Agronomic variability between commercial and experimental carrot genotypes with emphasis on multivariate analysis

Authors

DOI:

https://doi.org/10.33448/rsd-v10i13.21145

Keywords:

Daucus carota L.; Genetic breeding; Similarity; Multiple factor analysis.

Abstract

Carrots are among the five most cultivated vegetables in Brazil, which is why the search for commercial cultivars is a great challenge in breeding programs, because, in addition to being productive, these tuberous roots must present characteristics of fundamental interest for the producer to achieve acceptance of the product in the consumer market. Therefore, the objective of this work was to evaluate the level of similarity between summer carrot genotypes, and to establish the relationships between the variables, through agronomic and morphological characters of the plant and tuberous roots, in order to diagnose possible groupings in relation to the analyzed variables, using the techniques of multivariate and univariate analysis. The trial was conducted in a randomized block design with three replications, evaluating 11 summer carrot genotypes, with seven commercial genotypes available on the market (SV1099DT, Nativa, EX 4098, AGR-123, AGR-125, Juliana and Brazlândia) and four experimental hybrids. Fifteen agronomic characteristics of plants and tuberous roots were evaluated. Given the results, the multiple factor analysis technique was able to establish the similarities between the genotypes studied, showing high similarity between commercial genotypes present in the market and experimental hybrids. Exploratory factor analysis established the correlations of the variables and was able to project a separation between commercial and experimental genotypes. The specificities presented by the experimental genotypes were explained in the Scott-Knott mean test.

References

Abdi, H., Williams, L., & Valentin, D. (2013). Multiple factor analysis: principal component analysis for multitable and multiblock data sets. WIREs Computational Statatistics, 5 (2), 149-179. 10.1002/wics.1246

Araújo, J. C., Telhado, S. F. P., Sakai, R. H., Ledo, C. A. S., & Melo, P. C. T. (2016). Univariate and multivariate procedures for agronomic evaluation of organically grown tomato cultivars. Horticultura Brasileira 34: 374-380, 2016. 10.1590/S0102-05362016003011

Buratto, J. S., Santos Neto, J., & Moda-Cirino, V. (2016). Desempenho agronômico e dissimilaridade genética entre acessos de amendoim por variáveis multicategóricas. Scientia Agraria Paranaensis, 15(3), 324-331. 10.18188/sap.v15i3.13125

Carvalho, A. D. F., & Silva, G. O. (2017). Genetic divergence among carrot genotypes through agronomic characteristics. Revista Agro@mbiente, 11 (2), 137-144. 10.18227/1982-8470ragro.v11i2.3642

Cecon, P. R., Silva, F. F. E., Ferreira, A., Ferrão, R. G., Carneiro, A. P. S., Detmann, E., Faria, P.N., & Morais, T. S. S. (2008). Análise de medidas repetidas na avaliação de clones de café 'Conilon'. Pesquisa Agropecuária Brasileira, 43 (1), 1171-1176. 10.1590/S0100-204X2008000900011

Cruz, C. D., Carvalho, S. P., & Vencovsky, R. (1994). Estudos sobre a divergência genética II. Eficiência da predição do comportamento de híbridos com base na divergência de progenitores. Revista Ceres, v. 41(1) 183-194. http://www.ceres.ufv.br/ojs/index.php/ceres/article/view/2070

Cruz, C. D., Regazzi, A. J., & Carneiro, P. C. S. (2012). Modelos biométricos aplicados ao melhoramento genético. Ed. da UFV, 514p.

Escofier, B., & Pagès, J. (2008). Analyses factorielles simples et multiples: objectifs, méthodes et interprétation. (4a ed.), Dunod, 318 p.

FAOSTAT - Food and Agriculture Data. Produtividade da Cenoura (2017). http://www.fao.Org/faostat/en/#data/QC

Ferreira, E. B., Cavalcanti, P. P., & Nogueira D. A. (2021). ExpDes: Pacote Experimental Designs. URL <https://cran.rstudio.com/web/packa ges/ExpDes/index.html>. R package version 1.2.1.

Grangeiro, L. C., Azevêdo, P. E., Nunes, G. H. S., Dantas, M. S. M., & Cruz, C. A. (2012). Desempenho e divergência genética de cenoura ‘Brasília’ em função da procedência das sementes. Horticultura Brasileira, 30 (1), 137-142. 10.1590/S0102-05362012000100023

Grigolo, S., Fioreze, A. C. C. L., Denardi, S. & Vacari, J. (2018). Implicações da análise univariada e multivariada na dissimilaridade de acessos de feijão comum. Revista de Ciências Agroveterinárias 17(3), 351-360. https://www.revistas.udesc.br/index.php/agroveterinaria/article/view/9324

Hiranvarachat, B., & Devahastin, S. (2014). Enhancement of microwave-assisted extraction via intermittent radiation: Extraction of carotenoids from carrot peels. Journal of Food Engineering, 126 (1), 17-26, 2014. 10.1016/j.jfoodeng.2013.10.024

Jelihovschi, E., Faria, J. C., & Allaman, I. B. (2014). Scott-Knott: A Package for Performing the Scott-Knott Clustering Algorithm in R. Trends in Computational and Applied Mathematics, 15 (1), 3-17. 10.5540/tema.2014.015.01.0003

Maksylewicz, A., & Baranski, R. (2013) Intra-population genetic diversity of cultivated carrot (Daucus carota L.) assessed by analysis of microsatellite markers. Acta Biochimica Polonica, 60 (1) 753-760. https://pubmed.ncbi.nlm.nih.gov/24432327/

Nick, C., & Borém, A. (2016). Cenoura: do plantio à colheita. Editora UFV. 179 p.

Oliveira, C. D., Braz, L. T. E., & Banzatto, D. A. (2005). Adaptabilidade e estabilidade fenotípica de genótipos de cenoura. Horticultura Brasileira, 23 (3) 743-748. 10.1590/S0102-05362005000300011

Ossani, P. C., & Cirillo, M. A. (2021). MVar: Multivariate Analysis. URL <https://cran.r-project.org/web/packages/MVar/index.html>. R package version 2.1.7.

Rencher, A. C., & Christensen, W. F. (2012). Methods of Multivariate Analysis. (3a ed.), J. Wiley, 758 p.

R Development Core Team. (2021) R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna: Vienna University of Economics and Business. http://www.R-project.org

Scott, A. J., & Knott, M. (1974). A Cluster Analysis Method for Grouping Means in the Analysis of Variance. Biometrics, 30 (3), 507-512. https://www.jstor.org/stable/2529204

Souza, D. C., Ossani, P. C., Vilela, L. V., Cirillo, M. Â., Silva, L. F. L. S., & Xavier, J. B. (2019). Variabilidade genética entre cultivares comerciais e híbridos experimentais de morangueiro com ênfase em análise de múltiplos fatores. Magistra, 30: 48-59. https://magistraonline.ufrb.edu.br/index.php/magistra/article/view/725

Zanettini, M. H. B., & Cavalli, S. S. Variabilidade genética em função do modo de reprodução. In: Freitas LB, Bered F, editors. Genética e evolução vegetal.: Editora UFRGS; 2003. p.177-187.

Published

09/10/2021

How to Cite

SOUZA, D. C. de .; AZEVEDO, S. M. de .; OSSANI, P. C. .; FARIA, L. P. . de; MARQUES, W. A. A.; NARITA, G. A. Agronomic variability between commercial and experimental carrot genotypes with emphasis on multivariate analysis. Research, Society and Development, [S. l.], v. 10, n. 13, p. e173101321145, 2021. DOI: 10.33448/rsd-v10i13.21145. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/21145. Acesso em: 25 apr. 2024.

Issue

Section

Agrarian and Biological Sciences