Mega-environment analysis of maize breeding data from Brazil

Abstract

The development and recommendation of single cross maize hybrids (SH) to be used in extensive land areas (mega-environments), and in different crop seasons requires many experiments under numerous environmental conditions. The question we asked is if the data from these multi-environment experiments are sufficient to identify the best hybrid combinations. The aim of this study was to critically analyze the phenotype data of experiments of yield, established by a large seed producing company, under a high level of imbalance. Data from evaluation of 2770 SH were used from experiments conducted over four years, involving the first and second crop seasons, in 50 locations of different years and regions of Brazil. Different types of analysis were carried out and genetic and non-genetic components were estimated, with emphasis on the different interactions of the SH with the environments. Results showed that the coincidence of common hybrids in these experiments is normally small. The estimates of the correlations between of the hybrids coinciding in the environments two by two is of low magnitude. The hybrid × crop season interaction was always expressive; however, the interactions of hybrids and other environmental variables were also important. Under these conditions, alternatives were discussed for making with the information obtained from the experiments, can be more efficient on the process to obtain new hybrids by companies.

Employing factor analytic tools for selecting high‐performance and stable tropical maize hybrids

“…The slight reduction in generalized heritability from the general analysis to the seasonal analyses (Figure 3) occurred due to the smaller number of environments – thus, information –in the former. Nevertheless, the values for this parameter are consistent with the ones normally found in the literature for tropical maize (Dias, Gezan, Guimarães, Nazarian, et al., 2018; Cantelmo et al., 2017; Pereira et al., 2022). By concomitantly analysing the generalized heritability and experimental coefficient of variation values, we verified that there was high experimental precision, which grants credibility to the results.…”

“…Hence, if breeders aim at selecting for global adaptation, conducting analyses for each season and selecting the genotypes at the top positions of both rankings, for example, H29 and H38, yield the most reliable results. This occurs Crop Science because the environmental conditions may change drastically from one season to another (Nóia Júnior & Sentelhas, 2019;Pereira et al, 2022). Consequently, there is a difference in the allele expression between seasons.…”

Ascertaining yield and grain protein content stability in wheat genotypes having the Gpc-B1 gene using univariate, multivariate, and correlation analysis

“…The examined environments were grouped into mega-environments and further identification of mega-environment specific genotypes is the sustainable method to use GEI according to the interests of breeders ( Yan and Tinker, 2005 ). Investigation of mega-environments has been previously reported in different cereals, including wheat ( Gerrish et al, 2019 ), rice ( Krishnamurthy et al, 2017 ), maize ( Pererira et al, 2021 ), and barley ( Hernandez-Segundo et al, 2009 ).…”

Análise de fatores e modelagem de estruturas de (co)variâncias para ensaios multiambientes na seleção de linhagens e progênies de feijoeiro

“…Modelar os efeitos genéticos e residuais na presença da interação G×A permite obter um modelo mais realista para dados provenientes de experimentos multiambientes, uma vez que o efeito da interação é devido à heterogeneidade de variância genética e à falta de correlação de genótipos entre pares de ambientes . A eficiência da modelagem de diferentes estruturas de (co)variâncias para efeitos genéticos e não genéticos foi relatada como eficaz no milho (Dodig et al, 2021;Pereira et al, 2021); forragem (Sripathi et al, 2018), cana-de-açúcar (Balsalobre et al, 2016, trigo e feijão (Melo et al, 2020). Além disso, por meio desta abordagem, é possível aumentar a acurácia dos valores genéticos e, consequentemente, a eficiência da seleção (So e Edwards, 2011).…”