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In Italy rapid landslides are the most frequently occurring natural disasters and, after earthquakes, cause the highest number of victims. In this contribution we attempt to prove that there exist a tight connection between the presence of a specific soil type, namely andic soils, and the occurrence of the main catastrophic mudflows and debris flows occurred in Italy in the last decades. The study was performed by means of an integrated pedological and hydrological analysis on the detachment crowns of some of the most important catastrophic mudflows and debris flows occurred in Italy in the last decades and involving/evolving surface soils. The results at both regional (Campania) and National (Italy) scale clearly show that despite the large variability of the environmental settings of the studied sites there are indeed some striking homogeneous soil features in the detachment crowns including (i) soil morphology, (ii) andic features ranging from high to moderate, (iii) high water retention throughout a large range of pressure heads. Results seem to reveal clear cause-effect evidences between andic soils and the investigated catastrophic mudflows/debrisflows; this must be related to the unique physical properties of these soils inducing high landslide vulnerability.

A combination of indirect soil investigation by proximal soil sensors (PSS), based on geophysical (ARP, EMI), physical (Cone Index –CI– by ultrasound penetrometry) and spectrometric (γ-rays) techniques, as well as pedological surveys, was applied in the field to assess the spatial variability of soil pollution and physical degradation in an automobile-battery recycling plant in southern Italy. Five homogeneous zones (HZs) were identified by the PSS and characterized by soil profiles. CI measurements and field analysis showed clear features of physical (i.e., soil compaction, massive structure) degradation. XRF in situ (on profiles) analysis using portable equipment (pXRF) showed Pb, Cd and As concentrations exceeding the contamination thresholds provided by the Italian regulation for industrial land use up to 20 or 100 cm of depth. Hence, a validation procedure, based on pXRF field survey, was applied to the PSS approach used for the HZs identification. High consistency was found between the HZs and the PTEs in the most contaminated areas. Significant negative Pearson correlation coefficients were found between γ-rays dose rate and Pb, Cu, Zn, As and Ni; positive ones were found between γ-rays and autochthonous lithogenic elements (V, Ti, Mn, K, Sr, Nb, Zr, Rb, Th), confirming that higher radionuclide activity correlated with lower pollution levels.

In many contaminated sites and/or sites affected by a potentially toxic element (PTE) contamination, the spatial variability of soil contamination is a very complex issue.This is because the history of contamination in a specific industrial site is often lost in time and with very different modifications occurring over time. It is even hidden in the case of illegal waste dumping, for which type, quantity and localization of contaminants are unknown. Thus it is not known in advance (i) the spatial distribution of contamination, (ii) the knowledge about how contaminants have been distributed over and beneath the soil, (iii) how far contaminants have been reworked during the life time of the contaminated site.Despite these problems, it is self-evident that a detailed knowledge of the natural and the anthropogenic spatial variability of soil contamination and soil properties is of crucial importance in both site characterization and most importantly in site reclamation.Here we claim that the analysis of soil trenches in combination with pXRF can strongly support the understanding about processes behind soil contaminant distribution and this information can then be used in the following study of contaminated site characterization.This contribution focuses on how to acquire detailed knowledge of the spatial distribution of contamination in an agricultural area of southern Italy, 6 ha of farmland confiscated by the Italian Judiciary due to past illegal burial of industrial tannery wastes causing potential contamination by Cr, Zn and heavy hydrocarbons (C>12). After indirect geophysical and radiometric (i.e. soil gamma ray emissions) prospections, 8 sites for soil profiles and trenches (10 x 1.7 m wide) digging were identified.Over these trenches both morphological (i.e. colour, structure, plant roots, etc.) and elemental total content analysis (Olympus p-XRF) were performed. This analysis enable to identify three type of soil contaminant deposition which affected to various degree the Ap, Bw and C horizons of the investigated Silandic Andosol. The highest Cr and Zn content was found in the B horizon, where levels of 25000 ppm were measured in soil-pockets of muddy-grey materials, very likely hosting the original processing residues.

Over recent decades, andic soils have been increasingly found in non-volcanic mountain ecosystems (NVME) in many parts of the world. In Italy, this type of soil has proved to be greatly affected by aeolian deposits and to be widely distributed geographically. However, there are still many open questions, especially regarding the genesis of these soils. This paper focuses on five representative pedons taken from NVME. Soil forming processes and relationships between soil and underlying bedrock were investigated by using an integrated approach including geochemical, magnetic, mineralogical, micromorphological and particle size distribution (PSD) analysis. The 5 pedons showed andic character and similar morphology and micromorphology (e.g. silt illuviation, pedorelicts), as well as large differences in terms of soil reaction and clay mineralogy. The analysis of the vertical distribution of C, Al and Fe extracted in pyrophosphate along with related geochemical indices (e.g. C p /Al p ) enabled us to indicate andosolization as the main process of soil formation and to exclude podzolization. With respect to the soil-bedrock relationship, 2 soils showed great similarity to the underlying bedrock along with a clear aeolian particle size distribution footprint, whereas, in the remaining 3 soils, there was a clear soil-bedrock discontinuity and an evident volcanic fingerprint. Some of the soil-bedrock differences related to the geographical settings and latitude.

The world of viticulture connected to wine making has become a very important activity in many inland areas permitting both the generation of important income and the sustaining of agriculture systems. Recent progress in both crop modeling and Decision Support Systems (DSS) applied to viticulture promises important changes that combine both high quality production and environmental sustainability. However, most of this progress is only addressed at the farm level and does not challenge the viticulture landscape, which is a key issue when facing DOC, DOCG areas, wine growers' cooperatives and consortiums and strategic viticulture planning. Thus, this paper aims to demonstrate that a new type of DSS, which is developed on a Geospatial Cyberinfrastructure (GCI) platform, may provide an important web-based operational tool for high quality viticulture as it connects farm and landscape levels better. The GCI platform supports acquisition, management, processing of both static and dynamic data (e.g. pedological, daily climatic, and vineyard distribution), data visualization, and on-the-fly computer applications in order to perform simulation modeling (e.g. grapevine water stress, evaluation of ecosystem services, etc.). These are all potentially accessible via the Web. This is possible thanks to the implementation of a set of modeling clusters that is strongly rooted in soil-plant-atmosphere and physically based simulation modeling. The DSS tool, applied to an area of 20,000 ha in Southern Italy, is designed to address viticulture planning and management by providing operational support for farmers, farmer associations and decision makers involved in the viticulture landscape. Output of the system includes viticulture planning and management scenario analysis, maps and evaluation of potential and current plant water stress. The tool will also be demonstrated through a short selection of practical case studies.

Today it is evident that there are many contrasting demands on our landscape (e.g. food security, more sustainable agriculture, higher income in rural areas, etc.) as well as many land degradation problems. It has been proved that providing operational answers to these demands and problems is extremely difficult. Here we aim to demonstrate that a spatial decision support system based on geospatial cyberinfrastructure (GCI) can address all of the above, so producing a smart system for supporting decision making for agriculture, forestry, and urban planning with respect to the landscape. In this paper, we discuss methods and results of a special kind of GCI architecture, one that is highly focused on land management and soil conservation. The system allows us to obtain dynamic, multidisciplinary, multiscale, and multifunctional answers to agriculture, forestry, and urban planning issues through the Web. The system has been applied to and tested in an area of about 20 000 ha in the south of Italy, within the framework of a European LIFE+ project (SOILCONSWEB). The paper reports – as a case study – results from two different applications dealing with agriculture (olive growth tool) and environmental protection (soil capability to protect groundwater). Developed with the help of end users, the system is starting to be adopted by local communities. The system indirectly explores a change of paradigm for soil and landscape scientists. Indeed, the potential benefit is shown of overcoming current disciplinary fragmentation over landscape issues by offering – through a smart Web-based system – truly integrated geospatial knowledge that may be directly and freely used by any end user (www.landconsultingweb.eu). This may help bridge the last very important divide between scientists working on the landscape and end users.

Andic-like soils, along with Andosols, have previously been reported in non-volcanic ecosystems of the coastal and mountainous areas of the Calabria region (southern Italy), but little is known about their properties, soil formation processes and volcanic source areas. Chemical, mineralogical, microscopic, sub-microscopic and magnetic analyses were carried out on 8 soils which were selected from the major Calabrian geological sectors in order to assess relationships between parent material and bedrock. Soil chemical properties revealed an occurrence of andic or vitric properties in the studied soils as a whole, and the optical microscopy enabled to identify volcanic glass, micro-pumices and soil isotropic matrix, clearly indicating volcanic inputs. Three groups of soils, identified on the basis of the degree of andic properties, showed rather homogeneous also in terms of chemical properties, clay mineralogy and land use, as well as with regard to micromorphology and magnetic susceptibility. Part of the intra-group similarities was a consequence of similar distances between sites and volcanic source area, as suggested by the correlations found between ferrihydrite content, andic (i.e. % of ammonium oxalate extractable Al + 0.5 Fe and phosphate retention) and magnetic properties vs. distances from several Sicilian districts. The elemental composition obtained by SEM/EDS analyses of soil glass fragments supported these findings and related the origin of most of the studied soils to the high potassium calc-alkaline magmatism of the southernmost Eolian Islands (mainly Lipari and Vulcano), except for soils from the marine terraces of northern Calabria (PRA) and the Aspromonte Massif (DEL), which were both linked, albeit uncertainly, to Campanian volcanism. Soil magnetic properties were significantly (0.79**) correlated to the andic properties, so confirming the high performance of magnetic susceptibility as low cost proxy for the identification of volcanic constituents.

Soil sealing is considered among the most dangerous of land degradation processes on global, European and national scales. Despite important policy documents aiming to mitigate this soil threat, it currently shows no signs of abating, and current efforts often do not result in appropriate implementation of soil sealing mitigation in spatial planning, which represents the subject area governing soil sealing. In this paper, we show a spatial decision support system - based on a Geospatial Cyberinfrastructure - with the aim of applying it as an operational instrument aiming towards soil sealing mitigation. The system has the ambition to impact on those who take decision over soil sealing; typically, these are not agriculture experts but rather spatial planners. This tool, focusing on mitigating such crucial land degradation, allows the users - via the Web - to produce 'what-if' land planning scenarios thanks to the 'on-the-fly' modelling engines. Therefore, integrated geospatial quantitative data and procedures may be directly and freely used by planners. The tool has been applied to and tested in an area in the South of Italy. Results from two applications are reported: one addressing municipal planning and the other on a more detailed spatial scale. Furthermore, results include quantification of rural fragmentation, loss of soil ecosystem services and an estimate of soil sealing evolution over time. The tool was developed with the help of end users and indirectly explores a change of paradigm where soil science and landscape/urban planning work together to provide operational instruments that may be adopted by local communities in addressing soil sealing issues with a proactive approach.

The spatial analysis of soil properties by means of quantitative methods is useful to make predictions at sampled and unsampled locations. Two most important characteristics are tackled, namely the option of using complex and nonlinear models in contrast with (also very simple) linear approaches, and the opportunity to build spatial inference tools using horizons as basic soil components. The objective is to perform the spatial analysis of clay content for validation purposes in order to understand whether nonlinear methods can manage soil horizons, and to quantitatively measure how much they outperform simpler methods. This is addressed in a case study in which relatively few records are available to calibrate (train) such complex models. We built three models which are based on artificial neural networks, namely single artificial neural networks, median neural networks and bootstrap aggregating neural networks with genetic algorithms and principal component regression (BAGAP). We perform a validation procedure at three different levels of soil horizon aggregations (i.e. topsoil, profile and horizon pedological supports). The results show that neurocomputing performs best at any level of pedological support even when we use an ensemble of neural nets (i.e. BAGAP), which is very data intensive. BAGAP has the lowest RMSE at any level of pedological support with $$\hbox {RMSE}_\mathrm{BAGAP}^{Topsoil} = 7.2\,\%$$ , $$\hbox {RMSE}_\mathrm{BAGAP}^{Profile} = 7.8\,\%$$ and $$\hbox {RMSE}_\mathrm{BAGAP}^{Horizon} = 8.8\,\%$$ . We analysed in-depth artificial neural parameters, and included them in the “Appendix”, to provide the best tuned neural-based model to enable us to make suitable spatial predictions.

Soils with andic features are known to be very important as regards both ecosystem fertility and susceptibility to land degradation. In recent years there has been an increasing number of finds of these soils in non-volcanic mountain ecosystems (NVME) in different parts of the world under different environmental settings. In Italian NVME there has been extensive investigation proving that these soils have a much wider distribution than previously thought. But despite these important findings, very little is known about their genesis or properties. Against this background, our investigation aims to question the nature of parent material in andic soils in Italian NVME; we consider this question fundamental for proper understanding of such soils. We address the question of the parent material by highlighting similarities/dissimilarities between soils and their underlying bedrocks and ascertaining whether soils developed as a result of in situ weathering or because of eolian inputs. The study was conducted on 41 soil profiles distributed throughout Italian NVME, performing an integrated analysis of (i) grain-size distribution (GSD) by laser granulometry, (ii) end-member modelling of obtained GSD, (iii) soil-bedrock differences in magnetic susceptibility, and (iv) soil-bedrock differences based on the geochemistry of 38 elements. The final results from these investigations show that the investigated soils are mainly derived from eolian sediments and that weathering of the underlying bedrock plays a minor role. Two main types of eolian parent materials seem to coexist in different proportions according to the soil type: eolian sediments with a similar composition of the underlying bedrock (termed autochthonous loess) and eolian sediments of volcanic type (pyroclasts). Autochthonous loess represents the main component of the parent material in the following studied soils: Aluandic Andosols, Cambisols, Phaeozems and Podzol (67%, 63%, 44% and 73%, respectively). All these soils exhibit low to moderate andic features. Volcanic-type eolian sediments constitute about 90% of the parent material in most andic soils that are Silandic Andosols, but occur in different proportions in most of the investigated soils. The nature of soil parent materials surprisingly follows a clear trend with latitude, with autochthonous loess characterizing most parent materials in northern Italy whilst volcanic sediments form most parent materials in the south. In accordance with their latitude, other soils exhibit an intermediate behaviour. © 2013 Elsevier B.V.