Agricultural systems are under growing pressure, as soil degradation threatens food security and sustainable land use. Early detection through soil monitoring and precision agriculture is vital to prevent irreversible damage and enable timely conservation. This study evaluates a combined procedure based on electrical resistivity tomography and frequency-domain electromagnetic induction measurements, together with discrete soil sampling, to electrically characterize the soil, identify layers, and map the A horizon depth in a non-disturbing way. This work includes the design and implementation of a mounting electrode system, which reduces the installation time of electrical resistivity tomography surveys by 60% while maintaining data quality. The data were acquired in the oldest long-term agronomic experiment in South America, comprising seven rotation systems with three replicates each, totaling 21 rainfed plots, and representing contrasting management scenarios. Soil A horizon thickness maps of the entire experiment were obtained through two procedures. A comparison between mapping inputs, including all plots and only bare-soil plots, revealed minimal differences in unvegetated areas but notable discrepancies under plant cover, where vegetation increased fluctuations and noise. The present study provides a methodology for accurately assessing the spatial variability of the A horizon thickness by means of proximal sensing techniques. This contributes to the challenge of gathering fundamental soil information in a fast and cost-effective manner, critical for precision agricultura.

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.

Systematic land registration has become a key instrument for enhancing legal certainty and preventing agrarian disputes, yet administrative approaches alone face limitations due to data inaccuracies, overlapping claims, and boundary objections. This article examines the role of community participation in systematic land registration using a juridical-empirical and socio-legal approach, incorporating regulatory analysis, interviews, field observations, and land document review. The findings show that meaningful community participation serves as a mechanism for social verification of land data, early detection of potential disputes, and improved accountability in land administration. Its effectiveness depends on transparency, facilitation quality, inclusivity, and institutional integrity, while challenges include low legal literacy, local power dynamics, and limited evidentiary capacity. The study recommends strengthening participatory frameworks, data disclosure, and community-based legal assistance to support equitable and sustainable land registration.

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.

Sustainable sand filtration strategies for microplastic removal in irrigation water.

Cropland use intensity, stability, and crop transition dynamics in the Songhua River Basin (2000–2024): Implications for sustainable land use and food security

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.

Harmonised land evaluation frameworks are essential for sustainable land planning and policy development. Assessing land suitability is crucial for predicting agricultural and forestry potential but also for mitigating land degradation risks. Current land suitability maps in Portugal vary greatly in scale and methodology. This study presents the first nationally consistent framework to produce a harmonised land suitability map for mainland Portugal at a 1:100,000 scale following a recently updated WRB soil map. The latter was obtained by integrating legacy soil data with delineated land units according to soil-forming factors (climate, lithology, and relief). These land units were used to derive key land qualities, subsequently classified into constraint levels. Following FAO land evaluation principles, four land suitability levels for agriculture and forestry were assigned to 125 land units across three representative areas in southern Portugal. Relief and lithology emerged as main drivers of land suitability. Marginal agricultural lands are largely dominant (65.1–78.0%), followed by non-suitable lands (14.8–28.3%). Forestry suitability is mostly confined to moderate (61.5–69.4%) and marginal (30.6–37.4%) classes, reflecting the higher adaptability of forestry systems. High consistency was observed between the derived suitability classes and the latest land use/land cover map of Portugal. The framework enables decision-makers to identify areas suitable for intensive production while safeguarding lands vulnerable to degradation. It also provides a transferable tool for adaptive landscape management and sustainable land allocation, supporting policy development under changing environmental conditions in Mediterranean regions.

Abstract. Consequences of climate change are likely to pose severe challenges on agriculture in Southern Africa. Agroforestry systems (AFSs) can potentially alleviate some of the adverse effects and offer adaptation solutions to a sustainable land use. Positive effects of AFSs may include increasing soil carbon (C) and nitrogen concentrations, sustaining favourable nutrient cycling, protection against erosion and increased carbon sequestration. The influence of the AFS tree component on the soil water storage and thus water availability for the crops, however, is still relatively unknown. In this study we assessed the influence of Gliricidia sepium-maize intercropping on carbon cycling and water fluxes compared to maize as a sole crop at two well-established long-term experiments in central and southern Malawi, run by the World Agroforestry (ICRAF). Utilizing the field experiments of different durations (>10 and >30 years) at the two sites provided information regarding soil-specific impacts of gliricidia on water dynamics. We examined soil C contents and density fractionation as proxy for organic matter stability, soil physical and soil hydrological characteristics. We also monitored soil moisture and matric potential in different depths, determined retention curves on samples in the lab and from field data and analysed soil moisture responses to rainfall events to assess the influence of the AFS on water fluxes. Our results show a clear increase in C contents and stability as a result of the gliricidia impact compared to the control at the site with the generally lower baseline C contents. At this site, the treatment effect was not visible in soil physical characteristics such as porosity and bulk density, but in saturated hydraulic conductivity, which is rather a structural soil property. The soil water dynamics were influenced by several additional factors such as soil texture and interception. The gliricidia treatment showed greater soil water storage capacities and retained overall more water, while generally none of the plots neither control nor treatment were under severe water stress during the observation period. We also noticed a protective effect against soil drying below the topsoil potentially by more immediate/macropore infiltration into the subsoil under gliricidia. We conclude that, from a methodological point of view, assessing the effects on water fluxes requires respective field measurements as they cannot be deduced from soil physical characteristics directly. Overall, the AFS treatment of adding gliricidia into maize cultivation can have a considerable effect on nutrient and water dynamics in the system, however, this effect is also dependent on initial site conditions. A sensible AFS implementation can not only support carbon accumulation and stabilization but also increase the efficient use of available water, thus supporting different aspects towards sustainable agriculture in Malawi.

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.

Within the Sustainable Development Goals (SDGs), particularly those addressing poverty reduction, food security, climate action, and sustainable land use, rural mobility is increasingly recognised as a critical development issue. While rural development policies have traditionally prioritised rural–urban migration, considerably less attention has been paid to rural-to-rural intra-migration, despite its growing significance across Sub-Saharan Africa. The review process was informed by guidance on systematic reviews in the social sciences and reported in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. 34 literature articles were selected and reviewed for this systematic literature review. The New Economics of Labour Migration theory is the main theory used for this systematic literature and is complemented by the Push–Pull theory of migration. This systematic literature review examines the demographic, socioeconomic, environmental, and institutional drivers of rural-to-rural migration, with particular reference to Zambia and the Southern African region. The main drivers of intra migration include the shortage of land and accessing family land, youth life needs and desires, poor weather, drought due to climate change effects, the need to expand agricultural areas and increased diversification strategies by households. It is also indicated that deforestation, agricultural expansion, and rural development are environmental and land use implications of rural migration. The findings outline the importance of sound policy responses and implementation that deal with climate change adaptation, land governance, and sustainable use of land, and support for rural diversification development for equitable, sustainable transformation of rural areas.

Agricultural carbon projects are increasingly promoted as instruments to address climate change while supporting sustainable agriculture. These projects combine sustainable land management (SLM) practices with a carbon credit component, creating complex governance structures involving diverse actors with unequal power. Understanding the governance challenges that may impede their effective implementation is therefore essential. Despite growing evidence on the potential of digital tools in agriculture, their role in agricultural carbon projects remains underexplored. This study employs a qualitative case study of two pioneering carbon projects in Kenya, alongside a participatory and visual mapping tool (Process Net-Map), to engage with stakeholders. It combines concepts of principal-agent and bargaining power theory to analyse the governance challenges of agricultural carbon projects, and the potential role of digital tools in addressing them. The findings reveal layered principal-agent relationships in the carbon credit component, characterized by strict monitoring and external accountability, exacerbating information asymmetries and shifting performance risks to actors with limited bargaining power. While women play a pivotal role in implementation and monitoring, intra-household power relations constrain their control over assets and benefits, thereby reinforcing gender inequities. Digital tools currently support data collection and reporting, with potential to reduce transaction costs and improve accountability, but their use remains largely confined to the SLM component. Expanding digital tools beyond monitoring to support participation, transparency, and feedback could strengthen smallholder bargaining power. The current study contributes to literature by highlighting how the carbon credit component reshapes and intensifies existing SLM governance challenges and offers insights for project developers and policymakers seeking to promote more equitable and effective agricultural carbon initiatives.

Rangelands play a vital role in supporting livelihoods, biodiversity, and ecological balance across arid and semi-arid regions. However, these fragile ecosystems are increasingly threatened by overexploitation, land degradation, and unsustainable management practices. Understanding the human and behavioral dimensions of rangeland conservation has therefore become an urgent priority. Many of the world's rangelands, including those in Iran, have recently been exposed to destruction and serious damage. Collaboration among various stakeholders (especially pastoralists) in sustainable land use and management is considered a key factor in reducing this degradation. Guided by the Theory of Planned Behavior (TPB), this study tries to identify and analyze the behavioral nudges for the sustainable land use and management in Iran. This research employed a cross-sectional survey design involving 248 pastoralists in Fars Province, southern Iran, selected through simple random sampling. An extended version of the TPB was applied, incorporating two additional constructs-awareness of consequences and moral norms-to enhance its explanatory power in predicting sustainable land use intentions. Behavioral nudges, such as increased awareness of consequences, strengthening moral norms, perceived behavioral control, and attitudes, can lead pastoralists to sustainable land use and management, thereby helping to conserve rangelands. To operationalize the research, a cross-sectional survey of 248 pastoralists with livestock grazing certificates, who were selected using simple random sampling, was used. The results of the research showed that the constructs of attitude towards sustainable land use and management had a positive and significant effect on the intention towards sustainable land use and management (Beta = 0.292; T = 4.239; Sig = 0.001). The direct effects of two variables, awareness of consequences of rangelands' destruction (Beta = 0. 335; T = 3.333; Sig = 0.001) and moral norms of sustainable land use and management (Beta = 0. 323; T = 2.791; Sig = 0.005), were positive and significant on Intention. In addition, the results of this study showed that moral norms not only act as a constructive factor in the intention of the pastoralists towards sustainable land use and management, but also can play a mediating role for some other variables such as awareness of consequences of rangeland destruction. The results of SEM analysis showed that the extended TPB can explain 75% of the variance of pastoralists' behavioral intention, which shows the high explanatory power of the model. These findings provide practical insights for policymakers and land managers by emphasizing the need to design interventions that enhance moral and environmental awareness, promote participatory management, and align behavioral policies with local cultural norms. However, as this study is based on a cross-sectional design, causal inferences should be made cautiously, and future longitudinal research is recommended to validate these relationships over time.

This study examines the role of agents of change (AoCs) in promoting environmentally sustainable land management in Aotearoa New Zealand's (AoNZ's) agricultural system. Survey responses from 196 individuals across five organisational types highlighted significant sustainability challenges in nine agricultural sectors. Dairy was perceived as the least sustainable; mixed farming as the most sustainable, albeit still facing moderate environmental challenges. Respondents generally agreed that substantial or transformative changes in land management are necessary. The study assessed the perceived relevance and perceived organisational support for 12 human and 10 non‐human AoC types. While overall alignment between perceived relevance and perceived support was strong, inconsistencies and weaker alignment at the individual AoC level suggest additional factors influence support. Based on systems theory, AoCs were categorised by intervention depth using a leverage points framework, from shallow (e.g., policy parameters, feedbacks) to deep (e.g., system design, intent). Findings weakly supported hypothesis: shallow AoCs receive more recognition ( rho = –.16, p < 0.001) and support ( rho = –.12, p < 0.001) than deeper ones. This study underscores the importance of understanding AoCs within a systems framework and highlights the opportunity to strengthen support for AoCs driving transformative, system‐level change in AoNZ's land management practices.

Abstract Forest fragmentation poses a critical threat to ecosystem service functionality, particularly in rapidly urbanizing regions. This study investigates the spatio-temporal dynamics of forest fragmentation and its impacts on two essential ecosystem services, water provisioning and recreational value, within the Petaling District, Selangor, Malaysia, across the years 2005, 2014, and 2023. Using Landsat satellite imagery, supervised classification, and key landscape metrics (patch size, edge density, and connectivity), the research identified clear signs of intensified fragmentation. Although there was partial forest recovery post-2014, increased patchiness and elevated edge densities indicated persistent structural degradation. To understand how fragmentation affects ecosystem services at a local scale, the study used Geographically Weighted Regression (GWR). GWR is a spatial econometric approach that captures local variations and spatial non-stationarity. This is in contrast to conventional global models which assume uniform relationships across space. Significant spatial heterogeneity was found in the influence of fragmentation metrics on water provisioning and recreational services. The results can be interpreted as site-specific and thus important for adaptive conservation planning. Fragmentation metrics were found to influence water provisioning and recreational services unevenly across the landscape, with proximity to water bodies and developed areas serving as proxy indicators. Notably, areas with high edge density and reduced patch sizes were more vulnerable to hydrological service loss, while recreational value exhibited a spatially complex relationship, influenced by accessibility and landscape aesthetics. The findings underscore the urgent need for spatially adaptive planning and proactive forest governance, especially in urbanizing districts. By integrating ecological indicators with spatial econometrics, this research offers nuanced, site-specific insights that support conservation prioritization and sustainable land use strategies. The methodological framework and results contribute to broader discussions on urban ecological resilience and ecosystem service preservation under accelerating land-use pressures in tropical regions.

Abstract Flooding is one of the most frequent and destructive environmental hazards globally, and Indonesia is among the most affected countries. Understanding land use land cover (LULC) changes and flood distribution is essential for effective mitigation strategies. However, conventional methods using multiple processing environments are time-consuming, whereas integrating multi-sensor data within a single platform such as Google Earth Engine (GEE) improves efficiency and accuracy. This study aims to analyzes flood distribution and LULC changes in the Konaweha watershed from 2015 to 2024 using multi-temporal Sentinel-1 SAR and Landsat-8 optical imagery. Flooded areas were mapped using the Otsu thresholding combined with change detection, while LULC changes were identified using the Random Forest algorithm. The result reveal that flood inundation expanded from 6,709.24 ha in 2015 to 16,295.35 ha in 2020, before declining to 9,243.28 ha in 2024. Major LULC transitions included reductions in wetlands (12.82 %), primary forest (1.94 %), and agriculture (28.82 %), alongside increase in built-up areas (80.48 %), secondary forest (8.13 %), and water bodies (41.76 %). This finding indicate a strong correlation between flood occurrence and LULC changes, emphasizing the influence of environmental degradation on flood dynamics. The study contributes to global discourse on flood risk assessment by demonstrating the effectiveness of integrating multi-sensor remote sensing data for near real-time flood monitoring and sustainable land management planning.