Gully erosion remains one of the most serious environmental challenges affecting semi-arid regions by causing widespread damage to land, ecosystems, and livelihoods. These areas marked by low and irregular rainfall, fragile soils, and intense human pressure are particularly vulnerable to rapid land degradation. This review paper explores the environmental impacts of gully erosion, with a special focus on its implications for Nigeria. Drawing from a wide range of studies, it discusses the underlying causes and dynamics of gully formation and emphasizing on how human activities such as deforestation, overgrazing, and unsustainable farming practices have worsened natural vulnerabilities. The paper highlights the far-reaching consequences of gully erosion, including the loss of fertile agricultural land, decline in soil health, destruction of critical infrastructure, sedimentation of water bodies, and disruption of local ecosystems. In Nigeria, particularly in northern states like Adamawa, Yobe, and Borno, the problem has intensified due to increasing population pressures and shifting climatic conditions. Although various control measures from vegetation restoration to engineering solutions have been introduced, efforts are often hampered by limited resources, lack of awareness, and weak policy enforcement.This review calls for a more integrated and proactive approach to managing gully erosion by combining scientific knowledge, community participation, and effective governance. It also underscores the importance of promoting climate-resilient land management practices to protect vulnerable landscapes. By synthesizing existing research, the paper offers insights that can guide future studies, inform policy interventions, and support practical solutions aimed at mitigating the growing threat of gully erosion in Nigeria’s semi-arid regions and beyond.

Building Permit Systems (BPS) play a crucial role in delivering urban planning and land-use regulation. Land management, fragmented workflows, and poor data integration challenge effectiveness in rapidly developing countries like Saudi Arabia. To overcome traditional municipal BPS into a centralized, data-driven framework. Jeddah Municipality as a case study based on prior technology experience and research, following a hybrid method to meet study objectives. Interviews with municipal officials and surveys sent to 190 Authorized Engineering Offices (AEOs), a computation of the Relative Importance Index (RII) of 12 identified key factors, categorized into four dimensions of Process, Organization, Technology, and Information. The Framework of Building Permitting Systems (FBPS) demonstrates effective integration among municipalities by leveraging GIS and land-use regulations to enhance local governance, support overarching national objectives, and advance applications.

Abstract This study assessed changes in soil properties and pedon layers as a result of long‐term ecosystem exclosure at the Botanical and Ecotourism Garden (BEG) of Dilla University, South Ethiopia. The site holds significant geographical, archaeological, and ecological value, hosting over 408 plant species. Long‐term exclosure is a common land management practice mainly used for biodiversity conservation. However, its effects on soil properties and pedon development vary based on land topography and land use type. The study aimed to examine how long‐term ecosystem exclosure influences soil morphological, physical, and chemical properties across varying topographic positions, providing insights for sustainable land management. Field surveys and laboratory analyses were conducted on eight soil profiles (pedons) representing upper, middle, and lower slope positions. Soil samples were analyzed for texture, bulk density, pH, organic carbon (OC), nitrogen (N), phosphorus (P), cation exchange capacity (CEC), and exchangeable bases following standard methods. Key findings revealed variations in soil properties with topography: upper slopes had deeper, clay‐rich soils (Vertisols and Nitisols) with higher OC (1.2%–3.5%) and CEC (18–33 cmol kg −1 ), while middle slopes had shallow, gravelly Leptosols. Lower slopes featured sandy Fluvisols with alluvial deposits. Soil pH was slightly acidic (5.9–6.7), and available P was low (5.5–11.8 mg kg −1 ), indicating potential nutrient limitations. The study concludes that soil properties in the BEG are highly influenced by topography and land use, necessitating site‐specific conservation and fertility management to sustain its ecological and agricultural value.

A tobacco-rapeseed rotation model for economically sustainable phytoremediation of cadmium-contaminated farmland.

Land use and management intensity shape nitrogen cycling and microbial functions, driving environmental impacts in French urban soils

Global warming profoundly affects hydrological processes and regional aridity. However, the shifts in the arid–humid transition zone and its relationship to divergent surface and subsurface hydrological responses remain not fully understood. This study investigates the spatiotemporal aridity changes in China using hydroclimate datasets (1950–2022) and examines associated hydrological responses via remote sensing (RS) since the early 2000s. The results reveal that: (1) a pronounced ~32-year oscillatory pattern governs both the expansion and contraction of drylands and non-drylands, with China currently in a wetting phase; (2) a distinct climatic transitional zone is identified, and a distinct boundary emerges separating drylands and non-drylands, here referred to as China’s Arid–Humid Divide, reflecting the climatic equilibrium shaped by multiple monsoon systems and local topography; and (3) the nationwide expansion of surface water bodies, following the increase of groundwater storage in partial areas, was detected via recent RS data. These findings provide new insights into the mechanisms driving long-term aridity transitions and support climate adaptation and sustainable land management in China.

Novel carbon dynamics assessment framework reveals climate positive land management approaches across North America

Cumulative effects analysis to inform public land management in the United States: key characteristics and legal challenges

Semi-arid mallee landscapes are shaped by wildfires. Due to climate change, wildfires are expected to become more frequent and intense, making their management a conservation priority. Ants are often used as bioindicators in land management studies, as their composition, richness, and abundance respond to disturbances, including wildfires, both directly and indirectly through habitat modification. In the semi-arid zone of western New South Wales, Australia, we examined how time-since-fire influences ant species richness, abundance, and community composition and assessed whether fire-induced changes in vegetation composition explain the observed patterns in ant community structure. We sampled ants at five sites that last burned 3, 5, 9, 26, and 34 years ago, respectively. We identified 59 ant species from 20 genera in a total of 16,360 sampled ants. We found that post-fire ant communities exhibited higher abundance immediately after fire, while species richness increased with time-since-fire. Early successional stages with greater shrub density favored dominant and opportunistic ant species, whereas later stages with increased tree and grass cover supported more diverse ant communities. These results demonstrate that fire drives successional specialization in ant communities through niche filtering. Maintaining heterogeneous fire-age vegetation mosaics is therefore critical for conserving landscape-level biodiversity in fire-regulated mallee ecosystems.

Sustainable sand filtration strategies for microplastic removal in irrigation water.

Muddling through it: Analyzing public land manager perspectives on recreation allocation

Brazil is the world's leading coffee producer and increasingly adopts shaded agroforestry systems to enhance sustainability. However, the influence of topography on soil functionality within these systems remains insufficiently understood. This study evaluated soil physical and chemical properties across slope positions (Upper, Middle, and Lower Thirds) and depths (0-60cm) in a shaded coffee agroforestry system using multivariate statistics and Bayesian network modeling. Results revealed that upper slope positions exhibited greater macroporosity (15-20%) and lower bulk density (1.10-1.15g cm⁻3), whereas lower slope positions accumulated higher total organic carbon (2.5-3.0%) and microporosity (28-32%). Principal Component Analysis indicated that topography modulated soil porosity and carbon distribution, with total organic carbon (TOC) positively correlating with nutrient availability and negatively with acidity. Bayesian network analysis identified TOC as the most influential attribute, displaying the highest expected influence (1.25) and strength (1.15), along with elevated centrality in conserved environments. These results demonstrate that TOC functions as a central integrator linking soil structure, chemistry, and fertility across topographic gradients. Overall, shaded coffee agroforestry enhanced soil quality and functionality, particularly in upper slope areas, underscoring its potential for sustainable land management in tropical landscapes.

A long-term dataset of aboveground net primary productivity (ANPP) for global natural grasslands is essential for carbon dynamics modeling and sustainable land management. However, existing datasets are limited: they often fail to separate above- and below-ground productivity or reflect only post-disturbance conditions. To address these gaps, we developed a gridded annual ANPP dataset using machine learning, spanning historical (1958-2023) and future (2015-2100) periods. Historical ANPP data were derived from TerraClimate at 1/24° spatial resolution, while future projections came from CMIP6 models under SSP245 and SSP585 scenarios at 1/2° resolution. Our model performed robustly (R2 = 0.675 ± 0.009), showing temporal and spatial reliability through cross-validation with published products. Notably, systematic ANPP underestimation occurs in high-productivity regions (>700 g m-2) due to sparse field observations, so values in these areas should be interpreted with caution. Our dataset provides a spatially explicit baseline of climate-driven productivity, supporting precise evaluation of human impacts on grasslands and informing adaptive management under climate change.

Flood events occur recurrently in Padang City, Indonesia, generating substantial social, economic, and environmental consequences. This study aims to develop a geospatial flood vulnerability map using a probabilistic Naïve Bayes model integrated with GIS-based spatial analysis in ArcGIS. The model incorporates six key conditioning factors: rainfall, slope, soil type, landform, geology, and land use. The Naïve Bayes classifier achieved an overall accuracy of 97.69%, indicating high predictive capability and model reliability. The resulting vulnerability map categorizes the study area into three classes—low, moderate, and high vulnerability. High-vulnerability zones are predominantly concentrated in the western part of Padang City, primarily due to low-lying topography, upstream surface runoff accumulation, and tidal influences. This study presents a statistically grounded and computationally efficient framework that integrates probabilistic machine learning with spatial analysis for urban-scale flood vulnerability assessment. Compared to conventional deterministic approaches, the proposed method offers improved adaptability, rapid processing, and strong predictive performance. The framework provides valuable decision-support tools for flood risk mitigation, urban planning, and sustainable land management and can be applied to other flood-prone regions with comparable environmental characteristics. Keywords: Naïve Bayes classifier; flood vulnerability mapping; GIS-based spatial analysis; probabilistic modeling; urban flood risk; disaster mitigation.

Soil erosion poses a significant challenge to environmental sustainability, especially in regions with varying land-use patterns and topography. Soil erosion is a major environmental threat affecting soil quality, reservoir sedimentation, agricultural land, and watershed hydrology. This study aims to identify and classify homogeneous sub-watersheds in a mountainous watershed in Iran using GIS. Forty years of climate data, a high-resolution DEM, land-use maps, soil texture, and NDVI were applied to derive the main factors, while the P factor was determined based on slope classes and land-use types. The RUSLE results showed that annual soil erosion in the watershed had an average of about 7-ton ha⁻¹ year⁻¹, with more than 65% of the watershed area falling into the moderate to very high erosion classes. Average key factors were R = 78.08MJ·mm/ha·hr·year, K = 0.28 t·ha·h/MJ·mm·ha, LS = 1.62, and C = 0.39. The highest erosion occurred in areas with heavy rainfall, steep and long slopes, fine-textured soils, and sparse vegetation. Spatial autocorrelation analysis using Moran's I and the Getis-Ord Gi* statistic showed a clustered spatial pattern of erosion. High-high (HH) clusters, indicating severe erosion hotspots, were found in the southwest, while low-low (LL) clusters, representing minimal erosion coldspots, occurred in the north and northeast. These results support sub-watershed prioritization and indicate the need for targeted erosion control in high-rate zones. These results contribute to the development of more targeted and sustainable land management practices to mitigate soil erosion rates and improve watershed conservation efforts.

Background: Land transformation significantly impacts the physical, legal, and economic characteristics of agricultural resources. This study explores the role of economic-mathematical modeling in optimizing land use efficiency through land cover transformation in the Aghavnadzor community of Vayots Dzor, Armenia. Methods: The research focused on a 7.5-hectare area (1450-1600m altitude) from 2021 to 2024. Characterized by a dry subtropical climate and humus-poor gray soils, the site served as a controlled environment to minimize climatic variability. The methodology integrated soil chemical analysis, bonitation (soil quality) assessment, and cadastral valuation within an economic-mathematical framework to evaluate the transition from low-value to high-value agricultural land types. Result: Findings reveal that strategic land transformation substantially improves soil chemical composition, including organic matter and essential nutrients. Quantitative analysis showed a 20-30% increase in bonitation scores, leading to a proportional rise in cadastral value. The mathematical model successfully identified resource constraints and “bottlenecks,” providing a robust framework for sustainable land management. Notably, the transformation yielded a projected net community income of $17,000 USD, demonstrating significant local economic benefits. This integrated approach offers a scientifically grounded decision-making tool for land use planning. By linking soil properties with modern agricultural practices and economic outcomes, the study provides a scalable model for enhancing land productivity and rural financial stability in similar geographic regions.

Soil pH is a critical soil property governing nutrient availability and ecosystem functioning. Digital mapping of its spatial distribution is essential for precision agriculture and sustainable land management. This study performs a comparative analysis of six tree-based models coupled with residual kriging (RK) for 30 m resolution mapping of soil pH in Shayang County, China. Specifically, random forest (RF), extremely randomized trees (ERT), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost) were used. Based on 1343 soil samples and 32 environmental variables, experimental results demonstrate that the integration of RK enhanced the prediction accuracy of all standalone models by taking the spatial dependence of residuals into account. Among the models, CatBoost-RK achieved the best performance with an R2 of 0.7265, RMSE of 0.5072, and RPD of 1.9122, closely followed by ERT-RK and RF-RK. The analysis of variable importance identified soil type (ST) and mean annual precipitation (MAP) as the most critical factors affecting soil pH distribution. The generated 30 m resolution soil pH map reveals distinct patterns across different land use types, with croplands showing lower soil pH and grasslands exhibiting higher pH with greater variability. These findings confirm the effectiveness of the hybrid ML-RK framework and provide valuable insights for selecting optimal modeling strategies in digital soil mapping.

Abstract Anthropogenic activities, including anthropogenic climate change (ACC) and historical water and land management (HWLM), have intensified hydrological extremes in recent decades, with potential increases in flood risk. However, uncertainties in hydrological models and a lack of consideration for HWLM in climate models have limited the estimation and attribution of the changes in global‐scale flood risks. Based on a multi‐model ensemble of daily discharge simulations and projections from the latest Inter‐Sectoral Impact Model Intercomparison Project (ISIMIP 3), our estimation shows that flood risks significantly increased (slightly decreased) in the north (south) of 50°N during 1971–2019 when considering the combined effects of ACC and HWLM. Under continuing global warming (from 1.5°C to 3°C), 59.6%–69.4% of the global land area would witness an increasing flood risk, with 71.9%–87.5% of basin‐level risks attributable to anthropogenic activities, particularly in arid/tropical zones. Meanwhile, the flood risk in 38.8% of the global land area shows the anthropogenic signals before 2065. This increase is enlarged by greater flood magnitude and variability in the future, with flood magnitude being the dominant factor in most regions. We suggest that hydrological services and policymakers should implement adaptive strategies that consider anthropogenic increases in flood magnitude and variability, utilizing detected signals to guide global flood‐risk mitigation.

The influence of invasive plant species (IPS) on forest ecosystems has been of sufficient concern to warrant data acquisition on their presence and abundance. These data are needed at various spatial scales for both land management and policy purposes. One potential source of data are national forest inventories (NFIs) that often record IPS observations on sample plots that span relatively large spatial and temporal extents, thus providing data users with a wide range of analytical opportunities to meet desired information needs. In the NFI of the USA, which is conducted by the USA Forest Service's Forest Inventory and Analysis (FIA) program, IPS presence and foliar cover area are visually observed and recorded nationally on a spatially balanced subset of inventory plots measured each year. As with any sample-based assessment, the implementation of statistically appropriate methods is imperative for credibility of the final estimates. In this paper, statistical methods for calculating population estimates and associated measures of uncertainty for current status and change in IPS attributes are presented. The estimators address both presence and cover area estimation, as well as estimation of area proportions relative to total area domains. Also included are data and various numeric examples that assist in understanding the estimation steps and provide for verification of accuracy in results. The methods should be applicable to most IPS data collected in other forest inventories with little or no modification.

The traditional ecological knowledge (TEK) that is deeply embedded in the Ifugao Rice Terraces offers crucial insights for socio-ecological adaptation and resilience in times of climate change. It helps sustain local biodiversity and supports ecosystem processes while inspiring new design ideas that encourage an epistemological shift that involves integrating traditional knowledge with incremental innovations from the past into future solutions. The Second Terrace Lodge research-by-design project highlights the importance of the Ifugao community’s local landscape and cultural heritage, illustrating how traditional water and land management can be integrated into contemporary architecture and landscape design.