Planejamento experimental e descrição da produção de ervilha

• Ano de defesa: 2021
• Autor(a) principal: Tartaglia, Francieli de Lima
• Tipo de documento: Dissertação
• 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: Pisum sativum; Análise de trilha; Correlação canônica; Tamanho de parcela; Tamanho de amostra; Número de repetições; Modelos não lineares; Modelo logístico; Path analysis; Canonical correlation; Plot size; Sample size; Number of repetitions; Non-linear models; Logistic model; CNPQ::CIENCIAS AGRARIAS::AGRONOMIA
• Link de acesso: http://repositorio.ufsm.br/handle/1/24061

The pea is a legume cultivated annually, its grain has great nutritional value, being an important source of nutrients for human consumption. Thus, due to the importance of this vegetable garden, several researches are carried out. However, information for conducting experiments with high experimental precision is scarce for culture, as well as information on the application of nonlinear regression models to describe their production. In this sense, this work aims to evaluate the cause-and-effect relationships between the variables of pea production and verify if they follow the same trend between harvests and growing seasons, estimate the sample size, plot size, the number of repetitions and model the production cycle of the pea crop. Uniformity tests were conducted in the field in the years 2016, 2017 and 2018 in the experimental area of the Departament of Plant Science of the Federal University of Santa Maria - UFSM, in the municipality of Santa Maria - RS. The cultivar used was the Pea Grain 40. The characters of mass and total number of pods, length of pods, numbers and mass of grains per pod were measured. The relationships between the variables were estimated by Pearson's linear correlations and, later, the direct and indirect effects were unfolded by the trail analysis. Canonical correlation analysis was also carried out between the group of pod variables and grain variables. The plot size, sample size and number of repetitions were estimated, and the logistic nonlinear model was adjusted to characterize the production. The results show that pea production is affected by environmental conditions, however, it presented the same trend in the relationships between variables, in different harvests and growing seasons. The pod mass and grain number variables are the variables with the highest cause and effect relationships on the grain mass and can be used for the indirect selection of more productive plants. Plants with a lower pod mass provide pods with fewer grains and less grain mass. The plot size for evaluating the number of pods per plant and the mass of pods per plant for pea cultivation is eight and nine plants, respectively. The sample size for evaluating the number of pods per plant and the mass of pods per plant is eight plants in the direction of the line with a semi-amplitude of the confidence interval of 20% of the mean. For the variables number of pods per plant and pod mass per pea plant, 10 and 12 repetitions are required, respectively, to evaluate up to 20 treatments in the randomized block design and in the incomplete block design with up to 100 treatments for significant differences of 35 % between treatment averages. By adjusting the logistic model, it was found that season 1 was the most productive, with maximum increases in production in a shorter period (592.5 °C days-1 to produce 119.52g), causing a high production peak in relation to the other periods analyzed.