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Problems in correlation of Czech national soil classification and World Reference Base 2006
Geoderma 167-168 (2011) 54–60
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Problems in correlation of Czech national soil classification and World Reference Base 2006 Tereza Zádorová a,⁎, Vít Penížek a, b a b
Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Kamýcká 129, Praha 6, Czech Republic Land Management & Natural Hazards Unit, Institute for Environment & Sustainability, Joint Research Centre, European Commission, Via E. Fermi 2749, 21027 Ispra (VA), Italy
a r t i c l e
i n f o
Article history: Received 25 January 2011 Received in revised form 12 September 2011 Accepted 13 September 2011 Available online 2 November 2011 Keywords: Soil systematics Soil units Soil taxa conversion Soil maps
a b s t r a c t The use of legacy data in international soil mapping projects entails the demand for the harmonisation of soil data. Accurate correlation between national and international soil classification units is an important prerequisite in global soil mapping and acquisition of harmonised soil data usable in environmental applications. The correlation of soil units at different taxonomic levels was undertaken to relate the Czech national soil classification system with the World Reference Base (WRB) and evaluate the effectiveness of a semantic approach (analogical soil units provided by expert knowledge), and quantitative approach in the correlation. For the quantitave approach, a set of 433 soil profiles randomly selected from the Large-scale mapping of agricultural soils in Czechoslovakia was classified according to WRB using available analytical and morphological soil data. The study showed the necessity for an analytical approach and quantitative data use for reliable correlation between the two classification schemes. The general level of correlability at the higher taxonomic level can be considered as high (88%), whilst there is a significant variability of correlation accuracy between soil types. Conversion of some soil units, e.g. Rankers, Rendzinas, Pararendzinas, Černice, Černozems, Podzols or Luvizems requires analytical and morphological data of corresponding profiles. Relatively low correlability is caused by various factors. Different concepts of the soil unit and different setting of the criteria of the diagnostic soil properties are the most important. Some units such as Glejs, Fluvizems or Hnědozems can be correlated with a high probability of accurate assignment. High incompatibility was shown at the lower taxonomic level. Correlation of lower taxonomic units should be subject to analytical processing. © 2011 Elsevier B.V. All rights reserved.
1. Introduction Soil classification is a fundamental part of soil research and data processing serving mainly as an organisational framework for soil property description (Droogers and Bouma, 1997; Shi et al., 2006; Smith, 1963). It is also an important instrument in the communication of soil research results at national and international levels of soil science. The first version of the World Reference Base of Soil Resources (FAO, 1998) was introduced at the 16th World Congress of Soil Science in 1998 as a standard for soil classification correlation. The actual version of the WRB was presented in 2006 (IUSS Working Group WRB, 2006) and further updated in 2007. WRB, together with the Soil Taxonomy (USDA-NRCS, 1999), is an internationally used system for presentation of results in soil research. WRB is partly used for soil classification at a national level (e.g. Italy), whereas a high number of countries implement national classification in a different stage of correlability with WRB or Soil Taxonomy (Barreta-
⁎ Corresponding author. Tel.: + 420 224382591. E-mail address: [email protected] (T. Zádorová). 0016-7061/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.geoderma.2011.09.014
Bassols et al., 2006; Dazzi et al., 2009; Kleber et al., 2004; Reintam and Köster, 2006). IUSS Working Group WRB (2010) published guidelines for the creation of a legend for small-scale soil mapping in the WRB scheme, making WRB more suitable for designing basic soil maps. The majority of spatial soil data originates from national classification systems. The demand for harmonisation of national and international classification systems increased mainly within the context of the need for harmonised global soil data that can serve as input data in environmental modelling (e.g. climate changes). International projects on digital soil mapping (e.g. e-SOTER, GlobalSoilMap) and global soil database development are examples of such activities. Part of this task is the creation of soil maps in regions with national borders. This requires harmonisation of soil polygon classification, often based on the conversion of national taxa into global soil systems that reveal frequent discrepancies in diagnostics of some taxa at both higher and lower taxonomic levels. The difference in diagnostics may result from various factors: soil taxon of national classification is broader or narrower than a WRB group; taxa only partially coincide. In the correlation of soil map units, these diagnostic differences imply the need of primary field data use and soil profile reclassification (Krasilnikov et al., 2009; Shoba, 2002). Nevertheless, a lack of
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analytical data often results in semantic correlation based on simple translation of taxon name regardless of diagnostic characteristics. This approach was formerly applied in small scale general mapping, but the inaccuracy, particularly in detailed mapping is significant (Krasilnikov et al., 2009). For example, soil type Rendzina (designation used in many national classifications) can be correlated in WRB as Rendzic Leptosol, Cambisol or Regosol (Calcaric) or even as Rendzic Phaeozem when using the analytical data (Reintam and Köster, 2006). The possibility and accuracy of correlation depend mainly on limits of soil features required for diagnostics of a particular soil classification unit. The correlability of each soil unit then depends on the degree of similarity (or conformity) of the limits determining its diagnostics. The limits can vary significantly in different reference units (Shi et al., 2010). The variation can be caused by a diverse setting of quantitative or qualitative criteria or by a different concept of the soil unit (Krasilnikov et al., 2009). Different general approaches to soil classification represent another important obstacle to effective correlation. Historically, soil systematics development had three main phases. Classification based on soil properties changed into genetic classifications and then back to preference of soil properties represented by analytical and morphological features (Bockheim and Gennadiyev, 2000). The genetic approach or its remains persisting in several national classification systems can complicate the correlation and lower its accuracy. Recently, some authors accentuated the importance of consideration of soil processes for understanding the genetic basis of modern soil taxonomic systems and developing quantitative models of pedogenic systems (Bockheim and Gennadiyev, 2000). Gray et al. (2011) examined the relationship of WRB groups and soil forming factors such as climate and parent material. Soil mapping may be significantly facilitated by a clear knowledge of the relationships between soil group distribution and soil forming factors not only at the global mapping scale, but also at the level of national maps and their potential harmonisation with global soil classification (Birkeland, 1999; Gray et al., 2011). Different methods in soil data acquisition, especially the use of diverse analytical methods for soil properties can be fundamental mainly in the conversion of older data. Several studies on soil classification and correlation were published in recent years. The most extensive correlation project was the conversion of European national datasets into WRB 1998 for elaboration of the European Soil Database 1:1 M (ESBN, 2003). Shi et al. (2006, 2010) correlated Chinese soil classification with WRB and Soil Taxonomy. A comparison of Chinese soil classification with international classification was also undertaken by Gerasimova (2010). Reintam and Köster (2006) accentuated the significance of analytical properties of soils for the correlation with WRB and Soil Taxonomy. Gimenéz (2011) applied the WRB for the classification of some Argentinean soil units showing its applicability in the case of buried soils. Mojiri et al. (2011) compared the effectiveness of Soil Taxonomy and WRB in aridic soil classification. Traditional Indonesian soil classification was compared with a Soil Taxonomy scheme by Namriah and Badayos (2007). Taxonomic distances for the WRB were derived by Minasny et al. (2010) and the method was applied also for Hungarian soil units (Láng et al., 2010). Soil classification in the Czech Republic is actually represented by Czech Taxonomic Classification System of Soils (CTCSS; Němeček et al., 2001, 2011). The soil classification proceeds with previous systems: Morphogenetic Classification System of Soils (Hraško et al., 1991) and classification system of forest soils (Vokoun and Macků, 1996). The CTCSS classification was designed to meet the requirements for effective correlation of higher taxonomical units with international taxa. Consequently, the principle of morphogenetic feature preference was substituted for analytical data approach. Higher taxonomical unit assessment is based on diagnostic horizons and features below 0.25 m of depth (reduction of influence of different land use).
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The CTCSS is a multi-categorical system. Reference groups, soil types and subtypes reflect results of the long-term soil evolution. Reference groups represent a linkage to the highest taxonomic categories of the world reference systems (WRB, Soil Taxonomy) and the other wellknown soil classification systems (German, French, Canadian). Soil types (great soil groups) are a central taxonomic category, which involves soil being characterised by a specific sequence of diagnostic horizons and/or diagnostic features. Subtypes as subdivisions of the soil types include modal (typical), inter-grade representatives, extreme debasification degrees and extremes of the soil texture (Němeček et al., 2011). Subtypes serve the same role as the qualifiers in WRB, but their criteria vary slightly in some soil types in CTCSS (e.g. Arenic subtype has different limits in Černozem and in Kambizem). The presented study was performed to assess accuracy and effectiveness of correlation between Czech soil classification (as an example of national classification) and the international World Reference Base. The study also seeks to point out the major problems in their harmonisation and to evaluate the importance of the analytical data in the correlation process. 2. Material and methods Based on expert knowledge, the CTCSS provides for each of its reference groups an analogical soil unit according to WRB 2006 (Němeček et al., 2011; Table 1). In our study, the accuracy of this analogy was tested with a set of 433 soil profiles randomly selected from the reports of the Large-scale mapping of agricultural soil in Czechoslovakia (Ministry of Agriculture, 1963–1967). The number of profiles in each taxonomical unit was selected proportionally according to their spatial extent in the Czech Republic. Every profile was classified according to WRB using the following morphological and analytical profile data: profile stratigraphy, horizon thickness, soil colour, texture, organic carbon content, soil reaction, cation exchange capacity and base saturation. This test was possible for the majority of the taxonomical units of the national classification (except for Litozems, Koluvizems, Anthropozems, Kultizems and Organozems which were not included in the original field survey). The correlability level represents the largest relative number of profiles of a certain CTCSS soil unit falling after WRB classification into a certain WRB soil unit. This may or may not be the analogical soil unit. The correlability was evaluated as high if 80–100% of the Table 1 CTCSS soil types and their WRB analogical soil units according to Němeček et al. (2011). CTCSS soil type
WRB06 Reference Soil Group/qualifier
Litozem Ranker Rendzina Pararenzdina Regozem Fluvizem Koluvizem Smonice Černozem Černice Šedozem Hnědozem Luvizem Kambizem Pelozem Kryptopodzol Podzol Pseudoglej Stagnoglej Glej Organozem Kultizem Antropozem
Lithic Leptosol Leptosol (Skeletic) Rendzic Leptosol Leptosol (Calcaric) Regosol, Arenosol Fluvisol – Vertisol Chernozem Gleyic Chernozem Greyic Phaeozem Luvisol Albeluvisol Cambisol Cambisol (Clayic) Entic Podzol Podzol Stagnosol Stagnosol Gleysol Histosol Anthrosol Technosol
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Table 2 Correlability of soil units in CTCSS and WRB 2006 (soil types and Reference Soil Groups+ qualifiers). CTCSS
WRB 2006
Correlability (%)
Accesory units
Ranker Rendzina Pararendzina Regozem Regozem Arenic Fluvizem Smonice Černozem Černice Šedozem Hnědozem Luvizem Kambizem Pelozem Kryptopodzol Podzol Pseudoglej
Haplic Leptosol Leptosol (Calcaric) Leptosol (Calcaric) Regosol Arenosol Fluvisol Vertisol Chernozem Gleyic Chernozem Luvic Greyic Phaeozem Luvisol Albeluvisol Cambisol Cambisol (Clayic) Entic Podzol Podzol Stagnosol
67 75 92 80 100 100 100 91 67 100 100 63 94 50 100 67 74
Glej Total
Gleysol
100 88
Leptic Cambisol Cambisol (Eutric) Cambisol (Eutric) Chernozem – – – Phaeozem Gleyic Phaeozem – – Albic Luvisol Luvisol, Arenosol Cambisol (Siltic) – Cambisol (Dystric) Planosol, Stagnic Albeluvisol –
profiles were classified into the same soil unit. Values from 60% to 80% correspond to medium correlability, values under 60% to low correlability of the soil unit (Shi et al., 2010). Correlation was assessed in both higher taxonomical level (soil types in CTCSS and Reference Soil Groups (RSGs) in the WRB) and lower taxonomical level (subtype in CTCSS and qualifier in WRB). Reference groups (CTCSS) were originally intended for the correlation with RSGs (WRB). Soil types were chosen for the correlation in this study since the absolute majority of soil maps describes soil types. The other reason is that both soil types and RSGs are defined by strict diagnostic criteria which are
quantitatively comparable, whilst the Reference groups in CTCSS are classified only according to the prevailing soil-forming process. Correlation at the lower level included only subtypes/qualifiers occurring in more than 5 soil profiles in one or both of the classification systems. Correlated units were: arenic (CTCSS)/Arenic (WRB), pelic/Clayic, dystric/Dystric, luvic/Luvic, stagnic/Stagnic, gleyic/Gleyic. 3. Results and discussion 3.1. Conversion at high taxonomical soil units: soil type — Reference Soil Group Average correlability at the level of soil type — RSG was 88%. Overall accuracy of the correlation is high, but it differs significantly, from 50% to 100% within particular soil units (Table 2). The number of Czech soil types with high (80–100%), intermediate (60–80%) and low (less than 60%) correlability amounts to 10, 7 and 1, respectively. Shi et al. (2010) determined maximum referencibility of Chinese soil great groups and WRB between 29.4% and 100% when the high referencibility (more than 80%) was detected in 12 from 60 soil groups and low referencibility in 21 soil groups. Lower correlability is caused by various factors (Table 3). Different concepts of the soil unit and different setting of the limits of the diagnostic soil properties are the most important. Results showed that generally higher correlability occurs in soil units defined by diagnostic horizons, properties or materials with preference of morphological properties. Representatives of this group, Fluvizem (Fluvisol), Glej (Gleysol) and Smonice (Vertisol), show 100% homogeneity of the converted soil units. Shi et al. (2010) determine 71% referencibility of Fluvisol (neo-alluvial soil in Chinese classification) and only 48% of Gleysols (aquaeous soil in Chinese classification). Absolute accuracy was determined for Hnědozem and Šedozem, both defined by clay migration in the profile. Minimum values required for luvic/argic horizon in CTCSS are higher (clay ratio
Table 3 Problematic soil units with conflict conversion in WRB. CTCSS
WRB analogical soil unit
Rendzina, Pararendzina, Ranker
Leptosol
Černozem
Černice
Luvizem Kambizem
Pelozem
Podzol
Pseudoglej
a
Chernozem
Gleyic Chernozem
Albeluvisol Cambisol
Cambisol (Clayic)
Podzol
Stagnosol
Conflicting criteria WRB Coarse fragment % N 80% Calcic horizon/secondary carbonates Starting lower than 50 cm below the lower limit of mollic horizon Calcic horizon/secondary carbonates Starting lower than 50 cm below the lower limit of mollic horizon Albeluvic tonguing Present Clay ratio 1.2 in loamy soils, 3% in sandy soils, 8% in clayey soils Texture Loamy sand or coarser, if cumulative layers of finer texture are less than 15 cm thick Texture Not having texture of clay in a layer 30 cm or more thick SOC % in B horizon b 0.5% pH (H2O) (unless being cultivated) N 5.9 Abrupt textural change Present within 100 cm of the soil surface Albeluvic tonguing Present
CTCSS
WRB accessory soil unit
Ratea
⇒Cambisol
17%
⇒Phaeozem
9%
⇒ Gleyic Phaeozem
33%
⇒Albic Luvisol
37%
⇒Luvisol
4%
⇒Arenosol
2%
⇒Cambisol (Siltic)
50%
⇒Cambisol (Dystric)
16%
⇒Cambisol (Dystric)
16%
⇒Planosol
23%
⇒Stagnic Albeluvisol
3%
N 50% Starting depth not defined
Starting depth not defined
Not necessary Not taken as criterion, clay coatings necessary Not taken as criterion
Clay, sandy clay, silty clay
Not taken as criterion Not taken as criterion Not taken as criterion Not taken as criterion
The rate indicates the percentage of profiles correlated with different soil units due to conflicting criteria between WRB and CTCSS.
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Fig. 1. Correspondence and interrelation of soil units between CTCSS and WRB. The wide frames indicate WRB soil units correlated with given CTCSS soil unit. E.g. CTCSS Kambizem was correlated with WRB Cambisol, Luvisol, Regosol, Arenosol and Podzol.
between Bt and E horizons 1.3) than in WRB (clay ratio 1.2). This implies an automatic conversion of the luvic horizon in CTCSS to argic horizon in WRB. Šedozem conversion to Luvic Greyic Phaeozem may be considered as problematic. Greyic qualifier requires uncoated silt and sand grains on structural faces within 5 cm of the mineral soil surface, which can be considered as matter on argument in mixed plough layer. Regozem arenic was in 100% cases converted to Arenosol. The high accuracy results from an identical texture limit for both units. WRB requires less than 15 cm of texture finer than sand or loamy sand which can be a potential restraint in the correlation (sand or loamy sand in the whole profile in CTCSS). Conversion of other soil units shows lower accuracy and higher heterogeneity of converted soil units. The inhomogeneity results in more or less marked overlap of diagnosed soil units (Fig. 1). Leptosols in WRB have continuous rock starting within 25 cm of the soil surface or they have 80% or more coarse material averaged over the first 75 cm. For four CTCSS soil types, Leptosols are suggested to be the analogical soil unit: Litozems, Rankers, Rendzinas and Pararendzinas. Litozems were not included in the study. Rankers,
Rendzinas and Pararendzinas are defined by having 50% or more coarse material in the profile so that they were partially converted to Cambisols. Rankers were in 33% classified as Leptic Cambisol. Conversion of Rendzina (CTCSS) to Rendzic Leptosol (WRB) (Table 1) was not realised in any of the corresponding profiles. Rendzic qualifier in WRB is defined by a mollic horizon that contains or immediately overlies calcaric materials containing 40% or more calcium carbonate equivalent. Neither the high content of calcium carbonate, nor the criteria for the mollic horizon are met by the values of studied profiles. 75% of Rendzinas and 93% of Pararendzinas (CTCSS) are classified as Leptosols (Calcaric) in WRB. Reintam and Köster (2006) classify Rendzina profiles in WRB 1998 partially as Rendzic Leptosols and partially as Calcari-Mollic Cambisol and they discuss their possible conversion to Phaeozems. Accuracy of conversion of Černozem and Černice soil units is lowered by different requirements on calcic horizon position within the profile. WRB requires a calcic horizon, or concentrations of secondary carbonates starting within 50 cm below the lower limit of the mollic horizon for a Chernozem. This condition is not required in CTCSS. Deeper
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Table 4 Main causes of problems in the correlation of the CTCSS and the WRB. CTCSS soil type
WRB analogical soil unit
Different criteria or different concept
Different limit values
Indistinct wording of the criteria
Ranker Rendzina Pararendzina Regozem Fluvizem Smonice Černozem Černice Šedozem Hnědozem Luvizem Kambizem Pelozem Kryptopodzol Podzol Pseudoglej Glej
Leptosol (Skeletic) Rendzic Leptosol Leptosol (Calcaric) Regosol, Arenosol Fluvisol Vertisol Chernozem Gleyic Chernozem Greyic Phaeozem Luvisol Albeluvisol Cambisol Cambisol (Clayic) Entic Podzol Podzol Stagnosol Gleysol
– x – x – – – x – – x – – x x x –
x x x x – – x – – x x x x – x – x
– – – – – x – – x – – – – – – x x
different forms of Al and Fe) complicates (especially in the case of older legacy data) accurate classification. The decreased correlability of the Podzol soil unit is caused mainly by exceeding the spodic horizon pH limit (5.9 in WRB unless being cultivated, not set in CTCSS). The limit trespassing may be secondary caused by intensive amelioration or liming of forest soils. Soil with a higher pH in the spodic horizon must be classified as Cambisol (Dystric). Assignment to Cambisol (Dystric) can also result from the limit for organic carbon content in the spodic horizon (not assessed in CTCSS, 0.5% in WRB). According to our findings, three main issues influencing the correlability can be identified (Table 4): a different concept or different criteria, different limit values and an indistinct wording of the criteria in CTCSS. These correlation problems were identified even in soil units with 100% correlability (Table 2). This fact can be explained by the following reasons: 1) minimum values required in the diagnostic criteria are higher in CTCSS than in WRB (e.g. Hnědozem), 2) a very slight difference in CTCSS and WRB definitions of limit values/criteria (e.g. Smonice) that were not reflected by the studied dataset.
3.2. Conversion at lower taxonomical level: Soil subtype — qualifier
calcic horizons occurred in 9% of Černozems and 33% of Černice. These soils were classified as Phaeozems. Shi et al. (2010) identified only 61% referencibility in the Chernozem soil group in China. In CTCSS, Luvizems are represented by either a presence of albeluvic tonguing or a high clay ratio (N2.2) between E and Bt horizons. In WRB, the albeluvic tonguing is an essential diagnostic criterion for Albeluvisols. Luvizems having a significant textural change but not showing the albeluvic tonguing are classified as Albic Luvisols in WRB (37%). The presence of albeluvic tonguing influences also the conversion of Pseudoglej unit to its analogical RSG — Stagnosol. It can be present in Pseudoglejs Luvic in CTCSS, whilst it is restricted to Albeluvisols in WRB. Pseudoglej having an albeluvic tonguing must be automatically categorised in the Stagnic Albeluvisol unit (3% of Pseudoglejs). Pseudoglej profiles with a significant clay ratio meeting requirements of abrupt textural change were classified as Planosols (23%). Kambizems show high correlability that is given by broad scale of analytical and morphological features occurring in the unit. Accessory soil units are represented by Luvisols which are analogic to Kambizems Luvic (textural differentiation met requirements for argic horizon classification) and Arenosols correlated with extremely coarse Kambizems Arenic. In the Chinese classification, Cambisols reached the maximum referencibility in 28 out of 60 great soil groups (Shi et al., 2010). This fact fully illustrates the complexity of the soil unit. Pelozems fulfil criteria for Cambisol (Clayic) identification only in 50% of cases. Low correlability results from different limits of the clay content (Table 3). Half of the Pelozem profiles were classified as Cambisol (Siltic). Soil units defined by spodic horizon development demand a set of special laboratory analysis. A lack of detailed soil data (rates of
Lower taxonomical levels – soil subtype and qualifiers – represent various modifications of soil types and Reference Soil Groups, respectively. Correlation was analysed for the elements listed in Table 5. Correlability at the subtype/qualifier taxonomical level is significantly lower than in the case of soil type/RSG. This discrepancy is caused by a different setting of diagnostic criteria (Table 5). A difference in units characterised by textural properties is particularly high. Pelic (CTCSS) subtype was diagnosed in 111 profiles meeting the requirement of clay, sandy clay, silty clay, sandy clay loam, clay loam or silty clay loam. The textural range in CTCSS is much broader compared to the WRB setting (texture of clay) (Fig. 2). A difference in arenic soil unit is furthermore increased by the fact that the Arenic qualifier was not set for Cambisols in WRB. A contrasting situation was noted for the dystric unit. In the WRB classification, 81 soil profiles correspond to Dystric qualifier, whilst only 22 samples were classified as dystric in CTCSS. Disagreement is caused by the setting of a different base saturation (BS) limit in both systems (Table 5). Part of the profiles classified as Dystric in WRB corresponds to mesobasic variety in the CTCSS. The lower accuracy of luvic — Luvic correlability is due to a very changeable definition of the luvic subtype in CTCSS (both quantitative and qualitative criteria in different soil types). Problems in the correlation of hydromorphic subunits (Stagnic, Gleyic) result from two reasons: the depth of the hydromorphic feature occurence (100 cm in WRB, 60 cm in CTCSS) and an imprecise setting of the hydromorhic criteria (medium or strong redoximorphic/reducing conditions) in CTCSS. The lower number of profiles with stagnic or gleyic subtypes identified in CTCSS is also due to the fact that weaker features of hydromorphism (comparable to the Stagnic and Gleyic qualifiers of WRB) are dealt with in the next lower level of CTCSS, which is the variety level.
Table 5 Correlation at the lower taxonomical level. CTCSS subtype
Count
Criteria CTCSS
WRB qualifier
Count
Criteria WRB
Arenic Pelic Dystric Luvic Stagnic
19 111 22 52 47
Sand, loamy sand or sand, loamy sand, sandy loam Clay, sandy clay, silty clay, sandy clay loam, clay loam, silty clay loam BS less than 30% Variable criteria in different soil types Medium redoximorphic features within 60 cm from soil surface
Arenic Clayic Dystric Luvic Stagnic
1 29 81 28 110
Gleyic
15
Strong reducing conditions below 60 cm from soil surface
Gleyic
36
Sand, loamy sand in a layer 30 cm or more thick Clay in a layer 30 cm or more thick BS less than 50% Having a luvic horizon 25% or more of soil volume stagnic colour pattern within 100 cm from soil surface 25% or more of soil volume gleyic colour pattern within 100 cm from soil surface
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Fig. 2. Limit values of pelic – Clayic (left) and arenic – Arenic (right).
4. Conclusions The study showed the necessity of an analytical approach and quantitative data use in cross-reference and harmonisation of two soil classification systems, national CTCSS and international WRB, rather than relying solely on semantic correlation represented by equating similar taxonomic names as it has been done by providing the analogical soil names (Němeček et al., 2011). Discrepancies between the two approaches appeared at both lower (subtype) and higher (soil type) levels. The general level of correlability can be considered as high (88%), whilst there is a significant variability of correlation between soil types. The conversion of some soil units, e.g. Rankers, Rendzinas, Pararendzinas, Černice, Černozems, Podzols or Luvizems, has low accuracy and requires analytical and morphological data of corresponding profiles. Some units such as Glejs, Fluvizems or Hnědozems can be correlated with a high probability of accurate assignment. The study showed high incompatibility at the level of soil subtypes/qualifiers. Correlation at the lower taxonomical level should be subject to analytical processing of quantitative soil data. Three main causes of problems in correlating the WRB and the CTCSS are different concepts or criteria of the soil unit, different limit values and indistinct criteria in CTCSS. The most frequent problem is the different setting of limit values. The quantitative correlation approach involving an analysis of laboratory and morphological data of each soil profile is necessary in large-scale soil mapping. However, it is exceedingly demanding in general maps of middle and small scales. The correlation approach presented in our study does not solve the problem of the potential complexity of mapping units. The mapping unit that is originally represented by unique soil unit in national classification (CTCSS) can contain two or more WRB units (and vice versa). A map with WRB legend then should be presented as a soil association map even if the original map represented single soil typological units (IUSS Working Group WRB, 2010). In this case, the application of analytical properties for particular soil taxa statistically generated from the soil survey will be the appropriate approach. The statistical analysis will produce a representative soil profile which will be used for the correlation. This method can solve the conflict between high demand for data and accuracy of the cross-reference between different soil classification systems and fulfil the actual demand for quantitative data use at the same time.
CTCSS ranks amongst the national classification systems developed with the aim to be simply referencible with WRB. The study shows, that despite the similarity of soil taxa, the partial maintenance of the traditional concept of some soil groups results in significant problems in the correlation with WRB. National classifications should preserve their specificities to be accepted by the wider national community dealing with soil science. However, reliable correlation of national soil classification and the WRB scheme relies on careful correlation of individual soil profiles based on an analysis of quantitative soil data. Acknowledgements Authors acknowledge the financial support of EU (EU FP7-ENV2007-1 — e-SOTER: Regional pilot platform as EU contribution to a Global Soil Observing System) and the Ministry of Education, Youth and Sports (grant no. MSM 6046070901). References Barreta-Bassols, N., Zinck, J.A., Ranst, E.V., 2006. Local soil classification and comparison of indigenous and technical soil maps in a Mesoamerican community using spatial analysis. Geoderma 135, 140–162. Birkeland, P.W., 1999. Soils and Geomorphology, third ed. Oxford University Press, New York. Bockheim, J.G., Gennadiyev, A.N., 2000. The role of soil-forming processes in the definition of taxa in Soil Taxonomy and the World Soil Reference Base. Geoderma 95, 53–72. Dazzi, C., Papa, G.L., Palermo, V., 2009. Proposal for a new diagnostic horizon for WRB Anthrosols. Geoderma 151, 16–21. Droogers, P., Bouma, J., 1997. Soil survey input in exploratory modeling of sustainable soil management practices. Soil Science Society of America Journal 61, 1704–1710. European Soil Bureau Network, Scientific Committee, 2003. The European Soil Database (distribution version 2). EUR 19945 Online CD-ROM. FAO, 1998. World Reference Base for Soil Resources. FAO, Rome. Gerasimova, M.I., 2010. Chinese soil taxonomy: between the American and the international classification systems. Eurasian Soil Science 43, 945–949. Gimenéz, J.E., 2011. The world reference base for soil resources (WRB) and its application to some soils of Argentina. Geociencias 30, 15–21. Gray, J.M., Humphreys, G.S., Deckers, J.A., 2011. Distribution patterns of World Reference Base soil groups relative to soil forming factors. Geoderma 160, 373–383. Hraško, J., Němeček, J., Šály, R., Šurina, B., 1991. Morphogenetic Classification System of Soils of the CSFR. VÚPÚ, Bratislava, in Slovak. IUSS Working Group WRB, 2010. Guidelines for Constructing Small-Scale Map Legends Using the World Reference Base for Soil Resources. Addendum to the World Reference Base for Soil Resources, FAO, Rome.
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IUSS Working Group WRB, 2006. World Reference Base for Soil Resources 2006. FAO, Rome. Kleber, M., Mikutta, C., Jahn, R., 2004. Andosols in Germany — pedogenesis and properties. Catena 56, 67–83. Krasilnikov, P., Ibanez-Martí, J.-J., Arnold, R.W., Shoba, S., 2009. Handbook of Soil Terminology. Correlation and Classification, Earthscan, London. Láng, V., Fuchs, M., Waltner, I., Michéli, E., 2010. Taxonomic distance measurements applied for soil correlation. Agrokémia és Talajtan 59, 57–64. Minasny, B., McBratney, A., Hartemink, A.E., 2010. Global pedodiversity, taxonomic distance, and the World Reference Base. Geoderma 155, 132–139. Ministry of agriculture, 1963–1967. Large-scale Mapping of Agricultural Soils in Czechoslovakia. Final reports, in Czech. Mojiri, A., Jalalian, A., Honarjoo, N., 2011. Comparison between keys to soil taxonomy and WRB to classification of soils in Segzi Plain (Iran). Journal of Applied Sciences 11, 579–583. Namriah, Badayos, R.B., 2007. The soil and land classification system of Munanese Farmers in Muna Island, Southeast Sulawesi, Indonesia. Philippine Agricultural Scientist 90, 231–243.
Němeček, J., Macků, J., Vokoun, J., Vavříček, D., Novák, P., 2001. Czech Taxonomic Classification System of Soils. ČZU, in Czech, Praha. Němeček, J., Mühlhanselová, M., Macků, J., Vokoun, J., Vavříček, D., Novák, P., 2011. Czech Taxonomic Classification System of Soils. ČZU, Praha, in Czech. Reintam, E., Köster, T., 2006. The role of chemical indicators to correlate some Estonian soils with WRB and Soil Taxonomy criteria. Geoderma 136, 199–209. Shi, X.Z., Yu, D.S., Warner, E.D., Sun, W.X., Petersen, G.W., Gong, Z.T., 2006. Crossreference system for translating between Genetic Soil Classification of China and Soil Taxonomy. Soil Science Society of America Journal 70, 78–83. Shi, X.Z., Yu, D.S., Xu, S.X., Warner, E.D., Wang, H.J., Sun, W.X., Zhao, Y.C., Gong, Z.T., 2010. Cross-reference for relating Genetic Soil Classification of China with WRB at different scales. Geoderma 155, 344–350. Shoba, S.A. (Ed.), 2002. Soil Terminology and Correlation, Second ed. Centre of the Russian Academy of Sciences, Petrozavodsk. Smith, G.D., 1963. Objectives and basic assumptions of the new classification system. Soil Science 96, 6–16. USDA-NRCS, 1999. Soil Taxonomy, Agricultural Handbook No. 436, second ed. USDA. Vokoun, J., Macků, J., 1996. Classification System of Soils. ÚHÚL, Brandýs n.L, in Czech.
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Problems in correlation of Czech national soil classification and World Reference Base 2006 Tereza Zádorová a,⁎, Vít Penížek a, b a b
Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Kamýcká 129, Praha 6, Czech Republic Land Management & Natural Hazards Unit, Institute for Environment & Sustainability, Joint Research Centre, European Commission, Via E. Fermi 2749, 21027 Ispra (VA), Italy
a r t i c l e
i n f o
Article history: Received 25 January 2011 Received in revised form 12 September 2011 Accepted 13 September 2011 Available online 2 November 2011 Keywords: Soil systematics Soil units Soil taxa conversion Soil maps
a b s t r a c t The use of legacy data in international soil mapping projects entails the demand for the harmonisation of soil data. Accurate correlation between national and international soil classification units is an important prerequisite in global soil mapping and acquisition of harmonised soil data usable in environmental applications. The correlation of soil units at different taxonomic levels was undertaken to relate the Czech national soil classification system with the World Reference Base (WRB) and evaluate the effectiveness of a semantic approach (analogical soil units provided by expert knowledge), and quantitative approach in the correlation. For the quantitave approach, a set of 433 soil profiles randomly selected from the Large-scale mapping of agricultural soils in Czechoslovakia was classified according to WRB using available analytical and morphological soil data. The study showed the necessity for an analytical approach and quantitative data use for reliable correlation between the two classification schemes. The general level of correlability at the higher taxonomic level can be considered as high (88%), whilst there is a significant variability of correlation accuracy between soil types. Conversion of some soil units, e.g. Rankers, Rendzinas, Pararendzinas, Černice, Černozems, Podzols or Luvizems requires analytical and morphological data of corresponding profiles. Relatively low correlability is caused by various factors. Different concepts of the soil unit and different setting of the criteria of the diagnostic soil properties are the most important. Some units such as Glejs, Fluvizems or Hnědozems can be correlated with a high probability of accurate assignment. High incompatibility was shown at the lower taxonomic level. Correlation of lower taxonomic units should be subject to analytical processing. © 2011 Elsevier B.V. All rights reserved.
1. Introduction Soil classification is a fundamental part of soil research and data processing serving mainly as an organisational framework for soil property description (Droogers and Bouma, 1997; Shi et al., 2006; Smith, 1963). It is also an important instrument in the communication of soil research results at national and international levels of soil science. The first version of the World Reference Base of Soil Resources (FAO, 1998) was introduced at the 16th World Congress of Soil Science in 1998 as a standard for soil classification correlation. The actual version of the WRB was presented in 2006 (IUSS Working Group WRB, 2006) and further updated in 2007. WRB, together with the Soil Taxonomy (USDA-NRCS, 1999), is an internationally used system for presentation of results in soil research. WRB is partly used for soil classification at a national level (e.g. Italy), whereas a high number of countries implement national classification in a different stage of correlability with WRB or Soil Taxonomy (Barreta-
⁎ Corresponding author. Tel.: + 420 224382591. E-mail address: [email protected] (T. Zádorová). 0016-7061/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.geoderma.2011.09.014
Bassols et al., 2006; Dazzi et al., 2009; Kleber et al., 2004; Reintam and Köster, 2006). IUSS Working Group WRB (2010) published guidelines for the creation of a legend for small-scale soil mapping in the WRB scheme, making WRB more suitable for designing basic soil maps. The majority of spatial soil data originates from national classification systems. The demand for harmonisation of national and international classification systems increased mainly within the context of the need for harmonised global soil data that can serve as input data in environmental modelling (e.g. climate changes). International projects on digital soil mapping (e.g. e-SOTER, GlobalSoilMap) and global soil database development are examples of such activities. Part of this task is the creation of soil maps in regions with national borders. This requires harmonisation of soil polygon classification, often based on the conversion of national taxa into global soil systems that reveal frequent discrepancies in diagnostics of some taxa at both higher and lower taxonomic levels. The difference in diagnostics may result from various factors: soil taxon of national classification is broader or narrower than a WRB group; taxa only partially coincide. In the correlation of soil map units, these diagnostic differences imply the need of primary field data use and soil profile reclassification (Krasilnikov et al., 2009; Shoba, 2002). Nevertheless, a lack of
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analytical data often results in semantic correlation based on simple translation of taxon name regardless of diagnostic characteristics. This approach was formerly applied in small scale general mapping, but the inaccuracy, particularly in detailed mapping is significant (Krasilnikov et al., 2009). For example, soil type Rendzina (designation used in many national classifications) can be correlated in WRB as Rendzic Leptosol, Cambisol or Regosol (Calcaric) or even as Rendzic Phaeozem when using the analytical data (Reintam and Köster, 2006). The possibility and accuracy of correlation depend mainly on limits of soil features required for diagnostics of a particular soil classification unit. The correlability of each soil unit then depends on the degree of similarity (or conformity) of the limits determining its diagnostics. The limits can vary significantly in different reference units (Shi et al., 2010). The variation can be caused by a diverse setting of quantitative or qualitative criteria or by a different concept of the soil unit (Krasilnikov et al., 2009). Different general approaches to soil classification represent another important obstacle to effective correlation. Historically, soil systematics development had three main phases. Classification based on soil properties changed into genetic classifications and then back to preference of soil properties represented by analytical and morphological features (Bockheim and Gennadiyev, 2000). The genetic approach or its remains persisting in several national classification systems can complicate the correlation and lower its accuracy. Recently, some authors accentuated the importance of consideration of soil processes for understanding the genetic basis of modern soil taxonomic systems and developing quantitative models of pedogenic systems (Bockheim and Gennadiyev, 2000). Gray et al. (2011) examined the relationship of WRB groups and soil forming factors such as climate and parent material. Soil mapping may be significantly facilitated by a clear knowledge of the relationships between soil group distribution and soil forming factors not only at the global mapping scale, but also at the level of national maps and their potential harmonisation with global soil classification (Birkeland, 1999; Gray et al., 2011). Different methods in soil data acquisition, especially the use of diverse analytical methods for soil properties can be fundamental mainly in the conversion of older data. Several studies on soil classification and correlation were published in recent years. The most extensive correlation project was the conversion of European national datasets into WRB 1998 for elaboration of the European Soil Database 1:1 M (ESBN, 2003). Shi et al. (2006, 2010) correlated Chinese soil classification with WRB and Soil Taxonomy. A comparison of Chinese soil classification with international classification was also undertaken by Gerasimova (2010). Reintam and Köster (2006) accentuated the significance of analytical properties of soils for the correlation with WRB and Soil Taxonomy. Gimenéz (2011) applied the WRB for the classification of some Argentinean soil units showing its applicability in the case of buried soils. Mojiri et al. (2011) compared the effectiveness of Soil Taxonomy and WRB in aridic soil classification. Traditional Indonesian soil classification was compared with a Soil Taxonomy scheme by Namriah and Badayos (2007). Taxonomic distances for the WRB were derived by Minasny et al. (2010) and the method was applied also for Hungarian soil units (Láng et al., 2010). Soil classification in the Czech Republic is actually represented by Czech Taxonomic Classification System of Soils (CTCSS; Němeček et al., 2001, 2011). The soil classification proceeds with previous systems: Morphogenetic Classification System of Soils (Hraško et al., 1991) and classification system of forest soils (Vokoun and Macků, 1996). The CTCSS classification was designed to meet the requirements for effective correlation of higher taxonomical units with international taxa. Consequently, the principle of morphogenetic feature preference was substituted for analytical data approach. Higher taxonomical unit assessment is based on diagnostic horizons and features below 0.25 m of depth (reduction of influence of different land use).
55
The CTCSS is a multi-categorical system. Reference groups, soil types and subtypes reflect results of the long-term soil evolution. Reference groups represent a linkage to the highest taxonomic categories of the world reference systems (WRB, Soil Taxonomy) and the other wellknown soil classification systems (German, French, Canadian). Soil types (great soil groups) are a central taxonomic category, which involves soil being characterised by a specific sequence of diagnostic horizons and/or diagnostic features. Subtypes as subdivisions of the soil types include modal (typical), inter-grade representatives, extreme debasification degrees and extremes of the soil texture (Němeček et al., 2011). Subtypes serve the same role as the qualifiers in WRB, but their criteria vary slightly in some soil types in CTCSS (e.g. Arenic subtype has different limits in Černozem and in Kambizem). The presented study was performed to assess accuracy and effectiveness of correlation between Czech soil classification (as an example of national classification) and the international World Reference Base. The study also seeks to point out the major problems in their harmonisation and to evaluate the importance of the analytical data in the correlation process. 2. Material and methods Based on expert knowledge, the CTCSS provides for each of its reference groups an analogical soil unit according to WRB 2006 (Němeček et al., 2011; Table 1). In our study, the accuracy of this analogy was tested with a set of 433 soil profiles randomly selected from the reports of the Large-scale mapping of agricultural soil in Czechoslovakia (Ministry of Agriculture, 1963–1967). The number of profiles in each taxonomical unit was selected proportionally according to their spatial extent in the Czech Republic. Every profile was classified according to WRB using the following morphological and analytical profile data: profile stratigraphy, horizon thickness, soil colour, texture, organic carbon content, soil reaction, cation exchange capacity and base saturation. This test was possible for the majority of the taxonomical units of the national classification (except for Litozems, Koluvizems, Anthropozems, Kultizems and Organozems which were not included in the original field survey). The correlability level represents the largest relative number of profiles of a certain CTCSS soil unit falling after WRB classification into a certain WRB soil unit. This may or may not be the analogical soil unit. The correlability was evaluated as high if 80–100% of the Table 1 CTCSS soil types and their WRB analogical soil units according to Němeček et al. (2011). CTCSS soil type
WRB06 Reference Soil Group/qualifier
Litozem Ranker Rendzina Pararenzdina Regozem Fluvizem Koluvizem Smonice Černozem Černice Šedozem Hnědozem Luvizem Kambizem Pelozem Kryptopodzol Podzol Pseudoglej Stagnoglej Glej Organozem Kultizem Antropozem
Lithic Leptosol Leptosol (Skeletic) Rendzic Leptosol Leptosol (Calcaric) Regosol, Arenosol Fluvisol – Vertisol Chernozem Gleyic Chernozem Greyic Phaeozem Luvisol Albeluvisol Cambisol Cambisol (Clayic) Entic Podzol Podzol Stagnosol Stagnosol Gleysol Histosol Anthrosol Technosol
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Table 2 Correlability of soil units in CTCSS and WRB 2006 (soil types and Reference Soil Groups+ qualifiers). CTCSS
WRB 2006
Correlability (%)
Accesory units
Ranker Rendzina Pararendzina Regozem Regozem Arenic Fluvizem Smonice Černozem Černice Šedozem Hnědozem Luvizem Kambizem Pelozem Kryptopodzol Podzol Pseudoglej
Haplic Leptosol Leptosol (Calcaric) Leptosol (Calcaric) Regosol Arenosol Fluvisol Vertisol Chernozem Gleyic Chernozem Luvic Greyic Phaeozem Luvisol Albeluvisol Cambisol Cambisol (Clayic) Entic Podzol Podzol Stagnosol
67 75 92 80 100 100 100 91 67 100 100 63 94 50 100 67 74
Glej Total
Gleysol
100 88
Leptic Cambisol Cambisol (Eutric) Cambisol (Eutric) Chernozem – – – Phaeozem Gleyic Phaeozem – – Albic Luvisol Luvisol, Arenosol Cambisol (Siltic) – Cambisol (Dystric) Planosol, Stagnic Albeluvisol –
profiles were classified into the same soil unit. Values from 60% to 80% correspond to medium correlability, values under 60% to low correlability of the soil unit (Shi et al., 2010). Correlation was assessed in both higher taxonomical level (soil types in CTCSS and Reference Soil Groups (RSGs) in the WRB) and lower taxonomical level (subtype in CTCSS and qualifier in WRB). Reference groups (CTCSS) were originally intended for the correlation with RSGs (WRB). Soil types were chosen for the correlation in this study since the absolute majority of soil maps describes soil types. The other reason is that both soil types and RSGs are defined by strict diagnostic criteria which are
quantitatively comparable, whilst the Reference groups in CTCSS are classified only according to the prevailing soil-forming process. Correlation at the lower level included only subtypes/qualifiers occurring in more than 5 soil profiles in one or both of the classification systems. Correlated units were: arenic (CTCSS)/Arenic (WRB), pelic/Clayic, dystric/Dystric, luvic/Luvic, stagnic/Stagnic, gleyic/Gleyic. 3. Results and discussion 3.1. Conversion at high taxonomical soil units: soil type — Reference Soil Group Average correlability at the level of soil type — RSG was 88%. Overall accuracy of the correlation is high, but it differs significantly, from 50% to 100% within particular soil units (Table 2). The number of Czech soil types with high (80–100%), intermediate (60–80%) and low (less than 60%) correlability amounts to 10, 7 and 1, respectively. Shi et al. (2010) determined maximum referencibility of Chinese soil great groups and WRB between 29.4% and 100% when the high referencibility (more than 80%) was detected in 12 from 60 soil groups and low referencibility in 21 soil groups. Lower correlability is caused by various factors (Table 3). Different concepts of the soil unit and different setting of the limits of the diagnostic soil properties are the most important. Results showed that generally higher correlability occurs in soil units defined by diagnostic horizons, properties or materials with preference of morphological properties. Representatives of this group, Fluvizem (Fluvisol), Glej (Gleysol) and Smonice (Vertisol), show 100% homogeneity of the converted soil units. Shi et al. (2010) determine 71% referencibility of Fluvisol (neo-alluvial soil in Chinese classification) and only 48% of Gleysols (aquaeous soil in Chinese classification). Absolute accuracy was determined for Hnědozem and Šedozem, both defined by clay migration in the profile. Minimum values required for luvic/argic horizon in CTCSS are higher (clay ratio
Table 3 Problematic soil units with conflict conversion in WRB. CTCSS
WRB analogical soil unit
Rendzina, Pararendzina, Ranker
Leptosol
Černozem
Černice
Luvizem Kambizem
Pelozem
Podzol
Pseudoglej
a
Chernozem
Gleyic Chernozem
Albeluvisol Cambisol
Cambisol (Clayic)
Podzol
Stagnosol
Conflicting criteria WRB Coarse fragment % N 80% Calcic horizon/secondary carbonates Starting lower than 50 cm below the lower limit of mollic horizon Calcic horizon/secondary carbonates Starting lower than 50 cm below the lower limit of mollic horizon Albeluvic tonguing Present Clay ratio 1.2 in loamy soils, 3% in sandy soils, 8% in clayey soils Texture Loamy sand or coarser, if cumulative layers of finer texture are less than 15 cm thick Texture Not having texture of clay in a layer 30 cm or more thick SOC % in B horizon b 0.5% pH (H2O) (unless being cultivated) N 5.9 Abrupt textural change Present within 100 cm of the soil surface Albeluvic tonguing Present
CTCSS
WRB accessory soil unit
Ratea
⇒Cambisol
17%
⇒Phaeozem
9%
⇒ Gleyic Phaeozem
33%
⇒Albic Luvisol
37%
⇒Luvisol
4%
⇒Arenosol
2%
⇒Cambisol (Siltic)
50%
⇒Cambisol (Dystric)
16%
⇒Cambisol (Dystric)
16%
⇒Planosol
23%
⇒Stagnic Albeluvisol
3%
N 50% Starting depth not defined
Starting depth not defined
Not necessary Not taken as criterion, clay coatings necessary Not taken as criterion
Clay, sandy clay, silty clay
Not taken as criterion Not taken as criterion Not taken as criterion Not taken as criterion
The rate indicates the percentage of profiles correlated with different soil units due to conflicting criteria between WRB and CTCSS.
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Fig. 1. Correspondence and interrelation of soil units between CTCSS and WRB. The wide frames indicate WRB soil units correlated with given CTCSS soil unit. E.g. CTCSS Kambizem was correlated with WRB Cambisol, Luvisol, Regosol, Arenosol and Podzol.
between Bt and E horizons 1.3) than in WRB (clay ratio 1.2). This implies an automatic conversion of the luvic horizon in CTCSS to argic horizon in WRB. Šedozem conversion to Luvic Greyic Phaeozem may be considered as problematic. Greyic qualifier requires uncoated silt and sand grains on structural faces within 5 cm of the mineral soil surface, which can be considered as matter on argument in mixed plough layer. Regozem arenic was in 100% cases converted to Arenosol. The high accuracy results from an identical texture limit for both units. WRB requires less than 15 cm of texture finer than sand or loamy sand which can be a potential restraint in the correlation (sand or loamy sand in the whole profile in CTCSS). Conversion of other soil units shows lower accuracy and higher heterogeneity of converted soil units. The inhomogeneity results in more or less marked overlap of diagnosed soil units (Fig. 1). Leptosols in WRB have continuous rock starting within 25 cm of the soil surface or they have 80% or more coarse material averaged over the first 75 cm. For four CTCSS soil types, Leptosols are suggested to be the analogical soil unit: Litozems, Rankers, Rendzinas and Pararendzinas. Litozems were not included in the study. Rankers,
Rendzinas and Pararendzinas are defined by having 50% or more coarse material in the profile so that they were partially converted to Cambisols. Rankers were in 33% classified as Leptic Cambisol. Conversion of Rendzina (CTCSS) to Rendzic Leptosol (WRB) (Table 1) was not realised in any of the corresponding profiles. Rendzic qualifier in WRB is defined by a mollic horizon that contains or immediately overlies calcaric materials containing 40% or more calcium carbonate equivalent. Neither the high content of calcium carbonate, nor the criteria for the mollic horizon are met by the values of studied profiles. 75% of Rendzinas and 93% of Pararendzinas (CTCSS) are classified as Leptosols (Calcaric) in WRB. Reintam and Köster (2006) classify Rendzina profiles in WRB 1998 partially as Rendzic Leptosols and partially as Calcari-Mollic Cambisol and they discuss their possible conversion to Phaeozems. Accuracy of conversion of Černozem and Černice soil units is lowered by different requirements on calcic horizon position within the profile. WRB requires a calcic horizon, or concentrations of secondary carbonates starting within 50 cm below the lower limit of the mollic horizon for a Chernozem. This condition is not required in CTCSS. Deeper
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Table 4 Main causes of problems in the correlation of the CTCSS and the WRB. CTCSS soil type
WRB analogical soil unit
Different criteria or different concept
Different limit values
Indistinct wording of the criteria
Ranker Rendzina Pararendzina Regozem Fluvizem Smonice Černozem Černice Šedozem Hnědozem Luvizem Kambizem Pelozem Kryptopodzol Podzol Pseudoglej Glej
Leptosol (Skeletic) Rendzic Leptosol Leptosol (Calcaric) Regosol, Arenosol Fluvisol Vertisol Chernozem Gleyic Chernozem Greyic Phaeozem Luvisol Albeluvisol Cambisol Cambisol (Clayic) Entic Podzol Podzol Stagnosol Gleysol
– x – x – – – x – – x – – x x x –
x x x x – – x – – x x x x – x – x
– – – – – x – – x – – – – – – x x
different forms of Al and Fe) complicates (especially in the case of older legacy data) accurate classification. The decreased correlability of the Podzol soil unit is caused mainly by exceeding the spodic horizon pH limit (5.9 in WRB unless being cultivated, not set in CTCSS). The limit trespassing may be secondary caused by intensive amelioration or liming of forest soils. Soil with a higher pH in the spodic horizon must be classified as Cambisol (Dystric). Assignment to Cambisol (Dystric) can also result from the limit for organic carbon content in the spodic horizon (not assessed in CTCSS, 0.5% in WRB). According to our findings, three main issues influencing the correlability can be identified (Table 4): a different concept or different criteria, different limit values and an indistinct wording of the criteria in CTCSS. These correlation problems were identified even in soil units with 100% correlability (Table 2). This fact can be explained by the following reasons: 1) minimum values required in the diagnostic criteria are higher in CTCSS than in WRB (e.g. Hnědozem), 2) a very slight difference in CTCSS and WRB definitions of limit values/criteria (e.g. Smonice) that were not reflected by the studied dataset.
3.2. Conversion at lower taxonomical level: Soil subtype — qualifier
calcic horizons occurred in 9% of Černozems and 33% of Černice. These soils were classified as Phaeozems. Shi et al. (2010) identified only 61% referencibility in the Chernozem soil group in China. In CTCSS, Luvizems are represented by either a presence of albeluvic tonguing or a high clay ratio (N2.2) between E and Bt horizons. In WRB, the albeluvic tonguing is an essential diagnostic criterion for Albeluvisols. Luvizems having a significant textural change but not showing the albeluvic tonguing are classified as Albic Luvisols in WRB (37%). The presence of albeluvic tonguing influences also the conversion of Pseudoglej unit to its analogical RSG — Stagnosol. It can be present in Pseudoglejs Luvic in CTCSS, whilst it is restricted to Albeluvisols in WRB. Pseudoglej having an albeluvic tonguing must be automatically categorised in the Stagnic Albeluvisol unit (3% of Pseudoglejs). Pseudoglej profiles with a significant clay ratio meeting requirements of abrupt textural change were classified as Planosols (23%). Kambizems show high correlability that is given by broad scale of analytical and morphological features occurring in the unit. Accessory soil units are represented by Luvisols which are analogic to Kambizems Luvic (textural differentiation met requirements for argic horizon classification) and Arenosols correlated with extremely coarse Kambizems Arenic. In the Chinese classification, Cambisols reached the maximum referencibility in 28 out of 60 great soil groups (Shi et al., 2010). This fact fully illustrates the complexity of the soil unit. Pelozems fulfil criteria for Cambisol (Clayic) identification only in 50% of cases. Low correlability results from different limits of the clay content (Table 3). Half of the Pelozem profiles were classified as Cambisol (Siltic). Soil units defined by spodic horizon development demand a set of special laboratory analysis. A lack of detailed soil data (rates of
Lower taxonomical levels – soil subtype and qualifiers – represent various modifications of soil types and Reference Soil Groups, respectively. Correlation was analysed for the elements listed in Table 5. Correlability at the subtype/qualifier taxonomical level is significantly lower than in the case of soil type/RSG. This discrepancy is caused by a different setting of diagnostic criteria (Table 5). A difference in units characterised by textural properties is particularly high. Pelic (CTCSS) subtype was diagnosed in 111 profiles meeting the requirement of clay, sandy clay, silty clay, sandy clay loam, clay loam or silty clay loam. The textural range in CTCSS is much broader compared to the WRB setting (texture of clay) (Fig. 2). A difference in arenic soil unit is furthermore increased by the fact that the Arenic qualifier was not set for Cambisols in WRB. A contrasting situation was noted for the dystric unit. In the WRB classification, 81 soil profiles correspond to Dystric qualifier, whilst only 22 samples were classified as dystric in CTCSS. Disagreement is caused by the setting of a different base saturation (BS) limit in both systems (Table 5). Part of the profiles classified as Dystric in WRB corresponds to mesobasic variety in the CTCSS. The lower accuracy of luvic — Luvic correlability is due to a very changeable definition of the luvic subtype in CTCSS (both quantitative and qualitative criteria in different soil types). Problems in the correlation of hydromorphic subunits (Stagnic, Gleyic) result from two reasons: the depth of the hydromorphic feature occurence (100 cm in WRB, 60 cm in CTCSS) and an imprecise setting of the hydromorhic criteria (medium or strong redoximorphic/reducing conditions) in CTCSS. The lower number of profiles with stagnic or gleyic subtypes identified in CTCSS is also due to the fact that weaker features of hydromorphism (comparable to the Stagnic and Gleyic qualifiers of WRB) are dealt with in the next lower level of CTCSS, which is the variety level.
Table 5 Correlation at the lower taxonomical level. CTCSS subtype
Count
Criteria CTCSS
WRB qualifier
Count
Criteria WRB
Arenic Pelic Dystric Luvic Stagnic
19 111 22 52 47
Sand, loamy sand or sand, loamy sand, sandy loam Clay, sandy clay, silty clay, sandy clay loam, clay loam, silty clay loam BS less than 30% Variable criteria in different soil types Medium redoximorphic features within 60 cm from soil surface
Arenic Clayic Dystric Luvic Stagnic
1 29 81 28 110
Gleyic
15
Strong reducing conditions below 60 cm from soil surface
Gleyic
36
Sand, loamy sand in a layer 30 cm or more thick Clay in a layer 30 cm or more thick BS less than 50% Having a luvic horizon 25% or more of soil volume stagnic colour pattern within 100 cm from soil surface 25% or more of soil volume gleyic colour pattern within 100 cm from soil surface
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Fig. 2. Limit values of pelic – Clayic (left) and arenic – Arenic (right).
4. Conclusions The study showed the necessity of an analytical approach and quantitative data use in cross-reference and harmonisation of two soil classification systems, national CTCSS and international WRB, rather than relying solely on semantic correlation represented by equating similar taxonomic names as it has been done by providing the analogical soil names (Němeček et al., 2011). Discrepancies between the two approaches appeared at both lower (subtype) and higher (soil type) levels. The general level of correlability can be considered as high (88%), whilst there is a significant variability of correlation between soil types. The conversion of some soil units, e.g. Rankers, Rendzinas, Pararendzinas, Černice, Černozems, Podzols or Luvizems, has low accuracy and requires analytical and morphological data of corresponding profiles. Some units such as Glejs, Fluvizems or Hnědozems can be correlated with a high probability of accurate assignment. The study showed high incompatibility at the level of soil subtypes/qualifiers. Correlation at the lower taxonomical level should be subject to analytical processing of quantitative soil data. Three main causes of problems in correlating the WRB and the CTCSS are different concepts or criteria of the soil unit, different limit values and indistinct criteria in CTCSS. The most frequent problem is the different setting of limit values. The quantitative correlation approach involving an analysis of laboratory and morphological data of each soil profile is necessary in large-scale soil mapping. However, it is exceedingly demanding in general maps of middle and small scales. The correlation approach presented in our study does not solve the problem of the potential complexity of mapping units. The mapping unit that is originally represented by unique soil unit in national classification (CTCSS) can contain two or more WRB units (and vice versa). A map with WRB legend then should be presented as a soil association map even if the original map represented single soil typological units (IUSS Working Group WRB, 2010). In this case, the application of analytical properties for particular soil taxa statistically generated from the soil survey will be the appropriate approach. The statistical analysis will produce a representative soil profile which will be used for the correlation. This method can solve the conflict between high demand for data and accuracy of the cross-reference between different soil classification systems and fulfil the actual demand for quantitative data use at the same time.
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