Skip to main content
Log in

Genotype–Environment Interaction, Megaenvironments and Two-Table Coupling Methods for Sugarcane Yield Studies in Venezuela

  • Research Article
  • Published:
Sugar Tech Aims and scope Submit manuscript

Abstract

The selection of sugarcane cultivars adapted to different environments becomes difficult when there is genotype–environment interaction (GEI). The data were analyzed from twenty sugarcane genotypes evaluated in eight locations over two crop cycles to identify megaenvironments (ME), through GEI methods for higher cane yield measured in tons of cane per hectare (TCH) and percentage of sucrose (Pol% cane) using biplot multivariate GEI models. The best genotypes and the environment were determined with better mean yield by the two-table coupling coinertia method. Additive main effects and multiplicative interaction (AMMI) analyses revealed significant GEI with respect to both variables. The AMMI stability value exposed high genotypes stability for Pol% cane, but for TCH just G4, G8, G1, G20 and G17 have stability in all environments. The site-type regression SREG-GGE biplot showed two ME for TCH and one for Pol% cane. Although both yield variables showed mean negative correlation, through coinertia analysis it was possible to determine that G15, G17 and G13 were the best genotypes for both variables in all environments, besides “Los Tamarindos” was the best environment, with both variables correlated positively, and G11, G13, G12 could be considered the better genotypes. This work revealed the necessity of using coinertia as a complementary analysis to AMMI and GGE, which needs to be applied to determine the genotypes and environments that favor multiple yield variables, in order to increase the productivity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Akcura, Mevlüt, Seyfi Taner, and Yuksel Kaya. 2011. Evaluation of bread wheat genotypes under irrigated multi-environment conditions using GGE biplot analyses. Agriculture 98(1): 35–40.

    Google Scholar 

  • Chessel, Daniel, Anne B. Dufour, and J. Jean Thioulouse. 2004. The ade4 package-I-one-table methods. R News 4: 5–10.

    Google Scholar 

  • Crossa, José, Paul L. Cornelius, and Weikai Yan. 2002. Biplots of linear-bilinear models for studying crossover genotype × environment interaction. Crop Science 42: 619–633.

    Article  Google Scholar 

  • Culhane, Aedín, Guy Perrière, and Desmond G. Higgins. 2003. Cross-platform comparison and visualisation of gene expression data using co-inertia analysis. BMC Bioinformatics 4(1): 59.

    Article  PubMed  PubMed Central  Google Scholar 

  • De Mendiburu, Felipe. 2015. Agricolae: Statistical procedures for agricultural research. R package version 1.2-2. http://CRAN.R-project.org/package=agricolae.

  • Dolédec, Sylvain, and Daniel Chessel. 1994. Co-inertia analysis: An alternative method for studying species-environment relationships. Freshwater Biology 31: 277–294.

    Article  Google Scholar 

  • Dray, Stéphane, and Anne B. Dufour. 2007. The ade4 package: Implementing the duality diagram for ecologists. Journal of Statistical Software 22(4): 1–20.

    Article  Google Scholar 

  • Dray, Stéphane, Anne B. Dufour, and Daniel Chessel. 2007. The ade4 package-II: Two-table and K-table methods. R News 7(2): 47–52.

    Google Scholar 

  • Dray, Stéphane, Daniel Chessel, and Jean Thioulouse. 2003a. Procrustean co-inertia analysis for the linking of ecological tables. Ecoscience 10: 110–119.

    Google Scholar 

  • Dray, Stéphane, Daniel Chessel, and Jean Thioulouse. 2003b. Co-inertia analysis and the linking of ecological data tables. Ecology 84(11): 3078–3089.

    Article  Google Scholar 

  • Frutos, Elisa, María P. Galindo, and Victor Leiva. 2014. An interactive biplot implementation in R for modeling genotype-by-environment interaction. Stochastic Environmental Research and Risk Assessment 28(7): 1629–1641.

    Article  Google Scholar 

  • Ibáñez, Maximiliano, Mario Cavanagh, Natalia Bonamico, and Miguel Di Renzo. 2006. Análisis gráfico mediante biplot del comportamiento de híbridos de maíz. Revista de Investigaciones Agropecuarias 5(3): 83–93.

    Google Scholar 

  • Jalata, Zerihun. 2011. GGE-biplot analysis of multi-environment yield trials of barley (Hordeium vulgare. L.) genotypes in Southeastern Ethiopia Highlands. International Journal of Plant Breeding and Genetics 5(1): 59–75.

    Article  Google Scholar 

  • Kempton, Robert. 1984. The use of biplots in interpreting variety by environment interactions. The Journal of Agricultural Science 103: 123–135.

    Article  Google Scholar 

  • Mortazavian, Seyed, H. Nikkhah, F. Hassani, M. Sharif-al-Hosseini, M. Taheri, and M. Mahlooji. 2014. GGE biplot and AMMI analysis of yield performance of barley genotypes across different environments in Iran. Journal of Agricultural Science and Technology 16: 609–622.

    Google Scholar 

  • Ramburan, Sanesh, and Marvellous Zhou. 2011. Investigating sugarcane genotype × environment interactions under rainfed conditions in South Africa using variance components and biplot analysis. Proceedings of the South African Sugar Technology Association 84: 245–362.

    Google Scholar 

  • Rao, Srinivasa, Sanjana Reddy, Abhishek Rathore, Belum V. Reddy, and Sanjeev Panwar. 2011. Application GGE biplot and AMMI model to evaluate sweet sorghum (Sorghum bicolor) hybrids for genotype × environment interaction and seasonal adaptation. Indian Journal of Agricultural Science 81(5): 438–444.

    Google Scholar 

  • Rea, Ramón, and Orlando De Sousa-Vieira. 2002. Genotype × environment interaction in sugarcane yield trials in the central-western region of Venezuela. Interciencia 27(11): 620–624.

    Google Scholar 

  • Rea, Ramón, Orlando De Sousa-Vieira, Miguel Ramón, Gleenys Alejos, Alida Díaz, and Rosaura Briceño. 2011. AMMI analysis and its application to sugarcane regional trials in Venezuela. Sugar Tech 13(2): 108–113.

    Article  Google Scholar 

  • Rodríguez, Reynaldo, Yaquelin Puchades, Norge Bernal, Héctor J. Suárez, and Héctor García. 2012. Métodos estadísticos en el estudio de la interacción genotipo–ambiente en caña de azúcar. Ciencia en su PC 1: 47–60.

    Google Scholar 

  • Roostaei, Mozaffar, Reza Mohammadi, and Ahmed Amri. 2014. Rank correlation among different statistical models in ranking of winter wheat genotypes. The Crop Journal 2: 154–163.

    Article  Google Scholar 

  • Silveira, Luís, Volmir Kist, Thiago O. Paula, Márcio H. Barbosa, Luiz A. Peternelli, and Edelclaiton Daros. 2013. AMMI analysis to evaluate the adaptability and phenotypic stability of sugarcane. Scientia Agricola 70(1): 27–32.

    Google Scholar 

  • Yan, Weikai, and Nicholas A. Tinker. 2006. Biplot analysis of multi-environment trial data: Principles and applications. Canadian Journal of Plant Science 86: 623–664.

    Article  Google Scholar 

  • Yan, Weikai, Manjit S. Kang, Baoluo Ma, Sheila Woods, and Paul L. Cornelius. 2007. GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Science 47: 641–653.

    Google Scholar 

  • Yan, Weikai. 2002. Singular value partition for biplot analysis of multi-environment trial data. Agronomy Journal 94: 990–996.

    Article  Google Scholar 

  • Yan, Weikai. 2011. GGE Biplot vs. AMMI graphs for genotype-by-environment data analysis. Journal of the Indian Society of Agricultural Statistics 65(5): 181–193.

    Google Scholar 

Download references

Acknowledgments

The authors would like to thank the Venezuela’s National Institute for Agricultural Research (INIA) and the Venezuelan Endowment for Science, Technology and Innovation (FONACIT) for the financing of regional trials.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Leandro Balzano-Nogueira.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rea, R., De Sousa-Vieira, O., Díaz, A. et al. Genotype–Environment Interaction, Megaenvironments and Two-Table Coupling Methods for Sugarcane Yield Studies in Venezuela. Sugar Tech 18, 354–364 (2016). https://doi.org/10.1007/s12355-015-0407-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12355-015-0407-9

Keywords

Navigation