Qualidade da estimativa da umidade, utilizando TDR, em função da composição granulométrica e da densidade do solo

• Ano de defesa: 2013
• Autor(a) principal: Santos, Alex Elpidio dos
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Estadual de Maringá; Brasil; Departamento de Agronomia; Programa de Pós-Graduação em Agronomia; UEM; Maringá, PR; Centro de Ciências Agrárias
• Palavras-chave em Português: Irrigação; Umidade do solo; TDR; Solo; Densidade do solo; Composição granulométrica; Região Noroeste do Paraná; Curvas de calibração da TDR; Granulometria; Parná (Estado); Brasil.; TDR calibration curves; Granulometry; Soil moisture; Paraná (State); Brazil.; Ciências Agrárias; Agronomia
• Link de acesso: http://repositorio.uem.br:8080/jspui/handle/1/1293

Soil moisture estimate is highly important to make technical and economical decisions in irrigated agriculture. Among the methods used to measure the soil moisture, the Time Domain Reflectometry (TDR) technique stands out because it allows quick, nondestructive estimates, and supposedly with high reliability. It allows establishing with more accuracy the appropriate moment to irrigate, as well as the right amount of water to be applied. Although it was accepted in the early years of use of the technique that a universal model could be employed, several studies have shown that this technique is susceptible to soil related variables, which makes it essential to carry out a calibration. This study aimed to evaluate the effect of different granulometric compositions at different density values on the calibration curves. Samples were taken at three positions in a toposequence: at the top of the slope, at the bottom of the valley and in an intermediate position, which led to a clay content variation of 10% to 70%. It was verified that the granulometric composition variation influenced the TDR calibration, showing that the quality of a single model is unacceptable. It was also observed that the most recent assessment indicators used were not capable enough to describe the quality of the model adjustment. Residual analysis of the models shows that a model with an adjustment considered optimal by the evaluation indicators can display errors of available soil water estimation in the order of 50%. It was concluded therefore that the incorporation of physical variables of the soil, such as texture and soil density, to the model promotes moisture estimates closer to reality.