyping, the regression coefficients for ear width, length, and total number of grains (TNG) were 0.92, 0.88, and 0.62, respectively, demonstrating a strong association with manual measurements. The use of imaging for ear phenotyping is promising as a method for measuring corn components. It also enables the identification of the optimal timing to accurately estimate corn grain yield, leading to advancements in the agricultural imaging sector by streamlining the process of estimating corn production.