Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/29692
Título: Modelos de regressão na descrição do crescimento de frutos de amora-preta
Título(s) alternativo(s): Regression models in the description of fruit growth from blackberry
Autores: Muniz, Joel Augusto
Fernandes, Teles Jesus
Morais, Augusto Ramalho de
Oliveira, Deive Ciro de
Tadeu, Maraisa Hellen
Palavras-chave: Análise de regressão
Seleção de modelos
Comportamento sigmoide
Desenvolvimento de frutos
Regression analysis
Selection of models
Sigmoid behavior
Fruit development
Data do documento: 16-Jul-2018
Editor: Universidade Federal de Lavras
Citação: SILVA, E. M. da. Modelos de regressão na descrição do crescimento de frutos de amora-preta. 2018. 57 p. Dissertação (Mestrado em Estatística e Experimentação Agropecuária)-Universidade Federal de Lavras, Lavras, 2018.
Resumo: The Blackberry is a small fruit with several properties beneficial to human health and its cultivation is an alternative for small producers due to the fact that it has good financial returns. Studying the fruit growth over time is extremely important to understand its development, helping to better manage the crop, avoiding, for example, post-harvest loss, which is one of the aggravating factors of blackberry losses, since it has a short period of development. Thus, growth curves are highlighted in this type of study and modeling through statistical models helps to understand how such growth happens. The data were obtained in an experiment carried out at the Federal University of Lavras in 2015. The linear model and the non-linear models Brody, Logistic, Gompertz, double logistic and double Gompertz models were adjusted with the inclusion of the first-order autoregressive term when necessary. The objective of this work was to adjust linear and nonlinear models to describe the diameter and length growth of four cultivars of blackberry (Brazos, Choctaw, Guarani and Tupi). The estimation of the parameters was obtained through the least squares methods using the Gauss-Newton method, in addition to the "nls" and "glns" functions of the statistical software R. The comparison of the adjustments was made by Akaike (AIC), Bayesian information criterion (BIC), residual standard deviation (DPR) and adjusted determination coefficient (R²aj). The models described satisfactorily the data, with predominance for the linear first-degree model and the logistic-diphasic model.
URI: http://repositorio.ufla.br/jspui/handle/1/29692
Aparece nas coleções:Estatística e Experimentação Agropecuária - Mestrado (Dissertações)

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