Parâmetros biológicos da produção de tomateiro via modelo logístico

• Ano de defesa: 2018
• Autor(a) principal: Sari, Bruno Giacomini
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria; Brasil; Agronomia; UFSM; Programa de Pós-Graduação em Agronomia; Centro de Ciências Rurais
• Palavras-chave em Português: Solanum lycopersicum; Modelos de crescimento; Precocidade produtiva; Olerícolas de múltiplas colheitas; Growth models; Productive precocity; Multiple harvest vegetable crops; CNPQ::CIENCIAS AGRARIAS::AGRONOMIA
• Link de acesso: http://repositorio.ufsm.br/handle/1/15360

The multiple harvest vegetable crops are characterized by high variability between plants and crops. This characteristic limits the use of ANOVA, since the assumptions of normality, homogeneity and independence of residues are violated. However, there are a number of statistical approaches that can be used to analyze experiments with multiple harvested crops, between them nonlinear models. Therefore, this works aims to describe the productive behavior of the tomato over time (harvests) through non-linear models and indicates them as a statistical analysis tool to be used in tomato experiments. Two experiments (2015/2016 and 2016/2017) were conducted in the field in the Crop Science Department of the Federal University of Santa Maria (UFSM) with different tomato genotypes: Cordillera, Ellen and Santa Clara in the first year; and Cordillera and Gaucho in the second year. The fruits were harvested weekly, counted and weighed. The number and mass of fruits were consecutively accumulated (at each harvest), and the Brody, Gompertz, Logistic and von Bertalanffy models were fitted to these data. The best model was selected based on the value of the coefficient of determination (R2) and on the parametric nonlinearity. Finally, the critical points of the select model were obtained: maximum acceleration point, inflection point, maximum deceleration point, and asymptotic deceleration point. The variables showed sigmoide behavior, which allowed the adjustment of the growth models. Among the models tested, it was selected the one with high prediction capacity and low nonlinearity, indicating that estimates approximately unbiased. Based on the estimates of the parameters and the critical points of the selected model, inferences were made regarding total production, productive precocity and concentration of production. The logistic model was selected, independent of the year or genotype, because it presented low parametric nonlinearity, and based on the estimations of its parameters and critical points it was possible to make inferences regarding the productive behavior of the genotypes. In the first year, the Cordillera genotype was the most productive and the most precocious, reaching the peak of production approximately 85 days after the transplanting of the seedlings (DAT), with a concentrated production between approximately 82 and 89 DAT. The Ellen genotype was the least productive, but its behavior was similar: production was concentrated between 82 and 89 DAT, with a peak at 85 DAT. The Santa Clara genotype obtained an intermediate and early production, since it reached the peak of production only at 90 DAT, and concentrated its production between 92 and 102 DAT. In the second year, the genotype Gaucho presented a smaller production, but more concentrated than the Cordillera genotype. In the Gaucho genotype peak production was observed at approximately 90 DAT (concentrating between approximately 85 and 100 DAT), while the Cordillera peak genotype occurred around 100 days (concentrating between 92 and 110 DAT, approximately). The growth models proved to be an excellent alternative for the statistical analysis of experiments with multiple harvested olive groves. In addition, from its parameters and critical points, it is possible to make inferences about production, precocity and concentration of production. Although this work focuses on tomato culture, the models can be an alternative analysis for any olive cultivation.