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Blueberry (Vaccinium corymbosum) has an important role in the global market, with consumption increasing steadily in the past decades. Adapted to multiple environments, a relevant question when investigating fruit quality attributes is the performance of different genotypes over multiple environmental conditions. In this study, we hypothesize that the use of environmental covariables (ECs) can improve the understanding of genotype-by-environment interaction and guide breeders' decisions. To test it, we used twenty-four genotypes, evaluated across five harvest seasons (2018–2022) in four mega-environments in Florida (Citra FL, Central-North FL, Central FL and South FL), and investigated the impact of twenty-one environmental covariables on the phenotypic expression of five fruit quality traits (weight, total soluble solids, total titratable acidity, firmness, and size). Our contributions in this study are three: (I) first, we identified important temporal windows affecting the phenotypic plasticity for multiple traits; (II) using the environmental covariables, we draw attention on the importance of ECs that characterize the mega-environments and use such information in a mixed model framework to explain the genotype-by-environment interaction; and finally (III) we used linear regression (in the form of Finlay-Wilkinson regression) to estimate adaptability and stability metrics to select promising genotypes. Overall, these findings offer insights into the utility of environmental variables in explaining genotype stability and plasticity, thus, providing a framework to enhance predictive responses and optimize resource allocation in blueberry breeding. [ABSTRACT FROM AUTHOR]

Randomized complete block (RCB) design is the most widely used experimental design in biological sciences. As number of treatments increases, the block size become larger and it looses the capacity to control the variance within block, which is its original purpose. A method known as post hoc blocking could be used in these cases to improve the genetic parameter estimation and thus obtain an unbiased assessment of the performance of a given treatment. In trufgrass breeding, as other breeding program, this is a common challenge. The goal of this study was to test the capacity of different post hoc blocking designs to improve the genetic parameter estimation of zoysiagrass (Zoysia spp.). We evaluated two post hoc blocking designs; row–column (R–C) and incomplete block (IB) designs on five genotype trials located in Florida. The results showed that post hoc R–C design had superior model fitting than both the original RCB and the post hoc IB designs when studied at the single measurement level and at the site level. The narrow-sense heritability (0.24–0.40) and the genotype-by-measurement correlation (0.57–0.99) did not change significantly when R–C was compared to the original RCB design. The ranking of the top performing genotypes changed considerably when comparing RCB to R–C design, but the degree depended on the location analyzed. We conclude that the change in the ranking of the top (potentially select individuals) is coming from the better control of intrablock environmental variation, and this could potentially have a significant impact on the breeding selection process. [ABSTRACT FROM AUTHOR]