Relationships between geochemistry of basal till and chemistry of surface soil at forested sites in Finland

Applied Geochemistry 16 (2001) 123±136

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Relationships between geochemistry of basal till and chemistry of surface soil at forested sites in Finland Anne-Maj LahdenperaÈ a, Pekka Tamminen b, Timo Tarvainen c,* a

Geological Survey of Finland, PO Box 77, FIN-96101, Rovaniemi, Finland Finnish Forest Research Institute, PO Box 18, FIN-01301, Vantaa, Finland c Geological Survey of Finland, PO Box 96, FIN-02151, Espoo, Finland

b

Received 19 June 1998; accepted 14 March 2000 Editorial handling by M. Billett

Abstract Concentrations of several elements extractable with BaCl2 and acid ammonium acetate (pH 4.65) were measured in the organic and 4 mineral soil layers on the national forest inventory plots of the Finnish Forest Research Institute. The soil data also includes total concentrations of elements in the organic layer and site and soil physical characteristics. Data were compared with the aqua regia extractable element concentrations measured in the nationwide regional till geochemical mapping carried out by the Geological Survey of Finland. Correlations between concentrations in surface soil and underlying basal till were generally highest for K, Mg, Mn, P and Zn; in the organic layer and till they were highest for Cr and Cu. The strength of these correlations did not increase regularly from surface to deeper soil layers. All soil base cations with the exception of Ca, which is of relatively low solubility, were well correlated. The elements Zn, K, P, Al and Mn in till were the most reliable indicators of surface soil chemistry. Fuzzy clustering showed that the correlation between element concentrations in basal till and the two uppermost layers of mineral soil was better within areas of distinct till geochemistry, such as the schist belts in southwestern Finland, the Lake Ladoga±Bothnian Bay zone and the Kuusamo schist belt. Surface soil chemical variables were clearly better in discriminating fertility classes of forest sites than were element concentrations in basal till. The independent ability of till geochemistry to distinguish these productivity classes and to explain surface soil fertility was nevertheless demonstrated. 7 2000 Elsevier Science Ltd. All rights reserved.

1. Introduction The parent material of most forest soils in Finland is till. In addition to lithology and topography of bed-

* Corresponding author. Tel.: +358-20-55011; fax: +35820-55012. E-mail address: Timo.Tarvainen@gsf.® (T. Tarvainen).

rock, the composition of till depends on the weatherability of rocks, the variation in the direction of ¯ow of continental ice sheets, and the type and amount of redeposited drift (Koljonen, 1992). Though the tills in some areas of Finland are rich in silt and clay fractions (Lintinen, 1995), most are sandy (Virkkala, 1969). The two most important soil forming processes in Finland, podzolization and gleying, have had a signi®cant e€ect on tills during the last 10 Ka.The pH values

0883-2927/01/$ - see front matter 7 2000 Elsevier Science Ltd. All rights reserved. PII: S 0 8 8 3 - 2 9 2 7 ( 0 0 ) 0 0 0 2 1 - 4

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increase and concentrations of many nutrients decrease with an increase in depth in forest soil pro®les (Tamminen and Starr, 1990; Aario and Peuraniemi, 1991; Koljonen and Tanskanen, 1992). Tree growth in Finland is strongly governed by climate. The e€ective temperature sum decreases from south to north and from the coastal to inland areas. The relative e€ect of climate increases towards the north (Koivisto, 1970), while the relative e€ect of soil fertility on tree growth increases in the opposite direction (Kuusela, 1977). The southward trend also holds for soil nutrients, which are the most important component of site fertility (wood production potential) in the southern boreal zone (Valmari, 1921; Ilvessalo, 1923; Viro, 1951; Lipas, 1985; Tamminen, 1993). Nitrogen is the critical growth-limiting nutrient on mineral soils in Finland (Viro, 1967; Kukkola and SaramaÈki, 1983). However, forest growth also correlates positively with the amount of soil Ca (Viro, 1951; Lipas, 1985; Tamminen, 1993) and sometimes P (Kukkola and SaramaÈki, 1983; Tamminen, 1993), but negatively with K (Lipas, 1985; Tamminen, 1993). There is virtually no information available on the correlation between tree growth and micronutrients such as B, Cu and Zn. Peat soils tend to be characterized by shortages of P and K, while shortage of N is less of a problem (Kaunisto and Paavilainen, 1988). In mineral soils, there is a much better correlation between forest growth and soil nutrients than forest growth and soil texture (Tamminen, 1993). The last 2 decades have witnessed increasing concern over the supply of base cations in forest soils due to acidic deposition (Ulrich et al., 1979; BerdeÂn et al., 1987; Kauppi et al., 1990). Soils in southern Sweden appear to have become more acid and lost base cations during the last 50 a (BerdeÂn et al., 1987; HallbaÈcken, 1992; Falkengren-Grerup et al., 1987). Base cation pools are also expected to decrease in the future due to surface soil weathering, leaching and the removal of nutrients from the site along with timber (Kukkola and MaÈlkoÈnen, 1997). The addition of base cations to forest sites on mineral soils in Finland has not resulted in a positive growth response, which would suggest that chronic de®ciencies of base cations are in fact rare (Kukkola and SaramaÈki, 1983; Derome et al., 1986). Finnish till geochemistry has been surveyed both intensively and extensively, and comprehensive data are available for the whole country (Salminen, 1995). Till geochemical data give information, not only on basal till deposits, but also on bedrock chemistry (Salminen, 1995). Data on the geochemistry of sorted sediments are rare, although glacio¯uvial and aeolian sediments cover about 10% of the land area in a fairly evenly distributed fashion (Aaltonen, 1941; Anon, 1990). Forest soil fertility data, especially chemical data,

are sparse. Only about 500 systematically sampled sites, the permanent plots of the 8th national forest inventory (Finnish Forest Research Institute, 1986), have been analysed by uniform methods. Regional representativeness of the data is also poor, although several soil layers have been sampled per site and 1 to 4 analyses have been carried out per sample. There is little systematic information on the e€ects of till geochemistry on soil fertility or nutrient availability to plants in Finland (Lounamaa, 1956; Urvas and ErvioÈ, 1974). Variation in both till geochemistry and site fertility is substantial (Ilvessalo, 1960; Salminen, 1981; Salminen, 1995). The hypothesis that bedrock and till geochemistry a€ects the nutrient status of the surface soil has not yet been tested for Finnish forested mineral soils. However, Kauranne (1967) showed for heavy metals that ``when a distinct anomaly in humus has been detected it has always been accompanied by a corresponding anomaly in the glacial till''. The aims of this study were to: (1) investigate relationships between chemical properties of the surface soil and the underlying basal till; (2) evaluate the e€ect of surface soil texture on correlations between the surface soil and the basal till; and (3) understand the signi®cance of basal till geochemistry for forest site fertility. 2. Materials and methods 2.1. Till The chemical properties of till were derived from data collected during the Regional Geochemical Mapping carried out by the Geological Survey of Finland (Salminen, 1995). The till samples were taken at a mean sampling depth of 1.5±2 m from unaltered material preferably beneath the groundwater table. In areas of eskers and other waterlain sediments and in peat areas, the samples were taken from till below the deposits. The sampling density was one sample per 4 km2. Each sample was a composite of at least 4 subsamples taken from an area measuring about 1000 m2. The samples were taken with a portable percussion drill equipped with a through-¯ow bit. Analytical data were produced for 82,062 samples covering the whole of Finland (Salminen, 1995). The study was based on the ®ne-grained till fraction (<0.06 mm) which was separated for analysis with a plastic sieve. Samples were dried at 708C and hot aqua regia extraction (ISO/DIS 11466, 1992), referred to in the text with subscript `AR', was applied. The concentrations of Al, Ba, Ca, Co, Cr, Cu, Fe, K, La, Li, Mg, Mn, Mo, Ni, P, Pb, Sc, Sr, Th, Ti, V, Y, Zn and Zr were determined by inductively coupled argon plasma

A. LahdenperaÈ et al. / Applied Geochemistry 16 (2001) 123±136

emission spectrometry (ICP-AES). In addition to the samples proper, 2411 duplicate samples were collected, treated and analysed in the same way. The reliability of the analyses in the leaching and analysis steps was measured by reanalysing 3% of the samples as replicates. For geological samples the aqua regia dissolution is a partial leach, selective for certain minerals. Carbonates and most sulphide minerals, together with silicates such as olivine and biotite, dissolve well, whereas silicates such as muscovite and quartz are almost insoluble. 2.2. Forest soils Forest soil samples were collected from permanent sample plots of the 8th national forest inventory in 1986±1989 and 1995. Sample plots (300 m2) were situated on grids of 16  16 (or 32  32, 48  48, 64  64) km in southern Finland and at 24  32 (or 48  64, 72  96) km in northern Finland. Plots were described, among other things, for site fertility (forest site type), type and thickness of organic layer and stoneyness. Soil samples were taken from the organic layer (1± 14 cm) and from mineral soil layers at 0±5, 5±20, 20± 40 and 60±70 cm depth. Depending on the thickness of the organic layer, the organic sample was a composite sample of 10, 20 or 30 subsamples collected around the inventory plot. Organic subsamples were collected with a cylinder (diameter 58 mm). The mineral soil samples consisted of a composite of 5 subsamples for each layer, except for the 60±70 cm layer which consisted of a single sample. All samples were air dried at 35±408C. The organic samples were milled and mineral soil samples sieved to retain the <2 mm fraction. Particle size analysis was performed on 20±40 cm layer samples by the hydrometer method after pretreatment with H2O2 and HCl (Elonen, 1971). Particle size distribution was described in terms of clay (<2 mm) and ®nes (<63 mm) percentages, median diameter (mm) and sorting index=2
log10(D75%/D25%). Organic matter content was determined as loss-on-ignition (5508C/3 h). For organic samples the ash was digested with HCl in a water bath (Halonen et al., 1983), and the `total' concentrations of elements determined. Soil samples were extracted with both unbu€ered 0.1 M BaCl2 and buffered acid ammonium acetate (NH4OAc) (pH=4.65) with a ratio of 15 ml of sample powder to 150 ml of extractant. The suspension was left to stand overnight, shaken in the morning for 1 h, and then ®ltered (Halonen et al., 1983; Tamminen and Starr, 1990). Element concentrations were determined from ®ltered aliquots using an inductively coupled plasma emission spectrophotometer (ARL 3580). Element concentrations (mg/ kg) were calculated on an oven-dry mass basis for the <2 mm samples.

125

2.3. Statistical analysis As geochemical mapping of till was based on an irregular sampling grid, the data were ®rst interpolated and smoothed into a regular 1  1 km grid, where the grid values were calculated by using a moving weighted median in a circular window (radius 12 km) (Gustavsson et al., 1997). The interpolation and smoothing method may shift local anomalies to some extent, resulting in unfavourable correlations between samples and grid cells. However, the till geochemical data form large anomalies rather than small-scale patterns. Soil data were correlated with the corresponding interpolated geochemical till concentrations. The accuracy of the soil sampling coordinates was 0.1 km. Skewed variables (skewness > 1) were normalised by ln (loge) transformation. Element concentrations (aqua regia extraction), as measured in the ®ne fraction (<0.06 mm) of till, were ten to several hundred times higher than in surface mineral soil extracted with NH4OAc or BaCl2 (Table 1). Distributions of surface soil concentrations were also more skewed to the right than those of basal till concentrations, i.e. there were more exceptionally high values. The data were further classi®ed by means of the fuzzy c-means algorithm, to study the correlation between till and surface soil concentrations within relatively homogeneous soil groups. Fuzzy clustering allows each sample to be a member of one or more of the clusters (Frapporti, 1994). The samples were grouped into 4 clusters using the variables (i) aqua regia extractable K concentration of basal till, (ii) the proportion of ®ne fraction (<0.06 mm) of the surface soil and (iii) the sorting index of the surface soil. Aqua regia extactable K concentration re¯ects the abundance of trioctahedral micas and mixed-layer clay minerals in the parent till and correlates signi®cantly with concentrations of several trace elements in the basal till material (Salminen, 1995). The percentage of the ®ne fraction of till is one of the key variables used to divide Finnish tills into geologically homogeneous provinces (Lintinen, 1995), and the sorting index separates the soil data into samples representing tills and sorted sediments. Membership values of fuzzy clusters ranging from 0±1 were calculated for each soil plot by using the FCM program (Bedzek et al., 1984). The value of the weighting q-exponent, which determines the fuzziness, was 1.5. Although each soil plot had membership in more than one cluster, the plots were grouped on the basis of dominant cluster (highest membership value) for clustered rank correlation calculations (Section 3.2). Plots whose highest membership value for any cluster was less than 0.5 were not included in the correlation analysis.

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3. Results

3.2. Fuzzy clustering

3.1. Correlations between basal till and surface soil chemical variables

Rank correlation coecients were calculated separately for fuzzy clusters 1±4 (Fig. 5). The clusters are as follows:

Spearman's rank correlation coecients between elemental concentrations in basal till and surface soil were calculated (Tables 2(a)±2(c)). Only the organic and two uppermost mineral soil layers are presented in Table 2, because the correlations between the basal till and surface mineral soil did not improve with sampling depth in the surface soil. Relatively high K and Mg concentrations were found in both basal till and organic or upper soil layers in southwestern Finland and along the Lake Ladoga±Bothnian Bay zone (Figs. 1±3). Some of the basal till elements, especially Zn, were correlated with many elements in several layers of the surface soil (Tables 2(a)±2(c) and Fig. 4). In contrast, Ca in basal till was poorly correlated with soil Ca concentration. Correlations between the same elements in basal till and soil layers (0±5 cm) were highest for Zn, Ni (organic), K (organic), Cr (organic) and Mg (organic) (see Lax et al., 1995). The elements Zn, Al, K, P and Mn in basal till had on average the highest number of signi®cant correlations with surface soil elements (Table 2).

1. surface soil with ®ne texture and basal till with a relatively large amount of KAR in southwestern Finland, the Lake Ladoga±Bothnian Bay zone and the Kuusamo area; 2. surface soil with an average texture and basal till with a high KAR concentration in the same areas as cluster 1, plus central Lapland; 3. surface soil with an average texture and basal till with a low KAR concentration in granitic or felsic gneiss areas in many parts of the country; and 4. Coarse textured, well sorted surface soil and till with an average KAR concentration throughout the country. Correlations between concentrations in basal till and the two uppermost mineral soil layers were better within cluster 2 than in other clusters or in the whole data set (data not shown). For example, rank correlation coecients between MgAR and Mg 0±5 cm were 0.42 in cluster 2 and 0.27 in the whole data set. Correlations of MgAR and Mg 0±5 cm were not signi®cant in clusters 1, 3 and 4. In cluster 2, the correlations between ZnAR and

Table 1 Median concentrations of elements in basal till (interpolated values) and surface soil layers (mg/kg) at the study sites Element AR till n OMa (%) pH Al Ca Co Cr Cu Fe K Mg Mn Na Ni P S Zn a

477 ± ± 10,300 2700 7 27 20 16,100 1900 4030 164 ± 15 2748 ± 29

Total org.

NH4OAc org.

477 76

376 ±

± 2349 3161 ± 8.1 6.6 2792 868 565 3061 70 8.2 938 725 47

± 68 2071 ± ± ± 131 741 339 240 21 ± 211 155 21

OM=Organic matter content.

NH4OAc 0±5

NH4OAc 5±20

BaCl2 org.

BaCl2 0±5

BaCl2 5±20

BaCl2 20±40

BaCl2 60±70

381 ±

379 ±

476 76

485 4.9

478 3.8

465 2.4

333 1.0

± 196 66 ± ± ± 69 27 14 5.2 4.4 ± 5.6 20 1.3

± 418 38 ± ± ± 58 15 6.4 6.6 3.6 ± 3.6 33 0.6

4.0 210 301 ± ± ± 73 831 393 268 231 ± ± ± 38

4.3 186 84 ± ± ± 27 29 15 4.4 4.5 ± ± ± 1.6

4.9 96 47 ± ± ± 9.0 11 7.3 2.6 3.4 ± ± ± 0.6

5.2 36 31 ± ± ± 2.3 6.3 4.8 1.2 2.9 ± ± ± 0.37

5.0 11 19 ± ± ± 0.8 7.1 3.1 0.4 2.6 ± ± ± 0.3

A. LahdenperaÈ et al. / Applied Geochemistry 16 (2001) 123±136

Ca0±5 cm, K0±5 cm and Mg0±5 cm were 0.48, 0.52 and 0.51, respectively, while between ZnAR and Ca5±20 cm, K5±20 cm and Mg5±20 cm they were 0.35, 0.50 and 0.41, respectively. All these correlation coecients are higher than when the data set is considered as a whole

127

(Table 2). Aqua regia extractable P correlated best with the surface soil data within cluster 4. For example, correlation coecients between PAR and Ca5± 20 cm were 0.35 in cluster 4 and 0.26 in the whole data set.

Table 2 Spearman's rank correlation coecients between element concentrations in surface soil layers and the basal till. Only the highly signi®cant ( p R 0.001) correlation coecients are shown. Bold value is correlation between the same elements in both layers (Al±Al, Fe±Fe etc.). (a) Total concentrations in the surface organic layer (n = 477) and aqua regia extractable concentrations in basal till. (b) NH4OAc (normal text; n = 381) or BaCl2 (italic; n = 488) extractable concentrations in the 0±5 cm layer and aqua regia extractable concentrations in basal till. The highest coecient (NH4OAc or BaCl2) for each cell is shown. (c) NH4OAc (normal text; n = 381) or BaCl2 (italic; n = 488) extractable concentrations in the 5±20 cm layer and aqua regia extractable concentrations in basal till. Only the highest coecient (NH4OAc or BaCl2) for each cell is shown Basal till Al

Ca

Co

Cr

Cu

Fe

K

Mg

Mn

Ni

P

Zn

(a) Organic layer Al Ca Cr Cu Fe K Mg Mn Na Ni P Pb S Zn

0.25 ± 0.28 0.27 0.24 0.16 0.28 0.17 ± 0.23 ± ± ± 0.17

± ± ± ± ± 0.17 0.19 0.17 ± ± 0.17 ÿ0.20 ± ±

± ± 0.31 0.19 ± 0.22 0.31 0.18 ± 0.37 ± ÿ0.20 ± ±

± ± 0.36 ± ± ± 0.17 ± ± 0.38 ± ÿ0.29 ± ±

0.16 ± 0.32 0.30 0.20 0.20 0.35 0.20 ± 0.34 ± ± ± ±

0.16 ± 0.28 0.25 0.25 0.21 0.29 ± ± 0.32 ± ± ± ±

± 0.20 ± 0.39 0.17 0.37 0.38 0.28 ± ± 0.22 ± 0.22 0.20

± ± 0.27 0.25 0.17 0.23 0.31 0.16 ± 0.32 ± ± ± ±

± ± ± 0.33 0.21 0.31 0.36 0.28 ± 0.23 0.25 ± 0.17 0.17

± ± 0.34 ± ± ± 0.23 ± ± 0.38 ± ÿ0.25 ± ±

± 0.31 ± 0.32 0.23 0.27 0.31 0.33 ± ± 0.29 ± 0.31 0.20

0.24 0.29 ± 0.44 0.27 0.35 0.39 0.30 0.19 ± 0.25 0.21 0.31 0.35

(b) Layer 0±5 cm Al Ca Fe K Mg Mn Na P S Zn

0.26 0.26 0.30 0.31 0.33 0.29 0.18 0.22 0.28 0.25

± ± ± ± ± ± ± ± ± ±

± ± ± ± 0.23 0.18 ± ± ± ±

± ± ± ± ± ± ± ± ± ±

0.18 ± 0.22 ± 0.27 0.25 ± ± 0.18 ±

± ± 0.24 0.20 0.28 0.19 ± ± ± 0.18

0.20 0.23 0.22 0.31 0.35 0.28 0.18 0.20 ± 0.23

± ± 0.18 ± 0.27 ± ± ± ± ±

0.19 0.22 0.26 0.26 0.33 0.26 ± 0.20 ± 0.22

± ± ± ± ± ± ± ± ± ±

± 0.28 0.21 0.33 0.28 0.34 0.21 0.29 ± 0.20

0.30 0.38 0.36 0.45 0.44 0.38 0.28 0.34 0.30 0.40

(c) Layer 5±20 cm Al Ca Fe K Mg Mn Na P S Zn

0.17 0.21 0.20 0.34 0.29 0.21 0.22 0.20 ± ±

± ± ± ± 0.19 ± ± ± ± ±

± ± 0.19 0.22 0.27 ± ± ± ± ±

± ± ± ± 0.17 ± ± ± ± ±

± 0.19 ± 0.26 0.29 0.20 ± ± ± ±

± ± 0.25 0.30 0.30 ± ± ± ± ±

± 0.23 0.22 0.36 0.33 0.25 0.24 ± ± ±

± ± 0.22 0.24 0.30 ± ± ± ± ±

± 0.24 0.27 0.32 0.34 0.24 0.20 ± ± ±

± ± ± ± 0.20 ± ± ± ± ±

± 0.26 ± 0.27 0.28 0.28 0.24 0.18 ± ±

± 0.29 0.29 0.43 0.35 0.31 0.28 0.29 ± 0.23

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A. LahdenperaÈ et al. / Applied Geochemistry 16 (2001) 123±136

Fig. 1. Distribution of Mg in basal till (black and white scale) and Mg (BaCl2 extraction) in the surface soil layer 5±20 cm (circles).

A. LahdenperaÈ et al. / Applied Geochemistry 16 (2001) 123±136

129

Fig. 2. Distribution of K in basal till (black and white scale) and K (BaCl2 extraction) in the surface soil layer 5±20 cm (circles).

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A. LahdenperaÈ et al. / Applied Geochemistry 16 (2001) 123±136

Fig. 3. Distribution of K in basal till (black and white scale) and K (total) in the humus layer (circles).

A. LahdenperaÈ et al. / Applied Geochemistry 16 (2001) 123±136

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Fig. 4. Distribution of Zn in basal till (aqua regia extraction) and place names referred to in the text. CFGC, central Finland granitoid complex; CLGC, central Lapland granitoid complex; GB, granulite belt; L±B, Lake Ladoga±Bothnian Bay zone.

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Fig. 6. Distribution of the ratio (Ca0±5 cm/CaAR). Fig. 5. Sampling points classi®ed into clusters created by the fuzzy c-means clustering algorithm using as variables the proportion of the ®nes fraction of the surface soil, the sorting index of the surface soil and the aqua regia extractable K concentration of the basal till.

Table 3 Discriminant analyses of fertility classes with variables from (a) basal till data, (b) organic layer total concentrations, (c) the NH4OAc extraction of the organic and mineral soil layers 0±5 and 5±20 cm and (d) the BaCl2 extraction of organic and mineral soil layers 0±5 and 5±20 cm. The success of the analytical classi®cation is given as a proportion of correctly classi®ed observations Material

Variables

F-value

Success

(a) Basal till concentrations (n = 485) (b) Organic layer total concentrations (n = 476)

ln(Zn+1) ln(S+1) ln(Mg+1) ln(K+1) ln(Mg0±5+1) Korg ln(Ca5±20+1) ln(Mg0±5+1)

21.9 33.1 27.3 12.8 27.2 23.1 11.1 82.8

36.1 50.6

(c) NH4OAc extractable concentrations (n = 374) (d) BaCl2 extractable concentrations (n = 485)

58.6 50.1

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3.3. Relationship between site fertility measured by forest site type and basal till and surface soil chemical variables The forest site type, classi®ed on the basis of ground vegetation (Cajander, 1949), had been determined for each study site and is assumed to measure the wood production potential of the site. The capability of the chemical variables to predict fertility was evaluated with the help of this classi®cation. Individual site types were clustered into 4 site groups, measuring the relative edaphic fertility irrespective of climatic zone. The 4 site groups were: (1) The most fertile, herb-rich sites (n = 98); (2) Vaccinium myrtillus and equivalent medium fertility sites (n = 207); (3) Vaccinium vitis-idaea and equivalent medium fertility sites (n = 141); and (4) Calluna vulgaris and poorer sites (n = 39). Although the surface soil variables discriminated fertility classes clearly better than the basal till variables did (Table 3), the independent ability of till geochemistry to separate these productivity classes was nevertheless shown. The best basal till discriminator was Zn (Table 3), with K almost as good (separate F-value 19.7). Among the surface soil variables, total S, Mg and Ca of the organic layer (separate F-values 83, 74 and 72) and Mg and Ca of the mineral soil layers 0± 5 cm and 5±20 cm (separate F-values 83±55) were the best discriminators. The ®nal discriminant functions also included total or exchangeable K of the organic layer (Table 3), which were both poor discriminators (separate F-values 41±22). 3.4. Surface soil concentrations as a function of basal till concentrations An attempt was made to predict concentrations of the surface soil elements indicating site fertility from the basal till concentrations. Correlations between latitude (northing, km) and mineral soil ®ne fraction percentage with the ratio of surface soil/basal till concentration were best for Ca (Fig. 6) and Mg. Latitude was included in the regression models to remove the e€ect of variation in geographic location, and the ®ne fractions percentage was included to remove the

133

e€ect of variation in surface soil texture on elemental relationships. At the same level of basal till concentrations surface soil concentrations increased from north to south and from coarse to ®ne textured soils (Table 4). Zinc and P and Co in till were correlated to the surface soil concentrations (Tables 2 and 4). Within the basal till, Co concentrations correlated positively with Zn concentrations. Although Co exhibited lowest concentrations among the basal till elements (Table 1), partial correlations with surface soil Ca, Mg and S were higher than the correlations of Zn when the e€ect of latitude was removed. 4. Discussion The geochemical till data used in this study were based on a large amount of survey material (Salminen, 1995). However, the element concentrations in the till were interpolated, not sampled in situ and analysed. The parent material of the surface soils that were sampled was not always till; 35% of the sites lay on more or less sorted deposits. Although it would be reasonable to suppose that the relationships between basal till and surface soil chemistry would be closer between till and till soils than between till and other soils, this was only true for some elements. In spite of these weaknesses, the material examined covered a large part of the country and is highly representative. The rather loose statistical nature of the correlation between element concentrations was evident in the ®nding that only in 4 cases (Cr, K, P, Zn) out of 14 in the organic layer (Table 2(a)) and in 3 cases (Zn0±5 cm, Al5±20 cm, Zn5±20 cm) out of 20 in the mineral soil (Tables 2(b) and 2(c)), were correlations between the same elements (Crorg and CrAR, Korg and KAR etc.) highest. Furthermore, the failure to ®nd stronger correlations with depth indicated that the aqua regia extractable concentrations were not directly linked to soil elements extractable with NH4OAc and BaCl2. Basal till CaAR did not explain forest soil fertility at all (Tables 2 and 3), although the surface soil Ca concentration is known to be a good indicator of fertility (Valmari, 1921; Viro, 1951; Urvas and ErvioÈ, 1974;

Table 4 Total S concentration in the organic layer, BaCl2 extractable concentrations of Mg in the mineral soil layer 0±5 cm and Ca in the 5±20 cm layer as a function of latitude, percentage of mineral soil ®ne fraction and basal till concentrations. Regression coecients are standardised to demonstrate the relative importance of the independent variables Dependent

Regression coecients and F-values (in brackets)

R2

ln(Sorg+1)= ln(Mg0±5)= ln(Ca5±20)=

ÿ0.459
Northing (163)+0.228
Fines (41.6)+0.198
ZnAR (30.6)+0.127
PAR (12.7) 0.408
Fines (137)+0.309
ZnAR (78.4)ÿ0.262
Northing (56.1) 0.395
Fines (112)ÿ0.413
Northing (104)+0.234 lnCoAR (34.2)

0.45 0.44 0.38

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Tamminen, 1991). Soil Ca concentrations correlated best with basal till PAR and ZnAR concentrations (Table 2). Finnish soils are poor in carbonates and Ca in basal till is mainly incorporated in soluble Ca-plagioclases and amphiboles and interlayers of rocks containing these minerals. Fuzzy clustering showed that correlations between basal till and surface soil variables are stronger in tills from provinces containing geochemical anomalies (e.g. schist belts in southwestern Finland, the Lake Ladoga±Bothnian Bay zone, Kuusamo schist belt), than in the rest of the country. Aqua regia extractable P correlated best with surface soil data within cluster 4, which represents sorted deposits. Thus the geochemistry of the surrounding till explains part of the fertility of coarse grained glacio¯uvial sediments. Although surface soil chemical variables discriminated the fertility classes fairly well (Tamminen, 1993), to some extent so did the basal till elements (Table 3). It is possible that there is a regional covariation of factors linked to fertility, such as bedrock, basal till, surface soil texture, moisture, nutrients and vegetation, or there may be some direct connection between basal till and surface soil. The best indicators of fertility were variables broadly describing the base cation status of the organic layer and uppermost mineral soil layers (Table 3). Although N, which is the most important nutrient (Lipas, 1985; Tamminen, 1993), was not included in the investigation, S represents a nutrient closely linked to soil organic matter. It is worth noting that deeper surface soil separated the fertility classes less well than the uppermost soil layers. The material provided suitable test data for an examination of the possibility of estimating soil chemical variables from basal till variables. Although a relatively small part of the variation of surface soil variables could be explained by the basal till variables, the till variables nevertheless can be considered useful in such an estimation. Although no cause±e€ect relationship between element concentrations in basal till and surface soil can be established through correlation alone, the evidence of the data is convincing. The surface soil/basal till concentration ratio, i.e. the relative amount of the surface soil element, was not constant, although it was correlated with latitude and soil texture (Fig. 6, Table 4). In other words, if the concentration remains constant in the basal till, the warmer the climate and the ®ner textured the surface soil, the greater the amount of nutrients in the surface soil. A warmer climate speeds up weathering reactions (Olsson and Melkerud, 1991) and the decomposition of organic matter (Mikola, 1954; Johansson et al., 1995) and indirectly favours nutrient-rich vegetation (Viro, 1951; Kujala, 1964). In addition, plant roots are situated deeper in the soil pro®le in southern compared

to northern sites, exploiting greater soil volumes (Paavilainen, 1966) and producing at the same time more litter. In coarse textured forest soils, this organic matter is important in retaining water and nutrients; soils with a ®ner texture tend to have higher moisture and extractable nutrient contents (Viro, 1962; Tamminen, 1991). It is logical therefore that the surface soil/basal till concentrations ratios tend to be higher in ®ne-textured sites. Element concentrations in surface soils used to assess soil fertility do not provide a good description of till geochemistry, at least not in normal background areas. The connections between soil chemistry and basal till appear be stronger in distinct geochemically anomalous areas (Kauranne, 1967). 5. Conclusions A study of basal till and soil chemistry revealed loose statistical relationships between elemental concentrations in basal till and surface soil. Although geochemical information on basal till cannot be considered to be a useful tool for local fertility evaluation, correlations were surprisingly high for Zn, K, P, Al and Mn. The correlations were best in areas of till with geochemical anomalies. The usefulness of geochemical data for site fertility evaluation could be enhanced by the use of auxiliary ®eld or map information about sites and surface soils, such as detailed Quaternary deposit maps, forest compartment maps and moisture content measurements. References Aaltonen, V.T., 1941. Die Finnischen WaldboÈden nach den Erhebungen der zweiten ReichswaldschaÈtzung. Communicationes Instituti Forestalis Fenniae 29 (5) (in Finnish, with German summary). Aario, R., Peuraniemi, V., 1991. Average trends of till acidi®cation. In: Pulkkinen, E. (Ed.), Environmental Geochemistry in Northern Europe, Geol. Surv. Finland, Spec. Paper, vol. 9, pp. 21±28. Anon, 1990. Atlas of Finland. Folio 123±126. Geology. Appendix in English. Nat. Board Surv., Geograph. Soc. Finland. Helsinki. Bedzek, C.J., Ehrlich, R., Full, W., 1984. FCM: the fuzzy cmeans clustering algorithm. Comput. Geosci. 10, 191±203. BerdeÂn, M., Nilsson, S.I., Rosen, K., Tyler, G., 1987. Soil acidi®cation extent, causes and consequences. An evaluation of literature information and current research. Nat. Swedish Environ. Board 3292. Cajander, A.K., 1949. Forest types and their signi®cance. Acta Forestalia Fennica 56 (5). Derome, J., Kukkola, M., MaÈlkoÈnen, E., 1986. Forest liming on mineral soils. Results of Finnish experiments. Nat. Swedish Environ. Protect. Board. Report 3084.

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