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Ensuring the longevity of wooden constructions depends heavily on the preservation process. However, several traditional preservation methods involving fossil-based compounds have become outdated because they pose a significant risk to the environment and to human health. Therefore, the use of bio-based and bioactive solutions, such as essential oils, has emerged as a more sustainable alternative in protecting wood from biotic attacks. The entrapment of essential oils in polymeric carrier matrices provides protection against oxidation and subsequent degradation or rapid evaporation, which implies the loss of their biocidal effect. In this work, lignin as well as PLGA nanoparticles containing the essential oils from two different thyme species (Thymus capitatus and T. vulgaris) were applied on beech wood samples using spray coating. The prepared coatings were investigated using FTIR imaging, SEM, as well as LSM analysis. Release experiments were conducted to investigate the release behavior of the essential oils from their respective lignin and PLGA carrier materials. The study found that lignin nanoparticles were more effective at trapping and retaining essential oils than PLGA nanoparticles, despite having larger average particle diameters and a more uneven particle size distribution. An analysis of the lignin coatings showed that they formed a uniform layer that covered most of the surface pores. PLGA nanoparticles formed a film-like layer on the cell walls, and after leaching, larger areas of native wood were evident on the wood samples treated with PLGA NPs compared to the ones coated with lignin NPs. The loading capacity and efficiency varied with the type of essential oil, while the release behaviors were similar between the two essential oil types applied in this study.

The preservation of agricultural biodiversity and socioeconomic development are relevant both to enhance domestic production and to support innovation. In the search for new biomolecules, we have focused on the “Carciofo Ortano” landrace, growth in the northern part of the Lazio region. Artichoke cultivation generates substantial by-products, including leaves, stems, and roots, which could serve as valuable sources of biomolecules and prebiotic dietary fiber. To valorize the leaf waste of the “Carciofo Ortano” landrace, a multidisciplinary approach was applied. Chemical analysis using HPLC-DAD identified mono-O- and di-O-caffeoylquinic acids and the sesquiterpene cynaropicrin in all artichoke leaf extracts. SPME-GC/MS analyses detected aliphatic alcohols in the fresh leaf samples. Antiproliferative and cytotoxic studies on cancer (SH-SY5Y, MCF-7, MDA) and normal (MCF-10A) human cell lines revealed that leaf extracts induced a selective dose and time-dependent biological effect. While showing slight activity against environmental bacterial strains, artichoke leaf extracts exhibited significant antifungal activity against the phytopathogenic fungus Alternaria alternata. Overall, the results highlight the potential of “Carciofo Ortano” cultivation by-products as a rich source of biomolecules with versatile applications in humans, animals, and the environment.

Soil sand particles play a crucial role in soil erosion because they are more susceptible to being detached and transported by erosive forces than silt and clay particles. Therefore, in soil erosion assessment and mitigation, it is crucial to model and predict soil sand particles at unsampled locations using appropriate methods. The study was aimed to evaluate the ability of a multivariate approach based on non-stationary geostatistics to merge LiDAR and visible-near infrared (Vis-NIR) diffuse reflectance data with laboratory analyses to produce high-resolution maps of soil sand content. Remotely sensed, high-resolution LiDAR-derived topographic attributes can be used as auxiliary variables to estimate soil textural particle-size fractions. The proposed approach was compared with the commonly used univariate approach of ordinary kriging to evaluate the contribution of auxiliary variables. Soil samples (0–0.20 m depth) were collected at 135 locations within a 139 ha forest catchment with granitic parent material and subordinately alluvial deposits, where soils classified as Typic Xerumbrepts and Ultic Haploxeralf crop out. A number of linear trend models coupled with different auxiliary variables were compared. The best model for predicting sand content was the one with elevation derived from LIDAR data as the only auxiliary variable. Although the improvement in estimation over the univariate model was rather marginal, the proposed approach proved very flexible and scalable to include any type of auxiliary variable. The application of LiDAR data is expected to expand as it allows the high-resolution prediction of soil properties, generally insufficiently sampled, at different spatial scales.

Acidolysis lignins from the species Quercus cerris L. and Eucalyptus camaldulensis Dehnh. were isolated and characterized using high pressure size exclusion chromatography (HP-SEC), Fourier-transform (FTIR) infrared spectroscopy, analytical pyrolysis–gas chromatography–mass spectrometry (Py-GCMS), and two-dimensional heteronuclear single quantum coherence (2D HSQC) NMR spectroscopy. The acidolysis lignins from the two different species varied in chemical composition and structural characteristics, with Q. cerris L. lignin having a higher S/G ratio and higher molar mass averages with a bimodal molar mass distribution. The different analytical techniques FTIR spectroscopy, Py-GCMS, and 2D NMR spectroscopy provided consistent results regarding the S/G ratio of the lignins from the two wood species. Based on the determined high S/G ratio of both oak and eucalypt lignin, the two wood sources could be promoted as substrates for efficient lignin isolation in modern forest biorefineries in order to develop innovative lignin-based value-added biorefinery products.

The present study focused on the molecular, morphological, and nutritional characterisation of a globe artichoke landrace at risk of genetic erosion still cultivated in the municipality of Orte (Lazio Region, Central Italy) and therefore named “Carciofo Ortano”. Molecular analysis based on SSR and ISSR markers was carried out on 73 genotypes selected at random from 20 smallholdings located in the Orte countryside and 17 accessions of landraces/clones belonging to the main varietal types cultivated in Italy. The results confirmed that “Carciofo Ortano” belongs to the “Romanesco” varietal typology and revealed the presence within the landrace of two distinct genetic populations named Orte 1 and Orte 2. Despite the high level of within-population genetic variation detected, the two populations were genetically differentiated from each other and from the landraces/clones of the main varietal types cultivated in Italy. Morphological and nutritional characterisation was performed on representative genotypes for each of the two populations of the “Carciofo Ortano” and the four landraces/clones included in the varietal platform of the PGI “CARCIOFO ROMANESCO DEL LAZIO” used as reference genotypes (“Campagnano”, “Castellammare”, “C3”, and “Grato 1”). Principal component analysis showed that, of the 43 morphological descriptors considered, 12, including plant height, head shape index, head yield, and earliness, allowed a clear grouping of genotypes, distinguishing Orte 1 and Orte 2 populations from the reference genotypes. Regarding the nutritional composition of heads, particular attention should be devoted to the Orte 2 genotypes for their high dietary fibre, inulin, flavonoid, and phenol content, a feature that could be highly appreciated by the market.

Afforestation is one of the most effective processes for removing carbon dioxide from the atmosphere and combating global warming. Landsat data and machine learning approaches can be used to map afforestation (i) indirectly, by constructing two maps of the same area over different periods and then predicting changes, or (ii) directly, by constructing a single map and analyzing observations of change in both the response and remotely sensed variables. Of crucial importance, no comprehensive comparisons of direct and indirect approaches for afforestation monitoring are known to have been conducted to date. Afforestation maps estimated through the analysis of remotely sensed data may serve as intermediate products for guiding the selection of samples and the production of statistics. In this and similar studies, a huge effort is dedicated to collecting validation data. In turn, those validation datasets have varying sampling intensities in different areas, which complicates their use for assessing the accuracies of new maps. As a result, the work done to collect data is often not sufficiently exploited, with some validation datasets being used just once. In this study, we addressed two main aims. First, we implemented a methodology to reuse validation data acquired via stratified sampling with strata constructed from remote sensing maps. Second, we used this method for acquiring data for comparing map accuracy estimates and the precision of estimates for direct and indirect approaches for country-wide mapping of afforestation that occurred in Italy between 1985 and 2019. To facilitate these comparisons, we used Landsat imagery, random forest classification, and Google Earth Engine. The herein-presented method produced different accuracy estimates with 95% confidence interval and for different map classes. Afforestation accuracies ranged between 53 ± 5.9% for the indirect map class inside the buffer—defined as a stratum within 120 m of the forest/non-forest mask boundaries—and 26 ± 3.4% for the direct map outside the buffer. The accuracy in non-afforestation map classes was much greater, ranging from 87 ± 1.9% for the indirect map inside the buffer to 99 ± 1.3% for the direct map outside the buffer. Additionally, overall accuracies (with 95% CI) were estimated with large precision for both direct and indirect maps (87 ± 1.3% and 89 ± 1.6%, respectively), confirming (i) the effectiveness of the method we introduced for reusing samples and (ii) the relevance of remotely sensed data and machine learning for monitoring afforestation.

Afforestation processes, natural and anthropogenic, involve the conversion of other land uses to forest, and they represent one of the most important land use transformations, influencing numerous ecosystem services. Although remotely sensed data are commonly used to monitor forest disturbance, only a few reported studies have used these data to monitor afforestation. The objectives of this study were two fold: (1) to develop and illustrate a method that exploits the 1985–2019 Landsat time series for predicting afforestation areas at 30 m resolution at the national scale, and (2) to estimate afforestation areas statistically rigorously within Italian administrative regions and land elevation classes. We used a Landsat best-available-pixel time series (1985–2019) to calculate a set of temporal predictors that, together with the random forests prediction technique, facilitated construction of a map of afforested areas in Italy. Then, the map was used to guide selection of an estimation sample dataset which, after a complex photointerpretation phase, was used to estimate afforestation areas and associated confidence intervals. The classification approach achieved an accuracy of 87%. At the national level, the afforestation area between 1985 and 2019 covered 2.8 ± 0.2 million ha, corresponding to a potential C-sequestration of 200 million t. The administrative region with the largest afforested area was Sardinia, with 260,670 ± 58,522 ha, while the smallest area of 28,644 ± 12,114 ha was in Valle d’Aosta. Considering elevation classes of 200 m, the greatest afforestation area was between 400 and 600 m above sea level, where it was 549,497 ± 84,979 ha. Our results help to understand the afforestation process in Italy between 1985 and 2019 in relation to geographical location and altitude, and they could be the basis of further studies on the species composition of afforestation areas and land management conditions.

For the first time in human history, over half of the world’s population lives in urban areas. This rapid growth makes cities more vulnerable, increasing the need to monitor urban dynamics and its sustainability. The aim of this work is to examine the spatial extent of urban areas, to identify the urban–rural continuum, to understand urbanization processes, and to monitor Sustainable Development Goal 11. In this paper, we apply the methodology developed by the European Commission-Joint Research Center for the classification of the degree of urbanization of the Italian territory, using the ISPRA land consumption map and the ISTAT population data. The analysis shows that the availability of detailed and updated spatialized population data is essential to calculate SDG indicator 11.3.1, which assesses the ratio of land consumption rate to population growth rate. Three new indicators are also proposed to describe the main trends in urban sprawl, analyzing the spatial distribution of land consumption in terms of infill and settlement dispersion. The research shows good results in identifying class boundaries and describing the Italian urbanized landscape, highlighting the need for more detailed spatialized demographic data. The classification obtained lends itself to a variety of applications, such as monitoring land consumption, settlement dynamics, or the urban heat islands, and assessing the presence and state of green infrastructures in the urban context, driving the development of policies in urban areas toward sustainable choices focused on urban regeneration.

To combat global deforestation, monitoring forest disturbances at sub-annual scales is a key challenge. For this purpose, the new Planetscope nano-satellite constellation is a game changer, with a revisit time of 1 day and a pixel size of 3-m. We present a near-real time forest disturbance alert system based on PlanetScope imagery: the Thresholding Rewards and Penances algorithm (TRP). It produces a new forest change map as soon as a new PlanetScope image is acquired. To calibrate and validate TRP, a reference set was constructed as a complete census of five randomly selected study areas in Tuscany, Italy. We processed 572 PlanetScope images acquired between 1 May 2018 and 5 July 2019. TRP was used to construct forest change maps during the study period for which the final user’s accuracy was 86% and the final producer’s accuracy was 92%. In addition, we estimated the forest change area using an unbiased stratified estimator that can be used with a small sample of reference data. The 95% confidence interval for the sample-based estimate of 56.89 ha included the census-based area estimate of 56.19 ha.

The improvement of harvesting methodologies plays an important role in the optimization of wood production in a context of sustainable forest management. Different harvesting methods can be applied according to forest site-specific condition and the appropriate mechanization level depends on a number of factors. Therefore, efficiency and functionality of wood harvesting operations depend on several factors. The aim of this study is to analyze how the different harvesting processes affect operational costs and labor productivity in typical small-scale Italian harvesting companies. A multiple linear regression model (MLR) and artificial neural network (ANN) have been carried out to predict gross time, productivity and costs estimation in a series of qualitative and quantitative variables. The results have created a correct statistical model able to accurately estimate the technical parameters (work time and productivity) and economic parameters (costs per unit of product and per hectare) useful to the forestry entrepreneur to predict the results of the work in advance, considering only the values detectable of some characteristic elements of the worksite.

Three different formulations of bio-based polyurethane (PU), varying the weight ratio between Organosolv lignin and a commercial isocyanate, were synthesized. The coating formulations were characterized by SEM, pyrolysis-GC/MS, FTIR spectroscopy and FTIR mapping, which confirmed the successful formation of urethane bonds between commercial isocyanate and hydroxyl groups deriving from lignin. The coatings were applied on beech wood samples to measure color and contact angles, and eventually FTIR mapping of the coated wood samples was performed. FTIR mapping is an interesting tool to monitor the distribution of PU chemical bonds on the coating surface and to evaluate the homogeneity of the applied coating films. Increasing the lignin content of the PU coatings results in more red-yellow and darker tones, while the commercial PU coating is transparent. For a higher lignin concentration, the solid content as well as the weight gain of the applied coatings increase. A higher percentage of lignin in the prepared PU formulations leads to superficial cracks and therefore higher coating permeability compared to the commercial PU, but the prepared lignin-based PU coating still makes a raw wood surface significantly more hydrophobic. Apparently, additives such as film-formers with low surface tension to counteract cracks’ formation are necessary to improve the performance of lignin-based PU coatings.

Abstract Background Arundo donax has attracted renewed interest as a potential candidate energy crop for use in biomass-to-liquid fuel conversion processes and biorefineries. This is due to its high productivity, adaptability to marginal land conditions, and suitability for biofuel and biomaterial production. Despite its importance, the genomic resources currently available for supporting the improvement of this species are still limited. Results We used RNA sequencing (RNA-Seq) to de novo assemble and characterize the A. donax leaf transcriptome. The sequencing generated 1249 million clean reads that were assembled using single-k-mer and multi-k-mer approaches into 62,596 unique sequences (unitranscripts) with an N50 of 1134 bp. TransDecoder and Trinotate software suites were used to obtain putative coding sequences and annotate them by mapping to UniProtKB/Swiss-Prot and UniRef90 databases, searching for known transcripts, proteins, protein domains, and signal peptides. Furthermore, the unitranscripts were annotated by mapping them to the NCBI non-redundant, GO and KEGG pathway databases using Blast2GO. The transcriptome was also characterized by BLAST searches to investigate homologous transcripts of key genes involved in important metabolic pathways, such as lignin, cellulose, purine, and thiamine biosynthesis and carbon fixation. Moreover, a set of homologous transcripts of key genes involved in stomatal development and of genes coding for stress-associated proteins (SAPs) were identified. Additionally, 8364 simple sequence repeat (SSR) markers were identified and surveyed. SSRs appeared more abundant in non-coding regions (63.18%) than in coding regions (36.82%). This SSR dataset represents the first marker catalogue of A. donax. 53 SSRs (PolySSRs) were then predicted to be polymorphic between ecotype-specific assemblies, suggesting genetic variability in the studied ecotypes. Conclusions This study provides the first publicly available leaf transcriptome for the A. donax bioenergy crop. The functional annotation and characterization of the transcriptome will be highly useful for providing insight into the molecular mechanisms underlying its extreme adaptability. The identification of homologous transcripts involved in key metabolic pathways offers a platform for directing future efforts in genetic improvement of this species. Finally, the identified SSRs will facilitate the harnessing of untapped genetic diversity. This transcriptome should be of value to ongoing functional genomics and genetic studies in this crop of paramount economic importance.

Arsenic (As) ranks among the priority metal(loid)s that are of public health concern. In the environment, arsenic is present in different forms, organic or inorganic, featured by various toxicity levels. Bacteria have developed different strategies to deal with this toxicity involving different resistance genetic determinants. Bacterial strains of Rhodococcus genus, and more in general Actinobacteria phylum, have the ability to cope with high concentrations of toxic metalloids, although little is known on the molecular and genetic bases of these metabolic features. Here we show that Rhodococcus aetherivorans BCP1, an extremophilic actinobacterial strain able to tolerate high concentrations of organic solvents and toxic metalloids, can grow in the presence of high concentrations of As(V) (up to 240 mM) under aerobic growth conditions using glucose as sole carbon and energy source. Notably, BCP1 cells improved their growth performance as well as their capacity of reducing As(V) into As(III) when the concentration of As(V) is within 30–100 mM As(V). Genomic analysis of BCP1 compared to other actinobacterial strains revealed the presence of three gene clusters responsible for organic and inorganic arsenic resistance. In particular, two adjacent and divergently oriented ars gene clusters include three arsenate reductase genes (arsC1/2/3) involved in resistance mechanisms against As(V). A sequence similarity network (SSN) and phylogenetic analysis of these arsenate reductase genes indicated that two of them (ArsC2/3) are functionally related to thioredoxin (Trx)/thioredoxin reductase (TrxR)-dependent class and one of them (ArsC1) to the mycothiol (MSH)/mycoredoxin (Mrx)-dependent class. A targeted transcriptomic analysis performed by RT-qPCR indicated that the arsenate reductase genes as well as other genes included in the ars gene cluster (possible regulator gene, arsR, and arsenite extrusion genes, arsA, acr3, and arsD) are transcriptionally induced when BCP1 cells were exposed to As(V) supplied at two different sub-lethal concentrations. This work provides for the first time insights into the arsenic resistance mechanisms of a Rhodococcus strain, revealing some of the unique metabolic requirements for the environmental persistence of this bacterial genus and its possible use in bioremediation procedures of toxic metal contaminated sites.

A Landsat time series has been recognized as a viable source of information for monitoring and assessing forest disturbances and for continuous reporting on forest dynamics. This study focused on developing automated procedures for detecting disturbances in Mediterranean coppice forests which are characterized by rapid regrowth after a cut. Specifically, new methods specific to Mediterranean coppice forests are needed for mapping clearcut disturbances over time and for estimating related indicators in the context of Sustainable Forest Management and Biodiversity International monitoring frameworks. The aim of this work was to develop a new change detection algorithm for mapping clearcut disturbances in Mediterranean coppice forests with Landsat time series (LTS) using a short time window. Accuracy for the new algorithm, characterized as the Two Thresholds Method (TTM), was evaluated using an independent clearcut reference dataset over a temporal period of the 13 years between 2001 and 2013. TTM was also evaluated against two benchmark approaches: (i) LandTrendr, and (ii) the forest loss category of the Global Forest Change Map. Overall Accuracy for LandTrendr and TTM were greater than 0.94. Meanwhile, smaller accuracies were always obtained for the GFC. In particular, Producer’s Accuracy ranged between 0.45 and 0.84 for TTM and between 0.49 and 0.83 for LT, while for the GFC, PA ranged between 0 and 0.38. User’s Accuracy ranged between 0.86 and 0.96 for TTM and between 0.73 and 0.91 for LT, while for the GFC UA ranged between 0.19 and 1.00. Moreover, to illustrate the utility of TTM for mapping clearcut disturbances in Mediterranean coppice forests, we applied TTM to a Landsat scene that covered almost the entirety of the Tuscany region in Italy.

Shallow landslides are an increasing concern in Italy and worldwide because of the frequent association with vegetation management. As vegetation cover plays a fundamental role in slope stability, we developed a GIS-based model to evaluate the influence of plant roots on slope safety, and also included a landslide susceptibility map. The GIS-based model, 4SLIDE, is a physically based predictor for shallow landslides that combines geological, topographical, and hydrogeological data. The 4SLIDE combines the infinite slope model, TOPMODEL (for the estimation of the saturated water level), and a vegetation root strength model, which facilitates prediction of locations that are more susceptible for shallow landslides as a function of forest cover. The aim is to define the spatial distribution of Factor of Safety (FS) in steep-forested areas. The GIS-based model 4SLIDE was tested in a forest mountain watershed located in the Sila Greca (Cosenza, Calabria, South Italy) where almost 93% of the area is covered by forest. The sensitive ROC analysis (Receiver Operating Characteristic) indicates that the model has good predictive capability in identifying the areas sensitive to shallow landslides. The localization of areas at risk of landslides plays an important role in land management activities because landslides are among the most costly and dangerous hazards.

Sustainability and ecotoxicity issues call for innovations regarding eco-friendly adhesives in the production of biocomposite wood materials, and solutions involving nano-scale and bio-based compounds represent a valid and promising target. One possible approach is to increase the performance of adhesives such as polyvinyl acetate (PVAc) or melamine-urea-formaldehyde (MUF) by means of nanoparticles in order to obtain a material with better mechanical and environmental resistance. When applying cellulose-based nanoparticles or tannin, the concept of a circular economy is successfully implemented into the forest/wood value chain, and chances are created to develop new value chains using byproducts of forestry operations. In this study, assortments coming from young sweet chestnut (Castanea sativa Mill.) coppice stands were utilized for the preparation of single lap joint assemblies using different commercial adhesives (PVAc, MUF) and cellulose nanocrystals (CNC) and tannin as additives. The results showed that addition of CNC and tannin to PVAc glue increased tensile shear strength in lap joint tests presenting a promising base for future tests regarding the addition of CNC and tannin in MUF or PVAc adhesive formulations. Unfortunately, the tested bio-based additives did not reveal the same encouraging results when tested in the wet state.

The world is shifting to an innovation economy and forest biotechnology can play a major role in the bio-economy by providing farmers, producers, and consumers with tools that can better advance this transition. First-generation or conventional biofuels are primarily produced from food crops and are therefore limited in their ability to meet challenges for petroleum-product substitution and climate change mitigation, and to overcome the food-versus-fuel dilemma. In the longer term, forest lignocellulosic biomass will provide a unique renewable resource for large-scale production of bioenergy, biofuels and bio-products. These second-generation or advanced biofuels and bio-products have also the potential to avoid many of the issues facing the first-generation biofuels, particularly the competition concerning land and water used for food production. To expand the range of natural biological resources the rapidly evolving tools of biotechnology can ameliorate the conversion process, lower the conversion costs and also enhance target yield of forest biomass feedstock and the product of interest. Therefore, linking forest biotechnology with industrial biotechnology presents a promising approach to convert woody lignocellulosic biomass into biofuels and bio-products. Major advances and applications of forest biotechnology that are being achieved to competitively position forest biomass feedstocks with corn and other food crops are outlined. Finally, recommendations for future work are discussed.

Modern agriculture requires more efficient and low-impact products and formulations than traditional agrochemicals to improve crop yields. Iron is a micronutrient essential for plant growth and photosynthesis, but it is mostly present in insoluble forms in ecosystems so that it is often limiting for plants. This study was aimed at combining natural strategies and biodegradable nanostructured materials to create environmentally friendly and low-toxic bioactive products capable of both supplying iron to Fe-deficient plants and reducing the impact of agricultural products on the environment. Consequently, free-standing electrospun nanofibrous polycaprolactone/polyhydroxybutyrate thin membranes loaded with catechol (CL-NMs) as an iron-chelating natural agent (at two concentrations) were fabricated on purpose to mobilize Fe from insoluble forms and transfer it to duckweed (Lemna minor L.) plants. The effectiveness of CL-NMs in providing iron to Fe-deficient plants, upon catechol release, tested in duckweeds grown for 4 days under controlled hydroponic conditions, displayed temporal variations in both photosynthetic efficiency and biometric parameters measured by chlorophyll fluorescence and growth imaging. Duckweeds supplied with CL-NMs hosting higher catechol concentrations recovered most of the physiological and growth performances previously impaired by Fe limitation. The absence of short-term toxicity of these materials on duckweeds also proved the low impact on ecosystems of these products.

Plant roots play a key role in stabilizing slopes, particularly in the Mediterranean region, characterized by rough and unstable terrain. However, forest species differ in their stabilizing capacities. The purpose of this study is to fill the gap of knowledge on root biomechanical properties of relevant Mediterranean trees and shrubs in relation to slope stability. Root specimens of typical montane Mediterranean tree and shrub species were sampled in Southern Italy. Root characteristics, such as tensile strength (Tr) and root area ratio (RAR), were assessed from live roots sampled in trenches, while root cohesion was calculated. Power law functions yielded the best fit for the relationship of Tr versus root diameter; however, no significant relationship was found between root strength and root moisture content. RAR varied amongst different tree and shrub species. Roots of Quercus cerris L. were the most resistant to breaking under tension, while roots of Ilex aquifolium L. had the highest tensile strength among all shrub species. Results provide quantitative information on the role of root systems of montane Mediterranean forest species in stabilizing soils and will improve modeling of landslide susceptibility to the prevention and mitigation of natural hazards in mountain environments.

Lignin was isolated from wood wastes comprising Iroko sawdust (IR) and mixed sawdust from Iroko and Norway spruce (IRNS), furnished by a local wood houses producer. The respective acidolysis lignin fractions were structurally characterized using pyrolysis (Py)-GCMS, two-dimensional heteronuclear single quantum correlation nuclear magnetic resonance (2D HSQC NMR), Fourier-transform infrared FTIR and ultraviolet-visible (UV-VIS) spectroscopies, size exclusion chromatography, and standard wet-chemistry methods for Klason lignin and polysaccharides determination. The isolated lignin fractions were subsequently used for the preparation of lignin nanoparticles (LNPs) using a non-solvent method. LNPs were then used for wood surface treatment using a dip-coating technique. The coated wood samples were analyzed by colorimetry and scanning electron microscopy (SEM) before and after artificial weathering experiments in a UV chamber to investigate the UV protection potential of the LNPs coatings. Wood samples dip-coated with LNPs showed promising surface modifications resembling a sort of film of fused LNPs. Coatings made from IR-LNPs and IRNS-LNPs performed significantly better in artificial weathering experiments than uncoated reference samples.

The countries of the Mediterranean basin face several challenges regarding the sustainability of forest ecosystems and the delivery of crucial goods and services that they provide in a context of rapid global changes. Advancing scientific knowledge and foresting innovation is essential to ensure the sustainable management of Mediterranean forests and maximize the potential role of their unique goods and services in building a knowledge-based bioeconomy in the region. In this context, the European project FORESTERRA ("Enhancing FOrest RESearch in the MediTERRAnean through improved coordination and integration”) aims at reinforcing the scientific cooperation on Mediterranean forests through an ambitious transnational framework in order to reduce the existing research fragmentation and maximize the effectiveness of forest research activities. Within the FORESTERRA project framework, this work analyzed the infrastructures equipment of the Mediterranean countries belonging to the project Consortium. According to the European Commission, research infrastructures are facilities, resources and services that are used by the scientific communities to conduct research and foster innovation. To the best of our knowledge, the equipment and availability of infrastructures, in terms of experimental sites, research facilities and databases, have only rarely been explored. The aim of this paper was hence to identify complementarities, gaps and overlaps among the different forest research institutes in order to create a scientific network, optimize the resources and trigger collaborations.

Abiotic stresses have considerable negative impact on Mediterranean plant ecosystems and better comprehension of the genetic control of response and adaptation of trees to global changes is urgently needed. The Single Cell Gel Electrophoresis assay could be considered a good estimator of DNA damage in an individual eukaryotic cell. This method has been mainly employed in animal tissues, because the plant cell wall represents an obstacle for the extraction of nuclei; moreover, in Mediterranean woody species, especially in the sclerophyll plants, this procedure can be quite difficult because of the presence of sclerenchyma and hardened cells. On the other hand, these plants represent an interesting material to be studied because of the ability of these plants to tolerate abiotic stress. For instance, holm oak (Quercus ilex L.) has been selected as the model plant to identify critical levels of O3 for Southern European forests. Consequently, a quantitative method for the evaluation of cell injury of leaf tissues of this species is required. Optimal conditions for high-yield nuclei isolation were obtained by using protoplast technology and a detailed description of the method is provided and discussed. White poplar (Populus alba L.) was used as an internal control for protoplast isolation. Such a method has not been previously reported in newly fully developed leaves of holm oak. This method combined with Single Cell Gel Electrophoresis assay represents a new tool for testing the DNA integrity of leaf tissues in higher plants under stress conditions.

This study aims at conducting the first science mapping analysis of the Mediterranean forest research in order to elucidate its research structure and evolution. We applied a science mapping approach based on co-term and citation analyses to a set of scientific publications retrieved from the Elsevier's Scopus database over the period 1980-2014. The Scopus search retrieved 2,698 research papers and reviews published by 159 peer-reviewed journals. The total number of publications was around 1% (N = 17) during the period 1980-1989 and they reached 3% (N = 69) in the time slice 1990-1994. Since 1995, the number of publications increased exponentially, thus reaching 55% (N = 1,476) during the period 2010-2014. Within the thirty-four years considered, the retrieved publications were published by 88 countries. Among them, Spain was the most productive country, publishing 44% (N = 1,178) of total publications followed by Italy (18%, N = 482) and France (12%, N = 336). These countries also host the ten most productive scientific institutions in terms of number of publications in Mediterranean forest subjects. Forest Ecology and Management and Annals of Forest Science were the most active journals in publishing research in Mediterranean forest. During the period 1980-1994, the research topics were poorly characterized, but they become better defined during the time slice 1995-1999. Since 2000s, the clusters become well defined by research topics. Current status of Mediterranean forest research (20092014) was represented by four clusters, in which different research topics such as biodiversity and conservation, land-use and degradation, climate change effects on ecophysiological responses and soil were identified. Basic research in Mediterranean forest ecosystems is mainly conducted by ecophysiological research. Applied research was mainly represented by land-use and degradation, biodiversity and conservation and fire research topics. The citation analyses revealed highly cited terms in the Mediterranean forest research as they were represented by fire, biodiversity, carbon sequestration, climate change and global warming. Finally, our analysis also revealed the multidisciplinary role of climate change research. This study provides a first holistic view of the Mediterranean forest research that could be useful for researchers and policy makers as they may evaluate and analyze its historical evolution, as well as its structure and scientific production. We concluded that Mediterranean forest research represents an active scientific field.