Basilicata Region Research Articles

Rapid geomorphological evolution in the Mediterranean Basilicata region (southern Italy) during Little Ice Age

There is a general consensus that the increased frequency of extreme climatic events will quickly change the morphology of the landscapes in the Mediterranean area. Knowing and understanding the expected changes becomes crucial for defining future adaptive strategies. This paper deals with the very rapid geomorphological evolution in an emblematic Mediterranean area, the Basilicata region of southern Italy, during the last 300 years. The results show that the landscape evolution was mainly controlled by the extreme sensitivity of surface processes to climate change. Landslides, a new cycle of the formation of badland landforms such as calanchi and biancane, and an aggradation/incision phase in the main river valleys have completely reshaped the landscape of the Bradanic Trough, whereas fresh eroded material was moved to the coast. New data on valley-fill alluvium in the Basento River and one of its main left tributaries, the Vella stream, show ages ranging from 1700 to 1850. This deposition phase coincides with the Little Ice Age, an epoch of global climate variability. Previous literature showed that the alluvium of this age consists of a mappable stratigraphic unit outcropping in all the large alluvial valleys of the Basilicata and Mediterranean regions. Palaeoclimate and high-resolution palaeoflood data indicate that valley-fill alluviation occurred during a long humid period. Valley aggradation ended in approximately 1850 with the beginning of a new phase of historic arroyo cutting, mainly due to high magnitude-low frequency rainy events within prolonged drought periods. The stream incision drew the terminal dissection of the clayey slopes into biancana rounded morphologies. These data seem to be confirmed by the comparable heights of about 3 m, both of the biancane and of the fluvial terraces.

Land-Use Change Effects on Soil Erosion: The Case of Roman “Via Herculia” (Southern Italy)—Combining Historical Maps, Aerial Images and Soil Erosion Model

Land use and land cover (LULC) strongly influence soil erosion/sediment yield, and predicting changes in soil erosion is an important management strategy. Tracing the Earth’s past also helps us better understand the future evolution of the landscape, but research using modern mapping capabilities is hampered by the scarcity of historical landscape information. To fill the data gap and provide an example of how historical maps might be used in land-use change research, we combined an old paper map based on the IT Military Topographical Institute (ITM), aerial photos, and orthophotos to derive land-use history and landscape dynamics at fine scales for a segment of the Roman route “Via Herculia” located in the north-western sector of the Basilicata Region, Italy. Three LULC scenarios were then analysed to represent land use in 1870, 1974, and 2013. Starting from such scenarios, we applied a soil erosion model (Unit Stream Power Eosion and Deposition—USPED) to understand how land-use change over time has modified the areas subject to erosion and deposition. The results show an increase in erosion (from 17% to 20% of the total area) and sediment deposition (from 15% to 19%) over the period 1870–1974. In contrast, over the period of 1974–2013, the results show a decrease in gross erosion (from 20% to 14% of the total area) and sediment deposition (from 19% to 13%).

New Technique for Monitoring High Nature Value Farmland (HNVF) in Basilicata

The definition of High Nature Value Farmland Areas (HNVF) was provided by Andersen in 2003: “HNVF comprises those areas in Europe where agriculture is the major (usually the dominant) land use and where that agriculture supports or is associated with either a high species and habitat diversity, or the presence of species of European conservation concern or both”. The present work focuses on an overview of the techniques used to produce HNVF maps at different spatio-temporal resolutions. The proposed approach is based on the statistical approach. The study area is the Basilicata region (southern Italy) in 2012, mapped at municipal spatial resolution. The HNVF areas were identified by applying a threshold to the sum of the contributions of the main characterizing indicators. Three indicators contribute to the calculation of the HNVF areas: crop variability (CD Index), extensive practices (EP Index), and the presence of natural elements (Index Ne). Good agreement was found between our HNVF map and the results of the literature, although the analysis approaches were different. The main advantages of the proposed methodology derive from only free input data being used, and include remote sensing images and the adaptability to different spatial resolutions (local, regional, and national).

Analysing outpatient care access for planning purposes: The Basilicata Region experience

The delivery of healthcare services at regional level should be modeled on population needs, stemming from patients’ consumption pattern and trying to include unexpressed needs and exclude excesses of demand due to both moral hazard behaviors and inducements from the supply side. We propose a model able to estimate the frequency of access in outpatient care (OC) based on the characteristics of the population. According to empirical evidence, among the determinants of outpatient access we include variables addressing health, socioeconomic status and place of residence, plus variables related to the supply of services. We run generalized linear models for counting data of the Poisson family with the aim of both identifying the determinants of OC utilization and quantifying the related effects. We use the regional administrative database of Basilicata region, year 2019. Results are consistent with literature findings and provide new insights into the analysis of OC, suggesting that our model could easily be implemented by regional policymakers to plan the supply of ambulatory services on population needs.

Evaluation of Social Vulnerability to Flood Hazard in Basilicata Region (Southern Italy)

Floods are the most common natural hazards causing damage to properties and loss of life worldwide. They are not preventable but vulnerability assessments, hazard mitigation, and effective emergency management plans can reduce their impacts and facilitate recovery actions. Floods can have different impacts depending on the local physical conditions and on the social context represented by the economic and cultural patterns of a specific community. Social vulnerability is the susceptibility of social groups to the adverse impacts of natural hazards, including disproportionate death, injury, loss, or disruption of livelihood. Therefore, the social vulnerability analysis becomes of primary importance in understanding the main factors influencing the capacity of a specific community to anticipate, cope with, and recover from a flood event. In this context, this paper investigates the correlation between flood hazard and socio-economic factors across the Basilicata Region (southern Italy). The aim of this research is to evaluate flood hazard and social vulnerability index through a Geographic Information System (GIS) approach. Multivariate factor analysis was applied in this work to construct an overall social vulnerability index which was combined with the flood hazard distribution. Our results underline the presence of 107,587 people with a high level of both flood hazard and social vulnerability.

Spatially Correlated Nuclear Magnetic Resonance Profiles as a Tool for Precision Agriculture

Nuclear magnetic resonance (NMR) profiling, sample georeferentiaton, and geostatistics are applied to evaluate the spatial variability of metabolic expression of durum wheat in fields managed by precision agriculture. Durum wheat at three different vegetation stages, grown in two different places of the Basilicata region, in Italy, is analyzed by NMR. The spatial variability, within each field, of metabolites, quantified by NMR, is evidenced by appropriate geostatistic tools through the definition of a suitable metabolic index. Metabolic maps are compared to highlight the effects of soil and farming strategies.

Analysis of the Effect of Soil Erosion in Abandoned Agricultural Areas: The Case of NE Area of Basilicata Region (Southern Italy)

Land abandonment is among the most complex la nd use change processes driven by a multiplicity of anthropogenic and natural factors, such as agricultural over-exploitation, implementation of agricultural policies, socio-economic and climatic aspects. Therefore, it is necessary to deepen the effects of land abandonment based on methodologies that are as multidisciplinary as possible. Environmental and social problems related to abandonment include soil erosion and environmental degradation. Approaches combining GIS (Geographic Information System), remote sensing, and image analysis techniques allow for assessments and predictions based on integrating theoretical models with advanced geospatial and geostatistical models. One of the most widely used models for soil erosion estimation is the Revised Universal Soil Loss Equation (RUSLE). The present work developed a model using remote sensing and GIS tools to investigate some factors of the RUSLE equation to evaluate the adverse effects of soil erosion in areas covered by arable crops and subsequently abandoned. To identify potentially degraded areas, two factors of the RUSLE were related: the C Factor describing the vegetation cover of the soil and the A Factor representing the amount of potential soil erosion. Through statistical correlation analysis with the RUSLE factors, based on the deviations from the average erosion values and mapping of the areas of vegetation degradation relating to arable land, the areas identified and mapped are susceptible to soil degradation.

Combining Statistical, Displacement and Damage Analyses to Study Slow-Moving Landslides Interacting with Roads: Two Case Studies in Southern Italy

Slow-moving landslides are widespread natural hazards that can affect social and economic activities, causing damage to structures and infrastructures. This paper aims at proposing a procedure to analyze road damage induced by slow-moving landslides based on the joint use of landslide susceptibility maps, a road-damage database developed using Google Street View images and ground-displacement measurements derived from the interferometric processing of satellite SAR images. The procedure is applied to the municipalities of Vaglio Basilicata and Trivigno in the Basilicata region (southern Italy) following a matrix-based approach. First, a susceptibility analysis is carried out at the municipal scale, using data from landslide inventories and thematic information available over the entire municipalities. Then, the susceptibility index, the class of movement and the level of damage are calculated for the territorial units corresponding to the road corridors under investigation. Finally, the road networks are divided into stretches, each one characterized by a specific level of risk (or attention required) following the aggregation of the information provided by the performed analyses. The results highlight the importance of integrating all of these different approaches and data for obtaining quantitative information on the spatial and temporal behavior of slow-moving landslides affecting road networks.

The Longevity of Fruit Trees in Basilicata (Southern Italy): Implications for Agricultural Biodiversity Conservation

In the Mediterranean basin, agriculture and other forms of human land use have shaped the environment since ancient times. Intensive and extensive agricultural systems managed with a few cultured plant populations of improved varieties are a widespread reality in many Mediterranean countries. Despite this, historical cultural landscapes still exist in interior and less intensively managed rural areas. There, ancient fruit tree varieties have survived modern cultivation systems, preserving a unique genetic heritage. In this study, we mapped and characterized 106 living fruit trees of ancient varieties in the Basilicata region of southern Italy. Tree ages were determined through tree ring measurements and radiocarbon analyses. We uncovered some of the oldest scientifically dated fruit trees in the world. The oldest fruit species were olive (max age 680 ± 57 years), mulberry (647 ± 66 years), chestnut (636 ± 66 years), and pear (467 ± 89 years). These patriarchs hold a unique genetic resource; their preservation and genetic maintenance through agamic propagation are now promoted by the Lucan Agency for the Development and Innovation in Agriculture (ALSIA). Each tree also represents a hub for biodiversity conservation in agrarian ecosystems: their large architecture and time persistence guarantee ecological niches and micro-habitats suitable for flora and fauna species of conservation significance.

Making Sense of Oil Pollution: Thresholds of Harm in the Extractive Ecologies of Val d'Agri

This contribution investigates the politics of oil extraction and perceptibility in Val d'Agri, an area in the southern Italian region of Basilicata that sits on the largest onshore oil reservoir in the country. In this article, I examine the ways in which diffused industrial contamination emerges in the landscape and is intercepted and translated by human bodily senses. In doing so, I consider how material phenomena of slow and accretive environmental degradation have a spatial affect and shape ontologies of pollution in the region. To explore how oil becomes a highly political process of epistemological reconfiguration, I examine the rift between expert science and environmental monitoring, which are appropriated by oil corporations and the Italian State, and forms of knowledge that are mobilized by local citizens in an attempt to make sense of ecological degradation and claim environmental rights. Drawing from ethnographic fieldwork and open-source investigation, this contribution invokes accounts of situated practices of citizen sensing that navigate and assemble the evidence of slow and chronicle effects of hydrocarbon pollutants in Val d'Agri. Citizens generate fluxes and exchanges of knowledge that stimulate new political possibilities between inhabitants, activists, and experts and are constitutive of environmental resistance. I give resonance to the existing and emerging struggles around environmental justice in the region as an invitation to consider the possibilities of sensing and local knowledge to bring to the surface a different understanding of contamination and challenge the power of extractive economies.

Root and Shoot Growth of a Modern and an Old Tall Durum Wheat (Triticum durum Desf.) Variety under Dual-Purpose Management.

In dual-purpose cereal systems, the co-production of fodder and grain can increase farm profitability and reduce farming risks. Our work evaluated shoot and root growth in durum wheat (Triticum durum Desf.) under dual-purpose management in a medium-high rainfall area of southern Italy. We compared a modern variety (Core) with a tall ancient variety (Saragolle lucana) under traditional (NDP) and dual-purpose (DP) management and tested the hypothesis that clipping plants during the vegetative stage would reduce root growth and dewatering before anthesis, which is advantageous in drought-prone environments. Experiments were conducted in Bella (PZ), Basilicata region, southern Italy (40°42' N, 15°32' E) on a clay loam soil in 2021 in a split-plot design on 2 × 2 main plots and 1 × 2 split-plots with 6 replicates. The DP treatment consisted of simulated grazing by clipping plants at 5 cm from the ground 3 months after sowing (at first hollow stem). Forage Biomass was not different at p = 0.05 between varieties, with an average of 0.58 t ha-1 DM. Grain yield was not penalized by clipping (p = 0.05) and did not differ significantly between varieties. SPAD was always lower in the Saragolle variety and lowered by clipping. Defoliation delayed phenology in both cultivars but did not reduce the final number of spikes per square meter. Stomatal conductance was correlated to temperature, did not differ between cultivars, and was not influenced by clipping. Soil water depletion was monitored in modern wheat from the booting stage to the beginning of grain filling. Clipping did not result in a reduction in pre-anthesis water depletion, possibly due to evaporative losses. Root density was markedly reduced by clipping in core variety between 0.20 and 0.60 m and much less in Saragolle. Unclipped Saragolle produced thicker roots and higher root masses compared to clipped plants. Defoliated Saragolle shifted to finer roots, reducing root mass more than length. This may have reduced the metabolic cost of soil exploration, thereby increasing root foraging efficiency.

Influence of environmental conditions on the production of nutraceuticals in Italian edible plant landraces

Autochthonous plant varieties, also referred to as landraces, represent an important genetic resource, being well-adapted to the environment in which they have been selected. Landraces usually show profiles rich in nutraceuticals, making them an effective and valuable alternative to commercial agri-products, as well as potential candidates for crop improvement programs. Basilicata region is recognized as an Italian hotspot for agrobiodiversity, due to its complex orography. Thus, this work aimed to characterize and monitor, for two successive years, the content of secondary metabolites and related antioxidant properties of seven different species, four officinal (i.e., wild fennel - Feoniculum vulgare Mill.; oregano - Origanum vulgare L.; thyme - Thymus vulgaris L.; valerian - Valeriana officinalis L.) and three fruit species (i.e., fig - Ficus carica L. cv. Dottato; sweet cherry Prunus avium L. cv. Majatica; plum - Prunus domestica L. cv. Cascavella Gialla), collected in three different sites of this region. In detail, spectrophotometric tests were performed to assess the concentration of phenolic compounds, flavonoids, and – for officinal plants – also terpenoids, together with the antiradical activity (FRAP assays). In addition, to better typify the phytocomplexes of these landraces, HPLC-DAD and GC–MS analyses were carried out. In general, officinal plants showed higher values of nutraceutical compounds and related bioactivity with respect to fruit species. The data showed how different accessions of the same species had different phytochemical profiles, according to the sampling area and the year of collection, suggesting a role for both genetic and environmental factors in determining the observed results. Therefore, the final goal of this research was also to find a possible correlation between environmental factors and nutraceutics. The greatest correlation was found in valerian, where a lower water intake seemed to lead to a higher accumulation of antioxidants, and in plum, where the flavonoid content correlated positively with high temperatures. All these outcomes contribute at valorising Basilicata landraces for their aptitude to be high-quality foods and, at the same time, promoting the preservation of the agrobiodiversity for this region.

Comparison between Parametric and Non-Parametric Supervised Land Cover Classifications of Sentinel-2 MSI and Landsat-8 OLI Data

The present research aims at verifying whether there are significant differences between Land Use/Land Cover (LULC) classifications performed using Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) data—abbreviated as L8 and S2. To comprehend the degree of accuracy between these classifications, both L8 and S2 scenes covering the study area located in the Basilicata region (Italy) and acquired within a couple of days in August 2017 were considered. Both images were geometrically and atmospherically corrected and then resampled at 30 m. To identify the ground truth for training and validation, a LULC map and a forest map realized by the Basilicata region were used as references. Then, each point was verified through photo-interpretation using the orthophoto AGEA 2017 (spatial resolution of 20 cm) as a ground truth image and, only in doubtful cases, a direct GPS field survey. MLC and SVM supervised classifications were applied to both types of images and an error matrix was computed using the same reference points (ground truth) to evaluate the classification accuracy of different LULC classes. The contribution of S2′s red-edge bands in improving classifications was also verified. Definitively, ML classifications show better performance than SVM, and Landsat data provide higher accuracy than Sentinel-2.

A Procedure for the Quantitative Comparison of Rainfall and DInSAR-Based Surface Displacement Time Series in Slow-Moving Landslides: A Case Study in Southern Italy

Earth observation data are useful to analyze the impact of climate-related variables on geomorphological processes. This work aims at evaluating the impact of rainfall on slow-moving landslides, by means of a quantitative procedure for identifying satellite-based displacement clusters, comparing them with rainfall series, and applying statistical tests to evaluate their relationships at the regional scale. The chosen study area is the Basento catchment in the Basilicata region (southern Italy). Rainfall series are gathered from rain gauges and are analyzed to evaluate the presence of temporal trends. Ground displacements are obtained by applying the P-SBAS (Parallel Small BAseline Subset) to three datasets of Sentinel-1 images: T146 ascending orbit, and T51 and T124 descending orbits, for the period 2015–2020. The displacement series of the pixels located in areas mapped as landslides by the Italian Landslide Inventory and sited within rain gauge influence regions (defined as 10 km circular buffers) are studied. Those displacement series are analyzed and compared to the rainfall series to search for correlations, by employing statistical and non-parametric tests. In particular, two landslides are selected and investigated in detail. Significant results were obtained for the T124 descending orbit for both landslides, for a 3-day cumulative rainfall and a 7-day delay of the slope response. Challenges in the whole procedure are highlighted and possible solutions to overcome the raised problems are proposed. Given the replicability of the proposed quantitative procedure it might be applied to any study area.

Towards specific T–H relationships: FRIBAS database for better characterization of RC and URM buildings

FRIBAS database is an open access database (https://doi.org/10.5281/zenodo.6505442) composed of the characteristics of 312 buildings (71 masonry, 237 reinforced concrete and 4 mixed types). It collects and harmonizes data from different surveys performed on buildings in the Basilicata and Friuli Venezia Giulia regions (Southern and Northeastern Italy, respectively). Each building is defined by 37 parameters related to the building and foundation soil characteristics. The building and soil fundamental periods were experimentally estimated based on ambient noise measurements. FRIBAS gave us the opportunity to study the influence of the main characteristics of buildings and the soil-building interaction effect to their structural response. In this study, we have used the FRIBAS dataset to investigate how the building period varies as a function of construction materials and soil types. Our results motivate the need of going beyond a ‘one-fits-all’ numerical period–height (T–H) relationship for generic building typologies provided by seismic codes, towards specific T–H relationships that account for both soil and building typologies.

Integrated geophysical and geological surveys reveal new details of the large Montescaglioso (southern Italy) landslide of December 2013

A methodological approach based on the integration of different survey techniques may be particularly suitable for the study of areas with complex geology such as those affected by landslide phenomena. The results obtained by the application of combined electrical resistivity tomography (ERT) technique, ambient seismic noise measurements (single station and array), geological investigation and granulometric analysis to characterize and study a landslide occurred on December 3rd, 2013 in Basilicata region (southern Italy) are presented and discussed. The landslide partially affected the peri-urban area of Montescaglioso town and caused damages to small-medium enterprises, infrastructures, and housing. The analysis of ambient noise signals made possible to estimate the depth of the contacts between the main geological formations outcropping in the area and revealed the existence of directional resonance effects. The discontinuities observed by the ERT have confirmed the presence of different lithotypes and allowed illuminating the sliding surface at a depth of about 40 m in agreement with the seismic results and boreholes data. The shallow discontinuities inferred from geophysical surveys were supported also by the results of a detailed granulometric analysis performed on a geognostic survey, confirming the presence of a heterogeneous sedimentary deposit in the area. In such complex context, the acquisition of spatially distributed geophysical properties permitted to increase the knowledge of the geological setting of the area, reaching a level of detail that cannot be observed by applying only a sparse discrete direct sampling. The integration of all the obtained information allowed the delineation of geophysical discontinuities related to the presence of sliding or weakness surfaces and areas with high water content that could contribute to future activation of the movement. This information was crucial to better understand the nature of the landslide, contributing to the planning and the implementation of risk mitigation actions and to the design of remediation works.

Seismic fragility assessment of Balvano (Potenza, Italy) pre and post 1980 Irpinia's earthquake

The paper illustrates and discusses the results of a hybrid methodology to assess the seismic vulnerability of minor urban centres. The methodology, which allows for estimating damage scenarios when damaging data on buildings are not available, has been applied to the urban centre of Balvano. It is a small centre located on the meridional Apennine in Basilicata Region (Southern Italy), strongly hit by 1980 Irpinia's earthquake. Fragility curves, damage scenarios and expected annual losses before and after the 1980 seismic event have been defined and compared based on building features (obtained from interviews with local technicians and in-situ inspections), subdivision of buildings in typological classes and number of buildings for each class. As a result, (i) the damage scenario and vulnerability level of Balvano before 1980 was reconstructed and a good matching between the predicted, and occurred damage scenario resulted, and (ii) the seismic vulnerability following the reconstruction process of the Municipality was quantified and compared with that before 1980 earthquake, showing a significant reduction of vulnerability.

Correction to: “Predictive hybrid fragility models for urban scale seismic assessment: a case study in Basilicata Region (Italy)

Correction to: “Predictive hybrid fragility models for urban scale seismic assessment: a case study in Basilicata Region (Italy)

Exploiting machine learning methods with monthly routine milk recording data and climatic information to predict subclinical mastitis in Italian Mediterranean buffaloes

Mastitis has detrimental effects on the world's dairy industry, reducing animal health, milk production and quality, as well as income for farmers. In addition, consumers' growing interest in food safety and rational usage of antibiotics highlights the need to develop novel strategies to improve mastitis detection, prevention, and management. In the present study we applied machine learning (ML) analyses to predict presence or absence of subclinical mastitis in Italian Mediterranean buffaloes, exploiting information collected the previous month during routine milk recording procedures, as well as climatic data. The data set included 3,891 records of 1,038 buffaloes from 6 herds located in Basilicata Region (South Italy). Prediction models were developed using 4 different ML algorithms (Generalized Linear Model, Support Vector Machines, Random Forest, and Neural Network) and 2 data set splitting approaches for the creation of the training and test sets (by record or by animal ID number, always with 80% of the data used for model training and the remaining 20% for model testing). Support Vector Machine was the best method to predict high or low somatic cell count at the subsequent test-day record in the validation set, and therefore it was used to estimate the contribution of each feature to the best model. Independently from the data set splitting approach, the most important features were somatic cell score, differential somatic cell count, electrical conductivity, and milk production. Among climatic data, the most informative were temperature and relative humidity. When the data were split by animal ID, an improvement in models' predictive performance on the test set was observed, suggesting this as the most appropriate data splitting approach in data sets with repeated measures to avoid data leakage. According to different metrics, Neural Network was the best method for making predictions on the test set. Our findings confirmed the promising role of ML methods to improve prevention and surveillance of subclinical mastitis, exploiting the large amount of data currently available to identify animals that would possibly have high somatic cell count the subsequent month.

Sweet Cherry Diversity and Relationships in Modern and Local Varieties Based on SNP Markers

The sweet cherry is an important fruit species that is widespread globally. In addition to the well-known traditional and modern varieties, a myriad of landraces is present in Europe, as well as in southern Italy. This study aims to evaluate the population structure, genetic relationships, and cases of duplicate samples in a collection of 143 accessions using GBS-derived SNP markers. The genetic material under investigation includes modern commercial varieties, ancient European and American varieties, landraces, and individuals retrieved from small orchards. Some of the known varieties were genetically analyzed here for the first time. In addition, several genotypes were collected from the Basilicata region (southern Italy), an area largely unexplored for sweet cherry genetic resources. The relationships among genotypes were assessed using four different methods: allele frequency and ancestry estimation, principal component analysis, Neighbor-Joining tree, and identity-by-state estimation. The analyses returned quite congruent results and highlighted the presence of four main genetic groups, namely: (i) American varieties, (ii) the 'Germersdorfer-Ferrovia' cluster, (iii) the 'Burlat' group, and (iv) the group of Italian landraces. The main drivers of clustering were ancestry, geographical distribution, and some important traits such as self-compatibility. The sweet cherries from Basilicata, herewith examined for the first time, were mostly distributed within the group of Italian landraces, being particularly linked to the autochthonous varieties of the Campania region. However, some genotypes were outside this group, thus suggesting the introduction of genetic material from other Italian regions or from European countries. The considerable amount of American and European modern varieties analyzed are genetically very closely related, suggesting a reduced genetic basis. In addition, we highlighted the discriminating ability of SNP markers to distinguish between an original variety and its mutant. Overall, our results may be useful in defining conservation strategies for sweet cherry germplasm and developing future breeding programs to enlarge the genetic basis of commercial varieties.