Discrimination of management effects on soil parameters by using principal component analysis: a multivariate analysis case study

One of the major interests in soil analysis is the integrated evaluation of soil properties, which might be indicators of soil quality. Unsupervised methods of multivariate statistics are powerful tools for this integrated assessment and can help soil researchers to extract much more information from their data. A multivariate study was carried out in three farms from Guaı́ra, State of São Paulo, Brazil. Conventionally managed plots that intensively utilized pesticides and chemical fertilizers were compared with both non-disturbed forest areas and alternatively managed plots. The latter were under ecological farming employing effective microorganisms (EM) integrated with crop residues. Eight soil parameters were determined for each plot. Hierarchical cluster analysis (HCA) was used to verify the similarity among the plots. The multivariate approach of principal component analysis (PCA) allowed us to distinguish the areas as a function of the soil management and determine which are the most important parameters to characterize them. The forest areas presented higher microbial biomass with lower cellulolytics population than at cultivated sites. The alternative plots were characterized by higher microbial biomass and polysaccharide content with lower phosphate solubilizers and cellulolytics microorganisms colony counts than at the conventional areas. The higher observed levels of microbial biomass and polysaccharide content in the alternative areas can be attributed to the effects of the alternative soil amendment. All these effects can be clearer globally visualized with the aid of PCA, through the biplots.