Geografie 2023, 128, 437-457
https://doi.org/10.37040/geografie.2023.019
The spatial dependence of base saturation on forest soil grain and chemical composition seen through individual and typological divisions
Pavel Samec1,2
, Anna Tišlerová3, Matěj Horáček2, Gabriela Tomášová2, Miloš Kučera4
, Anna Tišlerová3, Matěj Horáček2, Gabriela Tomášová2, Miloš Kučera4
1Global Change Research Institute of the Czech Academy of Sciences, Brno, Czechia
2Mendel University in Brno, Faculty of Forestry and Wood Technology, Department of Geology and Soil Science, Brno, Czechia
3Mendel University in Brno, Faculty of Forestry and Wood Technology, Department of Forest Management and Applied Geoinformatics, Brno, Czechia
4Forest Management Institute Brandýs nad Labem, Czechia
Received January 2023
Accepted September 2023
References
1. ADHIKARI, K., KHEIR, R. B., GREVE, M. B, BØCHER, P. K., MALONE, B. P., MINASNY, B., MCBRATNEY, A. B., GREVE, M. H. (2013): High-Resolution 3-D Mapping of Soil Texture in Denmark. Soil Science Society of America Journal, 77, 3, 860−876.
<https://doi.org/10.2136/sssaj2012.0275>
2. BIVAND, R., YU, D. (2017): spgwr: Geographically Weighted Regression. R package version 0.6−32. https://CRAN.R-project.org/package=spgwr
3. BLAKEMORE, L. C., METSON, A. J. (1960): Micro-Determination of Cation-Exchange Capacity and Total Exchangeable Bases. Soil Science, 89, 4, 202−208.
<https://doi.org/10.1097/00010694-196004000-00005>
4. BLASER, P., WALTHERT, L., ZIMMERMANN, S., PANNATIER, G. E., LUSTER, J. (2008): Classification schemes for the acidity, base saturation, and acidification status of forest soils in Switzerland. Journal of Plant Nutrition and Soil Science, 171, 2, 163−170.
<https://doi.org/10.1002/jpln.200700008>
5. BOWMAN, A. (1984): An Alternative Method of Cross-validation for the Smoothing of Density Estimates. Biometrika, 71, 2, 353−360.
<https://doi.org/10.1093/biomet/71.2.353>
6. CARAVACA, F., LAX, A., ALBALADEJO, J. (1999): Organic matter, nutrient contents and cation exchange capacity in fine fractions from semiarid calcareous soils. Geoderma, 93, 3−4, 161–176.
<https://doi.org/10.1016/S0016-7061(99)00045-2>
7. COSBY, B. J., FERRIER, R. C., JENKINS, A., WRIGHT, R. F. (2001): Modelling the effects of acid deposition: refinements, adjustments and inclussion of nitrogen dynamics in the MAGIC model. Hydrology and Earth System Science, 5, 3, 499−517.
<https://doi.org/10.5194/hess-5-499-2001>
8. CRONAN, C. S., GRIGAL, D. F. (1995): Use of Calcium/Aluminium Ratios as Indicators of Stress in Forest Ecosystems. Journal of Environmental Quality, 24, 2, 209−226.
<https://doi.org/10.2134/jeq1995.00472425002400020002x>
9. FINKE, P. A., VANWALLEGHEM, T., OPOLOT, E., POESEN, J., DECKERS, J. (2013): Estimating the effect of tree uprooting on variation of soil horizon depth by confronting pedogenic simulations to measurements in a Belgian loess area. Journal of Geophysical Research, 118, 4, 2124−2139.
<https://doi.org/10.1002/jgrf.20153>
10. , K. (2010): Response Interpolation and Scale Sensitivity: Evidence Against 5-Point Scales. Journal of Usability Studies, 5, 3, 104−110.
11. FLECK, S., COOLS, N., DE VOS B., MEESENBURG, H., FISCHER R. (2016): The Level II aggregated forest soil condition database links soil physicochemical and hydraulic properties with long-term observations of forest condition in Europe. Annals of Forest Science, 73, 1, 945−957.
<https://doi.org/10.1007/s13595-016-0571-4>
12. GALEHOUSE, J. S. (1971): Sedimentation Analysis. San Francisco State College, San Francisco.
13. GALVÃO, S. L., FORMAGGIO, R. A., COUTO, G. E., ROBERTS, D. A. (2008): Relationships between mineralogical and chemical composition of tropical soils and topography from hyperspectral remote sensing data. ISPRS Journal of Photogrammetry and Remote Sensing, 63, 2, 259−271.
<https://doi.org/10.1016/j.isprsjprs.2007.09.006>
14. GOLDBERG, S., LEBRON, I., SUAREZ, D. L. (2000): Soil Colloidial Behavior. In: Sumner M. E. (ed.), Handbook of Soil Science, CRC Press.
15. GOLLINI, I., LU, B., CHARLTON, M., BRUNDSON, C., HARRIS, P. (2015): GWmodel: an R Package for Exploring Spatial Heterogeneity using geographically Weighted Models. Journal of Statistical Software, 63, 17, 1−50.
<https://doi.org/10.18637/jss.v063.i17>
16. HEITCAMP, F., AHRENS, B., EVERS, J., STEINICKE, C., MEESENBURG, H. (2020): Inference of forest soil nutrient regimes by integrating soil chemistry with fuzzy-logic: Regionwide application for stakeholders of Hesse, Germany. Geoderma Regional, 23, 1, e00340.
<https://doi.org/10.1016/j.geodrs.2020.e00340>
17. HOUBA, V. G. J., VAN DER LEE, J. J., NOVOZAMSKY, I., WALINGA, I. (1989): Soil Analysis Procedures. Soil and Plant Analysis, Part 5. Wageningen Agricultural University.
18. HRAŠKO, J., ČERVENKA, L., FACEK, Z., KOMÁR, J., NĚMEČEK, J., POSPÍŠIL, F., SIROVÝ, V. (1962): Rozbory pôd. Slovenské vydavateľstvo pôdohospodárskej literatúry, Bratislava.
19. IBÁŇEZ, J. J., KRASILNIKOV, P. V., SALDAŇA, A. (2012): Archieve and refugia of soil organisms: applying a pedodiversity framework for the conservation of biological and non-biological heritages. Journal of Applied Ecology,49, 6, 1267−1277.
<https://doi.org/10.1111/j.1365-2664.2012.02213.x>
20. JANKOVSKÁ, Z., ŠTĚRBA, P. (2007): Národní inventarizace lesů v České republice 2001−2004. Úvod, metody, výsledky. ÚHÚL Brandýs nad Labem.
21. JIANG, J., WANG, Y.-P., YU, M., CAO, N., YAN, J. (2018): Soil organic matter is important for acid buffering and reducing aluminum leaching from acidic forest soils. Chemical Geology, 501, 1, 86−94.
<https://doi.org/10.1016/j.chemgeo.2018.10.009>
22. LAFFAN, S. W., LEES, B. G. (2004): Predicting regolith properties using environmental correlation: a comparison of spatially local approaches. Geoderma, 120, 3−4, 241–258.
<https://doi.org/10.1016/j.geoderma.2003.09.007>
23. LEPŠ, J., ŠMILAUER, P. (2016): Biostatistika. Jihočeská univerzita v Českých Budějovicích.
24. MACKŮ, J., HOMOLOVÁ, K. (2007): Pedogenetické asociace lesních půd ČR. 1 : 500 000. ÚHÚL Brandýs nad Labem.
25. MANRIQUE, L. A., JONES, C. A., DYKE, P. T. (1991): Predicting Cation-Exchange Capacity from Soil Physical and Chemical Properties. Soil Science Society of America Journal, 55, 3, 787−794.
<https://doi.org/10.2136/sssaj1991.03615995005500030026x>
26. MEERSMANS, J., DE RIDDER, F., CANTERS, F., DE BAETS, S., VAN MOLLE, M. (2008): A multiple regression approach to assess the spatial distribution of Soil Organic Matter (SOC) at the regional scale (Flanders, Belgium). Geoderma, 143, 1−2, 1–13.
<https://doi.org/10.1016/j.geoderma.2007.08.025>
27. NELSON, D. W., SOMMERS, L. E. (1982): Total carbon, organic carbon, and organic matter. In: Page A.L. (ed.), Methods of soil analysis. Part 2. Chemical and microbiological properties. ASA, SSSA, Madison, 539−580.
<https://doi.org/10.2134/agronmonogr9.2.2ed.c29>
28. NĚMĚČEK, J., TOMÁŠEK, M. (1983): Geografie půd ČSR. Academia, Praha.
29. QUIRÓS-SEGOVIA, M., CONDÉZ RUIZ, S., DRÁPELA, K. (2016): Comparison of heightdiameter models based on geographically weighted regressions and linear mixed modelling applied to large scale forest inventory data. Forest Systems, 25, 3, e076.
<https://doi.org/10.5424/fs/2016253-09787>
30. R CORE TEAM (2022): R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
31. ROSS, D. S., MATSCHONAT, G., SKYLLBERG, U. (2008): Cation exchange in forest soils: the need for a new perspective. European Journal of Soil Science, 59, 6, 1141−1159.
<https://doi.org/10.1111/j.1365-2389.2008.01069.x>
32. , P. (2009): Revize biogeografické rajonizace pufračních schopností lesních půd. Acta Musei Beskidensis, 1, 1, 1−22.
33. SAMEC, P. (2014): The perspectives of biogeography at forestry environmental modelling. In: Second StatGIS Conference Proceedings. Palacký University Olomouc, 64−74.
34. SAMEC, P. (2020): Forest Soil Physico-Chemical Sorption Spatial Links in Central-European Systems of Site Geographical Divisions. In: Balková M., Kučera A., Samec P. (eds.), Contemplating Earth. Soil and Landscape Considerations. Mendel University in Brno, 75−89.
35. SAMEC, P., CAHA, J., TUČEK, P., ZAPLETAL, M., CUDLÍN, P., KUČERA, M. (2017): Discrimination between acute and chronic decline of Central European forests using map algebra of the growth condition and forest biomass fuzzy sets: A case study. Science of the Total Environment,599−600, 1, 899–909.
<https://doi.org/10.1016/j.scitotenv.2017.05.023>
36. SAMEC, P., MIKITA, T., BAJER, A. (2019): Catenas of grain size and chemical forest soil properties in Outer Western Carpathians of the Czech Republic characterized by principal component analysis. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 67, 3, 733−747.
<https://doi.org/10.11118/actaun201967030733>
37. SEDLÁČEK, J., JANDERKOVÁ, J., ŠEFRNA, L. (2009): Půdní asociace. 1 : 500 000. In: Hrčianová, T., Mackovčin, P., Zvara, I. (eds.), Atlas krajiny České republiky. MŽP, VÚKOZ, Praha, 134−135.
38. SELIGE, T., BÖHNER, J., SCHMIDHALTER, U. (2006): High resolution topsoil mapping using hyperspectral image and field data in multivariate regression modeling procedures. Geoderma, 136, 1−2, 235–244.
<https://doi.org/10.1016/j.geoderma.2006.03.050>
39. SEYBOLD, C. A., GROSSMAN, R. B., REINSCH, T. G. (2005): Predicting Cation Exchange Capacity for Soil Survey Using Linear Models. Soil Science Society of America Journal, 69, 3, 856−863.
<https://doi.org/10.2136/sssaj2004.0026>
40. SHI, Z., JI, W., VISCARRA ROSSEL, R. A., CHEN, S., ZHOU, Y. (2015): Prediction of soil organic matter using a spatially constrained local partial least squares regression and the Chinese vis-NIR spectral library. European Journal of Soil Science, 66, 4, 679−687.
<https://doi.org/10.1111/ejss.12272>
41. SKINNER, M. F., ZABOWSKI, D., HARRISON, R., LOWE, A., XUE, D. (2001): Measuring the cation exchange capacity of forest soils. Communications in Soil Science and Plant Analysis, 32, 11−12, 1751–1764.
<https://doi.org/10.1081/CSS-120000247>
42. SUMNER, M. E., MILLER, W. P. (1996): Cation Exchange Capacity and Exchange Coefficients. In: Sparks, D. L., Page, A. L., Helmke, P. A., Loeppert, R. H., Soltanpour, P. N., Tabatabai, M. A., Johnston, C. T., Sumner, M. E. (eds.), Methods of Soil Analysis. Part 3. Chemical Methods. Soil Science Society of America, American Society of Agronomy, Madison, 1201−1229.
43. SZABÓ, B., SZATMÁRI, G., TAKÁCS, K., LABORCZI, A., MAKÓ, A., RAJKAI, K., PÁSZTOR, L. (2019): Mapping soil hydraulic properties using random-forest-based pedotransfer functions and geostatistics. Hydrology and Earth System Sciences, 23, 6, 2615−2635.
<https://doi.org/10.5194/hess-23-2615-2019>
44. , K., KABALA, C., KARCZEWSKA, A., BOGACZ, A. (2010): Pools of available nutrients in soils from different altitudinal forest zones located in a monitoring system of the Karkonosze Mountains National Park, Poland. Polish Journal of Soil Science, 43, 2, 173−188.
45. , V., JURKOVSKÁ, L., FADRHONSOVÁ, V., HELLEBRANDOVÁ-NEUDERTOVÁ, K. (2013): Chemismus lesních půd ČR podle typologických kritérií – výsledky monitoringu lesních půd v rámci projektu EU „BioSoil“. Zprávy lesnického výzkumu, 58, 4, 314−323.
46. TEIXEIRA, D. D. B., MARQUES, J. JR., SIQUEIRA, D. S., VASCONCELOS, V., CARVALHO, O. A. JR., MARTINS, É. S., PEREIRA, G. T. (2017): Sample planning for quantifying and mapping magnetic susceptibility, clay content, and base saturation using auxiliary information. Geoderma, 305, 1, 208−218.
<https://doi.org/10.1016/j.geoderma.2017.06.001>
47. WALKEY, A., BLACK, I. A. (1934): An examination of the Degtjareff method for determining soil organic matter and a proposed modifications of the chromic acid titration method. Soil Science, 37, 1, 29−38.
<https://doi.org/10.1097/00010694-193401000-00003>
48. WHITE, R. E. (1987): Introduction to the Principles and Practice of Soil Science. Blackwell Scientific Publications, Oxford.
49. ZAR, J. H. (2010): Biostatistical Analysis. Prentice-Hall, New Jersey, 66−96.
50. ZBÍRAL, J., HONSA, I., MALÝ, S., ČIŽMÁR, D. (2004): Analýza půd III. Jednotné pracovní postupy. ÚKZÚZ, Brno.
51. ZENG, C., YANG, L., ZHU, A.-X., ROSSITER, D. G., LIU, J., LIU, J., QIN, C., WANG, D. (2016): Mapping soil organic matter concentration at different scales using a mixed geographically weighted regression method. Geoderma, 281, 1, 69−82.
<https://doi.org/10.1016/j.geoderma.2016.06.033>
52. ZHU, A.-X., QI, F., MOORE, A., BURT, J. E. (2010): Prediction of soil properties using fuzzy membership values. Geoderma, 158, 3−4, 199–206.
<https://doi.org/10.1016/j.geoderma.2010.05.001>
