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- W3196543006 abstract "Fruit breeding programs demand substantial investment for the development and selection of superior genotypes. Thus, statistical methods that allow a reliable genetic evaluation should be preferred. The present study aimed to estimate genetic parameters and genotypic values through the Markov chain Monte Carlo algorithm and perform the selection of Tahiti acid lime genotypes, based on a multitrait index. Twenty-four Tahiti acid lime genotypes were evaluated, from July 2017 to January 2019, for their vegetative, productive, and physical-chemical traits. Repeatability models with different residual variance structures were tested. The Deviance information criterion (DIC) was used to select the best model and test the significance of the random effects. A multiple trait index was used to identify superior genotypes, closely with the ideotype. According to DIC, models with diagonal residual variance structure presented best fit. Heritability estimates presented a downward trend through the harvests. Mean selective accuracies were moderate to very high (0.50≤r˜g^g) for all traits, which indicates high reliability in the genotype ranking. Desirable gains for all traits were achieved by the multitrait index. The Bayesian inference was adequate for the genetic evaluation; and the multitrait index allowed an efficient genetic selection." @default.
- W3196543006 created "2021-09-13" @default.
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- W3196543006 date "2021-12-01" @default.
- W3196543006 modified "2023-09-27" @default.
- W3196543006 title "Multitrait and multiharvest analyses for genetic assessment and selection of Tahiti acid lime genotypes through Bayesian inference" @default.
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- W3196543006 doi "https://doi.org/10.1016/j.scienta.2021.110536" @default.
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