Viticulture is one of the most important agricultural sectors in Europe, significantly contributing to its economy and social culture in several regions. Still, it is highly sensitive to climate conditions and thus exposed to climate change risks.This study aims to provide a very high-resolution (≈1 km) land suitability assessment for viticulture in Europe under current and future climate conditions by combining a Machine Learning (ML) ensemble model with multiple downscaled CMIP6 climate projections.A comprehensive set of 28 bioclimatic indices was initially considered, from which six key variables were selected using correlation analysis and Variance Inflation Factor (VIF). A species distribution modelling framework (Biomod2) was used to develop an ensemble model integrating six Machine Learning algorithms. Vineyard occurrence data were derived from the CORINE Land Cover Database. Projections were produced for a historical period (1981–2010) and two future time horizons (2041–2070 and 2071–2100) under SSP1–2.6, SSP3–7.0, and SSP5–8.5.The ensemble model demonstrated high predictive performance under the adopted SRE pseudo-absence strategy (TSS = 0.87, ROC = 0.99, Kappa = 0.87), although a sensitivity analysis based on random pseudo-absence selection yielded more conservative evaluation metrics. Results indicate a general northward latitudinal shift of climatically suitable areas for viticulture. Projections revealed moderate losses under SSP1–2.6 and pronounced contractions under SSP5–8.5, particularly in the Iberian Peninsula. Conversely, substantial suitability gains are projected in central and northern Europe, indicating regions where climatic conditions may become increasingly favorable for grapevine cultivation. Future projections should be interpreted as ensemble-mean climate suitability estimates, since climate-model uncertainty was not propagated through the complete SDM workflow.These findings highlight the potential redistribution of viticultural suitability across Europe under future climate conditions, with increasing challenges for traditional wine-producing regions, and emerging opportunities in central and northern Europe. However, the realization of future viticultural opportunities will also depend on non-climatic factors, including soil conditions, management practices, economic viability, and regulatory constraints.

Gentiana pneumonanthe L. (marsh gentian) is a wetland specialist and the exclusive host plant of the Alcon blue (Phengaris alcon). This species is considered a ‘flagship species' of wetland conservation, helping to raise awareness and support protection efforts. However, its long-term viability is increasingly threatened by climate change. In the Iberian Peninsula (IP), the species currently occurs within protected areas designated under Natura 2000 and RAMSAR. This study aims to identify the bio-ecological indicators that influence the viability and growth of marsh gentian, determine the suitable regions for its persistence, and assess the future suitability zones, particularly within protected areas. From an initial set of 14 bioclimatic variables (ISIMIP3b, processed using the CHELSA method at 1 km² resolution) and two topographic variables, four bio-ecological indicators were selected based on Pearson correlation and Variance Inflation Factor analyses (VIF): Thermicity Index (It), Ombrothermic Index (Io), Accumulated summer precipitation from June to August (RR_summer), and Maximum of the daily maximum temperature of the hottest month (August) (TXX_aug). Species distribution modelling was performed using the Biomod2 platform, applying six modelling algorithms (GLM, GAM, RF, GBM, CTA and MARS), which were evaluated using a combination of non-spatial metrics (TSS, AUCroc, BIAS, CSI) and a spatial metric (Boyce index). Projections were produced for a historical baseline (1995–2014) and future periods (2041–2060 and 2081–2100), under two anthropogenic radiative forcing scenarios (SSP3-7.0 and SSP5-8.5). The ensemble model demonstrated strong predictive performance (Boyce index = 0.98). Historically, 13.4% of the IP was climatically suitable for the species, primarily in mountainous regions. Under SSP3-7.0, suitable areas are projected to decline by 74.2% (2041–2060) and 99.3% (2081–2100), while under SSP5-8.5, the reductions are estimated at 75.5% and 99.9%, respectively. Although minor gains may occur in the Pyrenees (up to 3.5% under SSP3-7.0 in 2041–2060 and 0.05% under SSP5-8.5 in 2081–2100). Furthermore, most protected areas are expected to lose climatic suitability for species persistence. Such declines may disrupt ecological processes and directly threaten the survival of P. alcon. These findings underscore the urgent need for climate-informed land-use planning and effective habitat conservation strategies. Funding: This work is supported by National Funds by FCT – Portuguese Foundation for Science and Technology, under the projects UID/04033/2025: Centre for the Research and Technology of Agro-Environmental and Biological Sciences (https://doi.org/10.54499/UID/04033/2025) and LA/P/0126/2020 (https://doi.org/10.54499/LA/P/0126/2020). Teresa R. Freitas acknowledges the financial support of Fundação para a Ciência e a Tecnologia (FCT) through the Individual Scientific Employment Stimulus contract 2024.09620.CEECIND.

Hydroclimatic extremes in the Iberian Peninsula (IP) are projected to intensify under 21st-century climate change, with important consequences for water availability, agriculture, and infrastructure. This study quantifies spatial changes in key precipitation-based indicators, Consecutive Dry Days (CDD), frequency of extended dry spells (CDDn; spells >5 days), and the extreme-event contribution to total rainfall (R95pTOT), using ensemble means from nine CMIP6 global models downscaled with CHELSA v2.1 to ~1 km resolution. Historical conditions (1991–2010) and three future time slices (2041–2060, 2061–2080, 2081–2100) were evaluated for SSP1-2.6, SSP3-7.0 and SSP5-8.5. Analyses were performed at 30″ spatial resolution and aggregated by major Iberian basins to inform basin-scale governance and adaptation planning.Baseline climatology exhibits a marked NW–SE gradient, with humid Atlantic basins (e.g., Galicia, western Pyrenees) receiving 1,600–1,900 mm yr⁻¹, while southeastern interiors often fall below 300–400 mm yr⁻¹. Historical R95pTOT is highest (50–60 %) in Atlantic-influenced catchments, indicating a large share of total rainfall concentrated in very heavy events, and lowest (10–20 %) in Mediterranean southern basins dominated by convective storms. CDD and CDDn likewise show strong spatial contrasts: Atlantic margins present short CDD (

The Douro Demarcated Region (DDR) is a worldwide acknowledged winemaking region. Climate change is threatening the sector, as climatic shifts are expected. This work analyzed environmental and genetic traits modulating the rhizobiome of four vineyards in the DDR, focusing on understanding the hierarchy of ecological conductors for these communities. These vineyards’ terroir was environmentally, genetically, and culturally characterized. Rhizosphere bacterial community composition was analyzed using 16S metabarcoding from soil samples collected between July 2022 and January 2024. Results support the hypothesis of an ecological profiling hierarchy. The soil physicochemical properties likely acted as a primary environmental modulator, determining the composition of the bacterial microbiome and contributing to the diversity and richness of the bacterial communities. The major drivers among the soil’s physiochemistry were organic/inorganic profile, mainly influenced by organic matter content and pH. Rootstock genotype appears to exert a secondary selection on the microbiome, focusing on the microorganisms’ functional traits. The 1103-P rootstock positively influenced the abundance of copiotrophic bacteria, compared to the R110, demonstrating that the first recruits a more versatile, exploratory, and expansionist microbiome, whilst the second focuses on attracting a highly specialized community, more focused on maximizing energy gathering and optimizing resource use. This work demonstrates that the microbial terroir is a result of multiple factors, promoting abiotic modulation and host-mediated selection, which establishes a very specific community for each scenario. The assessment of these holistic dynamics is fundamental to establishing a baseline for future precision viticulture strategies, namely bio-inoculants, to support and promote a better grapevine adaptation to climate change.

The Mediterranean basin is affected by water shortages, particularly during the summer dry period. In olive orchards, this situation has increased the use of irrigation. Given the regional water scarcity, irrigation detection in perennial crops is needed to support policy development and sustainable water management. This study evaluates the ability of vegetation indices derived from Sentinel-2 data to distinguish irrigated and rainfed olive orchards in five Mediterranean countries (Greece, Italy, Morocco, Portugal, and Spain). A machine learning approach was applied to assess the discriminatory capacity of vegetation indices computed for winter (January, baseline) and summer water-stress conditions (August). Field-level median values were extracted and evaluated using eleven supervised classification algorithms. Ensemble-based models demonstrated higher classification performances than linear and probabilistic approaches. The highest global F1-score was obtained with Extremely Randomized Trees (ET) with F1-score = 0.843, followed by extra trees, and gradient boosting (GB), (F1-score ≈ 0.83). Within the single-index training approach, the moisture stress index (MSI) proved to be the most discriminative index (F1 = 0.771 with AdaBoost). The Normalized Difference Moisture Index also showed a strong performance with SVM (F1 = 0.772), highlighting the importance of shortwave-infrared moisture-sensitive indices for irrigation detection. These results indicate that irrigation discrimination in Mediterranean olive orchards is mainly related to canopy water status rather than greenness alone. The proposed approach combines physiological interpretability with computational scalability and is transferable to large-scale detection in heterogeneous agro-climatic conditions.

Context: Accurate cultivar-level yield predictions are crucial for ensuring agricultural resilience, but remain challenging due to complex Genotype & times; Environment (G & times;E) interactions and the limited integration of cultivarspecific information. Objective: This study aims to develop and demonstrate the effectiveness of a novel crop-climate fusion framework that integrates cultivar-level performance with phenology-aligned Climate Factors (CF), to explicitly capture G & times;E interactions and improve yield predictions using Machine Learning (ML) models. Methods: The Best Linear Unbiased Estimator (BLUE) was derived for 514 cultivars across three rice maturity groups using multi-environment cultivar trials (total 7136 observations) over 2017-2023. This indicator was coupled with 13 CF aligned to cultivar-specific phenological phases. ML models (RF, SVR and XGBoost) of different architectures were rigorously assessed in yield predictions for unseen cultivars through cultivarindependent train-test splits. Results: Models combining BLUE and CF features consistently outperform those using crop traits or CF alone, achieving R2 of 0.54-0.87 and normalized RMSE below 6% across maturity groups. While XGBoost exhibits a consistent advantage over RF and SVR, the ensemble mean model matches or slightly exceeds the best single model, showing the robustness across heterogeneous environments. However, the predictive accuracy varies markedly between years with notable decreases in low-yield years, underscoring the models' sensitivity to climatic anomalies and extreme events. SHAP analysis identifies BLUE as the most influential predictor, while higher baseline yield is linked to stronger panicle-sink formation and grain filling. Among climatic drivers, CF during the emergence-to-panicle initiation exert stronger influences on yield predictions. The relationships between CF and yields are complex and non-linear, with early-and medium-maturity cultivars mainly influenced by low-temperature and water availability indicators, whereas late-maturity cultivars are more affected by thermal ones. Conclusion: The proposed crop-climate fusion framework improves cultivar-specific yield predictions by integrating cultivar-level and climate factors. BLUE serves as a stable baseline for yield predictions, whereas CF during critical phases modulate this baseline. These findings highlight the importance of linking stable cultivar performance with stage-specific climatic responses for regional rice yield forecasting and cultivar improvement. Significance: The framework provides a practical method for accurate cultivar-level yield forecasting, while offering breeding-relevant insights into trait profiles and climatic sensitivities.

Climate change poses severe threats to agricultural systems in the Mediterranean Basin. Portugal is particularly exposed and vulnerable to projected warming and drying trends. This study provides a high-resolution bioclimatic characterisation of six Portuguese crops: grapevine, olive, wheat, maize, potato and tomato. Bioclimatic indicators (BIOCLIM) based on the CHELSA V2.1 km scale data for the historical period (1981–2010), as well as ensemble mean projections for 2041–2070, under SSP1-2.6 and SSP5-8.5, are used for this purpose. For each crop, all 19 BIOCLIM variables were extracted over the present cultivated area and subjected to Principal Component Analysis (PCA) to characterise the multivariate bioclimatic space. Future suitability was assessed using a multivariate envelope criterion requiring the PCA space to remain within the present observed range. In addition to area loss within current cultivated extents, potential gain areas, which refer to locations outside current cultivation but within the bioclimatic envelope, were identified across mainland Portugal. Under SSP1-2.6, losses ranged from 37 to 41% of the present cultivated area, whereas, under SSP5-8.5, losses ranged from 52 to 61%, with all six crops losing more than half of their present bioclimatic niche. Concurrent gains were, however, identified in northern Portugal and, at higher elevations, reflecting a northward and altitudinal shift in bioclimatic suitability. Once these gains are offset against losses, the net change in the suitable area ranged from −12% to −29% under SSP1-2.6, with maize alone expanding (+17%), and from −15% to −49% under SSP5-8.5. These projections do not account for future crop adaptation through physiological acclimation, cultivar improvement, or changes in management practices. Nonetheless, these results provide an impact assessment framework to support climate adaptation planning in Portuguese agriculture.

Climate change is modifying the thermal and hydric conditions that shape European viticulture in established wine-growing regions. A rule-based bioclimatic assessment approach was developed to evaluate historical and future viticultural preferability across Europe. Climatic thresholds were derived from vineyards within Protected Designation of Origin regions and complemented with values reported in the literature. Selected thermal and hydric indices were calculated from CHELSA climate data at a spatial resolution of 30 arc-seconds (~1 km). Future conditions were assessed for three periods, three Shared Socioeconomic Pathways, and a five-model ensemble. For each period and scenario, European regions were classified as preferable only when all selected indices remained within the defined thresholds. Spatial convergence across periods and scenarios was also evaluated to identify areas with persistent viticultural preferability. The projections show a marked redistribution of viticultural preferability across Europe. Losses increase in southern regions, particularly in the Iberian Peninsula, southern Italy, and Greece, mainly because of excessive heat and dryness. In contrast, northern and north-western Europe show substantial gains as current thermal constraints weaken. Central Europe presents mixed patterns of persistence and expansion. The convergence analysis confirms consistent losses in the south and gains at higher latitudes. This approach provides a basis for identifying areas where viticultural conditions are likely to persist, decline, or emerge under climate change. Areas classified as non-preferable may still remain viable through adaptation, although they are likely to require greater management effort. The results support long-term spatial planning, vineyard investment, and adaptation strategies in European viticulture.

Soil surface moisture (SSM) is a critical indicator of agricultural drought, yet high-resolution projections under climate change remain scarce. This study develops a machine learning framework to predict and project SSM at 1 km resolution across five European Living Labs (LLs), encompassing vineyards, olive groves, and fruit tree systems. Historical Sentinel-1 SSM observations (2014–2024) were used to train ensemble models (Random Forest, XGBoost, ExtraTrees, LightGBM) incorporating climate variables, soil texture, topography, and land use. Tree-based models achieved R2 values of 0.63–0.87. Vineyards showed the highest predictability (R2 ≈ 0.87), reflecting their sensitivity to short-term atmospheric demand and surface water availability, whereas olive groves were the least predictable (R2 ≈ 0.63–0.68), consistent with deeper rooting systems and greater drought buffering capacity. When forced with bias-corrected CMIP6 projections under SSP1-2.6 and SSP5-8.5 for 2041–2070, models indicate minimal changes under SSP1-2.6 but pronounced SSM declines of 8–24% under SSP5-8.5, with historically wetter regions experiencing the largest absolute losses. SHAP analysis confirmed precipitation and potential evapotranspiration as dominant predictors across all crops. This framework provides spatially explicit, crop-relevant SSM projections to support climate adaptation in European agricultural landscapes.

Crop models have been widely used to optimize nitrogen (N) applications for agronomic decision-making, but uncertainties in model calibration under varying N levels, particularly the effects of phenology choice for calibration, remain underexplored. This study employed the ORYZA v3 model, coupled with a global optimization algorithm, to assess how different calibration strategies affected predictions of leaf area index (LAI) and biomass in two rice varieties under four N levels (0, 90, 180, and 270 kg ha-1). The results indicate when the model is calibrated separately for each N level, predictive accuracy varies considerably for both LAI and biomass, reflecting the difference in crop response to N availability. When calibrating the model simultaneously with multiple N levels from a single phenology phase, variability from different selected phenology phases becomes the dominant source of model uncertainty, rather than N levels. Specifically, calibrations using measured data from the stem elongation to anthesis (SA) and anthesis to maturity (AM) phases across N levels provide the most accurate predictions for LAI (RMSE: 0.51–1.92 m² m-²; R²≥0.88) and biomass (RMSE: 551–2619 kg ha-1; R²≥0.96), respectively. In contrast, calibrations using measured data from early-season (transplanting to stem elongation) result in the least reliable predictions. Combined-phase calibrations using SA and AM phases result in the best predictions for both LAI and biomass, owing to their balanced representation of pre- and post-anthesis growth dynamics. This approach significantly reduces uncertainty from phase selection. However, variability in N application rates emerges as the primary uncertainty source in model simulations, emphasizing the importance of careful selection of N levels in calibration datasets, particularly when measured data span two-thirds of the growing season. These findings offer valuable insights into improved calibration practice for precise N management, highlighting the critical role of both phenology phase and N treatment selection.

Precipitation forecasting remains challenging due to the complexity of its driving mechanisms and model limitations in resolving short temporal scales and subgrid processes. This study provides a comprehensive assessment of the performance of the Application of Research to Operations at Mesoscale (AROME) model in forecasting precipitation over mainland Portugal, using observations from a network of automatic weather stations for the period 2022–2023. Forecast skill is evaluated using a combination of categorical metrics derived from contingency tables and spatial verification approaches, enabling a multi-scale analysis of model performance. Results reveal a decrease in model skill with increasing precipitation thresholds. However, model performance improves for longer accumulation periods and when evaluated over larger spatial neighbourhoods, highlighting the importance of phase errors. To identify skilful predictors of heavy precipitation events, convective conditions were analysed during two illustrative extreme events in the Douro (Northern Portugal) and Alentejo (Southern Portugal) wine regions. Thunderstorm diagnostic parameters derived from the AROME model show good agreement with observed lightning activity, demonstrating skill in identifying favourable conditions for deep convection. Additionally, the consistency across forecast lead times suggests that these indices can support early identification of convective activity. These findings highlight the potential of combining precipitation forecasts with thunderstorm diagnostic parameters to improve operational early warning systems and the implementation of suitable risk reduction measures.Acknowledgements: The authors acknowledge National Funds by FCT – Portuguese Foundation for Science and Technology, under the projects UID/04033/2025: Centre for the Research and Technology of Agro-Environmental and Biological Sciences (https://doi.org/10.54499/UID/04033/2025) and LA/P/0126/2020 (https://doi.org/10.54499/LA/P/0126/2020). Project WATERKNOW – Infraestrutura de Conhecimento Geoespacial para a Gestão Inteligente dos Recursos Hídricos (NORTE2030-FEDER-01392400)

Potential evapotranspiration (PET) is a key driver of agricultural water demand, its estimation remains uncertain, particularly in topographically complex, water-limited regions such as the Iberian Peninsula (IP). This study develops and applies a high-resolution PET framework that first evaluates PET across multiple climate datasets, then uses the most suitable dataset to quantify future changes in atmospheric water demand and assess their implications for regional climatic stress and crop evapotranspiration across the Iberian Peninsula. PET is computed using the Hargreaves method for CHELSA, E-OBS, and ERA5-Land, and compared with PET from the Copernicus Climate Change Service CMIP6 Atlas for 1991–2010. Among the evaluated datasets, CHELSA offers the highest spatial resolution while maintaining consistency with the other evaluated datasets, allowing the representation of fine-scale physiographic gradients that are not resolved in the coarser CMIP6 fields. Although this added spatial detail improves the representation of regional climatic variability, it does not necessarily imply higher accuracy in the absence of independent observational validation. CHELSA was therefore selected to analyse future PET under SSP1-2.6, SSP3-7.0, and SSP5-8.5 (2041–2100). A ranking approach is applied to temperature, precipitation, PET, and climatic water balance at the NUTS-2 level to regionalise climatic stress across the IP. Further, PET is combined with FAO-56 crop coefficients to map ETc for vineyards, olive groves, and fruit trees throughout the IP. Results show an increase of high PET values across the IP. Under SSP5-8.5, central and southern Iberia exceeds 1,400–1,600 mm/year by the late century, and even Atlantic and mountain regions are projected to experience substantially higher evaporative demand than during the historical reference period. Ranking analysis indicates that climatic stress is higher in the interior (e.g., Castilla-La-Mancha, Extremadura, Madrid, Castilla y León, and Centro-Portugal). Crop-specific ETc increases across all regions, with olive groves and fruit-tree orchards in southern and eastern IP showing the greatest intensification, thereby indicating greater potential pressure on irrigation systems for perennial agriculture. Hence, the study demonstrates the added value of high-resolution, climate-driven PET for impact assessment, while highlighting the need for adaptation in water management, crop zoning, and agricultural practices under future climate change.

Viticulture is highly vulnerable to weather variability and climate change. Growers increasingly face risks associated with extreme weather events, water scarcity, and emerging pests and diseases. To address these challenges, this study presents the development and implementation of the first operational digital decision support platform (DSP) tailored to Montenegrin vineyards within the MONTEVITIS project. The platform integrates IoT sensor data, national meteorological records and high-resolution global climate datasets to provide real-time monitoring and climate projections for vineyard management. The system was piloted in four vineyards representing diverse microclimatic and soil conditions of Montenegro. Key functionalities include phenology, irrigation and disease alerts supported by a user-friendly dashboard, map-based visualisation tools and data export functions. The pilot deployment demonstrated that combining heterogeneous data streams increases the reliability of outputs and enables timely, site-specific recommendations. Challenges identified during implementation include connectivity limitations, gaps in data and variable levels of digital expertise among growers; however, lessons learned point to the importance of continuous stakeholder engagement and institutional support for sustained use. The MONTEVITIS experience demonstrates how digital agriculture tools can bridge tradition and innovation in viticulture. By fostering collaboration between growers, researchers and policy makers, the platform enables adaptive strategies for climate resilience and sustainable vineyard management. Although the platform has been successfully deployed and tested under pilot conditions, a comprehensive long-term validation of its performance and impact on vineyard decision-making remains part of ongoing future work.

The Mediterranean-type climates on mainland Portugal generally provide suitable conditions for growing olive trees, though climate change may challenge their long-term sustainability. Historical (1995–2014) and projected future scenarios (2041–2060) of agroclimatic indices are developed herein to guide olive orchard (OR) management. Daily simulations from six Global Circulation Models are processed with the CHELSA method, using bias-adjusted ISIMIP3b climate projections based on CMIP6 simulations. Two Shared Socio-Economic Pathways (SSP) are considered: SSP3-7.0 (regional rivalry) and SSP5-8.5 (fossil-fuelled development). Daily data ( 1 km) are used to calculate the following indices: Consecutive Frost Days (CFD), Spring Heat Day (SPR32), Spring Maximum Temperature (SPRTX), Summer Heat Stress Days (SU40), Total rainfall October–May (WINRR). During the historical period, the North and Centre regions experienced a CFD between 0 and 35, whereas a reduction in CFD up to 9 days and 11 days will be expected under SSP3-7.0 and SSP5-8.5, respectively. In 1995–2014, higher SPR32 (3–12 days) and SPRTX (20–24 °C) are recorded in the inner southern regions, increasing to 24 days and 26 °C, respectively, under SSP5-8.5. In these areas, SU40 could reach 24 days in the future. WINRR will decrease by 100–140 mm (7

The study presented in this article contributes to the existing research on incorporating flakes of low-density polyethylene (LDPE) plastic waste into stone mastic asphalt (SMA) mixtures. Specifically, it evaluates the feasibility of incorporating waste plastic, with no commercial value, into the mixture and using conventional bitumen to replace the usual SMA polymer-modified bitumen. Marshall and volumetric properties were analyzed to define the most suitable mixtures for further study. The performance of a reference mixture and a plasticmodified mixture was evaluated in terms of water sensitivity, workability, rutting, stiffness, and fatigue resistance. Both mixtures performed well in all aspects. Compared to the reference mixture, the mixture with waste LDPE presented an excellent performance against rutting with almost no accumulated deformation and a slightly reduced behaviour against fatigue cracking. A mechanistic-empirical analysis was also conducted, showing that waste plastic-modified mixtures can enhance the pavement's structural behaviour. All in all, this study showed that it is possible to produce an SMA mixture using conventional bitumen incorporating waste LDPE, thereby contributing to a circular economy for plastics and helping to mitigate the problem related to their disposal.

Weighting is the process of assigning relative importance to life cycle inventory results or indicator results across impact categories, using weighting factors based on value choices. It is an optional step within Life Cycle Assessment (LCA) but plays an important role in interpreting and communicating the relative importance of different environmental impacts. As part of the Global LCIA Guidance (GLAM) project under the UN Life Cycle Initiative, a comprehensive review of weighting methods was conducted to better understand which approaches are most appropriate for different applications in LCA. Members of the GLAM weighting subtask identified and reviewed twenty-seven weighting methods. These methods were grouped into four categories: Multiple Criteria Decision Analysis (MCDA), monetary, data-driven and distance-to-target methods. Classifiers based on inherent features of the weighting methods were applied to support their inclusion or exclusion from further considerations. Each method then was assessed against a set of evaluation criteria defined by the subtask members. A color-code system (green, yellow or red) was applied to indicate the degree to which each method met each criterion to facilitate comparison and communication. Each method was briefly described with appropriate references, including examples of usage in LCA studies where available. The review results are summarized in a table that highlights the performance of each method against the evaluation criteria. All monetary methods are classified as trade-off rates, whereas there are MCDA methods and data-driven methods that can be either trade-off rates or importance coefficients. All distance-to-target methods are classified as importance coefficients. The ability of each method to incorporate temporal discounting or cultural differentiation varies, depending on the data availability and study design. None of the methods reviewed fully met all evaluation criteria, especially within the scope of the GLAM project. Some criteria (like Scientific validity) are sufficiently met by almost all of these methods. Existing weighting methods based on different approaches have both advantages and limitations. No single method is universally sufficient, and their validity depends on context. This comprehensive overview of available weighting methods provides a valuable starting point for practitioners seeking to identify suitable weighting method for specific LCA applications. To facilitate easy use, a software was also developed based on this review to support the selection of the most appropriate weighting method for LCA studies.

Climate change poses a significant challenge to agriculture in southern Angola, particularly for smallholder farming systems that are highly exposed and vulnerable, lacking the resources and capacity to respond effectively. This study analyses climate trends from 1950 to 2024 in Huíla, Namibe, and Cunene, focusing on eight variables: Tmax, Tmin, Tmean, PRCPTOT, R95p, R95pTOT, CDD, and CWD. Due to inconsistencies in local meteorological station data, ERA5-Land reanalysis was used. Trends such as rising Tmin in Namibe (+0.32 °C/decade), Tmean in Huíla (+0.16 °C/decade), and increased precipitation in Huíla (+29.3 mm/decade), along with fewer dry days in Namibe (–2.7 days/decade), were observed. Crop–climate relationships (2000–2023) were explored using a categorical contingency analysis. Maize showed its highest yield frequency (46%) during hot years; cassava and beans were more stable under cooler, drier conditions; millet yielded above average (31%) in dry years, confirming drought resilience; potatoes performed poorly in wet years (17% above-average yields). The contingency method provided insights where linear models were insufficient, helping to understand climate–yield interactions in data-limited environments. This study offers the first long-term climate–agriculture assessment for southern Angola, providing critical evidence for climate-informed agricultural strategies in regions with scarce and unreliable observational records. The findings emphasise the urgent need for adaptation policies focused on crop-specific climate vulnerabilities. They also demonstrate the value of combining reanalysis data and categorical analysis to overcome data gaps and guide sustainable agricultural planning.

Gentiana pneumonanthe L., a wetland specialist and exclusive host of the Alcon Blue (Phengaris alcon), is highly vulnerable to climate change. This study assessed the future climate suitability of the Iberian Peninsula (IP) for G. pneumonanthe. From 14 bioclimatic variables (ISIMIP3b, processed by CHELSA method at 1 km2) and two topographic variables, four bio-ecological indicators were selected using Pearson correlation and Variance Inflation Factors: Thermicity Index, Ombrothermic Index, Accumulated summer precipitation from June to August, and Maximum of the daily maximum temperature of August. A species distribution model platform (Biomod2) was applied for historical (1995–2014) and future periods (2041–2060, 2081–2100) under two anthropogenic radiative forcing scenarios (SSP3-7.0, SSP5-8.5). The ensemble model created shows a strong predictive performance (BOYCE: 0.98). Historically, 13.4% of the IP was climatically suitable, mainly in mountain areas. Under SSP3-7.0, suitable areas are projected to decline by 74.2% (2041–2060) and 99.3% (2081–2100); under SSP5-8.5, by 75.5% and 99.9%, respectively. While small gains may occur in the Pyrenees, most conservation protected areas (Natura 2000, RAMSAR) may lose suitability for species persistence. Such losses could disrupt ecological ecosystems and directly threaten the survival of P. alcon. These findings highlight the urgent need for climate-informed land-use planning and effective habitat conservation.

Agroclimatic indicators help convey information about climate variability and change in terms that are meaningful to the agricultural sector. This study evaluated climate projections for Angola, particularly for provinces with more significant agricultural potential. To this end, 15 predefined agroclimatic indicators in 2041–2070 and 2071–2099, under the anthropogenic forcing scenarios RCP4.5 and RCP8.5, were compared with the historical period 1981–2010 as a baseline. We selected two climate scenarios and two temporal horizons to obtain a comprehensive view of the potential impacts of climate change in Angola. Data were extracted within the geographic window of longitudes 10–24° E and latitudes 4–18° S and from five general circulation models (GCM), namely MIROC-ESM-CHEM, HadGEM2-ES, IPSL-CM5A-LR, GFDL-ESM2M, and NorESM1-M. The set averages of agroclimatic indicators and their differences between historical and future periods are discussed in relation to the likely implications for agriculture in Angola. The results show significant increases in average daily maximum (2–3 °C) and minimum (2–3 °C) temperatures in Angola. For the future, a generally significant reduction in precipitation (and its associated indicators) is expected in all areas of Angola, with the southwest region (Namibe and Huíla) recording the most pronounced decrease, up to 300 mm. At the same time, the maximum number of consecutive dry days will increase across the country, especially in the Northeast. A widespread increase in temperatures is expected, leading to hot and dry conditions in Angola that could lead to more frequent, intense, and prolonged extreme events, such as tropical nights, the maximum number of consecutive summer days, hot and rainy days, and warm period duration index periods. These changes can seriously affect agriculture, water resources, and ecosystems in Angola, thereby requiring adaptation strategies to reduce risks and adverse effects while ensuring the sustainability of the country’s natural resources and guaranteeing its food security.