Application of multivariate analysis of variance and related techniques in soil studies with substrate utilization tests

Hitzl, Wolfgang; Henrich, M.; Kessel, Markus; Insam, Heribert

doi:10.1016/s0167-7012(97)00047-x

Cited by 30 publications

(7 citation statements)

References 24 publications

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“…However, few works have used multivariate analysis in addition to univariate analysis to study soil parameters. Multivariate analysis is necessary to better understand the ecosystem-soil interaction (Hitzl et al, 1997;Park and Vlek, 2002;Vidal et al, 2004). On the basis of the magnitudes of the parameters analyzed for samples and/or variables, these techniques permit a joint evaluation of the diverse characteristics of the soil by using a grouping suggested by the data itself, rather than one defined ahead of time (Sena et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Applying multivariate methods to soil–solution interactions in carbonate media

González¹,

Quintana²,

Moreno³

et al. 2007

Geoderma

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Section: Introductionmentioning

confidence: 99%

Applying multivariate methods to soil–solution interactions in carbonate media

González¹,

Quintana²,

Moreno³

et al. 2007

Geoderma

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“…BIOLOG absorbency data were compressed to reduce the variables by factor analysis (Niklinska et al 2005). The use of factor analysis method here was safe for all replications and were included into analysis as a case so a higher ratio of numbers of replicates to the numbers of variables could be satisfied (Hitzl et al 1997). These PCs were used to represent the compressed characteristics of metal concentrations and BIOLOG profiles.…”

Section: Analytical Methodologymentioning

confidence: 99%

Efficiencies of different microbial parameters as indicator to assess slight metal pollutions in a farm field near a gold mining area

Wang

Dai

et al. 2009

Environ Monit Assess

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In order to monitor changes in the concentrations of metals in the soil, different microbial indices such as BIOLOG, microbial carbon (C(mic)), basal respiration, and culturable microbe's most probable number were used. We compared these methods and wanted to discover which method was the best at measuring slight changes in the amounts of heavy metals. Factor analyses were applied to the BIOLOG data and metal concentrations so the combined effects of heavy metals on microbes could be analyzed via statistical data reduction and the distribution patterns of metal concentration could also be revealed. The results showed that the BIOLOG method could barely detect subtle characteristic changes in the soil samples, while the C(mic) method was more sensitive. Furthermore, different heavy metals did not have the same origin/source, and their effects on microbial indices should be analyzed separately. Significant positive correlations between C(mic) and metals were observed and suggested the limitation of using traditional microbial parameters as metal pollution indicators. Among all the soil characteristics in our study, pH seemed to be the most active abiotic factor that affected microorganisms.

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“…The structure of fungal communities was correlated with the chemical constituents of the soil, organized according to the redundancy analysis (RDA). RDA has been used to correlate biotic factors with environmental conditions, seasonal fluctuations, and different conditions present in the areas evaluated, including physico-chemical characteristics of the soil [34][35][36]. The spatial coordinates of the fungal communities and the chemical properties of the soil are shown in Figure 4.…”

Section: Differences In Soil Fungal Microbial Community Structuresmentioning

confidence: 99%

Structure of Fungal Communities in Sub-Irrigated Agricultural Soil from Cerrado Floodplains

et al. 2016

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This study aimed to evaluate the influence of soybean cultivation on the fungal community structure in a tropical floodplain area. Soil samples were collected from two different soybean cropland sites and a control area under native vegetation. The soil samples were collected at a depth of 0-10 cm soil during the off-season in July 2013. The genetic structure of the soil fungal microbial community was analyzed using the automated ribosomal intergenic spacer analysis (ARISA) technique. Among the 26 phylotypes with abundance levels higher than 1% detected in the control area, five were also detected in the area cultivated for five years, and none of them was shared between the control area and the area cultivated for eight years. Analysis of similarity (ANOSIM) revealed differences in fungal community structure between the control area and the soybean cropland sites, and also between the soybean cropland sites. ANOSIM results were confirmed by multivariate statistics, which additionally revealed a nutrient-dependent relation for the fungal community structure in agricultural soil managed for eight consecutive years. The results indicated that land use affects soil chemical properties and richness and structure of the soil fungal microbial community in a tropical floodplain agricultural area, and the effects became more evident to the extent that soil was cultivated for soybean for more time.

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Application of multivariate analysis of variance and related techniques in soil studies with substrate utilization tests

Cited by 30 publications

References 24 publications

Applying multivariate methods to soil–solution interactions in carbonate media

Applying multivariate methods to soil–solution interactions in carbonate media

Efficiencies of different microbial parameters as indicator to assess slight metal pollutions in a farm field near a gold mining area

Structure of Fungal Communities in Sub-Irrigated Agricultural Soil from Cerrado Floodplains

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