The design and implementation of novel soil management techniques in order to enhance the environmental quality of the agricultural landscape, reducing degradation of soil and water resources and impairment of vital ecosystem functions, is at the forefront of several agricultural research and academic institutions worldwide. During this process, the research and field validation stages require effective monitoring frameworks, which demands sensible and efficient indicators. Soil microbial diversity potentially harbors the most sensitive soil quality indicators, but their use is hampered by difficulties in the analysis and interpretation of the data, especially when obtained by molecular biology techniques. This article presents a novel analytical framework to assess soil quality, incorporating bacterial community structure data derived from PCR-DGGE analysis into a principal component analysis, along with a total of 23 other physical, chemical, and biotic soil variables. Additionally, a procedure to integrate PCR-DGGE data from replicate samples into a single, composite cluster analysis is shown. The results demonstrated the applicability of the soil quality analytical framework proposed, and revealed that traditional shifting cultivation practices in the studied area are compatible with conservation of soil quality, provided that the fallow periods are maintained for a period longer than 5 years. Currently, stricter environmental laws that prevent farmers from cutting Atlantic Forest trees are leading them to reduce or eliminate the fallow periods. Our results showed that the consequences are significant reductions in soil aggregation indices and organic carbon contents, greater impacts on the bacterial community structure, with potential disruption of soil based ecosystem functions, such as erosion control, carbon sequestration, and hydrological regulation of the watershed.