This study investigates climate change impacts on spontaneous vegetation, focusing on the Mediterranean basin, a hotspot for climatic changes. Two case study areas, Monti Sibillini (central Italy, temperate) and Sidi Makhlouf (Southern Tunisia, arid), were selected for their contrasting climates and vegetation. Using WorldClim's CMCC‐ESM2 climate model, future vegetation distribution was predicted for 2050 and 2080 under SSP 245 (optimistic) and 585 (pessimistic) scenarios. Two spectral indices, NDVI (temperate area) and SAVI (arid area), served as vegetation proxies, classified into three classes using K‐means (NDVI: high = mainly associated with woodlands, medium = shrublands, continuous grasslands and crops, low = discontinuous grasslands, bare soil, and rocks; SAVI: high = mainly associated with woods, olive trees, and crops, medium = shrublands and sparse olive trees, low = bare soil and saline areas). Classes validated with ESA WorldCover 2020 data and sampled via 1390 presence‐only points. A set of 33 environmental variables (topography, soil, and bioclimatic) was screened using Pearson correlation matrices, and predictive models were built using four algorithms: MaxEnt, Random Forest, XG Boost, and Neural Network. Results revealed increasing temperatures and declining precipitation in both regions, confirming Mediterranean climate trends. Vegetation changes varied by area: in the temperate area, woodlands and shrublands were stable, but discontinuous grasslands expanded. In the arid area, woodlands gained suitable habitat, while bare soil declined under the pessimistic SSP 585 scenario. Both areas showed an upward shift for shrublands and grasslands. The models indicated significant shifts in areal distribution and environmental conditions, affecting habitat suitability and ecosystem dynamics. MaxEnt emerged as the most reliable algorithm for small presence‐only datasets, effectively predicting habitat suitability. The findings highlight significant vegetation redistribution and altered ecosystem dynamics due to climate change, underscoring the importance of these models in planning for future ecological challenges.

Background: Conservation agriculture has emerged as a sustainable strategy to improve soil quality while maintaining crop productivity and grain nutritional value. However, limited information is available on how agronomic practices influence the biological activity of durum wheat grain. This study, conducted within the framework of the PFRLab project, investigated the effects of no-tillage (NT) and conventional tillage (CT) under two nitrogen fertilization levels (0 and 180 kg ha⁻¹ N) on durum wheat grain quality and the biological responses of Caco-2 intestinal epithelial cells. Methods: Grain quality was evaluated by determining protein, lutein, and mineral contents. The biological activity of grain extracts was assessed by measuring Caco-2 cell viability using the MTT assay and intracellular oxidative activity using H₂DCFDA-derived fluorescence after 3, 24, and 48 h of incubation. Results: Grain protein and lutein contents tended to be higher under nitrogen fertilization, whereas differences between tillage systems were less consistent for the measured compositional traits. In contrast, distinct responses emerged in the cellular assays. NT-derived extracts generally maintained higher Caco-2 cell viability than CT-derived extracts, particularly after 24 and 48 h, while CT-derived extracts generally elicited higher H₂DCFDA-derived intracellular fluorescence. Nitrogen fertilization influenced both cellular endpoints, with effects varying according to incubation time and tillage system. Correlation analysis revealed negative but non-significant associations between intracellular fluorescence and cell viability at individual incubation times (r = −0.649 at 3 h, −0.353 at 24 h, and −0.198 at 48 h). Conclusions: The findings of this pilot exploratory study suggest that long-term no-tillage can maintain the measured durum wheat grain-quality traits at levels comparable to conventional tillage under Mediterranean conditions, while agronomic management is also associated with differences in the biological activity of grain extracts. The cellular findings provide complementary information rather than evidence of direct health effects or specific chemical mechanisms. Future studies integrating targeted phytochemical characterization of grain extracts, soil microbial analyses, and complementary cellular approaches may help clarify the relationships between agronomic management, grain composition, and biological activity.

Leaf Area Index (LAI) and Leaf Chlorophyll Content (LCC) are key vegetation indices for modeling energy and mass exchange between the atmosphere and land surfaces and can therefore be utilized for yield prediction. Consequently, suitable methods have been developed to retrieve LAI and LCC from remotely sensed data. Among these, the inversion of Radiative Transfer Models stands out as a promising approach, as it addresses the issue of limited transferability and minimizes the need for extensive field measurements also accounting for crop variability.The objective of this study is to assess the applicability of the Soil Canopy Observation of Photochemistry and Energy Fluxes (SCOPE) model for estimating LAI and LCC of potato (Solanum tuberosum L.) using time series of hyperspectral images captured by an uncrewed aerial vehicle. A field experiment was conducted in Belgium from June to October 2024, involving two potato varieties, early and late, subjected to two nitrogen fertilization levels and six different biostimulants. Throughout the crop growth cycle, hyperspectral UAV images were captured biweekly using the Specim AFX10 camera. On the same days, in situ measurements of LAI and LCC were performed. LAI and LCC were estimated using a look-up table (LUT) approach based on the inversion of the SCOPE model. A cost function (norm2 distance) was employed to sort the LUT and identify a set of spectra that minimized the distance between measured reflectance and simulated reflectance in the LUT. The estimated LAI and LCC values were then compared with their corresponding in situ measurements.Preliminary results indicate that simulated LAI and LCC showed potential for use in designing models to predict measured LAI and LCC (R2=0.26 and R2=0.30, respectively, p

The Mediterranean basin is a climate change hotspot, and this will strongly affect key crops such as durum wheat, a staple for millions and a major commodity in southern Europe. Future productivity remains uncertain, as climate change introduces both limiting and beneficial factors. Understanding crop responses is essential to design effective adaptation strategies and ensure food security.This study assesses the DSSAT-CERES-Wheat model’s ability to simulate durum wheat growth and yield under climate change across three contrasting Mediterranean environments and support adaptive strategies. The model was calibrated for Tirex, a widely cultivated variety, using long-term field data and Monte Carlo optimization, then evaluated with three independent trials in northern (Rovigo), central (Agugliano), and southern (Genzano) Italy, incorporating leaf area index data from Sentinel-2. Two CMIP5 scenarios (RCP 4.5 and RCP 8.5) and three temporal horizons (baseline 1991–2020, near future 2022–2050, far future 2070–2100) were used to simulate yields, evaluate irrigation as adaptative strategy, and assess water use efficiency (WUE).Calibration showed strong performance for grain yield (R²=0.98, d-stat=0.98, RMSE=0.3 t/ha), canopy biomass (R²=0.98, d-stat=0.62, RMSE=3 t/ha), and anthesis (R²=0.98, d-stat=0.84, RMSE=7.6 days). Evaluation confirmed good agreement for yield and biomass across sites, while LAI was less accurate.At Rovigo, climate change reduced yields most under RCP 8.5 near future (-1.8 t/ha, -40%). At Agugliano, responses depended on agronomic management: under enhanced conventional (standard nutrition, supplemental irrigation, and integrated pest management), yields declined most under RCP 4.5 near future (-1.8 t/ha, -28%) but increased under RCP 8.5 far future (+1.0 t/ha, +15%). Under zero-stress (fertigation and chemical pest control), yields increased across all scenarios, reaching the highest gain of +1.5 t/ha (+17%) for RCP 8.5 far future. At Genzano, limited effects were observed, with the largest near-future decline (-0.3 t ha⁻¹, -11%) and a far-future increase (+0.7 t/ha, +30%) under RCP 8.5. The achievement of higher yields in the far future compared to the near future across all scenarios may be due to projected increases in atmospheric CO₂, thereby partially offsetting yield losses caused by changes in temperature and precipitation.Full irrigation mitigated climate impacts. At Rovigo, it led to +3.9 t ha⁻¹ (+84%) under RCP 8.5 far future and improved WUE from 5 to 10–14 kg/mm. At Agugliano, irrigation increased yields under all scenarios, with the largest gain under RCP 8.5 far future (+3.6 t ha⁻¹, +54%) while maintaining a WUE of 10–15 kg/mm. At Genzano, irrigation produced smaller absolute but higher relative gain (+2.0 t/ha, +90%) under RCP 8.5 far future and WUE slightly increased.Full irrigation effectively stabilizes and increases wheat yields, but only modestly improves WUE, indicating that higher yields do not necessarily translate into greater water efficiency. Full irrigation should therefore be considered a theoretical upper limit rather than a realistic large-scale strategy due to water availability, cost, and infrastructure constraints. Effective climate adaptation in Mediterranean wheat requires combining agronomic management adjustments and genotype choice, supported by crop modeling to assess trade-offs among productivity, water use, and environmental sustainability.