MAPPING SOIL SPATIAL VARIABILITY AT HIGH DETAIL BY PROXIMAL SENSORS FOR A VINEYARD PLANNING

Planning new vineyard needs accurate information about soil features and their spatial variability. The use of soil proximal sensors, coupled by few detailed soil observations and analysis allows to obtain high detailed maps of soil variability at affordable costs. The work showed the methodology to interpolate the proximal sensors data and to delineate homogeneous area by clustering, corresponding to likely soil units. The description and analysis of one profile for each homogeneous area allowed to describe the soil features of each soil typological units and to produce useful thematic maps for vineyard planning

Effect of management of topsoil structure

This paper aims to show the effectiveness of different soil management strategies for improving soil structure in degraded areas within two vine farms inTuscany(Italy). The management practices adopted were: Composted organic amendment addition (COMP), Green Manure (GM), Dry mulching (DM) and Control (CONTR). Topsoil samples were taken at the beginning of the trial (2015) and two years later, and analyzed for bulk density (BD) and aggregate stability by wet sieving. The strategies adopted to restore the functionality of degraded vineyard soils diversely affected BD and aggregates stability. In both the farms COMP proved to be the best strategy to reduce BD, while GM and DM gave the best results in terms of aggregate stability increase

NEMATODE COOMUNITIES AS INDICATORS OF SOIL QUALITY IN VINEYARD SYSTEM: A CASE OF STUDY IN DEGRADED AREAS

The restoring effect of selective agronomic strategies on optimal soil functionality of degraded areas within organic vineyard was evaluated using the nematode community as an indicator of soil quality. Three different restoring strategies were implemented in two organic farms located in Tuscany (Italy). The relative abundance of nematode trophic groups and the maturity index showed that the use of compost improved soil biological quality and increased the abundance of predators. Instead, dry mulching and green manure applications were useful to control the most dangerous nematodes of grapevines, namely the virus-vector Xiphinema index (Longidoridae)

Comparing different approaches - data mining, geostatistic, and deterministic pedology - to assess the frequency of WRB Reference Soil Groups in the Italian soil regions

Estimating frequency of soil classes in map unit is always affected by some degree of uncertainty, especially at small scales, with a larger generalization. The aim of this study was to compare different possible approaches - data mining, geostatistic, deterministic pedology - to assess the frequency of WRB Reference Soil Groups (RSG) in the major Italian soil regions. In the soil map of Italy (Costantini et al., 2012), a list of the first five RSG was reported in each major 10 soil regions. The soil map was produced using the national soil geodatabase, which stored 22,015 analyzed and classified pedons, 1,413 soil typological unit (STU) and a set of auxiliary variables (lithology, land-use, DEM). Other variables were added, to better consider the influence of soil forming factors (slope, soil aridity index, carbon stock, soil inorganic carbon content, clay, sand, geography of soil regions and soil systems) and a grid at 1 km mesh was set up. The traditional deterministic pedology assessed the STU frequency according to the expert judgment presence in every elementary landscape which formed the mapping unit. Different data mining techniques were firstly compared in their ability to predict RSG through auxiliary variables (neural networks, random forests, boosted tree, supported vector machine (SVM)). We selected SVM according to the result of a testing set. A SVM model is a representation of the examples as points in space, mapped so that examples of separate categories are divided by a clear gap that is as wide as possible. The geostatistic algorithm we used was an indicator collocated cokriging. The class values of the auxiliary variables, available at all the points of the grid, were transformed in indicator variables (values 0, 1). A principal component analysis allowed us to select the variables that were able to explain the largest variability, and to correlate each RSG with the first principal component, which explained the 51% of the total variability. The principal component was used as collocated variable. The results were as many probability maps as the estimated WRB classes. They were summed up in a unique map, with the most probable class at each pixel. The first five more frequent RSG resulting from the three methods were compared. The outcomes were validated with a subset of the 10% of the pedons, kept out before the elaborations. The error estimate was produced for each estimated RSG. The first results, obtained in one of the most widespread soil region (plains and low hills of central and southern Italy) showed that the first two frequency classes were the same for all the three methods. The deterministic method differed from the others at the third position, while the statistical methods inverted the third and fourth position. An advantage of the SVM was the possibility to use in the same elaboration numeric and categorical variable, without any previous transformation, which reduced the processing time. A Bayesian validation indicated that the SVM method was as reliable as the indicator collocated cokriging, and better than the deterministic pedological approach

Comparing Different Approaches - Data Mining, Geostatistic, and Deterministic Pedology - to Assess the Frequency of WRB Reference Soil Groups in the Italian Soil Regions

The assessment of class frequency in soil map legends is affected by uncertainty, especially at small scales, where generalization is larger. The aim of this study was to test the hypothesis that data mining or geostatistic techniques provide better estimation of class frequency than traditional deterministic pedology in a national soil map. In the map of Italian soil regions compiled at 1:5,000,000 reference scale, soil classes were the WRB Reference Soil Groups (RSGs). Different data mining techniques, namely neural networks, random forests, boosted tree, classification and regression tree, supported vector machine (SVM), were tested and the last one gave the best RSGs predictions, using selected auxiliary variables and 22,015 classified soil profiles. Given the categorical target variable, the multi-collocated indicator cokriging was the algorithm chosen for the geostatistic approach. The first five more frequent RSGs resulting from the three methods were compared. The outcomes were validated with a Bayesian approach on a subset of 10% of geographically representative profiles, kept out before the elaborations. The most frequent classes were uniformly predicted by the three methods, which instead differentiated notably for the classes with a lower occurrence. The Bayesian validation indicated that the SVM method was as reliable as the multi-collocated indicator cokriging, and both more than the deterministic pedological approach. An advantage of the SVM was the possibility to use numeric and categorical variable in the same elaboration, without any previous transformation, which notably reduced the processing time

USING WRB TO MAP THE SOIL SYSTEMS OF ITALY

Aim of this work was to test the 2010 version of the WRB soil classification for compilating a map of the soil systems of Italy at 1:500,000 scale. The source of data was the national geodatabase storing information on 1,414 Soil Typological Units (STUs). Though, basically, we followed WRB criteria to prioritize soil qualifiers, however, it was necessary to work out an original methodology in the map legend representation to reproduce the high variability inside each delineation meanwhile avoiding any loss of information. Each map unit may represent a combination of three codominant STUs at the most. Dominant STUs were assessed summing up the occurrence of STUs in the Land Components (LCs) of every soil system, where each LC is a specific combination of morphology, lithology and land cover. STUs were classified according to the WRB soil classification system, at the third level, that is, reference soil group and first two qualifiers, when possible. Since the large number of delineations, map units grouping was needed to make the map more legible. Legend colours were organized according to soil regions groups firstly, then by considering the highest level of soil classification, so resulting a nidificated legend. The map showed 3,357 polygons and 704 map units. The most common STU were Calcaric Cambisols, by far followed by Calcaric Regosols, Eutric Cambisols, Haplic Calcisols, Vertic Cambisols, Cutanic Luvisols, Leptic Pheozems, Chromic Luvisols, Dystric Cambisols, Fluvic Cambisols, and others STUs belonging to almost all the WRB soil references. Keywords: geodatabase, soil system

Lung-borne systemic inflammation in mechanically ventilated infant rats due to high PEEP, oxygen, and hypocapnia

Background: Intensive care practice calls for ventilator adjustments due to fast-changing clinical conditions in ventilated critically ill children. These adaptations include positive end-expiratory pressure (PEEP), fraction of inspired oxygen (FiO2), and respiratory rate (RR). It is unclear which alterations in ventilator settings trigger a significant systemic inflammatory response. Methods: Fourteen-day old Wistar rat pups were randomized to the following groups: (a) “control” with tidal volume ~8 mL/kg, PEEP 5 cmH2O, FiO2 0.4, RR 90 min-1, (b) “PEEP 1”, (c) “PEEP 9” (d) “FiO2 0.21”, (e) “FiO2 1.0”, (f) “hypocapnia” with RR of 180 min-1, and (g) “hypercapnia” with RR of 60 min-1. Following 120 min of mechanical ventilation, plasma for inflammatory biomarker analyses was obtained by direct cardiac puncture at the end of the experiment. Results: Interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) were driven by FiO2 0.4 and 1.0 (P=0.02, P<0.01, respectively), tissue plasminogen activator inhibitor type-1 (tPAI-1) was increased by high PEEP (9 cmH2O, P<0.05) and hypocapnia (P<0.05), and TNF-α was significantly lower in hypercapnia (P<0.01). Tissue inhibitor of metalloproteinase-1 (TIMP-1), cytokine-induced neutrophil chemoattractant 1 (CINC-1), connective tissue growth factor (CTGF), and monocyte chemoattractant protein-1 (MCP-1) remained unaffected. Conclusion: Alterations of PEEP, FiO2, and respiratory frequency induced a significant systemic inflammatory response in plasma of infant rats. These findings underscore the importance of lung-protective ventilation strategies. However, future studies are needed to clarify whether ventilation induced systemic inflammation in animal models is pathophysiologically relevant to human infants

Preliminary Work Towards Publishing Vocabularies for Germplasm and Soil Data as Linked Data

The agINFRA project focuses on the production of interoperable data in agriculture, starting from the vocabularies and KOS used to classify and an-notate them. In this paper we report on our first steps in the direction of con-tributing to a LOD of agricultural data. In particular we look at germplasm data and soil data, which are still widely missing from the LOD landscape, seeming-ly because information managers in this field are still not very familiar with LOD practices