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Journal of Chemistry
Volume 2014 (2014), Article ID 748236, 8 pages
http://dx.doi.org/10.1155/2014/748236
Research Article

Comparison of the Chemical Properties of Forest Soil from the Silesian Beskid, Poland

1Faculty of Chemistry, Silesian University of Technology, B. Krzywoustego Street 6, 44-100 Gliwice, Poland
2Department of Energy Saving and Air Protection, Central Mining Institute, Pl. Gwarków 1, 40-166 Katowice, Poland

Received 20 May 2013; Revised 15 October 2013; Accepted 19 November 2013; Published 30 January 2014

Academic Editor: Athanasios Katsoyiannis

Copyright © 2014 Maria Zołotajkin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

There is spruce forests degradation observed in the Silesian Beskid. The aim of the work was the assessment of parameters diversifying organic layers of soils in two forest areas: degraded and healthy spruce forests of Silesian Beskid. 23 soil samples were collected from two fields—14 soil samples from a degraded forest and 9 soil samples from a forest, where pandemic dying of spruce is not observed. Implementation of hierarchical clustering to experimental data analysis allowed drawing a conclusion that the two forest areas vary significantly in terms of content of aluminium extracted with solutions of barium chloride (), sodium diphosphate (), and and in the amount of humus in soil.

1. Introduction

Aluminosilicates comprise most of the aluminium minerals in soil. The natural processes of aluminosilicates weathering lead to their conversion or decomposition. Temperature, air, and water are the factors influencing the above-mentioned transformations. Industrial development in the 20th century, resulting in higher concentration of nitrogen and sulphur oxides in the atmosphere, facilitated the processes of aluminosilicates decomposition in soil. Changes in soil solutions, decrease in soil pH and in the content of metal cations as Na+, K+, Ca2+, and Mg2+, and a considerable increase in the content of various ionic forms of aluminium are observed.

In 1980 Ulrich et al. [1] claimed that an increase in aluminium content in a soil solution is one of the main reasons for forests extinction. Until now, however, it is not clear which of the aluminium forms are phytotoxic. The aqua complex of aluminium is considered to be the main phytotoxic component [2, 3]. Toxic properties of aqua hydroxo complexes and are often reported in the literature as well as particularly phytotoxic characteristic of aluminium polymeric hydroxo complexes () [212]. Fluoride, sulphate, and organic complexes of aluminium are generally considered harmless, although some authors report toxic properties of the two first ones [2, 4, 7, 12, 13].

The toxic properties of aluminium depend not only on the absolute content of its inorganic complexes in soil, but also on the proportion of aluminium ions in the sum of exchangeable cations (CEC—cation exchange capacity). According to Ulrich et al. [1], an increase in the aluminium share to over 30% of CEC is the main reason for forests extinction. Use of the so-called chemical toxicity indicators, that is, molar ratios of selected cations and aluminium, for example, F1 = Ca exch/Alexch< 1 (cmol kg−1)/(cmol kg−1), F2 = Mgexch/Alexch < 0.1 (cmol kg−1)/(cmol kg−1), or F3 = Caexch/(Caexch + Alexch + Feexch) < 0.05 (cmol kg−1)/(cmol kg−1), is controversial according to some authors [1, 4, 1416]. The ratios F4 = (Caexch + Mgexch + Kexch)/Alexch (cmol kg−1)/(cmol kg−1) [17, 18] and F5 = (Caexch + Mgexch + Kexch + Mnexch + Feexch)/Alexch (cmol kg−1)/(cmol kg−1) [19, 20] are used as another toxicity indicators.

The Silesian Beskid is a part of West Beskid (West Carpathians). The area has been exposed to acid rains for years as coal-fired power stations (Rybnik Coal District in Poland and Ostrava-Karvina coal and mining region in Czech Republic), Trzyniec steel plant (Czech Republic) and Katowice agglomeration (Poland) are located in its close vicinity. It is also an attractive tourist destination which results in heavy traffic. In Table 1, we present the average annual loads (kgha−1), contributed by the precipitation of sulphates (VI) and the sum of nitrates (NO3 + NO2) in Silesia (The Silesian Voivodeship) where Beskid Slaski is located [21]. Forests spruce monocultures introduced in the 19th century dominate Silesian Beskid.

tab1
Table 1: Average annual loads (kg·ha−1) contributed by the precipitation of SO4 and (NO3 + NO2) in Silesia.

The spruce forest stands in Silesian Beskid have been heavily affected by decline in recent years. The youngest sprouts are prematurely yellowed and lose their needles, the trees’ crowns are dwindled, and eventually the trees wither. Pests’ plaque, like insects (xylophages Ips typographus), fungi (honey fungus Armillaria mellea), and so forth, spread. Deforestation is carried out on large areas to save uninfected trees.

In our previous papers the prediction of aluminium content in the soil of the Beskid mountain region was discussed [22, 23]. The aim of this study was the comparison of selected chemical properties of soil collected from two forest areas. The first object (Istebna) was the devastated area where spruce dieback, invasion of insects and fungi were observed and intensive sanitation felling was leading. The second object (Bukowiec) was healthy spruce forest without any dying of spruce. To select the parameters distinguishing these two areas, the hierarchical clustering analysis was used.

2. Materials and Methods

2.1. Soil Samples

Soil samples were collected at Wisła Forest District lying within the Silesian nappe. Its main trunk is Istebna and Godula sandstone. Acid brown soil was derived from this bedrock.

The data set studied included measurements of 13 chemical parameters in 23 soil samples collected from the organic layer O. Fourteen samples were collected in Istebna (along the mountain traverse) on the area of about 5 ha of spruce forest, infected with insect pests (xylophages), under deforestation. Three samples were taken at the same height in the land stripe of 200 m (every about 100 m). The heights of sampling points were from 690 to 850 m above sea level. In Bukowiec nine samples were taken on the area of about 2 ha 120-year-old spruce healthy forest, along the traverse from 645 to 720 m above the sea level.

The samples were dried in air, 2 mm sieved, and assayed for pHKCl potentiometrically [24]; organic matter content worg (% w/w) by combustion of a soil sample in a furnace at 500–550°C to constant mass [24]; total carbon Ctot (% w/w); total nitrogen Ntot (% w/w) with the use of instrumental method on PE CHNS/O 2400 (Perkin Elmer) device and Ctot (Ntot)−1 ratio; cation exchange capacity CEC (cmolc kg−1) according to ISO 11260 [25], based on Gillman’s method [26]; contents of calcium Caexch (cmolc kg−1); magnesium Mgexch (cmolc kg−1); potassium Kexch (cmolc kg−1); iron Feexch (cmolc kg−1), and manganese Mnexch (cmolc kg−1) in the exchangeable fraction by metal content determination with the use of AAS method in (0.1 mol l−1) BaCl2 soil extracts [25].

Aluminium was extracted with solutions of barium chloride and sodium pyrophosphate. Exchangeable aluminium (which reflects Al3+ in soil solution) was extracted with 0.1 mol l−1 solution of barium chloride Alexch (v/m (v/m = ratio of extraction solvent volume (cm3) to mass of air-dried soil sample (g))= 12, extraction time—3 hours) [25]. Aluminium Alpyr extracted with 0.1 mol l−1 solution of Na4P2O7 (v/m = 50, extraction time—16 hours) is considered to be the form associated with the soil organic matter in a sample. Aluminium content in extracts was determined with the use of AAS method and Varian SPECTRA AA880 device. The measurements’ accuracy was tested with a certified material GSJ JSO-2 % Al2O3.

Each assay was repeated at least 3 times. The results are given in Table 2.

tab2
Table 2: Range, average, and median of parameters measured in soil of Silesian Beskid.
2.2. Data Analysis

Hierarchical clustering analysis is a method which can be applied to multidimensional data sets, in order to study similarities of objects (e.g., soil samples) in the variables’ space (e.g., parameters), or similarities of variables in the objects space [2730]. Cluster analysis is characterized by the similarity measure used and the way the resulting subclusters are linked. The most popular similarity measure is Euclidean distance, whereas among the linkage methods the most popular ones are single linkage, complete linkage, average linkage, centroid linkage, and Ward linkage. In the study the Ward linkage was used. It is based on the inner squared distance of clusters, so that at each stage these two clusters are merged, for which the minimum increase in the total within group error sums of squares is observed. The results of hierarchical clustering are presented in a form of a dendrogram, along which axis indices of clustered objects (or variables) are displayed, and which axis shows the corresponding linkage distances (or an adequate measure of similarity) between the two objects or clusters, which are merged. The dendrogram reveals data structure (i.e., the subgroups of objects), but it allows no interpretation of the observed patterns in terms of the original variables (parameters). Therefore a simple visualization method was proposed, using colour map, which represents the studied data organized in matrix X (), but with objects and parameters ordered according to specific object and variable order (called “objorder” and “varorder”, resp.) from the Ward dendrograms [30].

The hierarchical cluster analysis was performed for parameters 1–10. The studied data were organized in matrix X (); that is, each row of matrix X represented one soil sample described by the first 10 parameters (Table 2). The data set was standardized as the measured parameters significantly differed in their ranges: where , denote the mean of the th column and its standard deviation, respectively.

3. Results and Discussion

The soil samples were taken from the layer O. The organic matter content varied widely from over a dozen to tens percent and was strongly correlated with the total carbon content, Ctot (R2 = 0.9838).

In both locations (Bukowiec and Istebna) (Table 2),(i)the exchangeable aluminium content was considerably higher than the toxicity level 1.11 cmolc kg−1 [4], and equaled on average 12.4 cmolc kg−1 in Istebna and 9.0 cmolc kg−1 in Bukowiec;(ii)aluminium content in CEC considerably exceeded the critical level by 30% (Figure 1) [1];(iii)the soils were very acid.

748236.fig.001
Figure 1: Aluminium proportion in CEC.

To explore the studied data set and to examine the similarities of the sampling sites, the hierarchical clustering methods were used. The results of the analysis presented below were based on the Euclidean distance and the Ward linkage algorithm. Clustering of the sampling sites in the parameter space described in Table 2 was presented in Figure 2.

fig2
Figure 2: Dendrograms of (a) sampling sites in the space of 10 measured parameters and (b) variables in the objects space.

The dendrogram shown in Figure 2(a) did not reveal the differences between soil samples collected in healthy and infected forest areas. It revealed, however, two district clusters of sampling sites; cluster A, containing all soil samples collected in Bukowiec (healthy forest), mixed with some of soil samples collected in Istebna (objects numbers 5–7, 11–13) and cluster B, containing the remaining soil samples from the infected Istebna forest. Moreover, two subclusters can be distinguished in the main cluster A: the first one (AI) with soil samples numbers 5, 7, and 11–13 from Istebna and soil samples numbers 15, 16, 18, and 20–22 from Bukowiec and the second one (AII), with soil sample number 6 from Istebna and soil samples numbers 17, 19, ad 23 from Bukowiec.

The dendrogram constructed for the variables (see Figure 2(b)) revealed two main classes thereof (A and B): class A, including variables numbers 2–6 and 8, which represented the total carbon and nitrogen contents and concentrations of Caexch, Mgexch, Kexch, Mnexch, respectively and class B, constituted by variables numbers 1, 7, 9, and 10 (pHKCl, Feexch, Alexch, and Alpyr).

The dendrogram of objects (soil samples) with the image of the data set with objects and variables sorted according to the “objorder” and “varorder” was presented in Figure 3. Simultaneous interpretation of the dendrogram of objects in variable space and the image of the data allowed drawing a conclusion that soil samples belonging to cluster A were characterized by relatively lower pHKCl and lower concentrations of Alpyr (parameters numbers 1 and 10). Moreover, the uniqueness of subcluster AI can be explained by relatively high concentration of Feexch (parameter number 7), whereas the uniqueness of subcluster AII stemmed from relatively high total carbon and nitrogen contents (parameters numbers 2 and 3) and the highest concentrations of Caexch from all tested soil samples. Soil samples belonging to cluster B were characterized by higher pHKCl and concentrations of Alexch and Alpyr (parameters numbers 1, 9, and 10).

fig3
Figure 3: Dendrogram of samples with visual complement in the space of ten parameters.

Another cluster models were constructed in order to find the basic soil parameters distinguished between the two studied forest areas. Clustering of the soil sampling sites in the parameter space described by pHKCl, total carbon content Ctot, exchangeable aluminium content Alexch, and organically bound aluminium content Alpyr was presented in Figure 4.

fig4
Figure 4: Dendrogram of samples with visual complement in the space of four parameters (pHKCl, total carbon content Ctot, exchangeable aluminium content Alexch, and organically bound content Alpyr).

The dendrogram constructed for soil samples in the space of four parameters allowed revealing the differences between soil samples collected in healthy and infected forests. Cluster A included all soil samples collected in healthy Bukowiec forest (objects numbers 15–23) with three soil samples from the infected Istebna forest (objects numbers 5, 6, and 11), whereas in Cluster B all the remaining, soil samples from the Istebna forest were located. Two subclusters were also distinguished in each cluster; in cluster A, subcluster AI collecting soil samples numbers 5, 6, and 11 (from Istebna) and soil samples numbers 16, 18–20 (from Bukowiec) and subcluster AII composed of soil samples numbers 15, 17, 21, and 22 (from Bukowiec) and one nongrouped in any subcluster soil sample no. 23 (from Bukowiec) and in cluster B, subcluster BI, containing soil samples numbers 1, 2, 9, 10, and 14 and the subcluster BII, including soil samples numbers 3, 4, 7, 8, 12, and 13. Based on the image of the data an explanation of the differences between healthy and infected forests can be given. Soil samples from the Bukowiec (located in cluster A) were characterized by relatively lower pHKCl and concentrations of Alpyr and Alexch than the remaining soil samples. Subcluster AI was distinguished mainly due to high concentration of Ctot, whereas subcluster AII due to the lowest concentrations of Alexch and Alpyr among all the soil samples tested. The uniqueness of soil sample no. 23 in cluster A was caused by the lowest pHKCl and the highest concentration of Ctot.

Soil samples from Istebna collected in cluster B were characterized by relatively higher pHKCl and low concentration of Ctot. Moreover, the uniqueness of subcluster BI was caused by relatively higher concentrations of Alexch and Alpyr. In subcluster BII soil sample no. 4 was characterized by the highest pHKCl and the lowest concentration of Ctot among all of the soil samples tested.

Values of chemical toxicity indexes F1F5, calcium content F6 = Caexch/(Caexch + Mgexch +Kexch + Mnexch + Feexch + Alexch)−1 (cmolc kg−1)/(cmolc kg−1), and molar calcium to magnesium ratio F7 = Caexch/Mgexch (cmol kg−1)/(cmol kg−1) in CEC of soil are given in Table 3. The values of Caexch (F1 < 1) and Mgexch (F2 < 0.1) content in soil were low in both locations. More reliable criterion [31] is F4, whose average value was higher in Bukowiec and equaled 0.4. The values of F3 computed for samples collected in Bukowiec, except for point 21, were higher than the critical value of 0.05. The results for samples from Istebna were ambiguous (for eight points F3 ≤ 0.05 and for six points F3 > 0.05). It can be claimed that the toxic properties of aluminium were affected also by the iron content in the exchangeable complex. The sum of aluminium and iron content in CEC was on average higher in Istebna samples than in Bukowiec. An average values of F5 and F6 were higher in Bukowiec. Soil samples differed also in terms of F7 coefficient values, which were on average higher in Bukowiec than in Istebna.

tab3
Table 3: Chemical toxicity index.

Interestingly, the results of molar ratios in CEC, computed as quotient of metal content (Kexch, Feexch, Mnexch, and Alexch), and F7 coefficient (Figure 5) were higher for soil samples collected in Istebna, particularly for iron and aluminium. The composition of an exchangeable complex seems to be the reason for different status of aluminium in soil samples of Bukowiec and Istebna.

fig5
Figure 5: Molar ratios of selected metals in exchangeable complex in soil samples collected in Istebna (in white) and Bukowiec (in black).

4. Summary

The hierarchical clustering methods have shown that the examined two areas of spruce forests in the Silesian Beskid with diametrically different health status differ in four chemical properties of the soil taken from the O level. First of all, the soil in the healthy forest (Bukowiec) contains 30% less exchangeable aluminium and more organic matter. Furthermore, it was found that this soil was more acid. Alpyr aluminium content is higher in the devastated forest area (Istebna). In addition, it was found that the absolute values of the cation exchange capacity of the soil in both regions were similar, but the molar ratio of metals in CEC was different.

Release of potentially toxic Al forms is very important consequence of soil acidification that may significantly contribute to forest extinction. Though concentrations of acidificants in atmosphere have decreased in the last decades (particularly SO4), forests are still endangered by long-term changes of soil conditions. It can be assumed that the high exchange Al form concentration in Istebna soil is one of the reasons for dying spruces. Weakened trees fall ill and are unable to defend themselves against insect pests.

The exchangeable aluminium content is lower in soils from Bukowiec. The composition of the CEC is more favorable which may explain much better condition of the forest.

Conflict of Interests

The authors declares that there is no conflict of interests regarding the publication of this paper.

References

  1. B. Ulrich, R. Mayer, and P. K. Khanna, “Chemical changes due to acid precipitation in a loess-derived soil in central Europe,” Soil Science, vol. 130, no. 4, pp. 193–199, 1980. View at Google Scholar · View at Scopus
  2. O. Drábek, L. Mládková, L. Borůvka, J. Szakova, A. Nikodem, and K. Nemecek, “Comparison of water—soluble and exchangeable form of Al in acid forest soils,” Journal of Inorganic Biochemistry, vol. 99, pp. 1788–1795, 2005. View at Google Scholar
  3. H. Klöppel, A. Fliedner, and W. Kördel, “Behaviour and ecotoxicology of aluminium in soil and water—review of the scientific literature,” Chemosphere, vol. 35, no. 1-2, pp. 353–363, 1997. View at Publisher · View at Google Scholar · View at Scopus
  4. M. Kotowski, L. Pawłowski, and X. Zhu, Aluminum in Environment, Wydawnictwo Politechniki Lubelskiej, Lublin University of Technology Publishing House, Lublin, Poland, 1995, (Polish).
  5. J. P. Boudot, T. Becquer, D. Merlet, and J. Rouiller, “Aluminium toxicity in decling forests: a general overview with a seasonal assessment in a silver fir forest in the Vosges Mountains (France),” Annales des Sciences Forestieres, vol. 51, no. 1, pp. 27–51, 1994. View at Google Scholar · View at Scopus
  6. O. Drábek, L. Borůvka, L. Mládková, and M. Kocarek, “Possible method of aluminium speciation in forest soils,” Journal of Inorganic Biochemistry, vol. 97, pp. 8–15, 2003. View at Google Scholar
  7. T. B. Kinraide, “Reconsidering the rhizotoxicity of hydroxyl, sulphate, and fluoride complexes of aluminium,” Journal of Experimental Botany, vol. 48, no. 310, pp. 1115–1124, 1997. View at Google Scholar · View at Scopus
  8. E. Matczak-Jon, “The role of aluminium in the environment,” Chemical News, vol. 49, pp. 9–10, 1995 (Polish). View at Google Scholar
  9. P. Matúš, “Evaluation of separation and determination of phytoavailable and phytotoxic aluminium species fractions in soil, sediment and water samples by five different methods,” Journal of Inorganic Biochemistry, vol. 101, no. 9, pp. 1214–1223, 2007. View at Publisher · View at Google Scholar · View at Scopus
  10. P. Matúš, J. Kubová, M. Bujdoš, and J. Medved', “Free aluminium extraction from various reference materials and acid soils with relation to plant availability,” Talanta, vol. 70, no. 5, pp. 996–1005, 2006. View at Publisher · View at Google Scholar · View at Scopus
  11. L. Mládková, L. Borůvka, O. Drábek, and R. Vasat, “Factors influencing distribution of different Al forms in forest soils of the Jizerskè hory Mts,” Journal of Forest Science, vol. 52, pp. 87–92, 2006. View at Google Scholar
  12. V. Manoharan, P. Loganathan, R. W. Tillman, and R. L. Parfitt, “Interactive effects of soil acidity and fluoride on soil solution aluminium chemistry and barley (Hordeum vulgare L.) root growth,” Environmental Pollution, vol. 145, no. 3, pp. 778–786, 2007. View at Publisher · View at Google Scholar · View at Scopus
  13. Z. Rengel, “Aluminium cycling in the soil-plant-animal-human continuum,” BioMetals, vol. 17, no. 6, pp. 669–689, 2004. View at Publisher · View at Google Scholar · View at Scopus
  14. C. S. Cronan and D. F. Grigal, “Use of calcium/aluminum ratios as indicators of stress in forest ecosystems,” Journal of Environmental Quality, vol. 24, no. 2, pp. 209–226, 1995. View at Google Scholar · View at Scopus
  15. H. A. De Wit, J. Mulder, P. H. Nygaard, and D. Aamlid, “Testing the aluminium toxicity hypothesis: a field manipulation experiment in mature spruce forest in Norway,” Water, Air, and Soil Pollution, vol. 130, no. 1–4, pp. 995–1000, 2001. View at Publisher · View at Google Scholar · View at Scopus
  16. H. A. de Wit, J. Mulder, P. H. Nygaard et al., “Aluminum: the need for a re-valuation of its toxicity and solubility in mature forest stands,” Water Air Soil Pollution Focus, vol. 1, pp. 103–118, 2001. View at Google Scholar
  17. J. Aherne, E. P. Farrell, J. Hall, B. Reynolds, and M. Hornung, “Using multiple chemical criteria for critical loads of acidity in maritime regions,” Water Air Soil Polluttion Focus, vol. 1, pp. 75–90, 2002. View at Google Scholar
  18. K. Hansen, L. Vesterdal, A. Bastrup-Birk, and J. Bille-Hansen, “Are indicators for critical load exceedance related to forest condition?” Water, Air, and Soil Pollution, vol. 183, no. 1–4, pp. 293–308, 2007. View at Publisher · View at Google Scholar · View at Scopus
  19. A. Göttlein, A. Heim, and E. Matzner, “Mobilization of aluminium in the rhizosphere soil solution of growing tree roots in an acidic soil,” Plant and Soil, vol. 211, no. 1, pp. 41–49, 1999. View at Publisher · View at Google Scholar · View at Scopus
  20. M. Holmberg, J. Mulder, M. Posch et al., “Critical loads of acidity for forest soils: tentative modifications,” Water Air Soil Pollution Focus, vol. 1, pp. 91–101, 2001. View at Google Scholar
  21. http://www.katowice.pios.gov.pl/monitoring/informacje/stan2012/rapimgw.pdf.
  22. A. Smoliński, M. Zołotajkin, J. Ciba, P. Dydo, and J. Kluczka, “PLS-EP algorithm to predict aluminum content in soils of Beskid Mountains region,” Chemosphere, vol. 76, no. 4, pp. 565–571, 2009. View at Publisher · View at Google Scholar · View at Scopus
  23. M. Zołotajkin, J. Ciba, J. Kluczka, M. Skwira, and A. Smoliński, “Exchangeable and bioavailable aluminium in the mountain forest soil of Barania Góra Range (Silesian Beskids, Poland),” Water, Air, and Soil Pollution, vol. 216, no. 1–4, pp. 571–580, 2011. View at Publisher · View at Google Scholar · View at Scopus
  24. A. Ostrowska, S. Gawliński, and Z. Szczubiałka, Methods for Analyzing and Assessing the Properties of Soil and Plants, Institute of Environmental Protection, Warszawa, Poland, 1991, (Polish).
  25. ISO, “11260 Soil quality—determination of effective cation exchange capacity and base saturation degree, using barium chloride solution”.
  26. G. P. Gillman, “A proposed method for the measurement of exchange properties of highly weathered soils,” Australian Journal of Soil Research, vol. 17, pp. 129–139, 1979. View at Google Scholar
  27. J. A. Hartigan, “Statistical theory in clustering,” Journal of Classification, vol. 2, no. 1, pp. 63–76, 1985. View at Publisher · View at Google Scholar · View at Scopus
  28. L. Kaufman and P. J. Rousseeuw, Finding Groups in Data, an Introduction to Cluster Analysis, John Wiley & Sons, New York, NY, USA, 1990.
  29. D. L. Massart and L. Kaufman, The Interpretation of Analytical Data by the Use of Cluster Analysis, John Wiley & Sons, New York, NY, USA, 1983.
  30. A. Smoliński, B. Walczak, and J. W. Einax, “Hierarchical clustering extended with visual complements of environmental data set,” Chemometrics and Intelligent Laboratory Systems, vol. 64, no. 1, pp. 45–54, 2002. View at Publisher · View at Google Scholar · View at Scopus
  31. L. Borůvka, V. Podrázsky, L. Mládková, I. Kuneš, and O. Drábek, “Some approaches to the research of forest soils affected by acidification in the Czech Republic,” Soil Science & Plant Nutrition, vol. 50, no. 5, pp. 745–749, 2005. View at Google Scholar