Genetic trends and multivariate interrelationships for grain quality of irrigated rice genotypes

Paulo Henrique Karling  Facchinello; Ivan Ricardo Carvalho; Eduardo Anibele  Streck; Gabriel Almeida  Aguiar; Janaína  Goveia; Michele  Feijó; Roberto Ramos  Pereira; Paulo Ricardo Reis  Fagundes; Murilo Vieira  Loro; Luciano Carlos Maia; Ariano Martins Magalhães  Júnior

doi:10.33158/ASB.r192.v9.2023

Paulo Henrique Karling Facchinello Faculdade de Agronomia Eliseu Maciel
Ivan Ricardo Carvalho Faculdade de Agronomia Eliseu Maciel
Eduardo Anibele Streck Instituto Federal Farroupilha
Gabriel Almeida Aguiar Instituto Federal Rio Grande do Sul
Janaína Goveia Faculdade de Agronomia Eliseu Maciel
Michele Feijó Faculdade de Agronomia Eliseu Maciel
Roberto Ramos Pereira Faculdade de Agronomia Eliseu Maciel
Paulo Ricardo Reis Fagundes Embrapa Clima Temperado
Murilo Vieira Loro Universidade Federal de Santa Maria
Luciano Carlos Maia Faculdade de Agronomia Eliseu Maciel
Ariano Martins Magalhães Júnior Embrapa Clima Temperado

DOI: https://doi.org/10.33158/ASB.r192.v9.2023

Keywords: Oryza sativa L, elite lineages, Ideotype, early selection, main components, clustering methods, genetic distance estimating algorithms

Abstract

For genetic improvement programs, researches with multivariate approaches in rice are fundamental, to define genetic trends, clusters and correlations of agronomic characters that together help selection procedures. This work aimed to reveal the agronomic performance, trends and genetic interrelationships of grain quality based on multivariate models applied to elite lines of irrigated rice. The experiment took place in the 2017/2018 harvest, held at Estação Experimental Terras Baixas (ETB), of Embrapa Clima Temperado. The study used randomized blocks design, with three replications. There were fifteen F6 lines and four control cultivars. Evaluation of intrinsic physical quality attributes with the aid of S21 grain statistical analyzer, as well as grain yield and mill yield (whole and broken grains). Performance of analysis of variance, genetic parameters and Scott Knott cluster test, linear correlation, canonical correlations, cluster analysis via generalized Mahalanobis distance, using the Toucher method, in addition to BIPLOT principal component analysis. The results showed that PH18502 and PH18701 strains presented better agronomic performance for the studied characters, by univariate analysis. The linear and canonical correlations presented demonstrate potential in the direction of selection of multiple characters and point to the possibility of indirect selection among the relevant agronomic characters for the production chain of irrigated rice.

Downloads

Download data is not yet available.

References

Benitez, L. C., Rodrigues, I. C. S., Arge, L. W. P., Ribeiro, M. V., & Braga, E. J. B. (2011). Análise multivariada da divergência genética de genótipos de arroz sob estresse salino durante a fase vegetativa. Revista Ciencia Agronomica, 42(2), 409–416. https://doi.org/10.1590/S1806-66902011000200021

Bhering, L. L. (2017). Rbio: A tool for biometric and statistical analysis using the R platform. Crop Breeding and Applied Biotechnology, 17(2), 187–190. https://doi.org/10.1590/1984-70332017v17n2s29

Carvalho, I. R., Souza, V. Q., Nardino, M., Follmann, D. N., Schmidt, D., & Baretta, D. (2015). Correlações canônicas entre caracteres morfológicos e componentes de produção em trigo de duplo propósito. Pesquisa Agropecuaria Brasileira, 50(8), 690–697. https://doi.org/10.1590/S0100-204X2015000800007

Carvalho, I. R., Nardino, M., Demari, G. H., Szareski, V. J., Aumonde, T. Z., Pedó, T., Monteiro, D. A., Pelegrin, A. J., Olivoto, T., Meira, D., & Souza, V. Q. (2016). Biometry and genetic breeding of dual-purpose wheat biometry and genetic breeding of dual-purpose. International Journal of Current Research, 8(7), 34539-34545.

Carvalho, I. R., Szareski, V. J., Mambrin, R. B., Ferrari, M., Pelegrin, A. J., Rosa, T. C., Peter, M., Silveira, D. C., Conte, G. G., Barbosa, M. H., & Souza, V. Q. (2018). Biometric models and maize genetic breeding: A review. Australian Journal of Crop Science, 12(11), 1796–1805.

Champagne, E. T., Bett-Garber, K. L., Fitzgerald, M. A., Grimm, C. C., Lea, J., Ohtsubo, K., Jongdee, S., Xie, L., Bassinello, P. Z., Resurreccion, A., Ahmad, R., Habibi, F., & Reinke, R. (2010). Important sensory properties differentiating premium rice varieties. Rice, 3(4), 270–281. https://doi.org/10.1007/s12284-010-9057-4

Cuevas, R. P., Pede, V. O., McKinley, J., Velarde, O., & Demont, M. (2016). Rice grain quality and consumer preferences: A case study of two rural towns in the Philippines. PLoS ONE, 11(3), 1–17. https://doi.org/10.1371/journal.pone.0150345

Falconer, D. S. (1981) Introdução à genética quantitativa. Viçosa: UFV.

Ferreira, D. F.(2011) Estatística Multivariada. (2nd ed.). Lavras: Editora UFLA.

Farias Filho, S., & Ferraz Júnior, S. L. (2009). A cultura do arroz em sistema de vazante na baixada maranhense, periferia do sudeste da amazônia. Pesquisa Agropecuária Tropical, 39(2), 82–91. http://www.redalyc.org/articulo.oa?id=253020166002

Hallauer, A. R., Carena, M. J., Miranda Filho, J. B. (1988). Quantitative genectic in maize breeding. (2nd ed.). Iowa State: University Press Ames.

Hongyu, K., Sandanielo, V. L. M., & Oliveira Junior, G. J. (2015). Análise de componentes principais : resumo teórico , aplicação e interpretação. E&S - Engineering and Science, 1(5), 83–90. https://doi.org/10.18607/ES20165053

Hosan, S., Sultana, N., Iftekharudduala, K., Ahmed, M. N. U., & Mia, S. (2010). Genetic Divergence in Landraces of Bangladesh Rice (Oryza sativa L.). The Agriculturists, 8(2), 28–34. https://doi.org/10.3329/agric.v8i2.7574

Khattree, R., & Naik, D. N. (2018). Applied Multivariate Statistics with SAS Software. (2nd ed.). Cary, NC, United States: Wiley.

Lin, Z., Wang, Z., Zhang, X., Liu, Z., Li, G., Wang, S., & Ding, Y. (2017). Complementary proteome and transcriptome profiling in developing grains of a notched-belly rice mutant reveals key pathways involved in chalkiness formation. Plant and Cell Physiology, 58(3), 560–573. https://doi.org/10.1093/pcp/pcx001

Liu, H., Long, S., Pinson, S. R. M., Tang, Z., Guerinot, M. L., Salt, D. E., Zhao, F., & Huang, X. (2021). Univariate and Multivariate QTL Analyses Reveal Covariance Among Mineral Elements in the Rice Ionome. Frontiers in Genetics, 12, Article 638555. https://doi.org/10.3389/fgene.2021.638555

Liu, Q., Zhou, X., Yang, L., & Li, T. (2009). Effects of Chalkiness on Cooking, Eating and Nutritional Qualities of Rice in Two indica Varieties. Rice Science, 16(2), 161–164. https://doi.org/10.1016/S1672-6308(08)60074-8

Lyra, W. S., Silva, E. C., Araújo, M. C. U., Fragoso, W. D., & Veras, G. (2010). Classificação periódica: um exemplo didático para ensinar análise de componentes principais. Química Nova, 33(7), 1594–1597. https://doi.org/10.1590/s0100-40422010000700030

Maghelly, O. R., Ogliari, J. B., Souza, R., Reichert Júnior, F. W., & Pinto, T. T. (2020). Milling yield components of local dryland rice varieties. Pesquisa Agropecuaria Tropical, 50, 1–9. https://doi.org/10.1590/1983-40632020V5065085

Magalhães Júnior, A.M., Morais, O. P., Fagundes, P. R. R., Colombari Filho, J. M., Franco, D. F., Cordeiro, A. C. C., Pereira, J. A., Rangel, P. H. N., Moura Neto, F. P., Streck, E. A., Aguiar, G. A., & Facchinello, P. H. K. (2017). BRS Pampeira: new irrigated rice cultivar with high yield potential. Crop Breeding and Applied Biotechnology, 17, 78-83.

Magalhães Júnior, A. M., Morais, O. P., Fagundes, P. R. R., Moura Neto, F. P, Franco, D. F., Neves, P. C. F., Nunes, C. D. M., Rangel, P. H. N., Petrin, J. A., & Severo, A. C. M. (2012). BRS Pampa: cultivar de arroz irrigado de alta produtividade e excelência na qualidade de grãos. Comunicado técnico, 282. Pelotas, RS: Embrapa Clima Temperado.

Magalhães Junior., A. M., Streck, E. A., Aguiar, G. A., & Facchinello, P. H. K. (2020). Industrial Quality. In: Oliveira, A. C.; Pegoraro, C., & Viana, V. E. The Future of Rice Demand: Quality Beyond Productivity. Cap. 3, p. 47-67. Switzerland AG: Springer Nature.

Morrison, D. F. (1978). Multivariate statistical methods. Tokyo: Mc Graw Hill.

Moreira, R. M. P., Ferreira, J. M., Takahashi, L. S. A., Vasconcelos, M. E. C., Geus, L. C., & Botti, L. (2009). Potencial agronômico e divergência genética entre genótipos de feijão-vagem de crescimento determinado. Semina: Ciências Agrárias, 30(4Sup1), 1051. https://doi.org/10.5433/1679-0359.2009v30n4sup1p1051

Nihad, S. A. I., Manidas, A. C., Hasan, K., Hasan, M. A. I., Honey, O., & Latif, M. A. (2021). Genetic variability, heritability, genetic advance and phylogenetic relationship between rice tungro virus resistant and susceptible genotypes revealed by morphological traits and SSR markers. Current Plant Biology, 25, 100194. https://doi.org/10.1016/j.cpb.2020.100194

Nikam, V. S., Takle, S. R., Patil, G. B., Mehta, A. M., & Jadeja, G. C. (2014). Genetic analysis and character association studies of physical and cooking quality traits in rice (Oryza sativa L.). Electronic Journal of Plant Breeding, 5(4), 765–770.

Panda, D., Sahu, N., Behera, P. K., & Lenka, K. (2020). Genetic variability of panicle architecture in indigenous rice landraces of Koraput region of Eastern Ghats of India for crop improvement. Physiology and Molecular Biology of Plants, 26(10), 1961–1971. https://doi.org/10.1007/s12298-020-00871-6

Ramalho, M. A. P., Ferreira, D. F., & Oliveira, A. C. (2012). Experimentation in genetics and plant breeding. Lavras: UFLA.

Sartori, G. M. S., Marchesan, E., Luz, D. S., Cassol, A. P. V., Figueiredo, M. C. S., Oliveira, M. A., Silveira, M. V. E., & Ferreira, R. B. (2011). Manejo da adubação e seus efeitos na ocorrência de algas e na produtividade de arroz irrigado em áreas com residual de imidazolinonas. Ciencia Rural, 41(8), 1323–1330. https://doi.org/10.1590/S0103-84782011000800005

Souza, J. R., Ferreira, E., Cargnelutti Filho, A., Boiça Jr, A. L., Chagas, E. F., & Mondego, J. M. (2009). Divergência genética de cultivares de arroz quanto à resistência a Tibraca limbativentris Stål (Hemiptera: Pentatomidae). Neotropical Entomology, 38(5), 671–676. https://doi.org/10.1590/s1519-566x2009000500018

SOSBAI – Sociedade Sul-Brasileira de Arroz Irrigado. (2016). Arroz irrigado: Recomendações Técnicas da Pesquisa para o Sul do Brasil. Pelotas, RS: SOSBAI.

Streck, E. A., Aguiar, G. A., Facchinello, P. H. K., Magalhães Júnior, A. M., Krüger, T. K., & Parfitt, J. M. B. (2019). Agronomic performance of rice cultivars under sprinkler and flood-irrigation system. Revista Brasileirade Ciencias Agrarias, 14(3), 1–7. https://doi.org/10.5039/agraria.v14i3a5661

Streck, E. A., Magalhães Júnior, A. M., Fagundes, P. R. R., Aguiar, G. A., Facchinello, P. H. K., & Oliveira, A. C. (2018). Adaptability and stability of flood-irrigated rice cultivars released to the subtropical region of Brazil. Pesquisa Agropecuaria Brasileira, 53(10), 1140–1149. https://doi.org/10.1590/S0100-204X2018001000007

Szareski, V. J., Carvalho, I. R., Kehl, K., Levien, A. M., Lautenchleger, F., Barbosa, M. H., Conte, G. G., Peter, M., Martins, A. B. N., Villela, F. A., Souza, V. Q., Gutkoski, L. C., Pedó, T., & Aumonde, T. Z. (2019). Genetic and phenotypic multi-character approach applied to multivariate models for wheat industrial quality analysis. Genetics and Molecular Research, 18(3), 1–14. https://doi.org/10.4238/gmr18223

Takeshi, A. (2019). Análise em amostras de arroz através de imagem digital. S21 rice statistical analyzer.