The Soil Erosion Risk Map of the Sicilian Region (1:250,000 Scale)

Assessing the risk areas of soil erosion by water at the regional level is relevant for current and future land planning of environmental actions to combat land degradation. The gravity of the risk is not only depending on the rate of soil erosion by water, but also on other factors, primarily soil depth and rock weatherability. The map of the soil erosion risk in the Sicilian region, expressed in terms of years to a complete loss of the fertile soil cover, is here presented as a methodological model. The degree of risk was not only estimated in function of the rate of soil erosion by water, but also of the depth of the fertile part of the profile, and of the weatherability degree of the underlying bedrock

SOILS WITH HIGH ORGANIC CARBON STORAGE CAPACITY IN DEPTH

Most studies about soil organic carbon (OC) stock focus on the topsoil storage capacity, however, it has been proved that OC can reach relatively high values also in depth. The aim of this work was a preliminary investigation of the soil types with a high OC content in depth and the relationship with the main pedogenetic factors. The dataset was the 1,414 Italian National Soil Typologies (STU). The selected attributes were: mean value of OC in the superficial functional horizon (L1); weighted average value between 50 and 100 cm (L2) and under 100 cm (L3); WRB classification; main lithology, morphology and land-use. About 92% of typologies had more than 0.58% of OC in L1, about 30% in L2, and 10% in L3. The highest OC contents were in L2 of Histosols, Umbrisols, Podzols, Vertisols, Andosols, and in L3 of Vertisols, Andosols, Fluvisols. STU on volcanic rocks, slope and residual deposits showed relatively higher accumulation in L2; soils on delta plane, lacustrine and alluvial deposits, both in L2 and L3. STU on upland plains, transitional areas with plateau in the mountain, high gradient mountains and low plains, showed higher OC content. Land-use was not significantly connected with OC content in depth. About 65% of the studied territory (47% of Italian surface) had a relatively high CO content in L2, and about 2% in L3. The main processes connected to soil CO storage capacity in depth were morphological, namely colluvium and alluvium, as well as pedological, in particular, podzolization and andisolization

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

Factors Influencing Soil Organic Carbon Stock Variations in Italy During the Last Three Decades

Soils contain about three times the amount of carbon globally available in vegetation, and about twice the amount in the atmosphere. However, soil organic carbon (SOC) has been reduced in many areas, while an increase in atmospheric CO2 has been detected. Recent research works have shown that it is likely that past changes in land use history and land management were the main reasons for the loss of carbon rather than higher temperatures and changes of precipitation resulting from climate change. The primary scope of this work was to estimate soil organic carbon stock (CS) variations in Italy during the last three decades and to relate them to land use changes. The study was also aimed at finding relationships between SOC and factors of pedogenesis, namely pedoclimate, morphology, lithology, and land use, but also at verifying the possible bias on SOC estimation caused by the use of data coming from different sources and laboratories. The soil database of Italy was the main source of information in this study. In the national soil database is stored information for 20,702 georeferentiated and dated observations (soil pro- files and minipits) analysed for routine soil parameters. Although the observations were collected from different sources, soil description and analysis were similar, because all the sources made reference to the Soil Taxonomy and WRB classification systems, and soil analyses followed the Italian official methods. Besides horizon description and analysis, soil observations had a set of site information including topography, lithology, and land use. The SOC and bulk density referred to the first 50 cm, thus CS was calculated on the basis of the weighted percentage of SOC, rock fragments volume, and bulk density. A set of geographic attributes were considered to spatialize point information, in particular, DEM (100 m) and derived SOTER morphological classification, soil regions (reference scale 1:5,000,000) and soil systems lithological groups (reference scale 1:500,000), soil moisture and temperature regimes (raster maps of 1 km pixel size), land cover (CORINE project, reference scale 1:100,000) at three reference dates: years 1990 and 2000, and an originalupdate to 2008, obtained with field point observations. The interpolation methodology used a multiple linear regression (MLR). CS was the target variable, while predictive variables were the geographic attributes. Basic statistical analysis was performed first, to find the predictive variables statistically related to CS and to verify the bias caused by different laboratories and surveys. After excluding the biased datasets, the best predictors were selected using a step-wise regression method with Akaike Information Criterion (AIC) as selection and stop criterion. The obtained MLR model made use of the following categorical attributes: (i) decade, (ii) land use, (iii) SOTER morphological class, (iv) soil region, (v) soil temperature regime, (vi) soil moisture regime, (vii) soil system lithology, (viii) soil temperature, (ix) soil aridity index (dry days per year), and, (x) elevation. The interaction between decade and land use variables was also considered in the model. Results indicated that CS was highly correlated with the kind of main type of land use (forest, meadow, arable land), soil moisture and temperature regimes, lithology, as well as morphological classes, and decreased notably in the second decade but slightly increased in the third one, passing form 3.32 Pg, to 2.74 Pg and 2.93 Pg respectively. The bias caused by the variables like “laboratory” and “survey source” could be as large as the 190%

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

USING A.R.P. PROXIMAL SURVEY TO MAP CALCIC HORIZON DEPTH IN VINEYARDS

The investigation of spatial variability of soil water retention capacity and depth is essential for a correct and economical planning of water supply of a vineyard. The advantage of measuring soil electrical properties by proximal sensors is the ability to operate with mobile and non-destructive tools, quicker than the traditional soil survey. A.R.P. (Automatic Resistivity Profiling) is a mobile soil electrical resistivity (ER) mapping system conceived by Geocarta (Paris, France), and it is comprised by a couple of transmitter sprocket-wheels, which inject current within the soil, and three couples of receiver sprocket-wheels, which measure the voltage-drop at three different depths, about 0-50, 0-100 and 0-170 cm. Ten vineyards of “Villa Albius” farm in Sicily region (southern Italy) were chosen to carry out the A.R.P. survey, for a overall surface of 45 hectares. The vineyards were located in a wide Plio-Pleistocene marine terrace, characterized by a few meters level of calcarenite, overlying partially cemented by calcium carbonate yellow sands. During the A.R.P. survey, 12 boreholes were described and sampled for the laboratory analysis and other 6 boreholes were carried out to validade the map. All soils showed a calcic horizon (Bk, BCk or Ck) with the upper limit at variable depths. The depth of calcic horizon (Dk) of each boreholes resulted significantly correlated to ER, especially with the ER0-100 (R2 = 0.83). Dk map was interpolated using the regression kriging and validated by the boreholes (R2 = 0.71) and with a NDVI map of the same vintage (R2 = 0.95)

TEMPRANILLO is a regulator of juvenility in plants

Many plants are incapable of flowering in inductive daylengths during the early juvenile vegetative phase (JVP). Arabidopsis mutants with reduced expression of TEMPRANILLO (TEM), a repressor of FLOWERING LOCUS T (FT) had a shorter JVP than wild-type plants. Reciprocal changes in mRNA expression of TEM and FT were observed in both Arabidopsis and antirrhinum, which correlated with the length of the JVP. FT expression was induced just prior to the end of the JVP and levels of TEM1 mRNA declined rapidly at the time when FT mRNA levels were shown to increase. TEM orthologs were isolated from antirrhinum (AmTEM) and olive (OeTEM) and were expressed most highly during their juvenile phase. AmTEM functionally complemented AtTEM1 in the tem1 mutant and over-expression of AmTEM prolonged the JVP through repression of FT and CONSTANS (CO). We propose that TEM may have a general role in regulating JVP in herbaceous and woody species

Geo-environmental mapping using physiographic analysis: constraints on the evaluation of land instability and groundwater pollution hazards in the Metropolitan District of Campinas, Brazil

Geo-environmental terrain assessments and territorial zoning are useful tools for the formulation and implementation of environmental management instruments (including policy-making, planning, and enforcement of statutory regulations). They usually involve a set of procedures and techniques for delimitation, characterisation and classification of terrain units. However, terrain assessments and zoning exercises are often costly and time-consuming, particularly when encompassing large areas, which in many cases prevent local agencies in developing countries from properly benefiting from such assessments. In the present paper, a low-cost technique based on the analysis of texture of satellite imagery was used for delimitation of terrain units. The delimited units were further analysed in two test areas situated in Southeast Brazil to provide estimates of land instability and the vulnerability of groundwater to pollution hazards. The implementation incorporated procedures for inferring the influences and potential implications of tectonic fractures and other discontinuities on ground behaviour and local groundwater flow. Terrain attributes such as degree of fracturing, bedrock lithology and weathered materials were explored as indicators of ground properties. The paper also discusses constraints on- and limitations of- the approaches taken