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

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)

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

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

Estimating carbon credits variations supplied from agricultural and forest soils of Italy between 1979 and 2008.

Soils contain approximately three times the world amount of organic carbon in vegetation and approximately the double of that present in the atmosphere. However, soil organic carbon (SOC) has been found lowering in many areas, while atmospheric CO2 was on increase. It is well known that there is a marked inter-dependence between SOC and climate, nevertheless, recent researches have demonstrated that changes of land use and management can cause gains or losses of SOC greater than climatic changes. Italy, which has joined the Kyoto Protocol, has decided to consider only forest management within the additional activities contemplated for the count of carbon credits, and to launch a monitoring campaign of SOC only in forests. The scope of this research work was to demonstrate that it is possible to estimate carbon credits variations supplied from both agricultural and forest soils of Italy during last the 3 decades (from 1979 to 2008), taking into account changes due to climate change. The soil database of Italy was the main source of information. SOC content was expressed as percentage by weight (dag kg-1) analysed by the Walkley-Black procedure and converted to ISO standard. The CRA - CMA (Research Unit for Climatology and Meteorology Applied to Agriculture) database was the source of information for climatic data. We considered the mean annual temperature and mean value of total annual precipitations of the two periods 1961-1990 and 1991-2006, and we mapped them by regression kriging with elevation and latitude as predictors. The soil organic carbon stock (CS) was calculated referring to the first 50 cm, obtaining a single value for every observation. A series of geographic attributes were used in order to spatialize site information. A linear multiple regression was used to interpolate the values, using the variable CS as target and the geographic attributes as predictive variables. The model also considered the interaction between decade, land use, and climate, to take into account the effect of climatic variables on the SOC content in the different land uses. The SOC variations due to climate change were then subtracted from the total, for the calculation of carbon credits that may be attributed to agricultural and forest management. Carbon credits were calculated following the Emission Trading System (EU-ETS, EU Directive 2003/87/EC), and the exchange rate given by the Carbon Dioxide Emission Allowances Electronic Trading System (SENDECO2) at September 2010.Our results indicate that CS highly correlates with the main groups of land use (forests, pastures, crop lands), as well as with soil humidity and temperature regimes, lithologies, and morphological classes. CS diminished remarkably in the second decade, while slightly recovered between the second and third decade. Climate change influence on SOC content was limited, as a whole, but relatively more pronounced in meadows. The Italian CS passed from 3,32 Pg in 1979- 1988, to 2,74 Pg in 1989-1998, and 2,93 Pg in 1999-2008. The equivalent lost of carbon credits occurred from the first to the second decade totalled some 24,260 M€, while in the following decade carbon credits recovered about 6,921 M€, mainly because of the SOC increase obtained in the arable lands. This study demonstrates the possibility to consider carbon credits from agricultural soils, in addition to forest. Therefore, Italy should extend also to agricultural soils (crop lands and meadows) the current monitoring of SOC for the time of engagement of the Kyoto Protocol

È possibile stimare le variazioni dei crediti di carbonio forniti dai suoli agricoli e forestali italiani

L’Italia ha aderito al Protocollo di Kyoto, ma ha deciso di eleggere solo la gestione forestale nell’ambito delle attività addizionali previste per contabilizzare i crediti di carbonio. Lo scopo principale di questo lavoro è stato quello di dimostrare che è possibile stimare le variazioni del contenuto di carbonio organico dei suoli in Italia durante gli ultimi 3 decenni (dal 1979 al 2008) e contabilizzare i crediti di carbonio originati dalle attività di gestione sia agricola che forestale

Variazioni di carbonio organico nei suoli italiani dal 1979 al 2008

I suoli contengono circa tre volte la quantit\ue0 di carbonio disponibile a livello mondiale nella vegetazione e circa il doppio di quella presente in atmosfera. Tuttavia il carbonio organico del suolo (SOC) si \ue8 ridotto in molte aree, mentre \ue8 stato rilevato un aumento della CO2 atmosferica. Ricerche recenti hanno dimostrato che sono stati i cambiamenti di uso e gestione del suolo a provocare le maggiori perdite di SOC nel recente passato, piuttosto che le pi\uf9 alte temperature e i cambiamenti delle precipitazioni conseguenti il cambiamento climatico. Lo scopo principale di questo lavoro \ue8 quello di stimare le variazioni del contenuto di carbonio organico dei suoli (carbon stock, CS) in Italia durante le ultime 3 decadi (dal 1979 al 2008) e di legarlo ai cambiamenti di uso del suolo. Lo studio ha come fine anche quello di studiare le relazioni tra SOC e i fattori della pedogenesi (pedoclima, morfologia, litologia e uso del suolo). La Banca Dati dei Suoli d\u2019Italia \ue8 stata la principale fonte di informazione. Il CS \ue8 stato calcolato a partire dai dati di SOC, densit\ue0 apparente e scheletro, i quali sono stati riferiti ai primi 50 cm di suolo, ottenendo un solo valore per ogni osservazione puntuale per mezzo della media pesata sulla base della profondit\ue0 degli orizzonti. Una serie di attributi geografici sono stati utilizzati per spazializzare le informazioni puntuali, in particolare il DEM (100 m) e le derivate classi morfologiche SOTER, le Soil Region d\u2019Italia (scala di riferimento 1:5.000000), i gruppi litologici dei Sistemi di Terre Italiani (scala di riferimento 1:500.000), i regimi di umidit\ue0 e temperatura del suolo (mappe raster con pixel di 1 km), l\u2019uso del suolo (progetto CORINE land cover, scala di riferimento 1:100.000; CORINE 2009) a due date di riferimento 1990 e 2000 e una carta di uso del suolo aggiornata al 2008 a partire da quella 2000, utilizzando punti di osservazione a terra. Il metodo di interpolazione utilizzato \ue8 stato quello della regressione multipla lineare (MLR), con il CS come variabile target e gli attributi geografici come variabili predittive. Un\u2019analisi statistica di base \ue8 stata realizzata per indagare singolarmente le relazioni fra le variabili predittive considerate e il CS. Infine \ue8 stato trovato un modello generale di regressione lineare multipla, considerando insieme tutte le variabili predittive. Le migliori variabili predittive sono state selezionate con una step-wise regression, utilizzando l\u2019Akaike Information Criterion (AIC) come criterio di selezione delle migliori variabili e del miglior modello finale. Il modello finale ottenuto considerava le seguenti variabili predittive: i) le decadi, ii) l\u2019uso del suolo, iii) le classi morfologiche SOTER, iv) le Soil Region, v) i regimi di temperatura del suolo, vi) i regimi di umidit\ue0 del suolo, vii) i gruppi litologici dei Sistemi di Terre, viii) la temperatura del suolo, ix) l\u2019indice di aridit\ue0 del suolo (giorni di suolo secco), e x) la quota. Nel modello \ue8 stata considerata anche l\u2019interazione fra la decade e l\u2019uso del suolo. I risultati indicano che il CS \ue8 altamente correlato con i principali raggruppamenti di uso del suolo (foreste, pascoli, aree agricole), con i regimi di umidit\ue0 e temperatura del suolo, con la litologia, con le classi morfologiche, ed \ue8 diminuito notevolmente nella seconda decade, mentre si \ue8 registrato un debole recupero fra la seconda e la terza decade, passando da 3,32 Pg, a 2,74 Pg e a 2,93 Pg rispettivamente