Propriedades microbiológicas e índice de qualidade de um argissolo subtropical submetido a sistemas de manejo

• Ano de defesa: 2020
• Autor(a) principal: Sobucki, Lisiane
• 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 Ciência do Solo; Centro de Ciências Rurais
• Palavras-chave em Português: Rendimento do milho; Classes interpretativas; Qualidade do solo; Zea mays L; Corn yield; Interpretative classes; Soil quality; CNPQ::CIENCIAS AGRARIAS::AGRONOMIA::CIENCIA DO SOLO
• Link de acesso: http://repositorio.ufsm.br/handle/1/21988

Soil quality can be assessed through chemical, physical and biological index and properties. However, there is a knowledge gap on the reference values for interpretation of soil microbiological properties. In this study, we objective was to define critical limits (upper and lower) and interpretive classes for soil microbiological properties; establish a minimum data set based on statistical associations between corn yield and soil microbiological and chemical properties; and to determine if the variables selected by the minimum data set influence the current corn yield in Typic Paleudult from Rio Grande do Sul, Brazil. The main results were the development of interpretative classes for microbial biomass C (108 e 331 mg C kg-1 soil), microbial biomass N (12 e 40 mg N kg-1 soil), basal respiration (0,46 e 1,12 mg C-CO2 kg soil h-1), arilsulfatase (166 e 433 μg p-nitrophenol g-1 dry soil h-1) and β-glycosidase activity (50 e 171 μg p-nitrophenol g-1 dry soil h-1). In addition, we indicate microbiological and chemical properties of the soil capable of predicting corn yield in (total nitrogen, pH content and Zn content). The results show that no-tillage associated with grass-legume crop systems improves soil quality. Therefore, the definition of critical limits was as an adequate tool for interpretation of soil microbiological properties and the variables selected to compose the minimum data set were significant to predict the current yield of corn.