Accounting for spatial trends in multi-environment diallel analysis in maize breeding

Abstract

Spatial trends represent an obstacle to genetic evaluation in maize breeding. Spatial analyses can correct spatial trends, which allow for an increase in selective accuracy. The objective of this study was to compare the spatial (SPA) and non-spatial (NSPA) models in diallel multi-environment trial analyses in maize breeding. The trials consisted of 78 inter-populational maize hybrids, tested in four environments (E1, E2, E3, and E4), with three replications, under a randomized complete block design. The SPA models accounted for autocorrelation among rows and columns by the inclusion of first-order autoregressive matrices (AR1 ⊗ AR1). Then, the rows and columns factors were included in the fixed and random parts of the model. Based on the Bayesian information criteria, the SPA models were used to analyze trials E3 and E4, while the NSPA model was used for analyzing trials E1 and E2. In the joint analysis, the compound symmetry structure for the genotypic effects presented the best fit. The likelihood ratio test showed that some effects changed regarding significance when the SPA and NSPA models were used. In addition, the heritability, selective accuracy, and selection gain were higher when the SPA models were used. This indicates the power of the SPA model in dealing with spatial trends. The SPA model exhibits higher reliability values and is recommended to be incorporated in the standard procedure of genetic evaluation in maize breeding. The analyses bring the parents 2, 10 and 12, as potential parents in this microregion.

Detection of Superior Rice Genotypes and Yield Stability under Different Nitrogen Levels Using AMMI Model and Stability Statistics

“…Breeders focus on the GEI effect to identify the yield stability of genotypes across different conditions and environments, which cannot be revealed by the separate effects of genotype or environment [ 10 , 11 , 12 , 13 , 14 ]. The GY heritability is exposed to variability across different environments [ 15 , 16 ], which hinders the accuracy of superior varietal selection processes [ 17 ]. Therefore, widely adapted genotypes with the ability to produce stable high yields across diversified environments constitute a major goal for rice breeders.…”

Simulation‐based decision‐making and implementation of tools in hybrid crop breeding pipelines

“…From a broader point of view, the study highlights the importance of maximizing the precision of phenotyping while also maximizing the number of genotypes. While it was not the topic of the study, previous work has shown similar results when comparing advanced experimental designs such as augmented p-rep designs or controlling for spatial variability in the field (Bernardeli et al, 2021;Coelho et al, 2021).…”

Evaluation of finger millet (Eleusine coracana (L.) Gaertn.) in multi-environment trials using enhanced statistical models

“…It could also be linked to the residual dependence of plots. When spatial trends were taken into account, relevant research on maize [ 36 ], sorghum [ 37 ], barely [ 38 ], potato [ 39 ], and soybean [ 40 ] likewise produced excellent results.…”

“…The results of MET data analysis showed that modeling GE interactions with FA models in combination with models for spatial variations resulted in a significant improvement in the estimates of genetic parameters, and this was demonstrated with evidence of heritability. Results from a related study on sorghum [ 41 ], durum wheat [ 42 ], and maize [ 36 ] revealed that modeling field spatial correlation plus MET through FA combined under a linear mixed model also indicated significant improvement in heritability analysis. The FA models were found to be useful not only for estimating and predicting GxE interaction effects, but also for estimating GxE variance and performing bi-plot analysis.…”

“…the residual dependence of plots. When spatial trends were taken into account, relevant research on maize [36], sorghum [37], barely [38], potato [39], and soybean [40] likewise produced excellent results. The ANOVA-based approach always assumes constant variance across environments and no correlation between environments.…”