In the context of current climate change and ecosystem degradation, agricultural sustainability has been positioned as a core strategic objective to meet food demands while preserving natural resources. The latest Intergovernmental Panel on Climate Change (IPCC) report states that the agricultural sector is responsible for about 23% of net anthropogenic greenhouse gas emissions, driven mainly by the intensive use of synthetic inputs such as fertilizers, pesticides, and non-regenerative soil management practices. Bio-based formulations represent a range of solutions derived from natural components (e.g., active ingredients from algae and plants) that stimulate healthy crop growth and protect against pests. Biostimulants are substances or organisms that enhance plants' resilience to biotic and abiotic factors. These biostimulants are essential agents in organic agriculture, as they improve the physiological pathways of plants, allow for enhanced nutrient uptake, and strengthen their resistance to the effects of extrinsic factors. Furthermore, they promote land health by stimulating beneficial microbiota and incorporating organic matter, which enables agroecosystem sustainability and eases organic certification. European fertilizer legislation and the European Biostimulants Industry Council (EBIC)'s guidelines require the marketing of a product as a biostimulant to be supported by scientific evidence demonstrating its efficacy and precise mechanism of action. This review presents an updated view of plant biostimulants, evaluating their efficacy, mechanisms of action, and impact on plants' resistance within sustainable soil management frameworks. It includes a discussion of various crops' resilience and land sustainability models, focusing mainly on empirical and conceptual approaches, while also acknowledging mathematical and simulation models as complementary tools for future research.

Soil fertility in sub-Saharan Africa is often degraded by poor farming. Microorganisms are essential to nutrient cycling and soil health, yet traditional methods often overlook their complexity. This study assessed the impact of land management on microbial diversity, enzyme activity, and nutrient availability in cashew-based agroforestry systems in Benin. Three integrated systems S1 (Sorghum bicolor–cattle), S2 (S. bicolor–cashew at low and high densities), and S3 (S. bicolor–cashew–cattle) were compared to a conventional monocrop (CFS). A randomized block design across four villages in Tchaourou yielded 72 soil samples, analyzed for microbial biomass carbon (MBC), enzyme activities (urease, dehydrogenase, phosphatase), and microbial populations. The results indicated that dehydrogenase activity increased from 16.59 to 56.46 μg TPF (triphenyl formazan) g⁻¹ soil (S2-HD to S3-LD; +240%). Phosphatase activity rose from 388.25 to 771.29 μmol PNP (p-nitrophenol) g⁻¹ soil (+99%). In contrast, CFS showed a decrease in dehydrogenase from 16.63 to 15.89 (-4.4%) and a slight increase in phosphatase from 299.21 to 312.58 (+4.5%). Urease activity peaked at 882.45 μmol NH₄⁺/g·h in S3-LD, a 239% increase over CFS, which declined from 317.54 to 260.23 μmol NH₄⁺/g·h (-18.1%) in the second year. MBC increased from 679.45 to 2,289.29 µg C/g soil (+237%) in integrated systems, while it decreased from 789.25 to 761.23 µg C/g soil (-3.5%) in CFS. SBR (soil basal respiration) improved from 4.02 to 6.71 mg C-CO₂/kg·h in integrated systems (+67%), while it rose modestly from 4.01 to 4.62 (+15.2%) in CFS. S3-LD consistently outperformed the other systems, highlighting the benefits of integrated farming for soil health. This system showed the highest levels of enzymatic activity, microbial biomass, and respiration, suggesting a more active and diverse microbial community that enhances nutrient cycling and fertility. Overall, integrated farming improved soil quality more than conventional farming, supporting productivity and ecosystem function.

The Himalayan region is experiencing evident climate change and increasing human interventions, leading to rapid alterations in land use and ecosystem balance, which highlights the necessity for sustainable planning using geospatial technology. Therefore, this study utilised satellite imagery (Landsat series) and Google earth engine to analyze decadal land use dynamics (2003, 2013, 2023) using the random forest algorithm and carbon storage data from Hamirpur district of the Indian Himalayas. The findings indicated significant shifts in land use over the two decades: the vegetation cover declined from 67% in 2003 to 61% in 2023, followed by a decrease in barren land from 2.6% to 1.8%, while waterbodies remained relatively constant at around 0.5%. In contrast, built-up areas increased notably from 4.3% to 7.1%, grasslands nearly tripled from 1% to 2.7%, and agricultural land showed a slight overall increase, stabilizing around 27%. Future land use projections using the CA–Markov model indicated a gradual decline in vegetation cover (0.83%) and a steady increase in built-up areas (6.25%), while other land cover classes are expected to remain relatively stable over time. The carbon storage estimation using the InVEST model indicated carbon variation from 0 to128 Mg ha^-1, and there was a decline in areas with very high carbon levels (6%) and an increase in medium and high carbon classes. Overall, the observed changes in land use dynamics and a decline in areas with very high carbon storage in the region highlight the need for integrated land management practices, including afforestation, controlled urban expansion, and the conservation of existing green cover to ensure sustainable development and enhanced carbon sequestration.

Ensuring the long-term erosional stability of post-mining landforms in Australia remains a first-order priority for the mining industry, particularly because these constructed landscapes must integrate seamlessly with the surrounding natural environment. This necessitates the evaluation of erosion rates not only on post-mining landforms—during both design and operational phases—but also on adjacent natural terrains. Landform evolution models (LEMs) offer a practical means for such assessments. However, accurately and reliably evaluating their performance across varying topographic, soil, and vegetation conditions remains a challenge. This study presents an evaluation of two landform evolution models—SIBERIA, widely applied within the Australian mining industry, and SSSPAM, a state-of-the-art coupled soilscape–landform model—using a natural catchment in the Upper Hunter region of Australia. This catchment, chosen for being analogous to a nearby mining site, was assessed under both dense and moderate grass cover conditions. High-resolution LiDAR-derived digital elevation data and site-specific parameters were used to perform the simulations. Field-based erosion estimates were obtained using sediment accumulation in a pond at the catchment outlet and the fallout radionuclide ¹³⁷Cs method. Sediment pond measurements indicated erosion rates ranging from 0.43 to 0.61 t/ha/yr, while the ¹³⁷Cs technique revealed maximum erosion and deposition rates of 1.5 t/ha/yr and 1.1 t/ha/yr, respectively. Model predictions varied with vegetation cover: SIBERIA estimated erosion at 1.07 t/ha/yr (dense grass) and 4.37 t/ha/yr (moderate grass), whereas SSSPAM predicted 0.35 t/ha/yr (dense grass) and 2.43 t/ha/yr (moderate grass). The results of both field-based methods and model evaluations are within comparable ranges, providing confidence in the models and their predictions. These findings highlight the utility of both field and modelling approaches in capturing erosion dynamics and offer valuable insights for model calibration and application in post-mining landscape design.

The utilization of fast-growing exotic species within degraded tropical landscapes presents a significant opportunity to restore ecosystem services whilst sustaining productive land uses. Gmelina arborea, which has been extensively cultivated in Central America, emerges as a promising candidate for sustainable plantation forestry, contingent upon management practices that prioritize resource efficiency and climate resilience. In this research, we undertook the calibration of the 3-PG (Physiological Principles Predicting Growth) model utilizing multi-site field data from northern Costa Rica to simulate growth, canopy dynamics, and water use patterns under a variety of climatic and edaphic conditions. The model demonstrated a high degree of accuracy, with prediction errors remaining below 5% for diameter and above-ground biomass distribution, and under 10% for leaf area index (LAI) and total stem volume. These findings reveal a propensity towards slight underestimation, albeit within acceptable margins for operational application. Additionally, the model exhibits considerable potential for genotype-level calibration, facilitating its deployment in selecting drought-resilient planting material and designing silvicultural practices that mitigate mortality risks under water-scarce conditions while enhancing the efficiency of water and nutrient utilization. In summation, the 3-PG model constitutes a valuable instrument for assessing species adaptability under prospective climate scenarios and optimizing plantation management or genetic selection in regions susceptible to drought. Its integration into landscape planning enhances the efficient allocation of natural resources, contributes to carbon sequestration, and advocates for restoration strategies that eschew competition with natural ecosystems.

Geodiversity can be described as the natural variety in geological, geomorphological, pedological, and hydrological elements in an area. The present study shows a geodiversity mapping of the urban coastal area of the province of Naples in order to assess its geodiversity potential and to support nature-based solutions (NBSs). This approach enabled a comprehensive understanding and assessment of biological and geological diversity, which constitute a priority for the landscape under investigation. This study focused on three types of ecosystem interventions: creation, restoration, and preservation. Each geodiversity unit was analyzed in terms of vegetation content to explore the following issues: correlation between the spatial distribution of physical and biological environments, assessment of biodiversity levels across the study area, and providing insights to improve NBSs for the local coastal environment. The results identified key spots for ecosystem interventions in the urban coastal area. Drawing upon this data, the study specifically examined municipalities southeast of Naples, at the foot of Mount Vesuvius, as they are highly urbanized areas experiencing significant momentum in urban regeneration projects. In conclusion, the study underscores the importance of the relationship between geodiversity and biodiversity and demonstrates the innovative use of geodiversity as a tool for effective land use planning and biodiversity conservation in dynamic coastal urban landscapes.

Rapid urbanization and agricultural intensification in Punjab, Pakistan, have triggered significant Land Use/Land Cover (LULC) changes, threatening the ecological balance and food security. Quantifying land entropy through LULC change analysis is essential for sustainable urban and agricultural planning, particularly in rapidly developing regions such as Punjab, Pakistan. This study aims to assess the spatial disorder and fragmentation of land caused by urban expansion and agricultural land conversion in major cities using multi-temporal Landsat and Sentinel-2 satellite imagery from 1995 to 2025. Employing supervised classification techniques and Shannon’s entropy index within a GIS framework, the research quantifies LULC transitions among agricultural land, built-up areas, barren land, and water bodies. Further incorporating machine learning (e.g., Random Forest, CNN) to predict future land entropy trends under different urbanization and policy scenarios would allow policymakers to simulate the effects of land use regulations, urban growth boundaries, or agricultural conservation policies.

1. Entropy Trends: Urban zones showed high entropy (SEI > 1.5), indicating chaotic growth, while agricultural areas exhibited moderate entropy (SEI 0.8–1.2) due to monoculture expansion.
2. LULC Shifts: A 15% net urban growth (1995–2025) encroached on 20% of the fertile farmland, with vegetation declining by 22% in central Punjab.
3. Hotspots: Lahore and Faisalabad districts had the highest entropy values (SEI > 1.8), correlating with GDP growth but also groundwater depletion.

The entropy index reveals increasing spatial disorder, particularly along urban–rural fringes, highlighting fragmented agricultural patches and unplanned urban growth.

This study concludes that quantifying land entropy via LULC changes provides critical insights into the dynamics of land transformation in Punjab, facilitating informed decision-making for sustainable urban expansion and agricultural conservation. Incorporating Earth observation data with entropy metrics offers a robust approach to monitoring land use patterns, enabling policymakers to balance development needs with environmental sustainability and food security objectives. Future work should integrate socio-economic drivers and predictive modelling to enhance planning frameworks in the region.

Cities face increasing water shortages due to climate change, intensified by rapid urban growth and rising consumption. Addressing these challenges requires integrated urban planning strategies that combine land-use decisions with sustainable water resource management. This dual approach is essential for balancing water availability with growing urban demand, particularly under conditions of climatic uncertainty. Integrated frameworks promote resource efficiency, stakeholder participation, and equitable distribution—principles that are central to sustainable urban development. This research evaluates the impacts of climate change on water resources at the metropolitan scale, investigates how rapid urbanization interacts with escalating water demand and climate variability, and compares management strategies across three different socio-environmental contexts to identify best practices. We also examine the role of demographic dynamics, spatial distribution, and governance in shaping water availability and access. Our methodology integrates geospatial analysis, policy review, and stakeholder consultation. GIS tools are used to map urban expansion, water infrastructure, and vulnerability zones using multi-temporal satellite data and hydrological stress indicators. Planning regulations and legal frameworks are analyzed to evaluate institutional coherence and identify areas for policy alignment. Semi-structured interviews with planners, engineers, and community stakeholders in Marrakesh, Amman, and Bucharest provide insights into local adaptation strategies and governance practices. Building on this interdisciplinary approach, we propose a planning methodology to strengthen water resilience in metropolitan areas, applying findings from Marrakesh and Amman to inform future strategies for Bucharest. These cities face shared pressures from water scarcity, climate change, and complex socio-economic dynamics. This study aims to enhance the resilience of urban water systems; improve efficiency and sustainability in water use; provide equitable access across social groups; increase institutional capacity for adaptive governance; and provide urban growth models that integrate environmental, social, and economic priorities to proactively address future water challenges.

With the burgeoning development of the low-altitude economy, China's spatial utilization model is experiencing a profound paradigm shift. As a result, both surface and low-altitude spaces are confronted with intense competition for spatial resources. This study employs a comprehensive mixed-methods approach, integrating cartographic theory analysis, three-dimensional spatial modeling, and in-depth comparative case studies in Shenzhen. The aim is to systematically unveil the crucial issues regarding the utilization of "surface-low altitude" spatial resources from a three-dimensional vantage point:
(1) There exists an overlap in the three-dimensional spatial utilization between low-altitude flight spaces (such as those occupied by drones) and existing ground property rights (such as high-rise buildings).
(2) The two-dimensional land management system encounters ambiguous boundary problems when addressing vertical space utilization.
(3) Inconsistent terminology leads to substantial discrepancies in the interpretation of low-altitude rights among different jurisdictional departments (such as transportation, aviation, and land management departments).
The research findings yield three fundamental insights: Firstly, the conceptual transition from planar "land" to three-dimensional "space resources" necessitates a redefinition of legal and technical frameworks. Secondly, through prototype testing, expanding the LADM (the ISO-19152, Land Administration Domain Model) to establish a three-dimensional land management system is anticipated to effectively handle integrated resource management of "surface-low altitude." Thirdly, the vertical-space-stratified governance strategy has the potential to unleash supplementary space resources within high-density urban regions. The findings of our research could offer a comprehensive perspective for the future governance of urban land and low-altitude areas. This stands in contrast to current practices that treat these two resource aspects in isolation.

The advent of hyperspectral remote sensing represented a breakthrough in the accurate, fast, and non-invasive estimation of important soil fertility parameters. The organic content in the soil acts as a strong indicator of soil fertility, which influences agricultural production and the global carbon cycle. The present study utilises non-imaging hyperspectral data in the spectral range of 350-2500nm collected proximally using an ASD FieldSpec spectroradiometer for estimating the soil organic carbon (SOC) content of a research farm in ICAR-Indian Agricultural Research Institute, New Delhi, India. The partial least squares regression (PLSR) scores were used as the independent variables for evaluating three multivariate regression models, such as support vector machine (SVM), random forest, and partial least squares regression, to estimate SOC. After pre-processing, the proximal spectral values were spatially interpolated using the ordinary kriging technique to construct a synthetic hyperspectral image of the experimental fields. The SVM outperformed other models, achieving an R² value of 0.83, which suggests an accurate prediction of SOC. On applying the regression model to this synthetic hyperspectral imagery, a high-resolution SOC map was generated. Our study indicated the potential of non-imaging proximal hyperspectral data for generating a high-resolution map showing the variability of organic content in the soil.