, 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


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.


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



Grain yield


Edaphoclimatic region


Ideal genotype


Value for cultivation and use


Genotype × environment interaction


Genotype main effect plus genotype × environment interaction


Genotype main effects plus genotypic × location interaction effect




Maturity group


Principal component


Singular values partition



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)


  1. Bhartiya A, Aditya JP, Kumari V, Kishore N, Purwar JP, Agrawal A, Kant L (2017) GGE biplot & AMMI analysis of yield stability in multi-environment trial of soybean [Glycine max (L.) Merrill] genotypes under rainfed condition of north western Himalayan hills. J Anim Plant Sci 27(1):227–238Google Scholar
  2. Branquinho RG, Duarte JB, Souza PIM, Silva Neto SP, Pacheco RM (2014) Estratificação ambiental e otimização de rede de ensaios de genótipos de soja no Cerrado. Pesquisa Agropecuária Brasileira 49(10):783–795. CrossRefGoogle Scholar
  3. Chen X, Wu B, Zhang Z (2016) Evaluation of adaptability and stability for important agronomic traits of oat (Avena spp.) germplasm resources. J Plant Genet Resour 17(4):577–585Google Scholar
  4. Kaster M, Farias JRB (2012) Regionalização dos testes de valor de cultivo e uso e da indicação de cultivares de soja: terceira aproximação. Embrapa Soja, Distrito de WartaGoogle Scholar
  5. Li XP, Li MY, Ling AJ, Hu XZ, Ma Z, Liu L, Li YX (2017) Effects of genotype and environment on avenanthramides and antioxidant activity of oats grown in northwestern China. J Cereal Sci 73:130–137. CrossRefGoogle Scholar
  6. Lopes MS, Reynolds MP, Manes Y, Singh RP, Crossa J, Braun HJ (2012) Genetic yield gains and changes in associated traits of CIMMYT spring bread wheat in a “historic” set representing 30 years of breeding. Crop Sci 52(3):1123–1131. CrossRefGoogle Scholar
  7. Mapa – Ministério da Agricultura, Pecuária e Abastecimento (2018) Registro Nacional de Cultivares—RNC. Accessed 06 May 2018
  8. Sánchez-Martín J, Rispail N, Flores F, Emeran AA, Sillero JC, Rubiales D, Prats E (2017) Higher rust resistance and similar yield of oat landraces versus cultivars under high temperature and drought. Agron Sustain Dev 37(1):3. CrossRefGoogle Scholar
  9. Silva CL, Bornhofen E, Todeschini MH, Milioli AS, Trevisan DM, Benin G (2015) Seleção de genótipos de trigo para rendimento de grãos e qualidade de panificação em ensaios multiambientes1. Revista Ceres 62(4):360–371. CrossRefGoogle Scholar
  10. Ullah H, Khalil IH, Khalil IA, Khattak GSS (2011) Performance of mungbean genotypes evaluated in multi-environmental trials using the GGE biplot method. Atlas J Biotechnol 1(1):1–8CrossRefGoogle Scholar
  11. Yan W (2001) GGEbiplot—a windows application for graphical analysis of multi-environment trial data and other types of two-way data. Agron J 93:1111–1118CrossRefGoogle Scholar
  12. Yan W (2014) Crop variety trials: data management and analysis. Wiley, ChichesterCrossRefGoogle Scholar
  13. Yan W (2015) Mega-environment analysis and test location evaluation based on unbalanced multiyear data. Crop Sci 55(1):113–122. CrossRefGoogle Scholar
  14. Yan W (2016) Analysis and handling of G × E in a practical breeding program. Crop Sci 56(5):2106–2118. CrossRefGoogle Scholar
  15. Yan W, Kang MS (2003) GGE biplot analysis: a graphical tool for breeders, geneticists, and agronomists, 1st edn. CRC Press, Boca RatonGoogle Scholar
  16. Yan W, Tinker NA (2006) Biplot analysis of multi-environment trial data: principles and applications. Can J Plant Sci 86(3):623–645. CrossRefGoogle Scholar
  17. Yan W, Kang MS, Ma B, Woods S, Cornelius PL (2007) GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci 47(2):643–653. CrossRefGoogle Scholar
  18. Zdziarski AD, Todeschini MH, Milioli AS, Woyann LG, Madureira A, Stoco MG, Benin G (2018) Key soybean maturity groups to increase grain yield in Brazil. Crop Sci 58:1155–1165. CrossRefGoogle Scholar

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

Personalised recommendations