Tese
Pressupostos multivariados e efeito dos parâmetros do modelo em análises multivariadas para ensaios com a aveia
Fecha
2023-02-17Autor
Sgarbossa, Jaqueline
Institución
Resumen
Oat is one of the main winter cereals grown in the world, used in human food and animal feed,
ground cover, straw production, and crop rotation in the no-tillage system. In order to enhance
the oat production systems, statistical techniques have been used to study the linear
relationships between characters, in order to identify characters that directly or indirectly favor
the selection of superior genotypes, among these techniques the linear correlation stands out,
and path analysis. When performing multivariate analyses such as path analysis, some statistical
assumptions must be met to avoid obtaining biased results. Furthermore, when working with
this technique, the parameters of the mathematical model referring to the experimental design
and treatments are disregarded, using only average observations, without stratifying the
possible effects. Therefore, this study was developed with the aim of analyzing the implications
of removing the parameters from the mathematical model on the results of Pearson correlation
analysis and path analysis, in field trials with the oat crop, cultivated in different years and
stratifying agricultural scenarios (with and without the use of fungicide). The experiments were
conducted from 2015 to 2019, in the municipality of Augusto Pestana, Rio Grande do Sul,
Brazil. The experimental design used was complete randomized blocks, with treatments
characterized by oat cultivars and fungicide applications, with three replications. For each year,
scenario, and data group, a multicollinearity diagnosis was performed, Pearson's correlation
coefficients were calculated, and a path analysis was performed. The occurrence of
multicollinearity generates biased path coefficients without biological interpretation, regardless
of the environment and data group analyzed. Removing parameters from the mathematical
model changes the explanatory capacity of characters in relation to yield variance, for all
environments, scenarios, and types of path analysis performed. Removing the effects of model
parameters results in changes in direction and magnitude (>50%) in the path coefficients
regardless of the environment, scenario, and type of path analysis performed.