Biological profile from soil using components analysis

Abstract

It is known that principal components analysis is used as a mathematical procedure that transforms a set of correlated response variables into a new set of non-correlated variables known as main components. The sensitivity of the variables in response to the changes in the soil environment under different health conditions of trees. In order to clarify the structure of interdependence between all properties, principal components analysis was applied to the data corresponding to all the soil properties studied. We analyzed this dataset and we found that the metabolic quotient (qCO2) and micC are the two parameters that differed most in the studied soil. The metabolic quotient (RES:micC) was higher in the presence of pathogens, while the biological quotient (micC:orgC) was lower. The score plots indicated that the samples can be divided into two groups (LRs-BAs and LRc, LRh, BAc, BAh), which suggests that the two different states of plant health conditions had a strong effect on soil properties. In PCA biochemical parameters explained 84 % of the system variability. Strong association between LRs with DH and b-Glu was observed. The score plot indicated that the samples can be defined into two groups: LRc, LRh, BAc, BAh and LRs, BAs which suggests that the presence of the patoghen had an effect on soil properties. The parameters studied showed the same variability within LR as BA soils. Our results empathize the importance of determining soil quality indicators and rizopherical bacterial communities in the presence of root pathogens. Bacterial communities from soil played a differential role in the presence of root pathogens. Microbiological activity correlated with biochemical properties from soil in locations with trees with fungal symptoms. We hypothesized that changes in the health state of trees could be related to changes in rhizospheric soil quality indicators.