Relações lineares entre variáveis em bactérias promotoras de crescimento e solubilizadoras de fósforo (P) na cultura da soja

• Ano de defesa: 2025
• Autor(a) principal: Rocha, Tiago Mateus
• 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: Glycine max L.; Remoção de parâmetros; Correlação linear de Pearson; Análise de componentes principais; Análise de trilha; Correlações canônicas; Multicolinearidade; Relações lineares; Removal of parameters; Principal component analysis; Path analysis; Canonical correlations; Multicollinearity; Linear relationships; CNPQ::CIENCIAS AGRARIAS::AGRONOMIA
• Link de acesso: http://repositorio.ufsm.br/handle/1/34531

The soybean crop (Glycine max L.) is of great economic importance in Brazil and worldwide, in the production of vegetable oil, biofuels and as a source of protein in animal nutrition. Phosphorus (P) stands out as one of the essential nutrients for growth and development, but much of it is not available in the soil for absorption, so it is important to look for alternatives to increase its availability, such as the use of nutrient fixing and solubilizing microorganisms. Multivariate analysis is used in soybean cultivation to study linear relationships, with the aim of identifying relevant characteristics for selecting superior genotypes. However, these techniques do not take into account the effects of the parameters of the mathematical model of the experimental design and the treatments, stratifying the effects and working only with average observations. Therefore, the aim of this work was to analyze the implications of removing the parameters of the mathematical model on the results of Pearson's linear correlation analysis, principal components, path analysis and canonical correlations in trials with growth-promoting bacteria in soybean cultivation. The field experiment was conducted during the 2019/2020 harvest in the municipality of Barra do Quaraí - RS. The design used was randomized blocks, with four replications. In the first factor: i) Bradyrhizobium spp. + Azospirillum spp; ii) Bradyrhizobium spp + Pseudomonas fluorescens; ii) Bradyrhizobium spp. + Bacillus subtilis; iv) Bradyrhizobium spp. + Bacillus subtilis + Bacillus megaterium; v) Bradyrhizobium spp. + Azospirillum spp. + Pseudomonas fluorescens; vi) Bradyrhizobium spp. + Azospirillum spp. + Bacillus subtilis; vii) Bradyrhizobium spp. + Azospirillum spp. + Bacillus subtilis + Bacillus megaterium; viii) Bradyrhizobium spp. + Azospirillum spp. + Pseudomonas fluorescens + Bacillus subtilis + Bacillus megaterium; and ix) no bacteria; and in the second factor four doses of phosphorus (P2O5): 0, 50, 100 and 150 kg ha-1. The number and dry mass of nodules, leaf phosphorus content, yield and thousand-grain mass, protein, oil, fiber and ash content, as well as palmitic, stearic, oleic, linoleic and linolenic fatty acids in the grains were measured. The parameters of the mathematical model were removed in different treatment effect stratification scenarios and compared with the general mathematical model (Traditional). Multivariate normality and multicollinearity were diagnosed for all scenarios. Removing parameters from the mathematical model increases the variance explained in principal component analysis. In Pearson's linear correlation, this method alters the significance as well as the magnitude and direction of the correlations. Stratifying the effects of the treatments increases the explanatory capacity of the characters in relation to the variance of the grain yield in the path analysis. Removing the effects of the model parameters results in changes in the direction and magnitude (>50%) of the path coefficients. In the canonical correlations, removing parameters changed the statistical significance and, when significant at 5%, increased the canonical correlation and explanatory power.