Concepts and Applications
Krasilnikov, P., Carré, F. & Montanarella, L. (eds.)
EUR 23290 EN - 2008
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Introduction Co-authorsChapter 1 Soil science and geostatistics (R. Webster) Chapter 2 Variography of discrete soil properties (P. Krasilnikov) Chapter 3 Spatial variability of forest litters in bilberry spruce forests of Fennoscandia (E. Solomatova, V. Sidorova) Chapter 4 Spatial distribution of the soil properties controlling soil resistance to erosion at a coﬀee growing farm in Sierra Sur de Oaxaca (N.E. ´ Garc´aCalder´n, Y. Uriostegui Delgado, G. Alvarez Arteaga, A. Ib´nez ı o a˜ Huerta, P. Krasilnikov) Chapter 5 Geostatistical analysis of the spatial structure of acidity and organic carbon in zonal soils of the Russian plain (P. Krasilnikov, V. Sidorova) Chapter 6 Eﬀect of beavers on variability of soil properties in southern Karelia (V. Sidorova, F. Fyodorov) Chapter 7 The use of geostatistical methods formapping soil horizons (V. Sidorova, P. Krasilnikov) Chapter 8 Spatial variability of soil hydro-physical properties: A case study in Herceghalom, Hungary (Cs. Farkas, K. Rajkai, M. Kert´sz, Zs. Bakacsi, e M. van Meirvenne) Chapter 9 The continuum dilemma in pedometrics and pedology (J.-J. Ib´nez, a˜ A. Salda˜a) n Bibliography (V. Sidorova) II IV 1 12 26
Geostatistics, which can be deﬁned as the tools for studying and predicting the spatial structure of georeferenced variables, have been mainly used in soil science during the past two decades. Since now, hundreds of geostatistical papers have been published on soil science issues (see bibliography ibid., this volume). The use of geostatistical tools in soil science is diverse andextensive. It can be for studying and predicting soil contamination in industrial areas, for building agrochemical maps at the ﬁeld level, or even to map physical and chemical soil properties for a global extent. The users of the output maps are going from soil scientists to environmental modelers. One of the speciﬁcity of geostatistical outputs is the assessment of the spatial accuracy associatedto the spatial prediction of the targeted variable. The results which are quantitative are then associated to a level of conﬁdence which is spatially variable. The spatial accuracy can then be integrated into environmental models, allowing for a quantitative assessment of soil scenarios. Geostatistics are one of the most popular tools of pedometrics (the application of mathematical and...