Bayesian AMMI applied to food-type soybean multi-environment trials

Gustavo Henrique Freiria, Leandro Simões Azeredo Gonçalves, Douglas Mariani Zeffa, Wilmar Ferreira Lima, Nelson da Silva Fonseca Júnior, Cássio Egídio Cavenaghi Prete, Inês Cristina de Batista Fonseca

Resumo


A complicating factor for the selection of plant strains is the influence of a genotype-environment (GE) interaction. The Bayesian approach is a tool to increase the efficiency of adaptability and stability methodologies. In this context, the objective of this study was to evaluate the linear and bi-linear parameters of the additive main effects and multiplicative interaction (AMMI) analysis using the Bayesian approach for selection of food-type soybean genotypes in multi-environment trials. The grain yields of five lipoxygenase-free lines intended for human consumption of from the soybean breeding program of the Londrina State University and two commercial standards (BRS 257 and BMX Potência RR) were evaluated in four counties of the State of Paraná, Brazil, in the 2014/15, 2015/16 and 2016/17 growing seasons. Of the evaluated lines, only UEL 110 and UEL 122 had positive posterior genotypic effects, exceeding a probability of 95%  against the commercial standard BRS 257. Only lines UEL 115 and UEL 123 did not contribute significantly to the GE interaction. Lines UEL 110 and UEL 122 proved adaptable to the largest number of environments with significant GE interaction and are therefore promising for the development of new food-type soybean cultivars. The use of AMMI1 (PC1 vs. effects genotypes) showed results for the stability of genotypes similar to AMMI2 (PC1 vs PC2), allowing a direct selection by the biplot for productivity and stability.


Palavras-chave


Glycine max; Bayesian inference; Genotype - environment; Functional food; Grain yield

Texto completo:

PDF

Referências


ARAÚJO, L. G. Seleção e análise dos modelos PARAFAC e Tucker e gráfico triplot com aplicação em interação tripla. Tese (Doutorado em Estatística e Experimentação Agronômica) - Escola Superior de Agricultura Luiz de Queiroz, Universidade de São Paulo, Piracicaba, 2009.

BERNARDO JÚNIOR, L. A. Y. et al. AMMI Bayesian models to study stability and adaptability in maize. Agronomy Journal, v. 110, n. 5, p. 1-12, 2018.

BRASIL. Ministério da Agricultura, Pecuária e Abastecimento. Cultivar Web: gerenciamento de informação. 2019. Disponível em: http://extranet.agricultura.gov.br/php/snpc/cultivarweb/cultivares_registradas.php?txt_ordem=&postado=1&acao=pesquisar&. Acesso em: 5 jan. 2019.

COTES, J. M. et al. A Bayesian approach for assessing the stability of genotypes. Crop Science, v. 46, n. 6, p. 2654-2665, 2006.

CROSSA, J. et al. Bayesian estimation of the additive main effects and multiplicative interaction model. Crop Science, v. 51, n. 4, p. 1458-1469, 2011.

CRUZ, C. D.; REGAZZI, A. J.; CARNEIRO, P. C. S. Modelos biométricos aplicados ao melhoramento genético. 4. ed. Viçosa, MG: Editora da UFV, 2012. 514 p. v. 1.

DAY, L. Proteins from land plants: potential resources for human nutrition’s and food security. Trends in Food Science & Technology, v. 32, n. 1, p. 25-42, 2013.

DESTRO, D. et al. Food-type soybean cooking time: a review. Crop Breeding and Applied Biotechnology, v. 13, n. 3, p. 194-199, 2013.

FIGUEIREDO, A. G. et al. Application of mixed models for evaluating stability and adaptability of maize using unbalanced data. Euphytica, v. 202, p. 393-409, 2015.

FREIRIA, G. H. et al. Productivity and chemical composition of food-type soybeans sown on different dates. Acta Scientiarum. Agronomy, v. 38, n. 3, p. 371-377, 2016.

FREIRIA, G. H. et al. Protein, lipid and isoflavone contents of food-type soybean lines at two sowing dates. Bioscience Journal, v. 34, n. 6, p. 1540-1550, 2018a.

FREIRIA, G. H. et al. Statistical methods to study adaptability and stability in breeding lines of food-type soybeans. Bragantia, v. 77, n. 2, p. 253-264, 2018b.

GAUCH, H. G. A simple protocol for AMMI analysis of yield trials. Crop Science, v. 53, p. 1860-1869, 2013.

JARQUÍN, D. et al. A hierarchical Bayesian estimation model for multienvironment plant breending trials in successive years. Crop Science, v. 56, n. 5, p. 1-17, 2016.

MA, W. et al. Genistein as a neuroprotective antioxidant attenuates redox imbalance induced by β-amyloid peptides 25–35 in PC12 cells. International Journal of Developmental Neuroscience, v. 28, n. 4, p. 289-295, 2010.

MEOTTI, G. V. et al. Épocas de semeadura e desempenho agronômicos de cultivares de soja. Pesquisa Agropecuária Brasileira, v. 47, n. 1, p. 14-21, 2012.

OLIVEIRA, L. A. et al. Credible intervals for scores in the AMMI with radom effects for genotype. Crop Science, v. 55, n. 2, p. 1-12, 2015.

OLIVEIRA, L. A. et al. Performance of cowpea genotypes in the Brazilian Midwest using the Bayesian additive main effects and multiplicative interaction model. Agronomy Journal, v. 10, n. 1, p. 147-154, 2018.

PEREZ-ELIZALDE, S.; JARQUÍN, D.; CROSSA, J. A general Bayesian estimation method of linear-bilinear models applied to plant breeding trials genotype x environment interaction. Journal of Agricultural, Biological and Environmental Statistics, v. 17, n. 1, p. 15-37, 2012.

RAFTERY, A. E.; LEWIS, S. M. [Practical Markov Chain Monte Carlo]: comment: one long run with diagnostics: implementation strategies for Markov Chain Monte Carlo. Statistical Science, v. 7, n. 4, p. 494-497, 1992.

RIGO, A. A. et al. Characterization of soybean cultivars genetically improved for human consumption. International Journal of Food Engineering, v. 1, n. 1, p. 1-7, 2015.

SILVA, C. P. et al. A Bayesian shrinkage approach for AMMI models. Plos one, v. 10, n. 7, p. e0131414, 2015.

VAN EEUWIJK, F. A.; BUSTOS-KORTS, D. V.; MALOSETTI, M. M. What should students in plant breeding know about the statistical aspects of genotype x environment interactions? Crop Science, v. 56, n. 5, p. 2119-2140, 2016.

VIELE, K.; SRINIVASAN, C. Parsimoniuos estimation of multiplicative interaction in analysis of variance using Kullback-Leibler information. Journal of Statistical Planning and Inference, v. 84, n. 1/2, p. 201-219, 2000.

ZOBEL, R. W.; WRIGHT, M. J.; GAUCH, H. G. Statistical analysis of a yield trial. Agronomy journal, v. 80, n. 3, p. 388-393, 1988.




Revista Ciência Agronômica ISSN 1806-6690 (online) 0045-6888 (impresso), Site: www.ccarevista.ufc.br, e-mail: ccarev@ufc.br - Fone: (85) 3366.9702 - Expediente: 2ª a 6ª feira - de 7 às 17h.