SOIL ANALYSIS AND NUMERICAL CLASSIFICATION OF THE LANORAIE DELTA, QUEBEC

Analysis and numerical classification of Lanoraie Delta soils were performed as part of an ecological study. After stratification, using detailed soil maps, 84 forest sites were randomly sampled. Eighteen soil descriptors, mainly morphologic, were retained for numerical analysis. Gower's similarity coefficient between profiles was used for the principal coordinate analysis (PCA) which brought out the most probable factors governing soil distribution. The first two axes of the PCA represented 15.5 and 6.2% of the total variance. Soil distribution was along two gradients: soil water regime (Xeric-Hydric) and genetic development (Gleysolic-Podzolic). Cluster analysis by complete linkage created five soil groups which were composed of 23, 9, 17, 25 and 10 soil individuals, respectively. The interpretation of these soil groups utilized the superposition of clusters onto the first two axes of the PCA. Each group was classified into a taxonomic subgroup (Canadian System of Soil Classification) and defined by a typical humus form. Key words: Lanoraie Delta, multivariate analysis, soil classification

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Numerical examination of a field classification of some red, yellow and grey earth profiles

Soil Research ◽

10.1071/sr9830343 ◽

1983 ◽

Vol 21 (4) ◽

pp. 343 ◽

Cited By ~ 1

Author(s):

RJ Coventry ◽

WT Williams

Keyword(s):

Three Dimensional ◽

Soil Classification ◽

Sampling Interval ◽

Soil Horizon ◽

Striking Difference ◽

Numerical Classification ◽

Soil Attributes ◽

Additional Information ◽

Positive Attribute

Numerical methods have been used to examine an existing and accepted field classification of 48 profiles of red, yellow, and grey earths (mainly Alfisols) from central north Queensland. The three-dimensional soils data (sites by depths by descriptors, which may be mixed in type) were converted to a form which appeared to the computer as a two-dimensional set of profiles by attributes. The soils data were from independent depth intervals, and no assumptions were made about the inter-relationships among soil layers tvithin a profile; nor were the values of any of the soil attributes weighted. In order to consider shallow profiles on the same total depth basis as the deep profiles, the absence of a soil horizon or sampling interval at depth has been regarded as a positive attribute in the numerical classification. Comparison of the traditional field classification and a numerical classification of the same soils dataset showed that certain soil attributes played an important role in both classifications. The most striking difference between them was the relative importance of soil colour attributes, from which it might be argued that field pedologists have assigned to colour a weight out of proportion to its real importance in soil classification. However, this attribute carries additional information about the mineral constituents and hydrological regimes of the soils, and represents information known to, and used by, the field pedologist but not revealed to the computer. The essential subjectivity in the choice of attributes for soil classification is demonstrated.

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Relationships between soil pH and base saturation – conclusions for Polish and international soil classifications

Soil Science Annual ◽

10.2478/ssa-2018-0021 ◽

2018 ◽

Vol 69 (4) ◽

pp. 206-214 ◽

Cited By ~ 2

Author(s):

Cezary Kabała ◽

Beata Łabaz

Keyword(s):

Soil Ph ◽

Soil Classification ◽

Exchange Capacity ◽

Base Saturation ◽

Control Section ◽

Soil Units ◽

Mineral Soils ◽

Diagnostic Horizons ◽

Soil Groups

Abstract Taking into account the fact that (a) measurement of the cation exchange capacity and base saturation is practically unavailable in the field, that formally makes impossible the reliable field classification of many soils, (b) base saturation is measured or calculated by various methods those results significantly differ, (c) base saturation and soil pH are highly positively correlated, it is suggested to replace the base saturation with pHw (measured in distilled/deionized water suspension) in the classification criteria for diagnostic horizons and soil units/subunits, both in the Polish Soil Classification and FAO-WRB. Based on statistical analysis of 4500 soil samples, the following pHw values are recommended instead of 50% base saturation: pHw <5.5 for umbric and pHw ≥5.5 for the mollic horizon, and for Chernozems, Kastanozems, Phaeozems (directly) and Umbrisols (indirectly). Furthermore, the pHw <4.7 may feature the Dystric qualifier in mineral soils and respective Reference Soil Groups of WRB; while the pHw ≥4.7 may feature the Eutric qualifier. The distinction between subtypes of the brown soils in the Polish Soil Classification may base on the pHw 4.7 or 5.0, but using different requirements of pH distribution in the depth control section. The replacement of the base saturation with pH refers to the formal soil classification only, and does not exclude the use of base saturation for professional soil characteristics.

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Using numerical classification of profiles based on Vis-NIR spectra to distinguish soils from the Piracicaba Region, Brazil

Revista Brasileira de Ciência do Solo ◽

10.1590/s0100-06832014000200002 ◽

2014 ◽

Vol 38 (2) ◽

pp. 372-385 ◽

Cited By ~ 12

Author(s):

Rodnei Rizzo ◽

José A. M. Demattê ◽

Fabrício da Silva Terra

Keyword(s):

Spectral Data ◽

Principal Components ◽

Visual Analysis ◽

Numerical Technique ◽

Correlation Coefficients ◽

Principal Component ◽

Soil Classification ◽

Numerical Classification ◽

Similarity Coefficients

Considering that information from soil reflectance spectra is underutilized in soil classification, this paper aimed to evaluate the relationship of soil physical, chemical properties and their spectra, to identify spectral patterns for soil classes, evaluate the use of numerical classification of profiles combined with spectral data for soil classification. We studied 20 soil profiles from the municipality of Piracicaba, State of São Paulo, Brazil, which were morphologically described and classified up to the 3rd category level of the Brazilian Soil Classification System (SiBCS). Subsequently, soil samples were collected from pedogenetic horizons and subjected to soil particle size and chemical analyses. Their Vis-NIR spectra were measured, followed by principal component analysis. Pearson's linear correlation coefficients were determined among the four principal components and the following soil properties: pH, organic matter, P, K, Ca, Mg, Al, CEC, base saturation, and Al saturation. We also carried out interpretation of the first three principal components and their relationships with soil classes defined by SiBCS. In addition, numerical classification of the profiles based on the OSACA algorithm was performed using spectral data as a basis. We determined the Normalized Mutual Information (NMI) and Uncertainty Coefficient (U). These coefficients represent the similarity between the numerical classification and the soil classes from SiBCS. Pearson's correlation coefficients were significant for the principal components when compared to sand, clay, Al content and soil color. Visual analysis of the principal component scores showed differences in the spectral behavior of the soil classes, mainly among Argissolos and the others soils. The NMI and U similarity coefficients showed values of 0.74 and 0.64, respectively, suggesting good similarity between the numerical and SiBCS classes. For example, numerical classification correctly distinguished Argissolos from Latossolos and Nitossolos. However, this mathematical technique was not able to distinguish Latossolos from Nitossolos Vermelho férricos, but the Cambissolos were well differentiated from other soil classes. The numerical technique proved to be effective and applicable to the soil classification process.

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Numerical classification of soil profiles on the basis of field morphological properties

Soil Research ◽

10.1071/sr9700043 ◽

1970 ◽

Vol 8 (1) ◽

pp. 43 ◽

Cited By ~ 23

Author(s):

NA Campbell ◽

MJ Mulcahy ◽

WM Mcarthur

Keyword(s):

Numerical Taxonomy ◽

Morphological Properties ◽

Soil Profiles ◽

Numerical Classification ◽

Classification Schemes ◽

Texture Profile ◽

Original Field ◽

Soil Groups ◽

Subsequent Identification

The use of numerical taxonomy to classify soils avoids classification schemes based on logical subdivision or keys by the application of objective numerical procedures to assess the similarity, and the subsequent identification, of soil groups. It was found that a combination of a hierarchical sorting strategy and a coordinate strategy avoided distortion of relationships and subjectivity in group recognition. The results of the numerical classification agreed closely with the original field grouping. Examination of the resulting soil groups indicated that texture profile was an important diagnostic property.

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Application of Energy Dispersive X-ray Fluorescence, γ-ray Spectrometry and Multivariate Statistical Approach for the Soil Classification of Samples from Douala – Cameroon

Radiation Physics and Chemistry ◽

10.1016/j.radphyschem.2021.109589 ◽

2021 ◽

pp. 109589

Author(s):

Joel Cebastien SHOUOP GUEMBOU ◽

Maurice MOYO. NDONTCHUENG ◽

Jilbert Eric MEKONGTSO NGUELEM ◽

Ousmanou MOTAPON ◽

David STRIVAY

Keyword(s):

Statistical Approach ◽

Soil Classification ◽

Energy Dispersive ◽

Multivariate Statistical ◽

X Ray ◽

Multivariate Statistical Approach ◽

Γ Ray

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Cross-Reference System for Translating Between Genetic Soil Classification of China and Soil Taxonomy

Soil Science Society of America Journal ◽

10.2136/sssaj2004.0318 ◽

2006 ◽

Vol 70 (1) ◽

pp. 78-83 ◽

Cited By ~ 114

Author(s):

X. Z. Shi ◽

D. S. Yu ◽

E. D. Warner ◽

W. X. Sun ◽

G. W. Petersen ◽

...

Keyword(s):

Reference System ◽

Soil Classification ◽

Soil Taxonomy ◽

Cross Reference

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Numerical classification of some viruses, bacteria and animals according to nearest-neighbour base sequence frequency

Journal of Molecular Biology ◽

10.1016/0022-2836(67)90354-3 ◽

1967 ◽

Vol 27 (1) ◽

pp. 107-112 ◽

Cited By ~ 9

Author(s):

Alan J.D. Bellett

Keyword(s):

Base Sequence ◽

Nearest Neighbour ◽

Numerical Classification

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Numerical classification of French vegetation of Carici caryophylleae – Genistetea lobelii J.-C. Klein 1972

Mediterranean Botany ◽

10.5209/mbot.68062 ◽

2021 ◽

Vol 42 ◽

pp. e68062

Author(s):

Pauline Delbosc ◽

Mathieu Le Dez ◽

Jean-Bernard Bouzillé ◽

Kevin Cianfaglione ◽

Frédéric Bioret

Keyword(s):

Expert System ◽

Dominant Species ◽

Numerical Classification ◽

Distribution Map ◽

Diagnostic Species ◽

Plant Associations ◽

Formal Definitions ◽

Twinspan Classification ◽

New Association

Carici-Genistetea lobelii Klein 1972 corresponds to cyrno-sardinian oromediterranean cushion scrub and related grasslands. In France, this class is only present in Corsica and the syntaxonomic scheme is debated among phytosociologists. The aim of this paper is to highlight the main plant associations of Carici-Genistetea lobelii Klein 1972 and to define the diagnostic species for each phytosociological unit. We compiled 519 vegetation plots and we applied EuropeanVegetationChecklist expert system for the classes of European vegetation to retain only vegetation plots belonging to Carici-Genistetea lobelii. We obtained a dataset with 189 vegetation plots and we classified them with Modified TWINSPAN classification. Our analyses recognized 6 plant associations and 3 sub-associations already described in the literature; and to describe a new alliance corresponding to the supra-mediterranean vegetations (Genistion salzmannii), a new association (Brimeuro fastigiatae-Juniperetum nanae) and its sub-association (alnetosum suaveolentis). For each of them, we identified diagnostic, constant and dominant species and produced their distribution map. Formal definitions were then written for each phytosociological unit (from subassociation to class) and grouped in an expert system to automatically classify the vegetations of Carici-Genistetea lobelii.

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