Modelos de regressão não linear para descrição do crescimento de plantas de alho

• Ano de defesa: 2012
• Autor(a) principal: Reis, Renata Maciel dos
• Orientador(a): Cecon, Paulo Roberto
• Banca de defesa: Ferreira, Adésio
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Viçosa
• Programa de Pós-Graduação: Mestrado em Estatística Aplicada e Biometria
• Departamento: Estatística Aplicada e Biometria
• País: BR
• Palavras-chave em Português: Allium sativum L.; Análise de agrupamento; Comparação de modelos
• Palavras-chave em Inglês: Allium sativum L.; Cluster analysis; Comparison models
• Área do conhecimento CNPq: CNPQ::CIENCIAS AGRARIAS
• Link de acesso: http://locus.ufv.br/handle/123456789/4056

The objective of this study was to choose a nonlinear regression model that best described the dry matter accumulation in different parts of the plant garlic over time (60, 90, 120 and 150 days after planting). Were used 20 garlic accessions belonging to the Vegetable Germplasm Bank of Universidade Federal de Viçosa. In order to work only with groups of similar accessions, was applied the cluster analysis in order to obtaining these clusters. The dry matter of leaf, pseudostem, bulb and root were defined as variables in this cluster analysis, which was conducted by the Ward algorithm, using as dissimilarity measure the Mahalanobis distance. Based on Mojena s method to determine the number of groups, was formed three groups of accessions, whose means of dry matter of bulb, of root and of the whole plant were used to fitting of seven nonlinear regression models, namely : Mitscherlich, Gompertz, Logistic, Meloun I Meloun II, von Bertalanffy and Brody. Aiming to choose the best fitted model to the three characteristics of each group were calculated coefficient of determination (R2), the error mean square (EMS) and the average deviation absolut error. Comparing the values of the evaluators found that, for the three characteristics of the three groups, the best fit model was the Logistic model.