Andic soils and catastrophic mudflows in Italy: morphological and hydropedological evidences

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

Soils of the Aversa plain (southern Italy)

The Aversa plain is one of the most important agricultural areas of the Campania region, combining the presence of very fertile soils, sites of great archaeological interest and growing residential urbanization. In this paper, the soil map (1:50,000 scale) of the Aversa plain is presented. Three main land systems (coastal, alluvial and foothill plains) characterized by different soil types (Andosols, Phaeozems, Cambisols, Vertisols, Arenosols, Histosols, Luvisols) have been identified. However, Andosols are the most widespread soil type (9768 ha) and, along with part of the Phaeozems and Cambisols, represent the most fertile soils of the Aversa plain (first and second classes of the land capability classification). In order to evaluate recent intense soil sealing, its impact over land capability classes was assessed during the last 60 years. Results show that soil sealing in the Aversa plain affected mainly the most fertile first- and second-class soils

Level II Oncoplastic Surgery as an Alternative Option to Mastectomy with Immediate Breast Reconstruction in the Neoadjuvant Setting: A Multidisciplinary Single Center Experience

: Oncoplastic surgery level II techniques (OPSII) are used in patients with operable breast cancer. There is no evidence regarding their safety and efficacy after neoadjuvant chemotherapy (NAC). The aim of this study was to compare the oncological and aesthetic outcomes of this technique compared with those observed in mastectomy with immediate breast reconstruction (MIBR), in post-NAC patients undergoing surgery between January 2016 and March 2021. Local disease-free survival (L-DFS), regional disease-free survival (R-DFS), distant disease-free survival (D-DFS), and overall survival (OS) were compared; the aesthetic results and quality of life (QoL) were evaluated using BREAST-Q. A total of 297 patients were included, 87 of whom underwent OPSII and 210 of whom underwent MIBR. After a median follow-up of 39.5 months, local recurrence had occurred in 3 patients in the OPSII group (3.4%), and in 13 patients in the MIBR group (6.1%) (p = 0.408). The three-year L-DFS rates were 95.1% for OPSII and 96.2% for MIBR (p = 0.286). The three-year R-DFS rates were 100% and 96.4%, respectively (p = 0.559). The three-year D-DFS rate were 90.7% and 89.7% (p = 0.849). The three-year OS rates were 95.7% and 95% (p = 0.394). BREAST-Q highlighted significant advantages in physical well-being for OPSII. No difference was shown for satisfaction with breasts (p = 0.656) or psychosocial well-being (p = 0.444). OPSII is safe and effective after NAC. It allows oncological and aesthetic outcomes with a high QoL, and is a safe alternative for locally advanced tumors which are partial responders to NAC

Coefficient shifts in geographical ecology: an empirical evaluation of spatial and non-spatial regression

Copyright © 2009 The Authors. Copyright © ECOGRAPHY 2009.A major focus of geographical ecology and macro ecology is to understand the causes of spatially structured ecological patterns. However, achieving this understanding can be complicated when using multiple regressions, because the relative importance of explanatory variables, as measured by regression coefficients, can shift depending on whether spatially explicit or non-spatial modelling is used. However, the extent to which coefficients may shift and why shifts occur are unclear. Here, we analyze the relationship between environmental predictors and the geographical distribution of species richness, body size, range size and abundance in 97 multi-factorial data sets. Our goal was to compare standardized partial regression coefficients of non-spatial ordinary least squares regressions (i.e. models fitted using ordinary least squares without taking autocorrelation into account; “OLS models” hereafter) and eight spatial methods to evaluate the frequency of coefficient shifts and identify characteristics of data that might predict when shifts are likely. We generated three metrics of coefficient shifts and eight characteristics of the data sets as predictors of shifts. Typical of ecological data, spatial autocorrelation in the residuals of OLS models was found in most data sets. The spatial models varied in the extent to which they minimized residual spatial autocorrelation. Patterns of coefficient shifts also varied among methods and datasets, although the magnitudes of shifts tended to be small in all cases. We were unable to identify strong predictors of shifts, including the levels of autocorrelation in either explanatory variables or model residuals. Thus, changes in coefficients between spatial and non-spatial methods depend on the method used and are largely idiosyncratic, making it difficult to predict when or why shifts occur. We conclude that the ecological importance of regression coefficients cannot be evaluated with confidence irrespective of whether spatially explicit modelling is used or not. Researchers may have little choice but to be more explicit about the uncertainty of models and more cautious in their interpretation

The LANDSUPPORT geospatial decision support system (S-DSS) vision: Operational tools to implement sustainability policies in land planning and management

Nowadays, there is contrasting evidence between the ongoing continuing and widespread environmental degradation and the many means to implement environmental sustainability actions starting from good policies (e.g. EU New Green Deal, CAP), powerful technologies (e.g. new satellites, drones, IoT sensors), large databases and large stakeholder engagement (e.g. EIP-AGRI, living labs). Here, we argue that to tackle the above contrasting issues dealing with land degradation, it is very much required to develop and use friendly and freely available web-based operational tools to support both the implementation of environmental and agriculture policies and enable to take positive environmental sustainability actions by all stakeholders. Our solution is the S-DSS LANDSUPPORT platform, consisting of a free web-based smart Geospatial CyberInfrastructure containing 15 macro-tools (and more than 100 elementary tools), co-designed with different types of stakeholders and their different needs, dealing with sustainability in agriculture, forestry and spatial planning. LANDSUPPORT condenses many features into one system, the main ones of which were (i) Web-GIS facilities, connection with (ii) satellite data, (iii) Earth Critical Zone data and (iv) climate datasets including climate change and weather forecast data, (v) data cube technology enabling us to read/write when dealing with very large datasets (e.g. daily climatic data obtained in real time for any region in Europe), (vi) a large set of static and dynamic modelling engines (e.g. crop growth, water balance, rural integrity, etc.) allowing uncertainty analysis and what if modelling and (vii) HPC (both CPU and GPU) to run simulation modelling 'on-the-fly' in real time. Two case studies (a third case is reported in the Supplementary materials), with their results and stats, covering different regions and spatial extents and using three distinct operational tools all connected to lower land degradation processes (Crop growth, Machine Learning Forest Simulator and GeOC), are featured in this paper to highlight the platform's functioning. Landsupport is used by a large community of stakeholders and will remain operational, open and free long after the project ends. This position is rooted in the evidence showing that we need to leave these tools as open as possible and engage as much as possible with a large community of users to protect soils and land

Cofin 2005 - Soluzioni tecniche innovative di fisica del suolo per l’analisi della variabilità spaziale dei suoli in relazione al rilevamento pedologico ed alla tutela e conservazione dell'ambiente

Il progetto sviluppa le seguenti problematiche di pedologia applicata: (a) applicazioni alla produttività primaria compatibile in alcuni ecosistemi agrari, pascolivi e forestali; (b) applicazioni all'effetto filtrante delle coltri pedologiche, rispetto all'inquinamento da nitrati in falda, in ecosistemi di pianura ad alta produttività agricola; (c) applicazioni dell'approccio metodologico sviluppato al rilevamento pedologico di nuove unità di paesaggio (UdP), geograficamente e geomorfologicamente, molto diverse dai siti investigati nei precedenti progetti (Lodi, Valchiavenna, Mustigarufi