Earth

Following the completion of the installation and commissioning of a solar sludge drying system serving the largest wastewater treatment plant on the island of Tenerife, a study has been carried out on the possibilities of implementing this type of infrastructure in other important plants in the Canary Archipelago. To this end and given the favorable climatic conditions found in the Canary Islands for this type of facility, the availability of land and possible impacts on surrounding areas have been studied. There are potential implementations on the islands. Thanks to these facilities, the volume of sludge to be transported to disposal or reuse areas is drastically reduced. The major drawback of these systems is the significant amount of land required, which is not always available on densely populated islands with rugged terrain. Full article

Seismic Shake-e is a free app that provides valuable data and tools related to earthquakes, covering the stages before, during, and after seismic events. In this text, we describe the main features of the Seismic Shake-e 2.1 (SSe) app, the considerations that guided its development, examples of its use, and the challenges for future versions. Version 1.0 of this app was awarded as one of the winners of EOVALUE: Call for Innovative Apps in environmental and social fields, a project by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. SSe recognizes two user levels: basic and intermediate/advanced. There are six modules for each level. The main topics of these modules for both user types are: (1) Accelerometer Networks (AN), (2) Seismograms Analyzer-e (SAe), (3) Seismic Design of Buildings (SDB), (4) Earthquake Preparedness (EP), (5) Earthquake Early Warning Systems (EEWS) & Tsunami Warning Systems (TWS), and (6) Earthquake Emergency Response & Recovery. The two key modules are AN and SAe: the first explains how to obtain seismic records, and the second provides tools for their analysis. We include some applications of SSe, along with their results and discussion. We also list the advantages of the main modules and discuss potential future developments and improvements. The uniqueness of this work is that we highlight the software’s essential features and demonstrate its applications. Full article

The Artificial Intelligence Forecasting System (AIFS), recently released by the European Centre for Medium-Range Weather Forecasts (ECMWF), represents a major shift in global weather prediction by replacing traditional physically based approaches with machine-learning methods. This study evaluates the impact of using AIFS as initial and lateral boundary conditions for the Weather Research and Forecasting (WRF) model, in contrast to the well-established physically based GFS. The aim of this work is to analyze the sensitivity of these different modelling configurations during three high-impact storms that affected Spain in 2025 and the effects of replacing GFS for AIFS as lateral and boundary conditions for WRF over the accuracy of operational forecasts. The analysis focuses on maximum wind gusts, accumulated precipitation, and the generation of meteorological warnings. Results show that AIFS substantially underestimates wind gusts with mean bias values between −13 and −25 km/h, and its forecasts differ markedly from those of GFS. When coupled with WRF, however, both AIFS-WRF and GFS-WRF produce similar results, with a general tendency to overestimate gusts, with mean bias values between 4 and 15 km/h. In all cases, WRF adds value, improving the representation of wind-related variables compared with the raw global model outputs. For accumulated precipitation, both WRF configurations reproduce the main rainfall patterns associated with the storms. AIFS-WRF shows a stronger tendency to overestimate precipitation, with RMSE values of 64, 23, and 12 mm for the different high-impact storms considered, although it also achieves the highest correlations. Finally, the analysis of meteorological warnings indicates that AIFS alone generates almost no wind gusts alerts. Once coupled with WRF, both configurations generate warnings in the regions where the most severe conditions occurred. Overall, while the added value of mesoscale models such as WRF is well established and confirmed here, the AI-based AIFS does not show clear advantages in comparison with traditional global models for these high-impact events being analyzed. Full article

Rapid urbanization in tropical Southeast Asia is transforming pervious land into impervious surfaces, intensifying the surface urban heat island (SUHI) effect and increasing the need for consistent urban thermal monitoring. This study assesses how impervious surface area (ISA) expansion relates to the urban thermal environment across five tropical megacities (Bangkok, Jakarta, Manila, Kuala Lumpur, and Ho Chi Minh City). AlphaEarth geospatial foundation model embeddings were used to reduce observation gaps caused by persistent cloud-cover, while MODIS land surface temperature (LST) was used to quantify the thermal response. We compared AlphaEarth classification against conventional Sentinel-2/NDVI approaches and an additional fairer annual Sentinel-2 full-band-plus-index Random Forest baseline, quantified ISA expansion for 2017–2024, and related ISA fraction to dry-season LST at 1 km resolution. Repeated random-holdout tests based on Google Earth Engine samples showed AlphaEarth mean IoU = 0.866 (95% CI: 0.857–0.875), compared with 0.758 (0.749–0.767) for the annual Sentinel-2 full-band-plus-index baseline and 0.686 (0.674–0.698) for the best single-date 5-index baseline. Spatial-block holdout tests gave similar but slightly lower values (AlphaEarth IoU = 0.859; annual Sentinel-2 baseline = 0.747; best single-date baseline = 0.673). Ho Chi Minh City experienced the fastest ISA expansion (+11.0 percentage points; slope = 1.48 pp yr^-1, 95% CI: 1.06–1.91), whereas Bangkok reached the highest ISA fraction (65.1%). ISA fraction and LST were consistently and positively associated across cities and years (Pearson r = 0.748–0.900), and mean SUHI intensity during 2017–2024 ranged from 4.01 °C in Bangkok to 8.51 °C in Manila. These results indicate that foundation model embeddings can support cloud-resilient mapping of impervious surface change and thereby improve assessment of tropical urban thermal environments, while also highlighting the need for independent ground-truth validation. Full article

Rapid urbanization imposes significant pressure on riverine water environments, yet the evolution of hydrochemical characteristics and dissolved organic matter (DOM) in rivers across urbanization gradients within developing regions, such as the Huaibei Plain, remains inadequately understood. Thus, this study investigates the hydrochemical and DOM characteristics of rivers across distinct urbanization gradients (suburban, peri-urban, and urban) in this area. Using an excitation–emission matrix coupled with a parallel factor analysis (EEM-PARAFAC) and hydrochemical analyses, we found that while rock weathering is the primary major ion source, human activities distinctly alter water profiles. Agriculturally dominated suburban rivers had significantly higher nitrate (NO₃⁻) concentrations than those in urban and peri-urban rivers. Their DOM was predominantly humic-like (C1, C3) with a high humification index (HIX), indicating a substantial input of soil-derived humic substances driven by runoff from the agricultural catchment. Conversely, urban and peri-urban rivers exhibited higher chloride (Cl⁻) concentrations due to domestic sewage. Their DOM was dominated by protein-like components (C2 and C4, averaging 65–68%), with high biological indices (BIX) reflecting autochthonous origins. Correlation analysis confirmed these anthropogenic impacts: NO₃⁻ positively correlated with humic-like components and HIX, while Cl⁻ strongly correlated with protein-like components. These findings confirm that DOM components and spectral indices are effective tracers of anthropogenic disturbance and hold promise for monitoring and predicting water quality, thus providing a scientific basis for improved water resource management and restoration strategies. Full article

Carbon is an essential component in the regulation of climate systems through the global biogeochemical cycle. However, changes in land use/land cover (LULC) have reduced the capacity of terrestrial ecosystems like watershed to store carbon. This shows the need for a policy framework that balances conservative objectives with agricultural demands, as watersheds are required to support carbon storage and food production. Previous studies have generally assessed carbon dynamics or LULC change separately, with limited integration of policy-driven scenarios. Therefore, this study aimed to conduct multi-scenario carbon storage modeling to evaluate LULC protection strategies in the Cimanuk Watershed, Indonesia, an area experiencing significant LULC pressures. The method used consisted of Support Vector Machine (SVM)–Markov, the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST), Geodetector, and Getis-Ord Gi*. A total of four scenarios were used to project LULC and carbon storage in 2042, which included Business as Usual (BAU), Paddy Field Protection (PFP), Forest Protection (FOP), and Paddy Field and Forest Protection (PFFOP). The results showed that forest area declined by 39,400 ha between 2015 and 2025, thereby reducing carbon storage. The PFFOP scenario was identified as the most viable, combining the protection of paddy fields and forests to balance agricultural production and carbon sequestration. Among the factors analyzed, slope exerted the greatest influence on carbon storage. Spatial cluster analysis showed that carbon hotspots were predominantly located in the upper Cimanuk sub-watershed. These results offered valuable insights into scenario-based sustainable watershed management to optimize carbon storage and maintain agricultural function. Furthermore, the proposed framework showed promising potential for application in other tropical watersheds, serving as a reference for decision-makers in sustainable watershed management. Full article

This study investigates the spatial distribution of ambient dose equivalent rates (ADER) on Avala and Kosmaj mountains, two protected landscapes located within the territory of the City of Belgrade, Serbia. Both sites, characterized by rich biodiversity and cultural heritage, were analyzed to assess their radiological safety and suitability for outdoor recreation. In mid-October 2025, in situ measurements were conducted at 42 sampling points using the Radex RD1503+ GM counter. The recorded values ranged from 0.085 to 0.2 µSv/h, remaining below the recommended safety threshold of 0.2 µSv/h. To visualize the gamma dose spatial variability, all field data were georeferenced and processed in QGIS 3.28.10 using the Inverse Distance Weighting (IDW) interpolation method. Integration of GIS and Remote Sensing techniques enabled the correlation between gamma radiation patterns, land cover, and elevation gradients derived from digital elevation models (DEMs). The comprehensive GIS-based approach confirms that Avala and Kosmaj maintain low natural background radiation levels comparable to global averages for similar geomorphological settings, and therefore are safe and suitable for sports, tourism and recreation. The applied combination of field dosimetry, Remote Sensing, and geostatistical modeling provides a valuable framework for continuous environmental monitoring and sustainable landscape management in protected mountainous landscapes in Central Serbia. Full article

Urban heat risk is intensifying globally, yet the relative contributions of climate warming and demographic restructuring to spatiotemporal risk change remain poorly understood, particularly in post-socialist cities experiencing simultaneous thermal intensification and population aging. This study develops a Heat Risk Population Index (HRPI) integrating satellite-derived land surface temperature, CERRA reanalysis air temperature, and census-based demographic sensitivity for 218 Zagreb neighborhood councils (2001–2024). A multi-scale analytical framework combining additive decomposition, enhanced partial correlations, and geographically weighted regression (GWR) was applied to disentangle the drivers of heat risk change. HRPI increased significantly across all neighborhood councils (mean ΔHRPI = 0.197, p < 0.001), with strong positive spatial autocorrelation (Moran’s I = 0.416). While air temperature change dominated the city-wide mean increase (72.1%), demographic sensitivity change explained the largest share of spatial variance across neighborhood councils (partial r = 0.677 vs. 0.524 for air temperature), driven by spatially heterogeneous demographic transitions—youth out-migration, aging-in-place in southeastern post-socialist estates, and gentrification in central districts. GWR substantially outperformed global OLS (ΔAICc = 60.1; Adj. R²: 0.649 → 0.816), with local demographic effect sizes varying fivefold across the city. These results demonstrate that heat risk drivers operate at distinct spatial scales: climate dominates city-wide magnitude while demographics determine spatial differentiation. Effective adaptation requires universal thermal interventions combined with spatially targeted demographic strategies in identified hotspot neighborhoods. The multi-scale framework is applicable to other post-socialist cities undergoing concurrent climate and demographic change. Full article

Drought is a recurrent hazard in the Vietnamese Mekong Delta (VMD), with major implications for agriculture, water resources, and rural livelihoods. This study assesses drought variability in Kien Giang Province, Vietnam, from 1992 to 2024 using two multiscale indicators: the Multivariate Standardized Precipitation Index (MSPI) and the Multivariate Standardized Precipitation Evapotranspiration Index (MSPEI). Principal Component Analysis (PCA) was applied to Standardized Precipitation Index (SPI)- and Precipitation Evapotranspiration Index (SPEI)-based time series spanning multiple accumulation periods (3–48 months) to derive integrated drought signals and to reduce redundancy across timescales. The results show that the first principal component (PC1) captured a high proportion of total variance across stations, indicating strong coherence in drought dynamics across the province. Both MSPI and MSPEI successfully identified major historical drought episodes, particularly the severe events of 2004–2005 and 2015–2016. However, the two indices differed in their temporal behaviour: MSPI responded more directly to precipitation deficits, whereas MSPEI showed slower post-drought recovery in recent years, suggesting greater sensitivity to evaporative demand and climatic water-balance stress. These differences indicate that evapotranspiration-sensitive indices may provide added analytical value in warming coastal environments. Overall, the combined multiscale framework offers a robust basis for drought monitoring, comparative assessment, and water-resource planning in Kien Giang and other drought-prone coastal delta settings. Full article

The Horn of Africa (Kenya, Djibouti, Uganda, Eritrea, Somalia, Ethiopia, South Sudan, and Sudan) faces the highest rates of hunger and malnutrition in the world, exacerbated by conflict and adverse weather conditions. These factors have serious health, educational, social, and economic consequences, especially for children under five and pregnant women. In this context, we analyze each country’s progress toward Sustainable Development Goals (SDGs) 1, 2, 3, and 4, which are closely linked to the eradication of hunger, improved health, and access to quality education. Using comparable data from the United Nations 2030 Agenda up to 2019, the achievement of the SDGs is assessed through a multidimensional approach based on Pena’s P₂ distance method, constructing a composite indicator that allows for robust cross-country comparisons. This method helps identify the key measures needed to prevent future humanitarian crises in the Horn of Africa, including providing urgent assistance to these countries in vital areas such as water, nutrition, education, sanitation, and child and maternal immunization. Factors related to the work of qualified healthcare personnel in treating diseases and improving maternal and neonatal health, as well as facilitating access to basic services such as clean drinking water and sanitation and ensuring girls’ access to primary education, top the rankings in terms of their correlation with greater progress by these countries in achieving these four SDGs, which are crucial for improving the well-being of their populations. Full article

Microplastic (MP) contamination has become a widespread environmental concern in coastal and freshwater wetlands, ecosystems that play a crucial role in hydrological regulation, nutrient cycling, and biodiversity conservation. Despite their ecological importance, research on MPs in wetlands remains fragmented and comparatively underexplored. This study presents a comprehensive bibliometric and visualization analysis of global research on MPs in coastal wetlands. A total of 17,523 publications were retrieved from the Web of Science Core Collection (2002–2025) using predefined search strings and screening criteria. Analytical tools, including VOSviewer version 1.6.20, were employed to examine co-authorship networks, country contributions, and keyword co-occurrence patterns. The results indicate a significant increase in MP-related publications after 2016, with China, the United States, and India emerging as leading contributors. However, wetland-specific studies constitute only a small fraction compared to marine-focused MP research, highlighting a substantial research gap. Key research themes identified include MP sources, transport pathways, sediment–water interactions, and ecotoxicological impacts. Additionally, there is growing attention to remediation approaches, particularly those involving TiO₂, ZnO, Fe₃O₄, and graphene derivatives, employing photocatalytic, magnetic, and adsorptive mechanisms. Overall, the findings underscore the limited focus on wetland ecosystems in MP research and emphasize the urgent need for integrated research efforts and management strategies to address MP contamination in these vulnerable ecosystems. Full article

Ports are increasingly recognized as actors that influence the sustainability of urban environments due to their spatial footprint, operational intensity, and close interaction with surrounding cities. As digital technologies become more embedded in infrastructure management, Digital Twins (DTs) are emerging in port systems as tools that can support more integrated and sustainable port–city development. This paper investigates how DT technologies applied in ports can contribute to broader urban sustainability objectives within port–city systems. The analysis is based on a synthesis of documented DT practices from selected European ports. Geographic Information System (GIS) visualization is used to illustrate the spatial relationship between port infrastructure and the surrounding urban environment, as well as to map the connections between DT application fields and relevant Sustainable Development Goals (SDGs). A comparative interpretation of the extent to which DT applications align with urban sustainability goals across the examined ports is achieved through the development of an SDG contribution scale. Insights derived from the European cases are subsequently contextualized for the Port of Piraeus, exploring how similar DT approaches could support both operational efficiency and the long-term climate resilience of the port–city environment. Overall, the findings provide practical insights for port authorities, urban planners, and policymakers seeking to align digital transformation strategies with sustainable and climate-responsive infrastructure development in port–city systems. Full article

Urban districts along major rivers are undergoing rapid transformation, yet long-term evidence on how redevelopment reshapes land cover and vegetation structure remains limited in post-socialist cities. This study examines the spatio-temporal evolution of land use and land cover (LULC) and vegetation dynamics along the Sava River corridor in Belgrade from 1990 to 2024. CORINE Land Cover (CLC) datasets were combined with Landsat-derived NDVI and MSAVI time series, while high-resolution Esri Wayback imagery was used for visual interpretation and qualitative corroboration of the detected land-cover and vegetation patterns. Beyond conventional NDVI/LULC assessments, the study integrates multi-decadal spectral trends with functional vegetation structure classification to evaluate canopy continuity and ecological configuration under contrasting redevelopment models. Results reveal a pronounced divergence between the two riverbanks. The left bank (New Belgrade) maintains stable land-cover composition and consistently higher NDVI and MSAVI values, indicating preserved green infrastructure and sustained canopy continuity. In contrast, the right bank (Belgrade Waterfront) experienced substantial land-cover conversion after 2006, with a statistically significant decline in vegetation greenness (NDVI −0.020 dec⁻¹, p < 0.001) and a marked increase in impervious surfaces. MSAVI-based functional classes indicate a shift from mixed low vegetation to predominantly sealed land, while tree canopy remained persistently low throughout redevelopment. The findings demonstrate measurable ecological simplification and canopy loss, even where nominal green areas remain present. By providing a rare multi-decadal, spatially explicit comparison of two contrasting planning paradigms within the same river corridor, the study contributes new empirical evidence on how governance and redevelopment models shape riparian ecological trajectories and sustainable urbanism in post-socialist cities. Strengthening blue-green infrastructure and restoring native riparian vegetation are essential for enhancing climate resilience and ensuring long-term riverfront sustainability. Full article

Mediterranean-type ecosystems (METs) occur on five continents and represent some of the most climatically constrained yet biologically rich regions on Earth. In these environments, legumes and their nitrogen-fixing rhizobial symbionts—including widely distributed genera such as Rhizobium, Bradyrhizobium, and Ensifer—play a pivotal role in sustaining plant productivity, nutrient cycling, and ecosystem resilience. This review synthesizes current knowledge on the environmental regulation of legume–Rhizobium symbiosis specifically within Mediterranean-type ecosystems, focusing on how nitrogen (N) and phosphorus (P) availability, light conditions, and carbon allocation trade-offs shape symbiotic performance across the five Mediterranean-type regions of the world (California, central Chile, the Cape Region of South Africa, southwestern Australia, and the Mediterranean Basin). By integrating physiological, ecological, and biogeochemical perspectives, we highlight how the shared features of these regions—strong seasonal drought, chronic nutrient limitation (particularly P in southwestern Australia and the Cape Region), recurrent fires, and exceptionally high plant diversity—constrain and, at the same time, favor the ecological success of symbiotic legumes. Throughout the review, we use case studies from key legume genera such as Lupinus in Chile and southwestern Australia, Virgilia and other Cape legumes in South Africa, Acacia in Australian kwongan and woodlands, and Medicago and Cytisus in the Mediterranean Basin and California to illustrate how general principles of legume–Rhizobium ecology manifest under Mediterranean-type climatic and edaphic constraints. Beyond summarizing established mechanisms, we critically examine the limitations of current metagenomic approaches, which often provide descriptive inventories of soil microbial communities without linking microbial composition to functional outcomes. We argue that advancing the field requires integrated, hypothesis-driven research that combines multi-omic tools with plant eco-physiology, soil nutrient dynamics, and temporal replication. Finally, we outline key priorities for future research, including the integration of functional ‘omics’, the study of microbiome interactions beyond rhizobia, the development of predictive models for Mediterranean-type ecosystems under climate change, and the application of symbiotic principles to restoration and agroecological management. By bridging molecular, physiological, and ecosystem perspectives, this review provides a conceptual framework for understanding and enhancing legume–Rhizobium symbiosis across five continents in a rapidly changing world. Full article

Hydrocarbon-contaminated sites are among the most common challenges for environmental professionals worldwide. Although bioremediation strategies have emerged, their efficiency in cleaning hydrocarbon-contaminated soil depends considerably on local conditions. This study presents a science-based framework to assess the potential for soil bioremediation based on site-specific conditions. At multiple depths, soil samples were collected from four locations (S1, S7, S13, and S16) within a historically contaminated heating plant site. Using a three-step framework based on the content of total petroleum hydrocarbons (TPH), hydrocarbon pollutant fractions, ecotoxicity, and microbial population density, the study quantitatively (using a scoring matrix) revealed considerable variability across locations regarding the potential for bioremediation. Thus, due to balanced parameter contributions, S16 has the most promising bioremediation potential. Location S1 may require additional effort to enhance microbial populations. Locations S7 and S13 have low scores, with S13 being the least suitable, requiring extensive efforts to improve site-specific conditions for bioremediation. By integrating chemical, biological, and ecological factors, this science-based framework emphasizes the importance of site pre-characterization, thus providing an evaluation tool for bioremediation applications at hydrocarbon-contaminated sites with similar data availability. Moreover, the pre-remediation matrix scoring evaluation results align with the in situ bioremediation efficiency observed at the site. Full article

Mountainous Mediterranean rivers provide essential ecosystem services but are increasingly affected by land-use change, hydraulic works, and inadequate wastewater management. This study investigates the links between geomorphological transformation and river water quality in the Central Eurytania drainage basin (Greece) over the past two decades, within the institutional framework of European and Greek environmental legislation, with emphasis on the protection and restoration of aquatic ecosystems. Georeferenced satellite imagery from 2003/2010 and 2023, Google Earth Engine (GEE, Python Earth Engine API: 1.7.20)-based spatial analysis, high-resolution UAV orthomosaics, and seasonal spectrophotometric analyses were integrated to assess spatial and temporal dynamics. Results indicate that land-use changes, including the construction of solar parks, expansion of tourism infrastructure, and partial agricultural abandonment, reflect ongoing socio-economic shifts influencing fluvial processes. Water-quality analyses further showed that channel alteration and wastewater inputs jointly degrade ecological conditions. The findings highlight the need for integrated watershed management focused on riparian buffer restoration, improved wastewater control, and systematic monitoring of hydromorphological change. The proposed interdisciplinary framework contributes to the assessment of environmental resilience in Mediterranean mountainous watersheds, which are increasingly vulnerable to climatic and socio-economic pressures. Full article

The agricultural sector is undergoing a profound digital transformation driven by artificial intelligence, the Internet of Things, remote sensing, robotics, blockchain, and edge computing, which are being integrated into crop monitoring, irrigation management, disease detection, and supply chain transparency systems. This study employs systematic evidence mapping to characterize the applications of emerging digital technologies in sustainable agriculture; it delineates technological trajectories, areas of application, implementation gaps, and opportunities for improvement. Adhering to the PRISMA 2020 reporting protocol, 101 peer-reviewed articles indexed in Scopus and Web of Science (2020–2025) were identified, screened, and subjected to integrated thematic and bibliometric synthesis, using RStudio Version: 2026.01.1+403 and VOSviewer 1.6.20 for data mining on keywords and technological evolution patterns. Results show that deep learning and computer vision models achieved diagnostic accuracies of 90–99%, smart irrigation systems reduced water consumption by 10–30%, predictive yield models frequently reported R² values above 0.80, and greenhouse automation reduced energy consumption by approximately 20–30%. Blockchain-based architectures improved traceability and secure data transmission by 15–20%, while remote sensing integration enhanced spatial estimation accuracy up to R² = 0.92. The findings demonstrate a measurable transition toward data-driven, resource-efficient agricultural ecosystems supported by validated digital architectures. However, interoperability limitations, lack of standardized performance metrics, scalability challenges, and uneven geographical implementation—identified in nearly 40% of studies—highlight the need for harmonized evaluation frameworks, cross-platform integration standards, and long-term field validation to ensure sustainable and scalable digital transformation. Full article

The long-term sustainability of Australian agriculture is fundamentally constrained by the capacity, condition, availability, and governance of soil resources. Australian soils are among the oldest and most weathered globally, highly heterogeneous, and often slow or effectively irreversible to recover once degraded. Traditional approaches centred on soil health, while valuable at paddock scale, are insufficient to address national-scale challenges related to spatial variability, data continuity, economic valuation, and policy integration. This paper examines soil security as a policy-relevant framework for supporting more sustainable Australian agriculture. Building on the dimensions of soil security (capacity, condition, capital, connectivity, and codification), we synthesise recent Australian case studies to show how soil security extends beyond soil health to integrate biophysical properties, digital soil infrastructure, socio-economic value, and governance mechanisms. Drawing on recent Australian case studies, this review identifies advances in digital soil mapping, national soil assessments, economic valuation of soil capital, stakeholder connectivity, and emerging policy frameworks, while also identifying persistent gaps in regulation, data standardisation, and institutional coordination. The paper argues that soil security can help operationalise 3-N agriculture—Net-Zero, Nature-Positive, and Nutrient-Balanced systems—by translating sustainability goals into spatially explicit, place-based decisions grounded in soil realities. By explicitly accounting for soil capacity limits, condition trajectories, capital value, information flows, and codified rules, soil security can support more realistic climate mitigation strategies, targeted nature-positive interventions, and durable nutrient security outcomes. We conclude that embedding soil security more explicitly within Australian agricultural research, policy, and governance would strengthen efforts to deliver productive, resilient, and socially legitimate food and fibre systems. Without soil security, sustainability frameworks may remain difficult to operationalise consistently; with soil security, they can be translated more effectively into measurable, place-based, and durable decisions. Full article

Climate variability and rising water scarcity are major challenges to agricultural sustainability, particularly in Mediterranean climates with high spatial heterogeneity. Agroclimatic zoning is a fundamental analytical tool for digital agriculture and climate-resilient agriculture. The current effort proposes an integrated agroclimatic and micro-agroclimatic zoning approach for Greece, based on the Aridity Index (AI), CORINE Land Cover 2018 land-use data, and topographic factors. Daily precipitation and reference evapotranspiration data from 139 meteorological stations and 382 rain gauges were spatially interpolated using Empirical Bayesian Kriging, identifying eight agroclimatic classes adapted to the country’s specific conditions. The results indicate a high degree of variability in space, with most agricultural areas being classified as dry to sub-humid, suggesting higher irrigation requirements and sensitivity to drought. Micro-agroclimatic zones have been identified by combining agroclimatic classes, land use, and elevation. Consequently, the derived zones can be used as groundwork for designing methodologies towards more efficient agrometeorological monitoring through the improved localization of IoT agrometeorological stations. Validation with the Köppen–Geiger climate classification reveals high spatial and statistical agreement (χ² = 248,454.09, df = 49, p < 0.001), proving the climatic validity of the proposed approach and its higher sensitivity to local water balance conditions. Full article