Impacto da cobertura do solo, declividade e precipitação na infiltração e escoamento de água e sedimentos

• Autor(a) principal: Vidaletti,Vitória Fenilli
• Data de Publicação: 2022
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações do UNIOESTE
• Texto Completo: https://tede.unioeste.br/handle/tede/6009
• topic: Manejo do solo; Porosidade; Produtividade; Soil Management; Porosity; Fertility; AGROENERGIA

The understanding of infiltration and surface runoff processes and their relationships with soil properties is essential for efficient soil and water management. Management systems significantly influence the infiltration of water into the soil, having a direct effect on the triggering of erosive processes, which influence soil and water conservation. Mathematical and statistical modeling is a tool that can estimate infiltration and consequent soil and water losses. The dissertation was divided into two chapters. Chapter 1 aims to simulate under laboratory conditions the effect of slope, rainfall intensity and straw quantity on the infiltration rate and establish a model that describes this behavior through Multiple Regression. Chapter 2 aims to simulate the same events on soil and water losses. For this, Principal Component Analysis was used to denote the influence of these variables in the process and thus, through Multiple Regression, establish a model that describes such an event. The experiment in a 3x3x3 factorial scheme (3, 8 and 20% of slope, 0, 5,10 Mg ha-1 of ground cover, 38.60, 62.40 and 74.48 mm h-1 of rainfall intensity) sought to assess how these changes influence the water infiltration process and, consequently, water and soil losses. The data were statistically analyzed with the aid of the R Studio Software to formulate models that describe the behavior of infiltration and loss of water and soil. The amount of straw had a greater influence on the infiltration rate, compared to precipitation and terrain slope, while precipitation had less influence. The adjusted model presented good quality, explaining 97% of the variability, adjusting itself accurately to the analyzed data at a 5% significance level. Precipitation had a greater influence on water losses, compared to other factors analyzed, while slope had a lesser influence. The fitted model was able to explain 99% of the data variability, with all coefficients significant at 5%. The amount of straw on the surface had a greater influence on soil losses, compared to other factors analyzed. The adjusted model has good quality, explaining 91% of the data variability.