Biometric approach applied to soybean genotypes cultivated in Rio Grande do Sul, Brazil
Abstract
The study had the purpose to evidence the agronomic performance, inter-relations of characters and the multivariate differentiation of soybean genotypes cultivated in the preferential season, in the state of Rio Grande do Sul, Brazil. In the crops season of 2017/2018, The experimental design was the completely randomized blocks, being 25 genotypes with three replicates. The data obtained was submitted to presuppositions based on normality and homogeneity of residual variances, variance analysis, Tocher method, Euclidian algorithm, linear correlations, relative contribution of characters by Singh and artificial neural networks. The agronomic performance of the genotypes presents superior seeds yield per plant through the elevated magnitude of reproductive nodes, legumes and seeds per plant. The plant height of the soybean is positively associated with the number of total nodes and reproductive nodes in the main stem and branches, where they are directly linked with the soybean productive potential. The most polymorphic characters correspond to the number and mass of thousand seeds, being possible to differentiate in a multivariate way the soybean genotypes though the similarity profiles.
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References
Balbinot-Junior, A. A., Procópio, S. O., Debiasi, H., Franchini, J. C., & Panison, F. (2015). Semeadura cruzada em cultivares de soja com tipo de crescimento determinado. Semina: Ciencias Agrarias, 36(3), 1215�??1225. https://doi.org/10.5433/1679-0359.2015v36n3p1215
Carvalho, I. R., Nardino, M., Demari, G. H., Bahry, C. A., Szareski, V. J., Pelissari, G., �?� de Souza, V. Q. (2016). Bi-segmented regression, factor analysis and AMMI applied to the analysis of adaptability and stability of soybean. Australian Journal of Crop Science, 10(10), 1410�??1416. https://doi.org/10.21475/ajcs.2016.10.10.pne63
Carvalho, I. R., Nardino, M., Demari, G. H., Szareski, V. J., Follmann, D. N., Pelegrin, A. J., Ferrari, M., Olivoto, T., Barbosa, M. H., Oliveira, A. C., Maia, L. C., & Souza, V. . Q. (2017). Relations among phenotypic traits of soybean pods and growth habit. African Journal of Agricultural Research, 12(6), 450�??458. https://doi.org/10.5897/ajar2016.11660
Carvalho, I. R., Szareski, V. J., Demari, G. H., Barbosa, M. H., Conte, G. G., Lima, L. F. S., �?� Pedó, T. (2018). Artificial Neural Network and Multivariate Models Applied to Morphological Traits and Seeds of Common Beans Genotypes. Journal of Agricultural Science, 10(11), 572. https://doi.org/10.5539/jas.v10n11p572
Carvalho, I. R., Nardino, M., & Souza, V. Q. (2017). Melhoramento e Cultivo da Soja. 1º ed. Porto Alegre: Cidadela. v. 366p
Cavalett, O., & Ortega, E. (2010). Integrated environmental assessment of biodiesel production from soybean in Brazil. Journal of Cleaner Production, 18(1), 55�??70. https://doi.org/10.1016/j.jclepro.2009.09.008
CONAB - Companhia Nacional de Abastecimento. (2018). Série histórica: soja. Brasília, DF: CONAB. Retrieved from https://www.conab.gov.br/conabweb/download/safra/SojaSerieHist.xls
Cruz, C. D. (2014). Modelos biométricos aplicados ao melhoramento genético (3rd ed.). Viçosa, MG: Editora UFV.
Bruin, J. L., & Pedersen, P. (2008). Effect of row spacing and seeding rate on soybean yield. Agronomy Journal, 100(3), 704�??710. https://doi.org/10.2134/agronj2007.0106
Dueñas, M., Hernández, T., Robredo, S., Lamparski, G., Estrella, I., & Muñoz, R. (2012). Bioactive Phenolic Compounds of Soybean (Glycine max cv. Merit): Modifications by Different Microbiological Fermentations. Polish Journal of Food and Nutrition Sciences, 62(4), 241�??250. https://doi.org/10.2478/v10222-012-0060-x
EMBRAPA �?? Empresa Brasileira de Pesquisa Agropecuária. (2014). Tecnologia de produção de Soja �?? Região Central do Brasil 2014. Londrina, PR: EMBRAPA SOJA. Retrieved from https://ainfo.cnptia.embrapa.br/digital/bitstream/item/95489/1/SP-16-online
Komori, E., Hamawaki, O. T., Souza, M. P., & Shigihara, D. (2004). Influência da época de semeadura e população de plantas sobre as características agronômicas da cultura da soja. Bioscience Journal, 20(3), 13�??19.
Sediyama, T. (2016). Produtividade da Soja,. Londrina, PR: Editora Mecenas.
Singh, D. (1981). The relative importance of characters affecting genetic divergence. Indian Journal of Genetics and Plant Breeding, 41(2), 237�??245. Retrieved from http://www.indianjournals.com/ijor.aspx?target=ijor:ijgpb&volume=41&issue=2&article=010&type=pdf
Streck, E. V., Kampf, N., Dalmolin, R. S. D., Klamt, E., Nascimento, P. C., Schneider, E., & Pinto, L. F. S. (2008). Solos do Rio Grande do Sul (2nd ed.). Porto Alegre, RS: EMATER-RS.
Szareski, V. J., Souza, V. Q., Carvalho, I. R., Nardino, M., Follmann, D. N. Demari, G. H., Ferrari, M., & Olivoto, T. (2015). Ambiente de cultivo e seus efeitos aos caracteres morfológicos e bromatológicos da soja. Revista Brasileira de Agropecuária Sustentável, 5(2), 79�??88.
Szareski, V. J., Carvalho, I. R., Kehl, K., Levien, A. M., da Rosa, T. C., Barbosa, M. H., �?� Aumonde, T. Z. (2018). Phenotypic and predicted genetic approaches for genotype ranking of wheat seed yield in Brazil. Genetics and Molecular Research, 17(3), 1�??13. https://doi.org/10.4238/gmr18026
Thompson, N. M., Larson, J. A., Lambert, D. M., Roberts, R. K., Mengistu, A., Bellaloui, N., & Walker, E. R. (2015). Mid-south soybean yield and net return as affected by plant population and row spacing. Agronomy Journal, 107(3), 979�??989. https://doi.org/10.2134/agronj14.0453
Vasconcelos, E. S., Cruz, C. D., Bhering, L. L., & Resende-Júnior, M. F. R. (2007). Método alternativo para análise de agrupamento. Pesquisa Agropecuária Brasileira, 42(10), 1421�??1428. https://doi.org/10.1590/s0100-204x2007001000008
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