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The Mediterranean diet (MD) is a world-renowned healthy dietary pattern. In the present study we analyse the climate sustainability of the MD and the greenhouse gas emissions (E GHG ) associated with current dietary patterns in Mediterranean and non-Mediterranean EU countries, focusing on the major deviations from the MD health and environmental targets in Mediterranean countries. The E GHG associated with dietary patterns were calculated for seven Mediterranean countries (Cyprus, Croatia, Greece, Italy, Portugal, Spain and Malta, referred to as 7MED) and the other 21 countries in the EU 28 (referred to as 21OTHER), using 2017 as the reference year. A new harmonised compilation of 3449 carbon footprint values of food commodities, based on a standardised methodology to extract information and assign optimal footprint values and uncertainties to food items, was used to estimate E GHG of food consumption. Our findings show that the E GHG associated with the ideal MD pattern, 2.3 kg CO 2 equivalents (CO 2 eq) capita −1 d −1 , are in line with planetary GHG climate targets, though GHG emissions associated with food consumption in Mediterranean countries strongly diverged from the ideal MD. Both MED and 21OTHER countries were found to have comparable dietary associated E GHG (4.46 and 4.03 kg CO 2 eq capita −1 d −1 respectively), almost double that expected from a sustainable dietary pattern. The primary factor of dietary divergence in 7MED countries was found to be meat overconsumption, which contributed to 60% of the E GHG daily excess (1.8 kg of CO 2 eq capita −1 d −1 ).

Informing and engaging citizens to adopt sustainable diets is a key strategy for reducing global environmental impacts of the agricultural and food sectors. In this respect, the first requisite to support citizens and actors of the food sector is to provide them a publicly available, reliable and ready to use synthesis of environmental pressures associated to food commodities. Here we introduce the SU-EATABLE LIFE database, a multilevel database of carbon (CF) and water (WF) footprint values of food commodities, based on a standardized methodology to extract information and assign optimal footprint values and uncertainties to food items, starting from peer-reviewed articles and grey literature. The database and its innovative methodological framework for uncertainty treatment and data quality assurance provides a solid basis for evaluating the impact of dietary shifts on global environmental policies, including climate mitigation through greenhouse gas emission reductions. The database ensures repeatability and further expansion, providing a reliable science-based tool for managers and researcher in the food sector. Measurement(s) carbon footprint of food products and related uncertainties • water footprint of food products and related uncertainties Technology Type(s) digital curation • statistical analysis Factor Type(s) Typologies, sub-tyologies and items of food products Sample Characteristic - Environment agroecosystem • food production system Sample Characteristic - Location global Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14115608

The occurrence of old-growth forests is quite limited in Mediterranean islands, which have been subject to particularly pronounced human impacts. Little is known about the carbon stocks of such peculiar ecosystems compared with different stages of secondary succession. We investigated the carbon variation in aboveground woody biomass, in litter and soil, and the nitrogen variation in litter and soil, in a 100 years long secondary succession in Mediterranean ecosystems. A vineyard, three stages of plant succession (high maquis, maquis-forest, and forest-maquis), and an old growth forest were compared. Soil samples at two soil depths (0-15 and 15-30 cm), and two litter types, relatively undecomposed and partly decomposed, were collected. Carbon stock in aboveground woody biomass increased from 6 Mg ha-1 in the vineyard to 105 Mg ha-1 in old growth forest. Along the secondary succession, soil carbon considerably increased from about 33 Mg ha-1 in the vineyard to about 69 Mg ha-1 in old growth forest. Soil nitrogen has more than doubled, ranging from 4.1 Mg ha-1 in the vineyard to 8.8 Mg ha-1 in old growth forest. Both soil parameters were found to be affected by successional stage and soil depth but not by their interaction. While the C/N ratio in the soil remained relatively constant during the succession, the C/N ratio of the litter strongly decreased, probably following the progressive increase in the holm oak contribution. While carbon content in litter decreased along the succession, nitrogen content slightly increased. Overall, carbon stock in aboveground woody biomass, litter and soil increased from about 48 Mg ha-1 in the vineyard to about 198 Mg ha-1 in old growth forest. The results of this study indicate that, even in Mediterranean environments, considerable amounts of carbon may be stored through secondary succession processes up to old growth forest.

In the context of the EU’s climate objectives, terrestrial ecosystems play a crucial role in mitigating climate change by acting as carbon sinks. Achieving the EU’s climate neutrality target requires not only reducing greenhouse gas emissions but also enhancing the current carbon dioxide removal through land-based solutions such as afforestation and sustainable soil management. The recently approved Carbon Removals and Carbon Farming Certification Regulation (EU) 2024/3012 aims to ensure transparency and reliability in carbon accounting, supporting carbon markets and land-based removals. However, monitoring, reporting, and verification (MRV) of these removals remain challenging due to ecosystem variability and land management complexities. While multiple technologies exist to support carbon accounting, selecting the most appropriate method for different contexts is not straightforward. This review evaluates five key carbon monitoring technologies: flux towers, satellite sensors, aerial light detection and ranging platforms, terrestrial laser scanning, and in situ internet of things devices. Their suitability for different application scenarios, specifically in forests and woody crops, is analyzed using an evaluation framework that takes into account spatial scale of observation, temporal resolution, accuracy, costs, and limitations. Although each technology offers specific advantages, no single method is universally optimal. By offering a comparative assessment of advantages and limitations of each technology, this review aims to assist land users, policymakers, researchers, and stakeholders in selecting the most effective solutions to ensure reliable MRV in carbon farming projects.

Since its first detection in 2013, Xylella fastidiosa subsp. pauca (Xfp) has caused a devastating Olive Quick Decline Syndrome (OQDS) outbreak in Southern Italy. Effective disease surveillance and treatment strategies are urgently needed to mitigate its impact. This study investigates the short-term (1.5 years) effects of thymol-based treatments on infected olive trees of the susceptible cultivar Cellina di Nardò in two orchards in Salento, Apulia region. Twenty trees per trial received a 3% thymol solution either alone or encapsulated in a cellulose nanoparticle carrier. Over two years, sap flux density and canopy-transmitted solar radiation were monitored using TreeTalker sensors, and spectral greenness indices were calculated. Xfp cell concentrations in plant tissues were quantified via qPCR. Neither thymol treatment halted disease progression nor significantly reduced bacterial load, though the Xfp cell concentration reduction increased over time in the preventive trial. Symptomatic trees exhibited increased sap flux density, though the treatment mitigated this effect in the curative trial. Greenness indices remained lower in infected trees, but the response to symptom severity was delayed. These findings underscore the need for longer-term studies, investigation of synergistic effects with other phytocompounds, and integration of real-time sensor data into adaptive disease management protocols.

HIGHLIGHTS A random forest algorithm effectively reduced the number of wavelengths. Selected wavelengths predicted fibre fraction composition with fair accuracy. A targeted NIR instrument is possible using reduced spectral data. Wavelength reduction must consider manufacturing cost, spectrum width, and potential customer base.

An eddy covariance (EC) GHG study was conducted at two comparable agroecosystems in the Central region of European Russia. The study was conducted in 2013 at the RTSAU Experimental Field with Umbric Albeluvisols (Moscow) and a private farm field with Chernozems in the Pristen area (Kursk region). Both studies involved barley crops, but the fields differed in climate, soil and technological conditions. Diurnal values of net ecosystem exchange (NEE) were two times higher in Kursk than in Moscow. The higher gross primary production (GPP) in Kursk was characterized by better climate and soil conditions and, partially, by the low intensity practices of semi-organic farming. GPP dynamics of the two agroecosystems were significantly different only during the first 50 days of vegetation; however, NEE seasonal differences persisted throughout the growing period, with the trends changing until the end of barley ripening. General trends for ecosystem respiration and GPP were determined by the crop phase. NEE seasonal dynamics showed that the Chernozem agroecosystem was characterized by an almost 2-fold increase in the range of CO2 fluxes, largely determined by hydrologic regime features. Since yield in Kursk was 50% lower than that in Moscow, it may be concluded that the use of modern fertilizing and crop protection systems converts a larger portion of GPP into yield mass.

Poplar is one of the most widespread fast-growing forest species. In Northern Italy, plantations are characterized by large interannual fluctuations, requiring frequent monitoring to inform on wood supply and to manage the stands. The use of radar satellite data is proving useful for forest monitoring, being weather independent and sensitive to the changes in forest canopy structure, but it has been scarcely tested in the case of poplar. Here, L-band ALOS2 (Advanced Land Observing Satellite-2) dual-pol data were tested to detect clear-cut plantations in consecutive years. ALOS2 quad-pol data were used to discriminate among different age classes, a much complex task than detecting poplar plantations extent. Results from different machine learning algorithms indicate that with dual-pol data, poplar forest can be discriminated from clear-cut areas with 80% overall accuracy, similar to what is usually obtained with optical data. With quad-pol data, four age classes were classified with moderate overall accuracy (73%) based on polarimetric decompositions, three 3 age classes with higher accuracy (87%) based on HV band. Sources of error are represented by poplar areas of intermediate age when stems, branches and leaves were not developed enough to detect by scattering mechanisms. This study demonstrates the feasibility of monitoring poplar plantations with satellite radar, which represents a growing source of information thanks to already-planned future satellite missions.

Tropical forests are major repositories of biodiversity, but are fast disappearing as land is converted to agriculture. Decision-makers need to know which of the remaining forests to prioritize for conservation, but the only spatial information on forest biodiversity has, until recently, come from a sparse network of ground-based plots. Here we explore whether airborne hyperspectral imagery can be used to predict the alpha diversity of upper canopy trees in a West African forest. The abundance of tree species were collected from 64 plots (each 1250 m(2) in size) within a Sierra Leonean national park, and Shannon-Wiener biodiversity indices were calculated. An airborne spectrometer measured reflectances of 186 bands in the visible and near-infrared spectral range at 1 m(2) resolution. The standard deviations of these reflectance values and their first-order derivatives were calculated for each plot from the c. 1250 pixels of hyperspectral information within them. Shannon-Wiener indices were then predicted from these plot-based reflectance statistics using a machine-learning algorithm (Random Forest). The regression model fitted the data well (pseudo-R(2) = 84.9%), and we show that standard deviations of green-band reflectances and infra-red region derivatives had the strongest explanatory powers. Our work shows that airborne hyperspectral sensing can be very effective at mapping canopy tree diversity, because its high spatial resolution allows within-plot heterogeneity in reflectance to be characterized, making it an effective tool for monitoring forest biodiversity over large geographic scales.

It is still unclear whether the exponential rise of atmospheric CO2 concentration has produced a fertilization effect on tropical forests, thus incrementing their growth rate, in the last two centuries. As many factors affect tree growth patterns, short -term studies might be influenced by the confounding effect of several interacting environmental variables on plant growth. Long-term analyses of tree growth can elucidate long-term trends of plant growth response to dominant drivers. The study of annual rings, applied to long tree-ring chronologies in tropical forest trees enables such analysis. Long-term tree-ring chronologies of three widespread African species were measured in Central Africa to analyze the growth of trees over the last two centuries. Growth trends were correlated to changes in global atmospheric CO2 concentration and local variations in the main climatic drivers, temperature and rainfall. Our results provided no evidence for a fertilization effect of CO2 on tree growth. On the contrary, an overall growth decline was observed for all three species in the last century, which appears to be significantly correlated to the increase in local temperature. These findings provide additional support to the global observations of a slowing down of C sequestration in the trunks of forest trees in recent decades. Data indicate that the CO2 increase alone has not been sufficient to obtain a tree growth increase in tropical trees. The effect of other changing environmental factors, like temperature, may have overridden the fertilization effect of CO2.