Ž .Catena 34 1998 113–129

Soil-age relationships and correlations: comparisonof chronosequences in the Ljubljana Basin,

Slovenia and USA

Natasa J. Vidic )ˇUniÕersity of Ljubljana, Agronomy Department, JamnikarjeÕa 101, 61000 Ljubljana, SloÕenia

Abstract

A comparison of soil development indices established in chronosequences from the LjubljanaŽ . Ž . Ž . Ž .Basin humid climate , California Mediterranean , Pennsylvania humid and New Mexico arid

suggests that the rates of soil development expressed by the indices increase with increasingprecipitation, even though some individual soil properties, notably rubification, develop faster inxeric areas. Parent material composition does not seem to affect the indices significantly.Therefore similarity of present climate and climatic history between compared chronosequencesremains a pre-requisite for soil-age correlations. The chronofunctions are not suitable for exactdating and age correlations because variability of soil development within each geomorphic

Ž .surface prevents the dependent variables soil properties being used to predict the independentŽ .variable soil age . Changes in age estimates affect calculated rates of soil development. In the

Ljubljana Basin chronosequence rates changed by up to 20% for semi-logarithmic chronofunctionsand by up to 180% for linear chronofunctions if minimum or maximum ages were used instead ofbest estimates. Better dating methods and more replicate profiles on each geomorphic surfacewould diminish these problems. Statistical techniques that account for variability in both variablesŽ .maximum likelihood estimates also help improve age correlations. Within the Ljubljana Basinchronosequence some time dependent soil properties can be used to discriminate between groupsof geomorphic surfaces, but each of them has a limited ability to do so because the confidenceintervals for deposits overlap. The inability of soil properties to discriminate between all membersof the chronosequence is attributed to the large intrinsic variability of soils, the insufficientnumber of replicate profiles on each deposit and the decreasing rate of soil development with age.q 1998 Elsevier Science B.V. All rights reserved.

Keywords: Soil development indices; Soil texture; Rubification; Clay films

) Fax: q38-6-61-123-1088; E-mail: natasa.vidic@bf.uni-lj.si

0341-8162r98r$19.00 q 1998 Elsevier Science B.V. All rights reserved.Ž .PII: S0341-8162 98 00085-X

( )N.J. VidicrCatena 34 1998 113–129114

1. Introduction

The soils formed on many Quaternary deposits are often used for relative dating ofŽ .the deposits Birkeland, 1984 . Because of repeated climatic oscillations, Quaternary

sedimentation was often cyclic, and soils on the deposits commonly form chronose-Ž .quences, which were defined by Jenny 1941, 1980 as groups of soils that have formed

under similar conditions of parent material composition, climate, slope and vegetation,but differ in age. Chronosequences allow soil development to be examined as a functionof time, and have become important because it should be possible to use trends in timedependent soil properties for age correlation.

Soil properties used for age correlations between different chronosequences need toshow systematic trends and should be independent of other soil forming factors, such as

Ž .parent material and climate. Soil development indices or SDIs Harden, 1982 minimisethe effects of parent material composition by quantifying the difference in several

Ž .morphological features between soil and parent material. Harden and Taylor 1983established that SDIs can be applied to very different soil types, increase significantlywith age and seem to increase at similar rates in different climates. Therefore they aretheoretically suitable for correlating chronosequences on different parent materials andin different climates.

This paper evaluates the suitability of SDIs for dating and age correlation byŽcomparing data for the Ljubljana Basin chronosequence Vidic et al., 1991,; Pavich and

.Vidic, 1993; Vidic, 1994; Vidic and Lobnik, 1997 and from other chronosequencesŽ . Ž .studied by Harden and Taylor 1983 and Busacca 1987 .

2. Materials and methods

2.1. The Ljubljana Basin chronosequence

The Ljubljana Basin chronosequence formed on a sequence of outwash terraceslocated in an intramontane basin in central Slovenia between latitudes 46800X and46825X N and longitudes 14800X and 14840X E along the Sava River and its tributaries. The

Ž .outwash deposits are sandy gravels composed mainly )80% of carbonate clastsŽ .limestone and dolomite , with some siliceous clastic and felsic volcanic rocks. Gravelsbeneath soils older than 62 ka are cemented by calcite to form conglomerate. Thepresent humid temperate climate determines the udic moisture and mesic soil tempera-ture regimes.

Ž .The chronosequence is post-incisive Vreeken, 1975 , soil formation beginning at thetime each of the deposits was stabilised and continuing to the present. The duration ofsoil formation has been determined by 10 Be and paleomagnetic measurements or

Ž .estimated from the topographic position relative height of the geomorphic surfaceŽ .Table 1 . The soil orders on each surface reflect different periods of pedogenesis under

Ž .udic moisture and mesic temperature regimes Table 2 . Mollisols rich in basic cationsŽ .prevail on young geomorphic surfaces F62 ka , and longer pedogenesis in a strongly

Ž . Ž .leaching regime has formed Alfisols 62 and 450 ka or Ultisols 980 ka and 1.8 Ma .

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Table 1Ž . 10Codes and previous names Penck and Bruckner, 1909 of deposits, with age estimates by Be and¨

Ž .paleomagnetic analyses Pavich and Vidic, 1993

Deposit codes Previous Estimated Uncertainty Dating methodsŽ . Ž .used in this study names age ka intervals ka

Qh 5 0–10 topographic position2

Qh 10 0–10 topographic position110Qt 32 20–35 Be710Qt 44 40–50 Be610Qt Wurm 62 50–70 Be¨510Qt Riss 450 435–515 Be4

Qt Mindel II 960 )780 paleomagnetic analyses310Qt Mindel I 980 780–1000 Be, paleomagnetic analyses2

Qt Gunz 1800 780–1800 paleomagnetic analyses¨110Qt Gunz 1800 )1000 Be¨1

Soil development on these deposits begins with the dissolution and leaching ofŽ .carbonates and weathering of silicates. Organic matter in epipedons , silt, clay and

resistant siliceous clasts accumulate. Soils 10–62 ka old have cambic horizons. Thepercentage of clay increases with age, and translocation of clay and iron compoundsfollows complete leaching of Ca and Mg, only adsorbed Ca and Mg remaining. Thiscauses formation of argillic horizons, which thicken with age. On the oldest surfaces theargillic horizons are transformed to kandic horizons. Soil thickness increases with timeto )800 cm, though there is much variability in thickness on each geomorphic surfacebecause soil pockets extend into the calcareous gravel and surface erosion has affected

Ž .all soils G450 ka old. Vidic and Lobnik 1997 give soil descriptions and SDI data.

2.2. Other chronosequences

Descriptions and data for chronosequences in California, Pennsylvania and NewŽ . Ž .Mexico were given by Harden and Taylor 1983 and Busacca 1987 . The character-

istics of the different chronosequences used in this paper are summarised in Table 3.

Table 2Ž .Classification of the Ljubljana Basin soils according to the US Soil Taxonomy Soil Survey Staff, 1992

Deposit Estimated Diagnostic Soil classificationŽ .code age ka field horizons

Qh 5 A-C Typic Rendoll2

Qh 10 A-Bw-C Typic Hapludoll1

Qt 32 A-Bw-C Typic Hapludoll7

Qt 44 A-Bw-C; A-Bt-C Typic Hapludoll6

Qt 62 A-Bt-C Typic Hapludoll; Typic Argiudoll; Mollic Hapludalf5

Qt 450 A-Bt-C Typic Hapludalf; Mollic Hapludalf4

Qt 960 A-Bt-C Typic Kanhapludult3

Qt 980 A-Bt-C Typic Haplohumult; Typic Hapludult; Typic Kanhapludult2

Qt 1800 A-Bt-C Typic Kanhapludult1

()

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Table 3Some characteristics of chronosequences compared in this study

a bChronosequence Ljubljana Basin SLO Merced CA, USA V e n tu ra C A , Susquehanna PA, Las Cruces NM, Sacramento Valleyb b b ccharacteristics USA USA USA CA, USA

Parent material predominantly car - granitic alluvial wave cut marine glacial till, outwash or alluvial fans and fan alluvial terraces ofŽbonate limestoneq terraces terraces covered eolian sand derived piedmonts derived mixed composition. Ždolom ite sandy by wedges of from mid-Paleozoic from monzonite rhyo- derived from ca. 65%

gravel outwash ter- arkosic alluvial sedimentary rocks lite and sedimentary basic metavolcanicraces, -20% of fel- and colluvial de- rocks rocks, the rest from

.sic volcanic and posits granodiorite , non-siliceo u s c las tic gravelly silt–loam tosedim. rocks sandy loam

Ž .Best estimates of 5–1800 Table 1 0.2, 3, 10, 40, 130, 1, 2, 40, 85 11, 15, 40, 250, 330 4, 20, 120, 290, 500, 0.6, 10, 40, 130, 250,ages of geomor- 250, 330, 600, 800 1600

Ž .phic surfaces ka 3000Soil temperature mesic thermic thermic mesic thermic thermicregimeSoil moisture udic xeric-inland xeric-coastal udic aridic xeric-inlandregime

a Ž . Ž .Vidic 1994 , Vidic and Lobnik 1997 .b Ž .Harden and Taylor 1983 .c Ž .Busacca 1987 .

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2.3. Field and laboratory methods

The soil profiles studied were located on parts of terraces that seemed to be leastaffected by erosion. The properties used for calculation of SDIs were described in the

Ž .field by standard methods Soil Survey Staff, 1992 , but particle size distributionŽ . Ž .Janitzky, 1986 and pH 1:2.5 soil:water ratio, Page, 1982 were measured in thelaboratory.

2.4. Calculation of soil deÕelopment indices

Ž . Ž . Ž .Following Harden 1982 , Harden and Taylor 1983 and Taylor 1988 , SDIs wereŽcalculated from the degrees to which soil properties colour, texture, dry and wet

.consistency, structure, clay films, pH change differed from those of the parent material.The SDI trends for the Ljubljana Basin chronosequence were discussed by Vidic and

Ž .Lobnik 1997 . Weighted means for soil profiles were obtained by dividing profileŽ .values horizon values multiplied by horizon thickness and summed for each profile by

the total thickness of the profile.

2.5. Statistical analyses

Ž .Least-squares regression analyses Davis, 1986 were used to determine chronofunc-tions. Linear, logarithmic, power and exponential models were tested for the Ljubljana

Ž .chronosequence. Following Bockheim 1980 , models which were statistically signifi-cant at the 0.01 confidence level and yielded the strongest correlation coefficients werechosen as the best chronofunctions. For comparison of different chronosequences, only

Ž .linear and logarithmic models were used. One-way analyses of variance Davis, 1986were used to calculate the means and confidence intervals for soils on each geomorphicsurface.

3. Results and discussion

3.1. Soil deÕelopment indices

Rates of soil development in the Ljubljana Basin chronosequence determined fromslopes of the chronofunctions decreased with time as logarithmic or power functions.The regression models that yielded the strongest correlations were semi-logarithmic in

Ž .most instances ysaqb log x, where yssoil development and xssoil age and lessŽ b. Ž .commonly power functions ysa=x Vidic and Lobnik, 1997 , indicating that SDIs

Ž . Ž .increased with the logarithm or power of time. Bockheim 1980 , Busacca 1987 ,Ž . Ž . Ž . Ž .Harden 1988 , Busacca and Singer 1989 , Reheis et al. 1989 and Harden et al. 1991

have found similar trends in overall soil development or the development of variousindividual soil properties.

ŽWhen soil property development using weighted means of profile property develop-.ment is compared between chronosequences in various parent materials and climatic

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settings, differences emerge between rates derived from the slopes of the chronofunc-Ž .tions or their shapes and even in the direction of development Figs. 1–3, Table 4 . For

Žexample, the rates of change in total-texture an index calculated from granulometric.data and wet consistency and pH for the Ljubljana Basin are considerably greater than

Ž .those for the other chronosequences Fig. 1a, b . Conversely, some of the rates aresimilar but the onset of property development lags behind that in other chronosequencesŽ . Ž .e.g., clay films in the Ljubljana Basin, Fig. 2a . Melanization soil darkening increaseswith time in all chronosequences except those of the Ljubljana Basin and MercedValley, where it decreases. Most of the properties increase or decrease with the

Ž .logarithm of time, though a few show linear relationships with time Figs. 1–3 . Someproperties, mainly in the arid Las Cruces chronosequence, fail to display statistically

Ž .significant trends with time Figs. 1–3, Table 4 .Ž .Soil properties are not always univariant Bockheim, 1980 , as their development

may depend partly on soil forming factors other than time. For example, rubification inthe Ljubljana Basin has been affected by climatic fluctuations, even though its develop-

Ž .ment increases with time so that older soils are redder than younger Fig. 3a . Earlygenerations of clay films seen in thin sections in older soils of the Ljubljana Basin are

Ž .stained red or reddish brown with hematite Vidic, 1997 , a mineral that cannot form inŽ .this area in present climatic conditions Schwertmann and Taylor, 1989 . These films

must have formed in warmer andror drier climatic conditions than the present. Pastclimatic conditions therefore brought the degree of rubification in the Ljubljana Basintowards that typical of a Mediterranean climate, as in the Sacramento and Merced

Ž .Valleys of California Fig. 3a .Development of soil properties may also be governed by thresholds that delay

Ž . Ždevelopment Vidic and Lobnik, 1997 , by step functions Busacca, 1987; Harden et al.,. Ž .1991 or by reversals in direction of development Vidic and Lobnik, 1997 . For

example, despite the strong leaching environment, the development of clay films in theŽ .Ljubljana Basin soils was delayed for about 50 ka Fig. 2a , after which it proceeded at a

Ž .similar rate to that in the inland xeric areas of California. Muhs 1984 described thistype of delay as an intrinsic threshold resulting from the inhibition of clay movementuntil decalcification has removed Ca and Mg ions, which act as strong clay flocculantsŽ .Buol et al., 1980 . In soils 62 ka old, where thin clay films occur, this threshold hasbeen passed. Soil properties showing trend reversals in the Ljubljana Basin are total-tex-

Ž . Ž .ture and pH, which reverse after 980 ka Fig. 1a, b Vidic and Lobnik, 1997 .Ž .Comparison of soil development using Profile development indices PDIs which

Ž .combine several soil properties Fig. 3b shows that the rate of soil development in theŽ .Ljubljana Basin exceeded those in drier areas Vidic and Lobnik, 1997 . This contradicts

Ž .the conclusion of Harden and Taylor 1983 , who found similar rates of soil develop-ment in xeric, aridic and humid regions. They compared four chronosequences formed

Ž .in similar parent materials Table 3 , so the differences in development rates between theLjubljana Basin and others considered in this paper may reflect differences in the parentmaterials. For example, a greater rate of change in total-texture would be expected insilt- and clay-rich soils formed on sandy gravel, as in the Ljubljana Basin, than insimilar textured soils formed on fine-grained alluvium, as in the Merced and Sacramentochronosequences. Similarly, a more rapid change in pH would be expected if the parent

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Fig. 1. Weighted means of profile indices of compared chronosequences versus time. The equations ofŽ .chronofunctions are given in Table 4. Because age x axis is logarithmic, the semi-logarithmic chronofunc-Ž . Žtions appear linear, and linear ones concave upwards. a Total-texture index calculated from textural data and

. Ž .wet consistency ; b pH-decrease.

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Fig. 2. Weighted means of profile indices of compared chronosequences versus time. The equations ofŽ .chronofunctions are given in Table 4. Because age x axis is logarithmic, the semi-logarithmic chronofunc-Ž . Ž . Ž .tions appear linear, and linear ones concave upwards. a Clay films; b Melanization soil darkening .

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Fig. 3. Weighted means of profile indices of compared chronosequences versus time. The equations ofŽ .chronofunctions are given in Table 4. Because age x axis is logarithmic, the semi-logarithmic chronofunc-

Ž . Ž .tions appear linear, and linear ones concave upwards. a Rubification; b Weighted means of profileŽdevelopment calculated by combining all time-dependent properties 7 for the Ljubljana Basin, 8 for other

.areas .

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Table 4Intercepts, slopes and correlation coefficients of chronofunctions shown in Figs. 1–3

Soil properties and chronosequences Regression Intercept a Slope b Rmodel

Weighted means of profile total-texturea dLjubljana Basin log y0.90 0.33 0.96

bMerced log y0.41 y0.13 0.81b e y6Ventura lin y0.03 4.54=10 0.95

bLas Cruces log y0.14 0.05 0.47bSusquehanna log y0.83 0.21 0.93

cSacramento log y0.29 0.12 0.88

Weighted means of profile pH-decreaseLjubljana Basin log y1.6 0.48 0.92

y7Merced lin 0.26 2.31=10 0.70fVentura NAfLas Cruces NAgSusquehanna NS

y7Sacramento lin 0.12 2.09=10 0.89

Weighted means of profile clay filmsLjubljana Basin log y0.78 0.18 0.85Merced log y0.58 0.18 0.82

y6Ventura lin y0.0016 3=10 0.97Las Cruces NASusquehanna log y0.19 0.08 0.43Sacramento log y0.58 0.21 0.95

Weighted means of profile melanizationLjubljana Basin log 0.75 y0.06 y0.55Merced log 0.4 y0.05 y0.55Ventura NSLas Cruces NSSusquehanna NS

y7Sacramento lin 0.25 2.45=10 0.76

Weighted means of profile rubificationLjubljana Basin log y0.25 0.10 0.75Merced log y0.39 0.16 0.74

y6Ventura lin 0.11 2.23=10 0.95Las Cruces NS

y6Susquehanna log 0.14 1.2=10 0.91Sacramento log y0.40 0.16 0.9

hWeighted means of profile deÕelopmentLjubljana Basin log y0.45 0.17 0.95Merced log y0.15 0.09 0.64

y6Ventura lin 0.12 2.33=10 0.83Las Cruces NSSusquehanna log y0.37 0.12 0.76Sacramento log y0.19 0.1 0.97

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Ž .material is calcareous pH)8 , as in the Ljubljana Basin, than where it is non-calcare-Ž .ous pH-6 . Also, as discussed above, the composition of the parent material delays

the onset of clay film formation. Nevertheless, despite these parent material effects, thestrongly leaching regime of the Ljubljana Basin has probably been the main influence onthe direction and rate of development of soil properties there.

The Sacramento and Merced Valley chronosequences offer an opportunity to test thesignificance of parent material influence on SDIs, as they are located in the same area,and have therefore been exposed to similar past and present climatic conditions, but the

Ž .two parent materials differ Table 3 . The Sacramento Valley alluvium is mixedŽ . Ž .sediment from metavolcanic rocks 65% and granodiorite 35% , whereas the Merced

alluvium is granitic in origin. The development rates of various soil properties areŽ . Ž .usually greater up to 15% in the Sacramento chronosequence Table 4 , and the shapes

Ž . Žof some functions are different Figs. 1–3a or even show different directions e.g.,.melanization . Nevertheless, the rate of cumulative soil development expressed by SDIs

Ž .combining several soil properties Fig. 3b does not differ significantly between the twoareas.

Ž .As PDIs and their means weighted according to soil thickness WPDIs based on allmeasured soil properties can be influenced by parent material composition, those basedonly on the most time-dependent soil properties, which may not be the same in all areas,should be more suitable for comparing rates of development. However, WPDIs may bemore universally useful than PDIs, especially for purposes of correlation, because they

Ž .minimise the bias introduced by variable profile thickness Birkeland et al., 1991 .To summarise, rates of soil morphology development depend more on climatic

conditions than on parent material differences, and seem to increase mainly withincreasing precipitation, though rubification may develop faster in Mediterranean andmore humid areas. Because some soil properties could have been affected by pastclimatic fluctuations, age correlations based on single properties, SDIs or WPDIs arereliable only between areas with similar present climates and past histories of climaticfluctuations.

3.2. Dating within chronosequences

Because of various problems in using chronofunctions for age estimates or correla-Ž .tions, which are listed below and by Schaetzl et al. 1994 , confidence intervals for

means of time-dependent soil properties were calculated for each geomorphic surface, to

Notes to Table 4:a Ž .Vidic and Lobnik 1997 .b Ž .Harden and Taylor 1983 .c Ž .Busacca 1987 .d Ž .Logarithmic: ys aq b log x ; ys weighted mean of profile index, xsage.eLinear: ys aq bx; ys weighted mean of profile index, xsage.f NA—data not available.g Ž .NS—regressions were not statistically significant 95% level .hCalculated from 7 properties for the Ljubljana Basin chronosequence, and from 8 for other chronosequences.

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Ž .Fig. 4. Confidence intervals 95% for means of soil indices for geomorphic surfaces 62, 450, 980 and 1800 kaof the Ljubljana Basin chronosequence. Confidence intervals for other surfaces could not be calculated becauseof the lack of replicate soil profiles. Confidence intervals that do not overlap can be used to distinguish

Ž .between geomorphic surfaces. Both weighted means of profile total-texture a and weighted means of profileŽdevelopment calculated with the four soil properties that show the most distinctive trends with time total

. Ž .texture, pH-decrease, rubification, clay films b can be used to distinguish 62 ka old surface from older ones.

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assess the possibility of estimating ages within the Ljubljana Basin chronosequence.Ž .Following Busacca 1987 , the criterion used was that the confidence intervals for

different geomorphic surfaces do not overlap. Few soil properties entirely satisfied thiscriterion. However, weighted means of soil development calculated from the four bestproperties and total-texture can be used to distinguish the F62 ka and G450 ka

Ž .surfaces Fig. 4a, b , and concentrations of Al O and Fe O can distinguish the 18002 3 2 3Ž .ka surface from the remainder Fig. 5a, b . The failure of most soil properties to

discriminate geomorphic surface can be attributed to the high intrinsic variability ofŽ .soils, the small numbers of replicates on each surface Busacca and Singer, 1989 and

the decreasing rate of development which results in small differences between the oldersoils.

3.3. The potential of chronofunctions for dating and correlation

Chronosequences have often been used to derive chronofunctions for dating andcorrelation purposes. However, many chronofunctions cannot be used for correlation

Ž .because of the nature of the data on which they are based Schaetzl et al., 1994 . Someof the problems are illustrated by the results of this study.

A common problem is the insufficient number of geomorphic surfaces identified.Terrestrial sedimentation is often localised and geomorphic surfaces of certain ages maybe absent, so that some time intervals are better represented than others. Older surfacesare less commonly preserved, and so younger surfaces with shorter age uncertaintyintervals are better documented. Table 3 illustrates this problem by showing the bestestimates of the ages of the surfaces in each chronosequence studied. Dating methodsand intervals of uncertainty also vary.

Spatial variability of soils over each geomorphic surface commonly limits the valueof chronofunctions for dating and correlation. The variability of soil properties on eachof the surfaces in this study is partly responsible for the overlapping confidence intervalsŽ .Figs. 4 and 5 . It is inherited partly from granulometric and mineralogical differences in

Ž .the alluvial parent materials Vidic, 1994 , which result from fluvial depositionalprocesses. A further complication is that the degree of variability may differ betweengeomorphic surfaces.

Additional variability is introduced into chronofunctions by age uncertainties. Evennumerical values obtained by radiometric dating methods have intervals of uncertainty.If maximum and minimum ages were used to derive chronofunctions in the LjubljanaBasin chronosequence, they resulted in rates of development differing by up to 20% for

Žsemi-logarithmic chronofunctions and up to 180% for linear chronofunctions Vidic and.Lobnik, 1997 . Even if reliable dates are available for all the geomorphic surfaces and

good chronofunctions are derived, there are further problems in using them for datingŽ . w xand correlation. Williams 1983 p. 195 stated that ‘‘a regression line cannot be used to

estimate a value for an independent variable from a known value of the dependentvariable, . . . because of the less than perfect correlation between the regressed variablesŽ .scatter on the graph ’’. Consequently, chronofunctions cannot be used for accuratedating because of the random variability within each geomorphic surface; they can beused to compare rates of soil development between different areas, but not for datingand correlation.

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Statistical techniques for representing the errors resulting from soil variability anddating uncertainties, such as two-way regression, are more suitable for dating andcorrelation. A technique based on maximum likelihood estimates with Monte Carlo

Ž .simulations, developed for soil chronosequence studies Switzer et al., 1988 , was usedto obtain dates for Holocene soils in the Cima Volcanic Field chronosequence usingSDIs and maximum likelihood estimates from the chronosequence at nearby Silver Lake

Ž .in the Southern Great Basin, USA Harden et al., 1991 . The dates agreed well withindependent radiometric ages, and their precision averaged 40% compared with an errorof 18% for the radiometric ages. However, satisfactory correlations are probably easierto achieve for more recent and better documented periods with linear soil-age relation-ships than for older periods with non-linear relationships.

4. Conclusions

The SDIs obtained for the Ljubljana Basin chronosequence increased with timeaccording to logarithmic or power functions, which indicate that the rate of developmentdecreased with time, as has been shown in other areas. However, the rates of change intotal-texture and pH in the Ljubljana Basin significantly exceed the rates in other areas.Conversely the onset of clay film formation was delayed longer, because of thecarbonate content of the parent material. Rubification develops faster in areas with aMediterranean climate, but has been influenced by past climatic fluctuations in theLjubljana Basin. Melanization increases with time in some areas, but decreases in others.Despite differences in the development of individual soil properties, overall soildevelopment seems to increase with increasing precipitation. Some differences in soildevelopment between the areas studied can be partly ascribed to differences in parentmaterial, but these do not affect SDIs significantly. PDIs and WPDIs expressing overallsoil development are less sensitive than individual soil properties to differences in parent

Ž .material. Harden and Taylor 1983 found that SDIs are independent of climaticconditions, but this was probably because they used chronosequences on similar parent

Žmaterials; in contrast, this study confirms the validity of climatic gradients Tedrow,.1968 . Similarity of present and past climatic conditions remains a pre-requisite for

correlations between different areas.Even if two chronosequences were formed in a similar climate, there are numerous

other problems limiting their values for dating. Variability of soil properties on eachgeomorphic surface prevents them being used as dependent variables to indicate age asthe independent variable. Other problems arise from the limited precision and accuracyof dating methods. Statistical techniques that can account for the age uncertainties andintrinsic variability of the soils, such as the maximum likelihood estimates developed by

Ž . Ž .Fig. 5. Confidence intervals 95% for means of soil major element data Vidic, 1994 for geomorphic surfaces62, 450, 980 and 1800 ka of the Ljubljana Basin chronosequence. Confidence intervals for other surfaces couldnot be calculated because of the lack of replicate soil profiles. Confidence intervals that do not overlap can be

Ž . Ž .used to distinguish between geomorphic surfaces. Both the percentage of Al O a and of Fe O b can be2 3 2 3

used to discriminate 62 and 1800 ka geomorphic surfaces, but cannot discriminate between 450 and 980 ka.

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Ž .Switzer et al. 1988 , can help with dating and correlation, but may be limited to recentperiods with linear soil-age relationships.

Within the Ljubljana Basin chronosequence, a few soil properties could be used todiscriminate between geomorphic surfaces, but only in a limited way. When theweighted means of profile development based on four properties, total-texture and theabundances of Fe and Al were combined, they could be used to discriminate betweengeomorphic surfaces dating from F62 ka, 450–980 ka and 1800 ka. The inability ofindividual properties to discriminate between all surfaces is attributed to the variabilityof the soils, the inadequate number of profile replicates studied on each surface and thedecreasing rate of soil development with increasing soil age. Analysis of additional soilprofiles would narrow the confidence intervals and thus improve the discrimination ofgeomorphic surfaces.

Acknowledgements

The study was supported by YUrUSA Scientific and Technological Cooperation,project USGSrJF 763, the University of Ljubljana, Slovenia and the University ofColorado, USA. It was part of a PhD thesis directed by P. Birkeland at the University ofColorado, Boulder. I thank M. Pavich, who helped with the soil profile descriptions andparticipated in the thesis committee, and E. Taylor and J. Slate, who advised on thecalculation of soil development indices. J. Harden and an anonymous reviewer provideduseful comments on the original manuscript.

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