Descrição do acúmulo da massa seca da planta de milho considerando a cultura antecessora por modelos não lineares

• Ano de defesa: 2022
• Autor(a) principal: Gonzaga, Natiele de Almeida
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Lavras; Programa de Pós-Graduação em Estatística e Experimentação Agropecuária; UFLA; brasil; Departamento de Estatística
• Palavras-chave em Português: Brachiaria brizantha; Feijão comum; Modelos não lineares; Curva de Gompertz; Modelo de Von Bertalanffy; Common bean; Milheto; Non-linear models; Gompertz curve; Von Bertalanffy Model; Estatística
• Link de acesso: http://repositorio.ufla.br/jspui/handle/1/55493

Maize is the most produced cereal in the world, being used both in human and animal food. Brazil ranks as the third largest producer in the world and the second largest exporter of this culture. However, despite its great importance for Brazilian agribusiness, the productivity of this culture is still considered low, therefore, it is necessary to study the growth of maize plants, which can help in the proper management of the culture and, consequently, in increasing productivity. Plant growth presents sigmoidal behavior, which is well adjusted through nonlinear models. Therefore, this study aimed to compare the fit of the non-linear Logístico, Gompertz and von Bertalanffy models to the data on the accumulation of total dry mass, stems, leaves and ears in maize plants, in gram/m2, cultivated with cover straws from common bean, millet and Brachiaria brizantha in relation to days after plant emergence. The analyzed data were obtained from Oliveira et al. (2013). The experiment was carried out in the summer of the 2007/2008 agricultural year, at Fazenda Capivara, located in the municipality of Santo Antônio de Goiás (GO). The assumptions of normality, homoscedasticity and residual independence were verified with the Shapiro-Wilk, Breusch- Pagan and Durbin-Watson tests, respectively, considering an autoregressive AR(1) error structure and heteroscedasticity of variances, when necessary. The models were fitted by the least squares method using the Gauss-Newton algorithm using the R software. The goodness of fit was evaluated based on the values of the coefficient of determination (R2), the residual standard deviation (DPR) and the criterion of Akaike Information (AIC). The non-linear models used adequately described the growth of the dry mass of the maize plant considering the previous crop, with the Gompertz and von Bertalanffy models presenting the best adjustments for the dry mass of the stems, the Logístico and von Bertalanffy models for the dry mass of the culms. ears, the Gompertz model for the dry mass of the leaves and the Gompertz and von Bertalanffy models for the total dry mass, based on the quality evaluators used. In the predecessor crop of common bean, there was a greater accumulation of dry mass of maize stalks and leaves.