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Index selection can improve the selection efficiency in a rice recurrent selection population

A Correction to this article was published on 02 June 2021

This article has been updated

Abstract

To attain success with selection process, the breeders need to understand the genetic variability into each population developed by breeding program. In this purpose, breeders need to estimate the variance components and genetic and phenotypic parameters. Besides, they need to have the ability to select simultaneously genetically top progenies for multiple traits. This study used a field trial, with a panel of 198 S0:2 lowland rice progenies, evaluated in three environments during two years in order to: (i) estimate genetic parameters for different traits in a rice breeding population, (ii) evaluate the impact of direct selection and selection performed through three selection indices in a rice population developed by recurrent selection, and (iii) compare three selection indices using different economic weights with direct and indirect selection. Significant genetic variation was found among progenies for: grain yield, plant height, days to flowering, panicle blast, leaf scald and grain discoloration. The direct selection provided high gains for single trait selection, but this method may results in undesirable changes in related traits. Thus, between the evaluated indices and in comparison, to indirect selection the Mulamba and Mock index provided simultaneously greater gains for all the evaluated traits. Furthermore, we found that when the random weights and \(b\)-variation index were used as economic weight, it was found lower gains or no gains, showing that the gains attained with Smith and Hazel and Tai indices were linked to economic weights used.

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Fig. 1
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Fig. 4

Availability of data and material

All data generated or analysed during this study are included in this published article. The phenotypic data are included in Fig. 1.

Change history

Abbreviations

DAS:

Days after the sowing

GY:

Grain yield

PH:

Plant height

DF:

Days to flowering

PB:

Panicle blast

Ls:

Leaf scald

Gd:

Grain discoloration

ANOVA:

Analysis of variance

\({\sigma }_{g}^{2}\) :

Genetic variance

\({\sigma }_{P}^{2}\) :

Phenotypic variance

\({\sigma }_{e}^{2}\) :

Error variance among plots

\({h}^{2}\) :

Broad-sense heritability

CV g :

Genotypic coefficient of variation

R 2 :

Coefficient of determination

DS :

Direct selection

IS :

Indirect selection

SH :

Smith and Hazel index

TA :

Tai index

MM :

Mulamba and Mock index

GSD :

Genetic standard deviation

RW :

Random weights

MANOVA:

Multivariate analysis of variance

G x E:

Genotype x environment interaction

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Acknowledgements

The authors dedicate this article to the memory of Dr Orlando Peixoto de Morais, an outstanding scientist and recognized mentor of the rice breeding team at Embrapa Rice and Beans, that in an earlier version of this text, he was involved, but he passed away in the meantime. The authors thank all the Embrapa staff who contributed with field trials. We also wish to thank the National Council for the Improvement of Higher Education (CAPES) for granting scholarship to PHRG

Funding

This research received funding from Embrapa through the rice breeding program for the experiments carried out with lowland rice progenies and 2) from Capes for the fellowship of the first author.

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ACCC, PPT, PHNR and APC designed the experiments. ACCC, PPT, PHRG and APC phenotyped the panel. PHRG performed the statistical analysis. PHRG and PGSM interpreted the phenotypic results and wrote the paper, which was edited and approved by all co-authors.

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Correspondence to Paulo Henrique Ramos Guimarães.

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Ramos Guimarães, P.H., Guimarães Santos Melo, P., Centeno Cordeiro, A.C. et al. Index selection can improve the selection efficiency in a rice recurrent selection population. Euphytica 217, 95 (2021). https://doi.org/10.1007/s10681-021-02819-7

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  • DOI: https://doi.org/10.1007/s10681-021-02819-7

Keywords

  • Genetic improvement
  • Quantitative genetics
  • Predicted gains
  • Economic weight