Hostname: page-component-7c8c6479df-24hb2 Total loading time: 0 Render date: 2024-03-19T05:51:15.782Z Has data issue: false hasContentIssue false

Applying the generalized additive main effects and multiplicative interaction model to analysis of maize genotypes resistant to grey leaf spot

Published online by Cambridge University Press:  22 December 2016

C. R. L. ACORSI
Affiliation:
Departamento de Estatística (DES), Universidade Estadual de Maringá (UEM), Av. Colombo, 5·790 – Zip Code 87020-900 Jd. Universitário, Maringá – Paraná, Brazil
T. A. GUEDES
Affiliation:
Departamento de Estatística (DES), Universidade Estadual de Maringá (UEM), Av. Colombo, 5·790 – Zip Code 87020-900 Jd. Universitário, Maringá – Paraná, Brazil
M. M. D. COAN*
Affiliation:
Departamento de Agronomia (DAG), Universidade Estadual de Maringá (UEM), Av. Colombo, 5·790 – Zip Code 87020-900 Jd. Universitário, Maringá – Paraná, Brazil
R. J. B. PINTO
Affiliation:
Departamento de Agronomia (DAG), Universidade Estadual de Maringá (UEM), Av. Colombo, 5·790 – Zip Code 87020-900 Jd. Universitário, Maringá – Paraná, Brazil
C. A. SCAPIM
Affiliation:
Departamento de Agronomia (DAG), Universidade Estadual de Maringá (UEM), Av. Colombo, 5·790 – Zip Code 87020-900 Jd. Universitário, Maringá – Paraná, Brazil
C. A. P. PACHECO
Affiliation:
Embrapa Milho e Sorgo, CNPMS, Rodovia MG 424 km 45, CP 285 – Zip Code 35701-970 – Sete Lagoas, MG, Brazil
P. E. O. GUIMARÃES
Affiliation:
Embrapa Milho e Sorgo, CNPMS, Rodovia MG 424 km 45, CP 285 – Zip Code 35701-970 – Sete Lagoas, MG, Brazil
C. R. CASELA
Affiliation:
Embrapa Milho e Sorgo, CNPMS, Rodovia MG 424 km 45, CP 285 – Zip Code 35701-970 – Sete Lagoas, MG, Brazil
*
*To whom all correspondence should be addressed. Email: marloncoan@gmail.com

Summary

Analysing the stability and adaptation of cultivars to different environments is always necessary before recommending them for planting on large areas. Additive main effects and multiplicative interaction (AMMI) models have been used to analyse genotype-by-environment interactions (G × E). AMMI models require data with homogeneous variance, normal errors and additive effects. However, agronomic data do not always conform to these statistical assumptions. The objective of the present study was to analyse G × E interactions for severity and incidence of grey leaf spot, a foliar disease in maize caused by Cercospora zeae-maydis, using a generalized AMMI model. Data were collected and evaluated for 36 maize cultivars from experiments carried out in nine Brazilian regions in 2010/11 by the Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA – Milho e Sorgo). Only two of three stable genotypes defined by a quasi-likelihood model with a logistic link function could be recommended for their desirable agronomic characteristics. Four growing locations in which the genotypes were stable were identified, but in only one of these was stability associated with very severe grey leaf spot disease. Cultivars adapted to specific locations with low percentage disease severity were also identified.

Type
Crops and Soils Research Papers
Copyright
Copyright © Cambridge University Press 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Agresti, A. (2002). Categorical Data Analysis. New Jersey, USA: John Wiley & Sons.Google Scholar
Allard, R. W. (1999). Principles of Plant Breeding. New York, USA: John Wiley & Sons.Google Scholar
Annicchiarico, P., Bellah, F. & Chiari, T. (2005). Defining subregions and estimating benefits for a specific-adaptation strategy by breeding programs: a case study. Crop Science 45, 17411749.Google Scholar
Bhatia, A. & Munkvold, G. P. (2002). Relationships of environmental and cultural factors with severity of gray leaf spot in maize. Plant Disease 86, 11271133.CrossRefGoogle ScholarPubMed
Brito, A. H., Von Pinho, R. G., Pozza, E. A., Pereira, J. L. A. R. & Faria Filho, E. M. (2007). Efeito da Cercosporiose no rendimento de híbridos comerciais de milho. Fitopatologia Brasileira 32, 472479.CrossRefGoogle Scholar
Cordeiro, G. M. & Demétrio, C. G. B. (2008). Modelos Lineares Generalizados e Extensões. Piracicaba, Brazil: Escola Superior de Agricultura “Luiz de Queiroz” – Universidade de São Paulo.Google Scholar
Crossa, J., Fox, P. N., Pfeiffer, W. H., Rajaram, S. & Gauch, H. G. Jr (1991). AMMI adjustment for statistical analysis of an international wheat yield trial. Theoretical and Applied Genetics 81, 2737.CrossRefGoogle Scholar
Cruz, C. D., Regazzi, A. J. & Carneiro, P. C. S. (2006). Modelos Biométricos Aplicados ao Melhoramento Genético. Viçosa, Brazil: Universidade Federal de Viçosa.Google Scholar
Dobson, A. J. A. (2002). Introduction to Generalized Linear Models. New York, USA: Chapman & Hall CRC.Google Scholar
Duarte, J. B. & Vencovsky, R. (1999). Interação Genótipos x Ambientes uma introdução à análize “AMMI”. Monograph Series, 9. Ribeirão Preto, Brazil: Sociedade Brasileira de Genética.Google Scholar
Fantin, G. M., Brunelli, K. R., Resende, I. C. & Duarte, A. P. (2001). A mancha de Cercospora do milho. Campinas, Brazil: Instituto Agronômico de Campinas.Google Scholar
Fornasieri Filho, D. (2007). Manual da Cultura do Milho. Jaboticabal, Brazil: Funep.Google Scholar
Ferreira, D. F. (2008). Estatística Multivariada. Lavras, Brazil: Universidade Federal de Lavras.Google Scholar
Ferreira, D. F., Demétrio, C. G. B., Manly, B. F. J., Machado, A. A., Vencovsky, R. (2006). Statistical models in agriculture: biometrical methods for evaluating phenotypic stability in plant breeding. Cerne 12, 373388.Google Scholar
Gabriel, R. (1998). Generalized bilinear regression. Biometrika 85, 689700.Google Scholar
Gauch, H. G. & Zobel, R. W. (1988). Predictive and postdictive success of statistical analyses of yield trials. Theoretical and Applied Genetics 76, 110.Google Scholar
Gauch, H. G. & Zobel, R. W. (1996). AMMI analysis of yield trials. In Genotype by Environment Interaction (Eds Kang, M. S. & Gauch, H. G.), pp. 85122. New York, USA: Boca Raton CRC Press.CrossRefGoogle Scholar
Gollob, H. F. (1968). A statistical model which combines features of factor analytic and analysis of variance techniques. Psychometrika 33, 73115.Google Scholar
Gower, J. C. (1995). A general theory of biplots. In Recent Advances in Descriptive Multivariate Analysis (Ed. Krzanowski, W. J.), pp. 283303. Royal Statistical Society Lecture Notes, 2. Oxford: Clarendon Press.Google Scholar
Hadi, A. F., Mattjik, A. A. & Sumertajaya, I. M. (2010). Generalized AMMI models for assessing the endurance of soybean to leaf pest. Jurnal Ilmu Dasar 11, 151159.Google Scholar
Ientilucci, E. J. (2003). Using the Singular Value Decomposition. New York, USA: Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology. Available from: http://www.cis.rit.edu/~ejipci/Reports/svd.pdf (verified 27 October 2016).Google Scholar
Kempton, R. A. (1984). The use of biplots in interpreting variety by environment interactions. Journal of Agricultural Science, Cambridge 103, 123135.Google Scholar
Kroonenberg, P. M. (1997). Introduction to Biplots for G × E Tables. Research Report #51. Leiden: Leiden University.Google Scholar
McCullagh, P. & Nelder, J. A. (1989). Generalized Linear Models. London, UK: Chapman & Hall.CrossRefGoogle Scholar
Pacheco, R. M., Duarte, J. B., Assunção, M. S., Junior, J. N. & Chaves, A. A. P. (2003). Zoneamento e adaptação produtiva de genótipos de soja de ciclo médio de maturação para Goiás. Pesquisa Agropecuária Tropical 33, 2327.Google Scholar
Paula, G. A. (2004). Modelos de Regressão com Apoio Computacional. São Paulo, Brazil: Instituto de Matemática e Estatística da Universidade de São Paulo (IME-USP)..Google Scholar
R Development Core Team (2013). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Available from: http://www.R-project.org/ Google Scholar
Rencher, A. C. (2002). Methods of Multivariate Analysis, 2nd edn. New York, USA: John Wiley & Sons.CrossRefGoogle Scholar
Searle, S. R., Casella, G. & McCulloch, C. E. (1992). Variance Components. New York, USA: John Wiley & Sons.Google Scholar
Smith, A. B., Cullis, B. R. & Thompson, R. (2005). The analysis of crop cultivar breeding and evaluation trials: an overview of current mixed model approaches. Journal of Agricultural Science, Cambridge 143, 449462.Google Scholar
Sumertajaya, I. M. (2007). Analisis Statistik Interaksi Genotipe Dengan Lingkungan. Bogor, Indonesia: Departemen Statistik, Fakultas Matematika dan IPA, IPB (Abstract in English).Google Scholar
Tarakanovas, P. & Ruzgas, V. (2006). Additive main effect and multiplicative interaction analysis of grain yield of wheat varieties in Lithuania. Agronomy Research 4, 9198.Google Scholar
Turner, H. & Firth, D. (2009). Generalized Nonlinear Models in R: An Overview of the gnm Package (R Package Version 0.10–0). Warwick, UK: University of Warwick.Google Scholar
van Eeuwijk, F. A. (1995). Multiplicative interaction in generalized linear models. Biometrics 51, 10171032.Google Scholar
Wedderburn, R. W. M. (1974). Quasi-likelihood functions, generalized linear models and the Gauss-Newton method. Biometrika 61, 439447.Google Scholar
Zobel, R. W., Wright, M. J. & Gauch, H. G. (1988). Statistical analysis of a yield trial. Agronomy Journal 80, 388393.Google Scholar