Natã Balssan Moura, Ivan Ricardo Carvalho, Kassiana Kehl, Leonardo Cesar Pradebon, Murilo Vieira Loro, Eduarda Donadel Port, Inaê Carolina Sfalcin, José Antonio Gonzalez da Silva, Adriano Udich Bester


Background. Wheat is a staple food crop and easily accessible to the population, so the biofortification of wheat grains is substantial to mitigate malnutrition. Objective. To evidence and select wheat genotypes based on nutritional multi-characters of grains based on genetic parameters. Methodology. Experiments were carried out in the 2019 agricultural season in five wheat areas of the state of Rio Grande do Sul, in two sowing seasons, in the municipalities of Cachoeira do Sul, Cruz Alta, Santo Augusto, São Gabriel and Vacaria. The experimental design was randomized blocks, organized in a factorial scheme with 10 cultivation environments (5 sites by two sowing dates) and 30 genotypes, with three replications. To carry out the selection of genotypes, the WAASB, AMMI, GGE and BLUP methodologies were applied. Results. In terms of lipids and fibers, three mega environments were formed, highlighting the genotypes BRS 327, CD 1550, Ametista, CD 1303 and BRS 331, respectively. For mineral material, there was the formation of two mega environments and the genotypes that stood out were Quartz and Tbio Toruk, while for carbohydrate there was the formation of a mega environment and the genotype that stood out was CD 1550. The Tbio Mestre and LG Prisma genotypes were the ideal genotypes, with high performance in the Cachoeira do Sul environment – Sowing 2nd fortnight. Tbio Iguaçú expressed high levels of lipids in Santo Augusto – Seeding 1st fortnight, São Gabriel – Seeding 2nd fortnight and Vacaria – Seeding 2nd fortnight. ORS 1405 and Tbio Iguaçú expressed high levels of carbohydrates in the Vacaria - Seeding 2nd fortnight environment. Heritabilities without interaction effects were high, which characterizes high genotypic additive variance. Implications. The current results indicate that there is genetic variability, making it possible to select genotypes with greater expression of nutrients in the grains. Conclusion. The TBio Mestre, CD 1440, LG Prisma and Marfim genotypes expressed greater performance and stability of the evaluated traits.


Triticum aestivum; genotype selection; WAASB; GGE; BLUP; AMMI.

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Bender, A. N., Carvalho, I. R., Martins, T., Moura, N., Silva, J. L. S., Lautenchleger, F., Ferreira, M. T., Hutra, D. J., Loro, M. V. and Souza, V. Q., 2021. Plant arrangement and its effects on yield and bromatological quality of wheat submitted to different cutting systems. Research, Society and Development, 10(2), pp. 1-15.

Carvalho, I. R., Nardino, M., Ferrari, M., Pelegrin, A. J., Demari, G. H., Szareski, V. J., Follmann, D. N., Bahry, C. A., Souza, V. Q., Oliveira, A. C. and Maia, L. C., 2016. Genetic variability among common black bean (Phaseolus vulgaris L.,) accessions in southern Brazil. Australian Journal of Crop Science, 10(10), pp. 1474-1483. DOI:

Carvalho, I. R., Silva, J. A. G., Ferreira, L. L., Barbosa, M. H., Mambrin, R. B., Fachi, S. M., Conte, G. G. andSouza, V. Q., 2019. Research Article Heritability profiles defined by hierarchal models and artificial neural networks for dual-purpose wheat attributes. Genetics and Molecular Research, 18(3), pp. 1-16.

Conab, Companhia Nacional de Abastecimento. Acompanhamento safra Brasileira: maio de 2022. Disponível em: Acesso: 19/04/2022.

Cotrim, M. F., Farias, F. J. C., Carvalho, L. P., Teodoro, L. P. R., Bhering, L. L. and Teodoro, P. E., 2019. Environmental stratification in the brazilian cerrado on the yield and fiber quality of cotton genotypes. Bioscience Journal, 35(5), pp. 1349-1355.

Chiang, S., Chen, C. and Chang, C., 2006. Effect of wheat flour protein compositions on the quality of deep-fried gluten balls. Cereal Chemistry. v.97(4), pp. 666-673.

Hallauer, A. R. J. B. and Miranda, F., 1988. Quantitative Genetics in Maize Breeding, 2.ed. Iowa State University Press, Ames, 650p.

Pimentel, A. J. B., Guimarães, J. F. R., Souza, M. A. D., Resende, M. D. V. D., Moura, L. M., Rocha, J. R. D. A. S., and Ribeiro, G., 2014. Estimação de parâmetros genéticos e predição de valor genético aditivo de trigo utilizando modelos mistos. Pesquisa Agropecuária Brasileira, 49(11), pp. 882-890.

Resende, M.D.V., 2000. Análise estatística de modelos mistos via REML/BLUP na experimentação em melhoramento de plantas perenes. Colombo: Embrapa Florestas, 101p. (Embrapa Florestas. Documentos, 47).

Follmann, D. N., Souza, V. Q. D., Cargnelutti Filho, A., Demari, G. H., Nardino, M., Olivoto, T., Carvalho, I. R., Silva, A. D. B., Meira, D. and Meier, C., 2019. Agronomic performance and genetic dissimilarity of second-harvest soybean cultivars using REML/BLUP and Gower’s algorithm. Bragantia, 78(2), pp. 197-207.

Santos, H.G., Jacomine, P. K. T., Anjos, L. H. C., Oliveira, V. A., Lumbreras, J. F., Coelho, M. R., Almeida, J. A., Cunha, T. J. F., and Oliveira, J. B., 2013. Sistema brasileiro de classificação de solos. 3.ed. Brasília: Embrapa, 353p.

Streck, E.V., Kämpf, N., Dalmolin, R. S. D., Klamt, E., Nascimento, P. C., Schneider, P., Giasson, E. and Pinto, L. F. S., 2008. Solos do Rio Grande do Sul. 2.ed. Porto Alegre: UFRGS: Emater/RS-Ascar, 222p.

Szareski, V. J., Carvalho, I. R., Kehl, K., Levien, A. M., Nardino, M., Dellagostin, S. M., Demari, G. H., Lautenchleger, F., Villela, F. A., Pedó, T., Souza, V. Q. and Aumonde, T. Z., 2018. Adaptability and stability of wheat genotypes according to the phenotypic index of seed vigor. Pesquisa Agropecuária Brasileira, 53(6), pp. 727-735.

Szareski, V. J., Carvalho, I. R., Kehl, K., Levien, A. M., Nardino, M., Demari, G. H., Lautenchleger, F., Souza, V. Q., Pedó, T. and Aumonde, T. Z., 2017. Univariate, multivariate techniques and mixed models applied to the adaptability and stability of wheat in the Rio Grande do Sul State. Genetics and Molecular Research. 16(3), pp. 1-13.

Szareski, V. J., Carvalho, I. R., Kehl, K., Levien, A. M., Barbosa, M. H., Conte, G. G., Peter, M., Villela, F. A., Souza, V. Q., Gutkoski, L. C., Pedo, T. and Aumonde, T. Z., 2019. Research Article Genetic and phenotypic multi-character approach applied to multivariate models for wheat industrial quality analysis. Genetics and Molecular Research, 18(3), pp. 1.

Yan, W., Hunt, L. A., Sheng, Q. and Szlavnics, Z., 2000. Cultivar evaluation and mega-environment investigation based on the GGE Biplot. Crop Science. 40(3), pp. 597-605.

Olivoto, T., Lúcio, A. D. C., Silva, J. A. G., Sari, B. G. and Diel, M. I., 2019. Mean performance and stability in multi-environment trials II: selection based on multiple traits. Agronomy Journal, 111 (6), pp. 2961 - 2969.



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