Modelos não lineares para descrição do crescimento e desenvolvimento de cultivares de girassol

• Autor(a) principal: Mello, Anderson Chuquel
• Data de Publicação: 2021
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Manancial - Repositório Digital da UFSM
• dARK ID: ark:/26339/001300000xx5x
• Texto Completo: http://repositorio.ufsm.br/handle/1/22941
• topic: Curva de crescimento; Helianthus annuus L.; Heterocedasticidade; Regressão não linear; Growth curve; Heteroscedasticity; Nonlinear regression; CNPQ::CIENCIAS AGRARIAS::AGRONOMIA

Sunflower (Helianthus annuus L.) originates from the American continent and produces excellent quality oil. Modeling is an important tool to characterize growth and development, as it allows simulating the real behavior of plants. Therefore, this study aimed to apply nonlinear models to describe the growth and development of three sunflower cultivars; verify the importance of meeting the assumptions in the quality of the adjustment; use parameter estimates for practical applications and comparisons of cultivar growth and development patterns; and, define the coordinates of the critical points of the models that present the best fit. The data used come from nine uniformity trials with sunflower cultivars Aguará 6, Nusol 4510 and Rhino, in three sowing times, conducted in the experimental area of the Federal University of Santa Maria in Frederico Westphalen – RS/Brazil in the 2019/2020 crop year and resulted in three studies. Plant height (PH), fresh plant mass (FPM) and number of leaves (NL) data were adjusted as a function of the accumulated thermal sum (ATs) of 10 plants randomly collected in the uniformity test, using logistic models ( L), Gompertz (G), Brody (B) and von Bertalanffy (VB). The parameters were estimated using the method of ordinary least squares (MQO) or generalized least squares (MQG). In the presence of violations, the power method was used to structure the variance. Parameter estimates were compared by overlapping confidence intervals (CI95%) and the goodness of fit of the models to the data was measured by the adjusted coefficient of determination (R2adj), Akaike's information criterion (AIC), Bayesian information criterion (BIC), and through intrinsic (IN) and parametric (PE) nonlinearity. Statistical analyzes were performed using Microsoft Office Excel® and R software. In the first study with the cultivar Rhino, the results showed that models L and G satisfactorily describe the growth curve in PH. Model L has the best fit, being the most adequate to characterize the growth curve. The estimated critical points provide important information for managing the crop. The second study shows that the insertion of the power structure into the models results in a better fit of L and G to the FPM data. Cultivars Aguará 6 and Nusol 4510 are better described by model L, and showed the highest growth phase in the first season. Cultivar Rhino is best described by the Gompertz model and shows a reduction in the growth phase in the first season. In the third study, model L was the most suitable for describing the NL development of cultivars Aguará 6 and Rhino while G is more suitable for Nusol 4510. Model B should not be used to describe the NL development of sunflower cultivars. Critical points allow to differentiate cultivars according to the development pattern. Aguará 6 and Rhino reach the inflection point (IP) at 50% of the asymptote, while Nusol 4510 reaches the IP at 37% of the asymptote.