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.
Highlights
Maize (Zea mays L.) is the most cultivated crop worldwide [1]
When comparing the NSPA and SPA models, the results demonstrated the importance of spatial analysis
The adoption of multi-environment trials (MET) permits a better understanding of the genetic effect, as it enables the breeder to account for the GxE interaction effect
Summary
Maize (Zea mays L.) is the most cultivated crop worldwide [1] Quantitative traits, such as grain yield and plant height, are controlled by several genes and are highly influenced by the environment [2]. Diallel mating designs are used for progeny tests [5] and are widely adopted in plant breeding [6,7,8,9]. 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) [10, 11]. 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 [11, 12]
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