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Euphytica

, 215:113 | Cite as

Across year and year-by-year GGE biplot analysis to evaluate soybean performance and stability in multi-environment trials

  • Samuel Cristian Dalló
  • Andrei Daniel Zdziarski
  • Leomar Guilherme Woyann
  • Anderson Simionato Milioli
  • Rodrigo Zanella
  • Josiane Conte
  • Giovani BeninEmail author
Article
  • 110 Downloads

Abstract

Breeding companies release new high-yielding soybean genotypes every year. The main trait is grain yield (GY), and the performance need to be evaluated by statistical methods to identify superior genotypes. Biplot analysis are good tools for selecting superior genotypes and to increase efficiency in selection. This study aimed to: (a) identify genotypes with high GY and stability in edaphoclimatic regions (ECR) in southern of Brazil; (b) compare the year-by-year and across-years analyses to identify ideal genotype (IG) for each ECR; and (c) identify the association between genotypes and locations using the across-year approach. GY data from yield trials performed in the crop seasons of 2013, 2014, and 2015 were used. The analysis of IG was performed for each year independently and across the years. Moreover, environment-genotype relationship analysis was used to identify the association between genotypes and ECR. Genotypes with high GY and widely adapted for each ECR were identify. The across-year analysis was superior to the year-by-year analysis. However, only genotypes evaluated in more than 1 year and in sufficient locations provide accurate information about GY and stability; otherwise, the results should be adopted with caution.

Keywords

Glycine max (L.) Merrill Grain yield Stability Genotype × environment interaction GGE biplot 

Abbreviations

GY

Grain yield

ECR

Edaphoclimatic region

IG

Ideal genotype

VCU

Value for cultivation and use

GEI

Genotype × environment interaction

GGE

Genotype main effect plus genotype × environment interaction

GGL

Genotype main effects plus genotypic × location interaction effect

MR

Macroregion

MG

Maturity group

PC

Principal component

SVP

Singular values partition

Notes

Acknowledgements

The authors thank GDM Seeds and the breeders Marcos Norio Matsumoto, Nizio Fernando Giasson and Jair Rogério Unfried for providing the datasets and for their contributions in the accomplishment of this work. To Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for granting the masters and doctoral scholarships. Funding was provided by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq).

Supplementary material

10681_2019_2438_MOESM1_ESM.docx (562 kb)
Supplementary material 1 (DOCX 561 kb)

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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Samuel Cristian Dalló
    • 1
  • Andrei Daniel Zdziarski
    • 1
  • Leomar Guilherme Woyann
    • 1
  • Anderson Simionato Milioli
    • 1
  • Rodrigo Zanella
    • 1
  • Josiane Conte
    • 1
  • Giovani Benin
    • 1
    Email author
  1. 1.Federal University of Technology – Paraná, Campus Pato BrancoPato BrancoBrazil

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