Number of trials to estimate the condition number in rye traits

Ismael Mario Márcio Neu, Alberto Cargnelutti Filho, Daniela Lixinski Silveira, Rafael Vieira Pezzini, Cirineu Tolfo Bandeira

Resumo


Multicollinearity must be diagnosed in multivariate analyses. Among the indicators, the condition number can be used to quantify the degree of multicollinearity. Hence, this study sought to determine the number of measurements (trials) necessary to estimate the number of condition in linear correlation matrices between rye traits. Five uniformity trials were carried out with ‘BRS Progresso’ rye, and eight morphological traits and eight productive traits were evaluated, forming two groups. In each group of traits, six cases (combinations of traits) were planned and the multicollinearity diagnosis was performed. Repeatability analyses were performed using the following methods: analysis of variance, principal component analysis, and structural analysis, and the number of measurements (trials) was determined for different levels of precision. A higher condition number of repeatability coefficients was obtained by the principal component methods (based on correlation and variance and covariance matrices) and structural analysis based on the variance and covariance matrix. A greater number of measurements (trials) is necessary to estimate the number of conditions in productive traits compared to morphological ones. One trial is enough to efficiently estimate the condition number with a minimum accuracy of 80% in morphological and productive traits of rye, whereas at least three trials are required for 95% accuracy.

Palavras-chave


Secale cereale L. Repeatability analysis. Multicollinearity. Experimental planning.

Texto completo:

PDF (English)

Referências


ABEYWARDENA, V. An application of principal component analysis in genetics. Journal of Genetics, v. 61, n. 1, p. 27–51, 1972.

ALVARES, C. A. et al. Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift, v. 22, n. 6, p. 711–728, 2013.

ALVES, B. M. et al. Linear relations among phenological, morphological, productive and protein-nutritional traits in early maturing and super-early maturing maize genotypes. Journal of Cereal Science, v. 70, p. 229–239, 2016a.

ALVES, B. M. et al. Correlações canônicas entre caracteres agronômicos e nutricionais proteicos e energéticos em genótipos de milho. Revista Brasileira de Milho e Sorgo, v. 15, n. 2, p. 171–185, 2016b.

ALVES, B. M. et al. Multicollinearity in canonical correlation analysis in maize. Genetics and Molecular Research, v. 16, n. 1, p. 1–14, 2017.

AZEVEDO, A. M. et al. Estudo da repetibilidade genética em clones de couve. Horticultura Brasileira, v. 34, n. 1, p. 54–58, 2016.

BAIER, A. C. Centeio. Passo Fundo - RS: EMBRAPA Trigo, 1994. Disponível em: . Acesso em: 20 set. 2019.

CARGNELUTTI FILHO, A. et al. Análise de repetibilidade de caracteres forrageiros de genótipos de Panicum maximum, avaliados com e sem restrição solar. Ciência Rural, v. 34, n. 3, p. 723–729, 2004.

CAVALCANTE, M. et al. Coeficiente de repetibilidade e parâmetros genéticos em capim-elefante. Pesquisa Agropecuária Brasileira, v. 47, n. 4, p. 569–575, 2012.

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

DIEL, M. I. et al. Repeatability coefficients and number of measurements for evaluating traits in strawberry. Acta Scientiarum. Agronomy, v. 42, n. e43357, p. 1–9, 2020.

DORMANN, C. F. et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography, v. 36, n. 1, p. 27–46, 2013.

DUARTE, A. B.; FERREIRA, D. DE O.; SILVA, F. L. DA. Repeatability and the optimal number of measurements for screening of soybean cultivars under water deficit. Revista Ciência Agronômica, v. 53, n. 2, p. 1–13, 2022.

EMBRAPA. Centeio: BRS Progresso. Empresa Brasileira de Pesquisa Agropecuária - Passo Fundo - RS, 2013. Disponível em:

FERNANDES, F. D. et al. Repeatability, number of harvests, and phenotypic stability of dry matter yield and quality traits of Panicum maximum Jacq. Acta Scientiarum. Animal Sciences, v. 39, n. 2, p. 149–155, 2017.

FIGUEIREDO FILHO, D. et al. O que fazer e o que não fazer com a regressão: Pressupostos e aplicações do modelo linear de mínimos quadrados ordinários (MQO). Revista Política Hoje, v. 20, n. 1, p. 44–99, 2011.

GOODHUE, D. L.; LEWIS, W.; THOMPSON, R. Multicollinearity and measurement error statistical blind spot: correcting for excessive false positives in regression and PLS. MIS Quarterly, v. 41, n. 3, p. 667–684, 2017.

GUJARATI, D. N.; PORTER, D. C. Econometria básica. 5. ed. Porto Alegre: AMGH Editora Ltda, 2011. 920 p.

HAIR, J. F. et al. Análise multivariada de dados. 6. ed. Porto Alegre, Brasil: Bookman, 2009. 688 p.

MATSUO, É. et al. Análise da repetibilidade em alguns descritores morfológicos para soja. Ciência Rural, v. 42, n. 2, p. 189–196, 2012.

MEIRA, D. et al. Multivariate analysis revealed genetic divergence and promising traits for indirect selection in black oat. Revista Brasileira de Ciências Agrárias - Brazilian Journal of Agricultural Sciences, v. 14, n. 4, p. 1–7, 2019.

MONTGOMERY, D. C. et al. Introduction to linear regression analysis. 5. ed. New Jersey: John Wiley & Sons, Inc., 2012. 672 p.

OLIVOTO, T. et al. Multicollinearity in path analysis: A simple method to reduce its effects. Agronomy Journal, v. 109, n. 1, p. 131–142, 2017.

PAGLIOSA, E. S. et al. Repeatability of pre-harvest sprouting in wheat. American Journal of Plant Sciences, v. 05, n. 11, p. 1607–1613, 2014.

R TEAM CORE. R: A language and environment for statistical computing.Vienna, Áustria - R Foundation for Statistical Computing, 2021. Disponível em: . Acesso em: 10 ago. 2021

SANTOS, H. G. DOS et al. Sistema brasileiro de classificação de solos. 5. ed. Brasília, DF: Embrapa: Embrapa Solos, 2018. 590 p.

SOUZA, Y. P. DE et al. Repeatability and minimum number of evaluations for morpho-agronomic characters of elephant-grass for energy purposes. Revista Brasileira de Ciências Agrárias - Brazilian Journal of Agricultural Sciences, v. 12, n. 3, p. 391–397, 2017.

TOEBE, M. et al. Dimensionamento amostral e associação linear entre caracteres de Crotalaria spectabilis. Bragantia, v. 76, n. 1, p. 45–53, 2017.

TOEBE, M.; CARGNELUTTI FILHO, A. Não normalidade multivariada e multicolinearidade na análise de trilha em milho. Pesquisa Agropecuária Brasileira, v. 48, n. 5, p. 466–477, 2013.

TORRES, F. E. et al. Minimum number of measurements for accurate evaluation of qualitative traits in Urochloa brizantha. Journal of Agronomy, v. 14, n. 3, p. 180–184, 2015.




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.