Stability of wheat genotypes for grain yield using the GGE Biplot methodology

Autores

  • Volmir Sergio Marchioro UFSM
  • Ricardo Reffatti Bastiani UFSM
  • Luís Antônio Klein UFSM
  • Évelyn Clarissa Mühl Ignacio UFSM
  • Ketlyn Mäger Kittlaus UFSM
  • Marlon Ribeiro Feldens UFSM

Palavras-chave:

Triticum aestivum L., plant breeding, cereals

Resumo

Wheat plays an important role in global agriculture, being one of the most widely cultivated cereals. Wheat cultivation in each region depends on the stability of grain yield in the given environment. The objective of this study was to identify the most productive and stable genotypes over three years of cultivation in the municipality of Frederico Westphalen, using GGE Biplot analysis. Twelve wheat genotypes were cultivated in a randomized complete block design, with three replicates over three years from 2019 to 2021. The experimental units consisted of six 4-meter rows, spaced 0.17 m apart. Grain yield was measured 3 meters from the four central rows. The data obtained was subjected to analysis of variance, which verified interactions between genotypes and years, and then performed GGE Biplot analysis. The grain yield of the wheat genotypes was influenced by the environmental conditions during the three years of cultivation. The genotypes UFSMFW 1-07 and USFMFW 1-04 were more productive in specific years, with the best performance for grain yield and stability being that of the genotype UFSMFW 1-08.

Referências

Companhia Nacional de Abastecimento (Conab). Acompanhamento da Safra Brasileira: grãos, safra 2023/24, 9º levantamento. Brasília: Conab; 2024 [citado 2024 jun 20]. Available from: https://www.conab.gov.br

Sousa CN, Gomes EP, Moreira JC, Sartori JF, Del Duca LJ, Scheeren PL, et al. Criação de cultivares de trigo no Centro Nacional de Pesquisa de Trigo – período de 1986 a 1990. Pesqui Agropecu Bras. 1998; 33:553-63. Available from: https://apct.sede.embrapa.br

Fayeun LS, Alake GC, Akinlolu AO. GGE biplot analysis of fluted pumpkin (Telfairia occidentalis) landraces evaluated for marketable leaf yield in Southwest Nigeria. J Saudi Soc Agric Sci. 2018; 17(4):416-23. https://doi.org/10.1016/j.jssas.2016.10.001

Branquinho RG, Duarte JB, Souza PI, Silva SP Neto, Pacheco RM. Estratificação ambiental e otimização de rede de ensaios de genótipos de soja no Cerrado. Pesqui Agropecu Bras. 2014; 49(10):783-95. Available from: https://doi.org/10.1590/S0100-204X2014001000005

Roozeboom KL, Schapaugh WT, Tuinstra MR, Vanderlip RL, Milliken GA. Testing wheat in variable environments: genotype × environment interaction effects and grouping test locations. Crop Sci. 2008; 48(1):317-30. Available from: https://doi.org/10.2135/cropsci2007.04.0209

Noerwijati N, Taryono N, Prajitno D. Fresh tuber yield stability analysis of fifteen cassava genotypes across five environments in East Java (Indonesia) using GGE biplot. Energy Procedia. 2014; 47(2014):156-65. Available from: https://doi.org/10.1016/j.egypro.2014.01.209

Yan W. Analysis and handling of G × E in a practical breeding program. Crop Sci. 2016; 56(5):2106-18. Available from: https://doi.org/10.2135/cropsci2015.06.0336

Santos HG, Jacomine PK, Anjos LH, Oliveira VA, Oliveira JB, Lumbreras JF, et al. Sistema brasileiro de classificação de solos. 5th ed. Brasília: Embrapa; 2018.

Alvares AC, Alcarde C, Stape JL, Sentelhas PC, Moraes JL, Sparovek G. Köppen’s climate classification map for Brazil. Meteorol Z. 2013; 22(6):711-28. Available from: https://doi.org/10.1127/0941-2948/2013/0507

Yan W, Rajcan I. Biplot analysis of test sites and trait relations of soybean in Ontario. Crop Sci. 2002; 42(1):11-20. Available from: https://doi.org/10.2135/cropsci2002.1100

Yan W, Hunt LA, Sheng Q, Szlavnics Z. Cultivar evaluation and megaenvironment investigation based on GGE biplot. Crop Sci. 2000; 40(3):597-605. Available from: https://doi.org/10.2135/cropsci2000.403597x

R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2020 [citado 2023 mai 20]. Available from: https://www.R-project.org

Olivoto T, Lúcio AD. Metan: An R package for multi-environment trial analysis. Methods Ecol Evol. 2020; 11(6):783-9. Available from: https://doi.org/10.1111/2041-210X.13384

Yan W, Kang MS. GGE biplot analysis: a graphical tool for breeders, geneticists, and agronomists. Boca Raton: CRC Press; 2003. Available from: https://doi.org/10.1201/9781420040371

Nornberg R, Ribeiro G, Silveira G, Luche HS, Baretta D, Tessmann W, et al. Adaptabilidade e estabilidade de genótipos de aveia branca. Rev Acad Cienc Agrar Ambient. 2014; 12(3):181-90. Available from: https://doi.org/10.7213/academica.12.03.AO03

Silva RR, Benin G, Silva GO, Marchioro VS, Almeida JL, Matei G. Adaptabilidade e estabilidade de cultivares de trigo em diferentes épocas de semeadura no Paraná. Pesqui Agropecu Bras. 2011; 46(11):1439-47. Available from: https://doi.org/10.1590/S0100-204X2011001100004

Silva CL, Bornhofen E, Todeschini MH, Milioli AS, Trevisan DM, Benin G. Seleção de genótipos de trigo para rendimento de grãos e qualidade de panificação em ensaios multiambientes. Rev Ceres. 2015; 62(4):360-71. Available from: https://doi.org/10.1590/0034-737X201562040005

Oteros J, García-Mozo H, Botey R, Mestre A, Galán C. Variations in cereal crop phenology in Spain over the last twenty-six years (1986–2012). Clim Change. 2015; 130:545-58. Available from: https://doi.org/10.1007/s10584-015-1363-9

Yan W, Kang MS, Ma B, Woods S, Cornelius PL. GGE biplot vs. AMMI analysis of genotype-by-environment data. 2007; 47(2):643-655. Available from: https://doi.org/10.2135/cropsci2006.06.0374

Farshadfar E, Rashidi M, Jowkar MM, Zali H. GGE biplot analysis of genotype × environment interaction in chickpea genotypes. Eur J Exp Biol. 2013; 3(1):417-23. Available from: https://www.pelagiaresearchlibrary

Yan W. Singular-value partitioning in biplot analysis of multienvironment trial data. Agron J. 2002; 94(5):990-6. Available from: https://doi.org/10.2134/agronj2002.9900

Silva CM, Mezzomo HC, Casagrande CR, Lima WL, Olivoto T, Nardino M. Selection of tropical wheat lines based on classical and modern parameters of adaptability and stability. Bulg J Agric Sci. 2021; 27(5):933-41. Available from: https://www.agrojournal.org

Oliveira TR, Carvalho HW, Oliveira G, Costa EF, Gravina GA, Santos RD, et al. Hybrid maize selection through GGE biplot analysis. Bragantia. 2019; 78(2):166-74. Available from: https://doi.org/10.1590/1678-4499.20170438

Allard RW, Bradshaw AD. Implications of genotype-environmental interactions in applied plant breeding. Crop Sci. 1964; 4(5):503-8. Available from: https://doi.org/10.2135/cropsci1964.0011183X000400050021x

Mushayi M, Shimelis H, Derera J, Shayanowako AI, Mathew I. Multi-environmental evaluation of maize hybrids developed from tropical and temperate lines. Euphytica. 2020; 216(84):1-14. Available from: https://doi.org/10.1007/s10681-020-02618-6

Yan W, Tinker A. Biplot analysis of multi-environment trial data: principles and applications. Can J Plant Sci. 2006; 86(3):623-45. Available from: https://doi.org/10.4141/P05-169

Hongyu K, Silva FL, Oliveira AC, Sarti DA, Araujo LB, Dias CT. Comparação entre os modelos AMMI e GGE Biplot para os dados de ensaios multi-ambientais. Rev Bras Biom. 2015; 33(2):139-55. Available from: https://biometria.ufla.br/antigos

Downloads

Publicado

2025-10-25

Como Citar

Marchioro, V. S., Reffatti Bastiani, R., Klein, L. A., Mühl Ignacio, Évelyn C., Mäger Kittlaus, K., & Ribeiro Feldens, M. (2025). Stability of wheat genotypes for grain yield using the GGE Biplot methodology. Revista Ceres, 72, e72031. Recuperado de https://ojs.ceres.ufv.br/ceres/article/view/8209

Edição

v. 72 (2025)

Seção

PLANT BREEDING APPLIED TO AGRICULTURE

Licença

Creative Commons License
Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.

Artigos mais lidos pelo mesmo(s) autor(es)