Article

New approaches to the exploitation of former soil survey data

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Abstract

A number of soil data is available from the soil and land resource surveys carried out in the past. Modern mathematical and statistical methods and computer development enable new analyses and exploitation of these data. This is required by more complex understanding of soil functions, danger of soil degradation, different agricultural practices, etc. This contribution shows some examples of new exploitation of soil data resulting from a soil survey done in the 1960's. Selected region is characterized by high heterogeneity and variability of soils and natural conditions in general. Characteristics (pH, CEC, organic carbon content, texture, etc.) of more than 600 profiles of agricultural soils were used. Different pedometric methods were applied to evaluate these data. Those included: 1) geostatistics for the analysis of soil spatial dependence and kriging for spatial prediction; 2) multivariate statistical and geostatistical methods for the analysis of interrelations among soil properties and for the determination of principal factors controlling soil heterogeneity in the region; 3) numerical classification for the delineation of soil categories defined according to different objectives (classical soil categorisation, soil vulnerability to pollution by risk elements, suitability to agricultural production, a.o.); 4) pedotransfer functions for modelling the behaviour of chemicals in soils. The results of numerical classification were compared to the results of traditional soil classification. The technology of geographical information systems was used for processing of the resulting applied maps. It was shown that the data of the former soil survey may be used for current purposes using modern methods of their processing. New soil surveys should be therefore focused mainly on special tasks on limited areas, verification of the older data, and validation of the results of mathematical simulation and prediction.

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... uses hidden units to extract useful information from inputs and use them to predict outputs Carré and Girard (2002), McBratney et al. (2003), Moonjun et al. (2010), Jafari et al. (2013), Bodaghabadi et al. (2015)Fuzzy systems Indicates uncertainty in predictive and predicted traits or classes. Using fuzzy logic, they draw a certain input to an expected outputZhu et al. (1997),Boruvka et al. (2002),Carré and Girard (2002),Rizzo et al. (2016),Mohamed (2020) ...
Chapter
Land management is the most challenging issue in the evolving world today, especially in urban areas where most people live. As the population grows, it must be planned to yield enough energy and food, and preserve some species (plants and animals) on Earth. Thus, the obvious need for mapping and efficient soil management in meeting the growing request for food and other basic requirements of communities worldwide. Mapping the spatial distribution of soil formation parameters is very important and serious for information on soil management decisions. Soil formation parameters are different in terms of presence and composition in each landscape, so it is necessary that all information on land resources are collected so that land planners can obtain land use with the maximum yield from each plot of land. Soil maps allow for studying adequate information on soil types and their distribution, and it is required for a variety of planning and usage, including urban planning, forestry, agriculture, grazing, watershed management and engineering. Digital soil mapping (DSM) is a subfield of soil science that requires three components: input data for field and laboratory observations, the process used in terms of spatial and non-spatial soil inference systems, and the output in the form of rasters of forecast along with forecast uncertainty. Digital mapping approaches using data mining and environmental covariates involve climate, organisms, relief, parent material, and time or age of soil which can be determined in soil mapping. The advancement of DSM is due to the confluence of several factors, including the increased availability of spatial data (satellite imagery), computing power for data processing, and the improvement of data mining tools and GIS and geostatistics. This chapter examines the history of soil mapping, environmental covariates, role of remote sensing in DSM, soil inference systems and quality assessments in the DSM. Soil data is needed to address agriculture, natural resource management, development of urban areas and urbanism issues such as water and food security, climate change, land degradation and biodiversity management. Therefore, an updated and accurate soil information are demanded in the studies of international organisations.
... Some soil types may have many pedon sites while others may have a few. These limits the applications of statistical and geostatistical methods which are commonly used in digital soil mapping (Myers, 1994;Boruvka et al., 2002;Diem and Comrie, 2002;Penížek and Borůvka, 2004;Lagacherie, 2008). An alternative is a similarity-based approach (Holt, 1999;Zhu et al., 2010;Pla et al., 2013), which makes soil predictions at unsampled locations based on environmental similarities of the sampled locations. ...
Article
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... (Penizek and Boruvka, 2004). That means that quite often two profiles were located very close to each other, but represented different soils, and therefore they were quite different (Boruvka et al., 2003). This leads to very high nugget in the variograms of soil properties, which certainly caused problems for reasonable interpolation. ...
Thesis
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Article
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... The sites were chosen to describe different soil units. That means that quite often two profiles were located very close to each other, but represented different soils, and therefore they were quite different (Boruvka et al., 2003). This leads to very high nugget in the variograms of soil properties, which certainly caused problems for reasonable interpolation. ...
Conference Paper
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