cited soil structure, moisture, light interception, pathogen infections, and even crop management. As main environmental sources, Burgueño et al. In diallel MET analyses, there are many sources of variation. In MET analyses, the REML/BLUP procedure allows modeling different residuals and genetic covariance structures and may be applied to unbalanced data. In addition, it allows the prediction of additive and dominance genetic effects and the gains with selection, derived directly from parents and hybrid selection. In diallel analyses, the REML/BLUP procedure allows the estimation of additive and dominance genetic variances, as well as the narrow- and broad-sense heritabilities. However, diallel analyses are still underused, even being an effective procedure for genetic evaluation. The mixed model methodology, or the residual maximum likelihood (REML)/best linear unbiased prediction (BLUP) procedure, has been widely adopted for analyzing MET in plant breeding. There are few examples of diallel multi-environment trials (MET) in maize breeding. The GCA is given by the mean of the performance of a particular individual in combination with many others, and the SCA is the genetic effect of a specific cross. These mating designs allow the evaluation of general and specific combining abilities, which are additive genetic effects based on general combining ability (GCA), and dominance genetic effect based on specific combining ability (SCA). ĭiallel mating designs are used for progeny tests and are widely adopted in plant breeding. In this scenario, the genotype-by-environment (G × E) interaction, also known as phenotypic plasticity, plays an essential role in phenotypic expression and can lead to difficulties in genetic selection. Quantitative traits, such as grain yield and plant height, are controlled by several genes and are highly influenced by the environment. Maize ( Zea mays L.) is the most cultivated crop worldwide. The analyses bring the parents 2, 10 and 12, as potential parents in this microregion. The SPA model exhibits higher reliability values and is recommended to be incorporated in the standard procedure of genetic evaluation in maize breeding. This indicates the power of the SPA model in dealing with spatial trends. In addition, the heritability, selective accuracy, and selection gain were higher when the SPA models were used. The likelihood ratio test showed that some effects changed regarding significance when the SPA and NSPA models were used. In the joint analysis, the compound symmetry structure for the genotypic effects presented the best fit. 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. Then, the rows and columns factors were included in the fixed and random parts of the model. The SPA models accounted for autocorrelation among rows and columns by the inclusion of first-order autoregressive matrices (AR1 ⊗ AR1). 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.
#ASREML R AR1 AT TRIAL#
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. Spatial analyses can correct spatial trends, which allow for an increase in selective accuracy. Spatial trends represent an obstacle to genetic evaluation in maize breeding.
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