The rice soils of Cambodia. II. Statistical discrimination of soil properties by the Cambodian Agronomic Soil Classification system (CASC)

2006 ◽  
Vol 16 (1) ◽  
pp. 20-26 ◽  
Author(s):  
T. Oberthür ◽  
A. Dobermann ◽  
P. White
Keyword(s):  
Soil Properties ◽  
Classification System ◽  
Soil Classification ◽  
Statistical Discrimination ◽  
Rice Soils

scholarly journals Harmonisation of a large-scale historical database with the actual Czech soil classification system

2020 ◽  
Vol 15 (No. 2) ◽  
pp. 101-115 ◽  
Author(s):  
Tereza Zádorová ◽  
Daniel Žížala ◽  
Vít Penížek ◽  
Aleš Vaněk
Keyword(s):  
Soil Properties ◽  
Classification System ◽  
Large Scale ◽  
Soil Classification ◽  
Study Region ◽  
Taxonomic Distance ◽  
Historical Database ◽  
Legacy Data ◽  
Soil Units ◽  
Data Harmonisation

The possibility of the adequate use of data and maps from historical soil surveys depends, to a large measure, on their harmonisation. Legacy data originating from a large-scale national mapping campaign, “Systematic soil survey of agricultural soils in Czechoslovakia (SSS, 1961–1971)”, were harmonised and converted according to the actual system of soil classification and descriptions used in Czechia – the Czech taxonomic soil classification system (CTSCS). Applying the methods of taxonomic distance and quantitative analysis and reclassification of the selected soil properties, the conversion of two types of mapping soil units with different detailed soil information (General soil representative (GSR), and Basic soil representative (BSR)) to their counterparts in the CTSCS has been effectuated. The results proved the good potential of the used methods for the soil data harmonisation. The closeness of the concepts of the two classifications was shown when a number of soil classes had only one counterpart with a very low taxonomic distance. On the contrary, soils with variable soil properties were approximating several related units. The additional information on the soil skeleton content, texture, depth and parent material, available for the BSR units, showed the potential in the specification of some units, though the harmonisation of the soil texture turned out to problematic due to the different categorisation of soil particles. The validation of the results in the study region showed a good overall accuracy (75% for GSR, 76.1% for BSR) for both spatial soil units, when better performance has been observed in BSR. The conversion accuracy differed significantly in the individual soil units, and ranged from almost 100% in Fluvizems to 0% in Anthropozems. The extreme cases of a complete mis-classification can be attributed to inconsistencies originating in the historical database and maps. The study showed the potential of modern quantitative methods in the legacy data harmonisation and also the necessity of a critical approach to historical databases and maps.

scholarly journals Development of the basic soil classification of Vladimir Fridland in the classification of soils of Russia 2004/2008

2020 ◽  
pp. 5-20
Author(s):  
M. I. Gerasimova ◽  
N. B. Khitrov ◽  
I. I. Lebedeva
Keyword(s):  
Soil Properties ◽  
Classification System ◽  
Soil Classification ◽  
Genetic Component ◽  
The World ◽  
Conceptual Background ◽  
Diagnostic Horizons

The basic three-component classification system of soils of the world was published by V.M. Fridland in 1982, and its profile-genetic component was taken as a basis for the classification of soils of Russia (1997– 2004–2008). Unlike the former systems, in that of Fridland the priority is given to soil properties, and this conceptual background is transferred into the new Russian system. The substantive-genetic principles of both systems are implemented in diagnostic horizons and genetic properties; both systems have similar hierarchy of taxa, nomenclature, keys. Changes introduced in the classification of soils of Russia derive either of proposals forwarded in the course of its application, or of information accumulated. They concern the improvement of definitions and introduction of new diagnostic elements; however, the main principles are preserved in the existing and forthcoming versions.  

Ecological niches of major soil types in Russia: Geographical aspects of the new Russian soil classification system

2009 ◽  
Vol 42 (9) ◽  
pp. 967-975 ◽  
Author(s):  
V. D. Tonkonogov ◽  
I. I. Lebedeva ◽  
M. I. Gerasimova ◽  
S. F. Khokhlov
Keyword(s):  
Classification System ◽  
Soil Classification ◽  
Soil Types ◽  
Ecological Niches ◽  
Russian Soil

Computerized Cone Penetration Test for Soil Classification

2008 ◽  
Vol 2053 (1) ◽  
pp. 47-64 ◽  
Author(s):  
Murad Y. Abu-Farsakh ◽  
Zhongjie Zhang ◽  
Mehmet Tumay ◽  
Mark Morvant
Keyword(s):  
Classification System ◽  
Soil Classification ◽  
Cone Penetration Test ◽  
Classification Systems ◽  
Fuzzy Classification ◽  
Classification Methods ◽  
Penetration Test ◽  
Cone Penetration ◽  
Cone Tip Resistance ◽  
Tip Resistance

Computerized MS-Windows Visual Basic software of a cone penetration test (CPT) for soil classification was developed as part of an extensive effort to facilitate the implementation of CPT technology in many geotechnical engineering applications. Five CPT soil engineering classification systems were implemented as a handy, user-friendly, software tool for geotechnical engineers. In the probabilistic region estimation and fuzzy classification methods, a conformal transformation is first applied to determine the profile of soil classification index (U) with depth from cone tip resistance (qc) and friction ratio (Rf). A statistical correlation was established in the probabilistic region estimation method between the U index and the compositional soil type given by the Unified Soil Classification System. Conversely, the CPT fuzzy classification emphasizes the certainty of soil behavior. The Schmertmann and Douglas and Olsen methods provide soil classification charts based on cone tip resistance and friction ratio. However, Robertson et al. proposed a three-dimensional classification system that is presented in two charts: one chart uses corrected tip resistance (qt) and friction ratio (Rf); the other chart uses qt and pore pressure parameter (Bq) as input data. Five sites in Louisiana were selected for this study. For each site, CPT tests and the corresponding soil boring results were correlated. The soil classification results obtained using the five different CPT soil classification methods were compared.

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