Across many oases in semi-arid and arid regions, water availability is steadily declining while soil salinity is increasing at an alarming rate. To address these challenges, the SuLaMo project proposed integrated sustainable land and water management concepts for agriculture, incorporating desalination as a viable source of irrigation water. This study experimentally evaluated a pilot-scale, photovoltaic-driven membrane capacitive deionization (MCDI) system for the desalination of brackish groundwater (TDS = 6.3 mS cm -1 ). The investigation focused on finding practical steps to: (1) evaluate the effect of flow rate on adsorption capacity; (2) optimize the system configuration to maximize water recovery; (3) evaluate the long-term performance and the effect of scaling, electrode configuration and selective ion removal on system efficiency. Additionally, the effect of soil salinisation was monitored during the trials. The results demonstrated the feasibility of MCDI for desalinating low-brackish water and mitigating long-term soil salinization. A trade-off between ion removal efficiency (RE), water recovery (WR), and energy consumption (SEC) was evaluated using a Pareto method. Steps for systematically optimizing the process are suggested. Pilot at low-cleaning operation resulted in optimized values of RE = 45%, WR = 69% and SEC = 2.75 kWh m -3 .m -3 . However, prolonged operation with infrequent cleaning resulted in significant scaling effects, a lower net flow rate and increased SEC. This indicates that frequent cleaning is essential, particularly when treating hard water. Further improvements in electrode adsorption capacity and anti-scaling operational strategies are necessary for the large-scale agricultural application of MCDI. • PV-MCDI was found to be feasible for desalinating low-brackish water in arid environments in Morocco. • Changes in flow demonstrate a trade-off effect between water recovery and specific energy consumption. • Brine disposal remains a critical issue, even with high water recovery rates. • A systematic optimization of pilot-scale MCDI to maximize water recovery is suggested. • The findings highlight the potential of combining MCDI with photovoltaic (PV) systems.

North African countries are increasingly facing climate change, natural resource degradation, and food crises. Algerian regions such as Laghouat are one of the hotspots where problems such as soil degradation, desertification, and water scarcity are experienced. Current agricultural production systems are not responding to future needs and are inadequate to address these problems. Agroecology emerges as a promising alternative that can respond to growing future needs by providing resilient and sustainable production systems. This study investigates the factors affecting farmers’ adaptation to agroecology in Laghouat, Algeria, using Elinor Ostrom’s Social Ecological Systems Framework (SESF). We apply our mixed-methods methodology in the field to systematically examine the complex relationships of the system, resource systems and, governance, and actors. Our findings suggest that the negative impacts of unsustainable agricultural practices, combined with climate change and misguided policies, are leading to a problematic trend that results in a system that is losing its resilience and sustainability and is becoming increasingly vulnerable. However, the study also highlights that farmer training, incentives to support the adoption of environmentally friendly practices, and strong social networks can significantly increase the transition to sustainable agroecology. These insights underline the need for integrated and collaborative strategies to achieve sustainable soil management, and hence more resilient agricultural system. • Agroecology can induce sustainable agricultural land management and improve soil health in a systematic manner. • Transdisciplinary approach helps understand SES by integrating social, ecological and economic aspects of soil and land use. • Participatory and site-specific methodology has been developed to implement Social Ecological Systems framework.

The quality of the soil is greatly influenced by soil management practices that either raise or decrease the criteria of soil quality. Therefore, it is crucial to monitor our soil to determine whether agricultural practices have a major positive or negative impact on it. The Havza district in Samsun, Turkey, which is a semi-humid climate region, experiences intensive agricultural land use, diverse soil-forming conditions, and increasing pressure from management practices, making it a vulnerable agroecosystem necessitating monitoring. Thus, this research was carried out to assess and predict the soil quality of this region. 217 soil samples were collected from the study area, and 33 soil quality parameters were selected and analyzed. The data were subjected to the Integrated Quality Index (IQI) and the Artificial Neural Network (ANN). This is to support sustainable land management, productivity, and long-term soil conservation under increasing human and climatic pressure. In addition, the Total Dataset (TDS) of soil parameters was subjected to Principal Component Analysis (PCA), and 13 soil quality parameters were chosen for the creation of a Minimum Dataset (MDS), and spatial distribution maps of the study area were created. The result showed that the Soil Quality Index (SQI) determined by IQI was similar to that predicted by ANN, with R2 values of 0.999, 0.970, and 0.987 for training, validation, and testing, respectively. The distribution maps show sporadic low-quality areas within the interior, with the lowest quality in the northern middle part of the study area. The overall soil quality was classified as medium quality. Also, the distribution maps provide valuable information for land management, ecosystem management, and the sustainability of agricultural farmlands.

Soil salinity drives land degradation and vegetation loss, posing a significant challenge for agriculture and global efforts to combat desertification. Enhancing land degradation monitoring methodologies is essential and aligns with the mission of the United Nations Convention to Combat Desertification (UNCCD), which aims to promote sustainable land management. In this context, this study aimed to develop soil salinity prediction models (SSPMs) for monitoring land degradation caused by soil salinity using Sentinel-2 (S2) satellite data and geographic information system (GIS) techniques. An area of 17.5 km2 (1750 ha) of Sabkhat Ghuwaymid, located in the Al-Qassim region of Saudi Arabia, was delineated as the study area for developing SSPMs. A total of 118 soil samples were collected using a grid sampling method between February 19 and 22, 2024. The collected samples were analysed for texture, acidity (pH: 6.97–8.61), total dissolved solids (TDS: 4.25–62.60 g L-1), and electrical conductivity (EC: 8.49–125.23 dS m-1) as an indicator of soil salinity. Soil analyses were performed using a hydrometer technique and Ohaus pH and EC meters. A promising model for predicting and mapping soil salinity was developed using S2 satellite imagery in combination with stepwise multiple linear regression (SMLR) analysis. The developed model demonstrated a correlation coefficient (R2) of 0.61 during development and 0.66 during validation, with a P-value < 0.05, and an RMSE of 12%. These results indicate that S2-based SSPMs provide a reliable and cost-effective approach for monitoring soil salinity, thereby supporting sustainable land management in arid environments.

Accurate digital mapping of soil classes remains essential for sustainable land management. The DSMART (Disaggregation and Harmonization of Soil Map Units through Resampled Classification Trees) algorithm is a probabilistic soil subgroup mapping technique that utilizes environmental covariates in conjunction with legacy soil data. The objective of this study is to disaggregate soil subgroups from existing 1:20,000 scale soil maps using DSMART in a topographically diverse region of Adana, Türkiye. A total of 231 legacy map units were harmonized and resampled to 20m resolution. A total of 40 soil profiles were collected using a conditioned Latin Hypercube Sampling (c-LHS) design. Predictors were derived from a 5-meter digital elevation model (DEM), and NDVI was derived from 20-meter Sentinel-2 images and legacy soil information. The DSMART algorithm was implemented in R using the "C5.0" decision tree model, producing multiple realizations and subgroup probability surfaces. The accuracy of the model was evaluated using Kappa statistics and Shannon entropy. The most prevalent subgroups, Typic Xerofluvent (Tx), Typic Calcixerept (Tc), and Typic Xerorthent (To), were reliably predicted, particularly in heterogeneous landscapes. Subgroups such as Fluventic Haploxerept (Fh) and Oxyaquic Xerofluvent (Ox) demonstrated elevated uncertainty, attributable to insufficient data representation. For instance, while Typic Calcixerept (Tc), Typic Xerofluvent (Tx), and Typic Xerorthent (To) achieved high accuracies of 96.4%, 90.9%, and 88.7% respectively, Ox exhibited the highest misclassification rate (~ 40%) and Fh could not be reliably predicted due to extremely limited samples. The most significant predictors were found to be elevation and curvature-based topographic indices. The overall model demonstrated an accuracy of 85.2%, and the probability surfaces exhibited smoother transitions in comparison to traditional polygon-based soil maps. The DSMART methodology facilitates the generation of detailed, probabilistic soil maps, thereby exceeding the precision of classical survey techniques by accounting for uncertainty and spatial heterogeneity. The findings of this study contribute to the development of soil management strategies for Mediterranean agricultural regions.

Microbial death pathways (MDPs) are increasingly recognized as key drivers of terrestrial carbon cycling, primarily through their regulation of microbial necromass carbon (MNC), a critical pool in global carbon dynamics. Yet explicit representation of MDPs in soil organic carbon (SOC) models remains limited. Here, we developed and evaluated three SOC models that differ in their structure of MNC pool: the multiple-pathway necromass (MPN) model, which partitions microbial necromass carbon (MNC) into four MDP-derived subpools; the dual necromass (DUN) model, which differentiates two necromass pools with distinct decay rates; and the single necromass (SIN) model, which aggregates necromass into a single pool. Using a unified data assimilation framework and SOC observations from six major agricultural regions in China, we found that the MPN model consistently outperformed DUN and SIN models across most regions, producing necromass subpool dynamics, scenario responses, and parameter sensitivities that closely reflect the mechanistic understanding of MDPs. In cold or nutrient-limited regions, however, the simpler DUN model performed similarly while requiring fewer parameters, emphasizing the importance of balancing model complexity with regional ecological constraints. Our results demonstrate that explicitly incorporating MDPs enhances the robustness and mechanistic realism of SOC simulations and provides a robust foundation for more explicit representations of MDPs to assess the soil carbon sequestration potential and guide sustainable land management.

Understanding the spatiotemporal dynamics of vegetation in response to climatic variability is critical for ecosystem monitoring and sustainable land management, particularly in ecologically sensitive regions. Northern Iran, specifically Guilan Province, with its humid subtropical climate and complex topography, is highly vulnerable to hydroclimatic fluctuations. However, comprehensive assessments integrating multi-source satellite data remain limited. This study aims to investigate spatiotemporal vegetation-climate interactions in Guilan Province from 2020 to 2024 using an integrated analytical framework developed within Google Earth Engine (GEE). The objectives are to quantify interannual changes in vegetation greenness (NDVI and EVI), assess relationships with land surface temperature (LST) and precipitation, and develop a novel Vegetation-Climate Sensitivity Index (VCSI) to evaluate ecological vulnerability. Methodology employed multi-source satellite datasets: Landsat 8-derived NDVI and EVI, MODIS MOD11A2 LST, and CHIRPS precipitation data. All datasets were harmonized to 1km resolution and analyzed within GEE. The VCSI was constructed by integrating standardized vegetation indices with normalized precipitation and LST. Spatial autocorrelation analyses (Moran's I, Getis-Ord Gi*) identified clustering patterns of vegetation-climate sensitivity. Results revealed marked interannual variability, with 2022 exhibiting the lowest NDVI/EVI and highest LST, indicating severe climatic stress, followed by partial ecosystem recovery in 2024. Strong positive correlations were found between vegetation indices and precipitation (r = 0.88-0.92), while negative correlations with LST (r = -0.62) confirmed thermal stress, particularly during summer (mean summer VCSI = 56.96). Lagged response analysis showed vegetation responded to precipitation with one- to two-month delays, highlighting soil moisture retention effects. The VCSI effectively captured spatial heterogeneity: high values (resilient zones) concentrated in forested highlands of the west and north, while low values (vulnerable zones) characterized agricultural lowlands and urbanized areas. Spatial autocorrelation confirmed strong clustering of ecosystem sensitivity (Global Moran's I > 0.93, p < 0.001), with persistent hotspots in highland forests and coldspots in lowland anthropogenic landscapes, intensified during 2022 climatic stress. In conclusion, vegetation responses to climate variability in Guilan Province are spatially structured, temporally lagged, and highly sensitive to hydroclimatic drivers. The integrated GEE-based framework combining VCSI with spatial statistics provides a robust approach for mapping ecological vulnerability. Findings offer actionable insights for adaptive land-use planning and climate adaptation interventions in northern Iran and similar humid subtropical regions.

The degradation of soil health due to intensive pesticide application has emerged as a critical global challenge, undermining ecological sustainability and agricultural productivity. In regions of high agronomic activity such as Dehradun, India, unsustainable practices including monocropping and excessive agrochemical inputs have been implicated in the decline of soil fertility and microbial diversity. This study employs 16S rRNA gene (V3-V4) amplicon-based sequencing to characterize shifts in bacterial community structure for agricultural farming and non-farming soils. Complementary physicochemical analyses revealed significant associations between soil health parameters and microbial community dynamics. Taxonomic profiling revealed distinct microbial signatures in pesticide-contaminated soils, with a notable enrichment of the phyla Proteobacteria, Acidobacteria, Firmicutes, and Actinobacteria. Dominant genera such as Bacillus sp., Chungangia sp., and Streptomyces sp. were identified, indicating their potential functional roles in biogeochemical cycling and adaptive resilience under chemical stress. Functional prediction using PICRUSt2 highlighted key microbial pathways associated with amino acid synthesis, fatty acid synthesis, degradation of aromatic compound, and other essential biochemical processes. These findings highlight the ecological significance of microbial communities in maintaining soil functionality and offer insights into the development of sustainable land management strategies in pesticide-impacted agroecosystems.

ABSTRACT This study examines the changes in land-use land-cover (LULC) across India over seven years, with a particular focus on the impacts of the COVID-19 lockdown in 2020. Using high-resolution Sentinel-2 10-m data, a short-term analysis of LULC dynamics from 2017 to 2023 was conducted. The unprecedented lockdown measures in 2020 provided a unique opportunity to observe the environmental effects of reduced human activity. Key findings include significant reductions in urban expansion and industrial activity during the lockdown, leading to temporary increases in water areas, green cover, flooded vegetation, and improved air quality in many regions. Agricultural areas such as crops and rangeland experienced varying impacts, with some shifts in crop patterns due to labour shortages and supply chain disruptions. The study highlights the resilience of natural systems in the face of reduced anthropogenic pressure and underscores the importance of continuous monitoring for sustainable land management. Our analysis leverages advanced remote sensing techniques to provide detailed spatial and temporal insights into LULC changes, contributing valuable data for policymakers and environmental planners aiming to balance development and conservation in post-pandemic recovery efforts.

The spatial variability of soil attributes plays an important role in hydrological processes, soil fertility, and environmental conservation in tropical semiarid regions. Texture, organic matter (OM), and available phosphorus (P) directly influence nutrient dynamics and the risk of phosphorus export to water bodies. This study analyzes the spatial distribution of soil texture, OM, and P in different soil classes of the Potengi River Basin (PRB), assessing their relationship with weathering and phosphorus mobility. A total of 110 soil samples were collected from different pedological classes, following standardized physical and chemical analysis methods. The data were spatially interpolated using the IDW method in ArcMap 10.8, and statistical analyses, including correlation, principal component analysis (PCA), and two-way cluster analysis, were applied to identify distribution patterns. The results revealed a predominance of sandy soils, moderate OM levels, and high phosphorus content. PCA identified two soil groups: Group 1, composed of more developed soils with higher clay and OM content, and Group 2, consisting of less developed soils with a higher risk of phosphorus export. The negative correlation between P and clay content emphasized the influence of texture on nutrient retention and mobility. This study highlights the relevance of spatial analyses for soil quality assessment and provides essential insights for sustainable land management strategies aimed at mitigating diffuse phosphorus pollution in semiarid watersheds.

Enset-based farming systems are promoted as a sustainable land management strategy in Ethiopia, yet comprehensive evidence of their contribution to soil fertility remains limited. This study assessed the role of three Enset-based farming systems (Enset-dominated, Enset-coffee, and Enset-coffee-fruit based farming) in improving soil fertility compared to adjacent croplands in central Ethiopia. A total of 60 composite soil samples were collected from 0-20 cm and 20-40 cm soil depths across 30 paired plots. Standard laboratory methods were used to analyze soil physicochemical properties, and soil organic carbon stocks were calculated. Statistical analyses included one-way ANOVA, Fisher's LSD post-hoc test, and paired t-tests. Results showed that Enset-based systems had significantly higher (p < 0.05) soil organic matter, total nitrogen, and exchangeable bases (Ca2+, K+, and Mg2+) compared to adjacent croplands at both soil depths. Enset-based systems have significantly higher cation exchange capacity and base saturation at 0-20cm depth compared to adjacent croplands. Most notably, soil organic carbon stocks in Enset with coffee (137.8 ± 27.3 Mg ha-1) and Enset with coffee-fruit (127.3 ± 19.2 Mg ha-1) systems were significantly higher (p < 0.05) than in adjacent croplands (93.5 ± 15.8 and 92.3 ± 16.5 Mg ha-1, respectively). However, soil organic carbon stocks in Enset-dominated systems did not vary significantly from those in croplands. We conclude that integrated Enset-based farming systems, particularly those incorporating perennial crops, substantially enhance soil fertility and carbon sequestration. These findings support policy integration of Enset-based farming systems as climate-smart agricultural practices for sustainable land management in the Ethiopian highlands.

Environmental geophysics holds significant but underutilized potential for tackling Indonesia’s diverse environmental challenges by supporting investigations of groundwater contamination, water infiltration, and the accumulation of metals in soils and crops—issues crucial for agriculture, public health, and sustainable land management. Emerging technologies such as UAV-assisted imaging, airborne surveys, and oceanographic geophysical observations further demonstrate the flexibility of modern geophysics in studying coastal processes, wave behaviour, and coral reef conditions. However, its application—especially in urban Indonesia—remains limited due to physical obstructions, cultural noise, restricted workspace, regulatory hurdles, and safety concerns, while the absence of geophysical test sites (GTS) and a shortage of skilled practitioners constrain methodological advancement and training. Based on bibliometric analysis of Scopus-indexed publications, this study shows that integration between geophysics and environmental science is growing but still insufficient. Strengthening environmental geophysics in Indonesia requires developing dedicated training and calibration facilities, fostering interdisciplinary collaboration, and adopting technological innovations tailored to dense urban and complex geological settings so that geophysics can play a more effective role in environmental monitoring, resource sustainability, and resilience to ecological and urban challenges.

This research discusses the salinity cycle of irrigated and rain-fed soils in the Integral University Agricultural Farm, Lucknow, Uttar Pradesh, India. Sustainable agriculture is a significant challenge facing soil salinization especially in arid and semi-arid areas where irrigation activities, climatic fluctuations and poor drainage further leads to the soil piling up the soluble salts in the soil profile. The research will be used to compare the main salinity parameters in two opposite land-use systems, irrigation and rain-fed, in the same agroecological environment. A comparative cross-sectional design was used and systematic sampling of soils was done on the surface layer (0 15 cm). Laboratory tests were conducted in accordance with the standard procedures to establish physicochemical parameters, such as pH, electrical conductivity (EC), total dissolved solids (TDS), chloride (Cl -), carbonate (CO 3 2-), and bicarbonate (HCO3-). The values were compared to the internationally accepted FAO salinity classification standards. The results show that there is a significant variation in the salinity levels of rain-fed and irrigated soils. Irrigated soils have a greater salt content as a result of continuous irrigation with groundwater, high evapotranspiration and low drainage. In comparison, rain-fed soils are less salty, and this is affected by seasonal rainfall and natural processes of leaching. The findings indicate the importance of irrigation activities and water quality in influencing the dynamics of soil salinity. This work presents a scientific background of the salinity changes in alluvial soils of the Indo-Gangetic Plains and highlights the fact that site-specific management measures, such as better irrigation, drainage and constant monitoring of the soils are necessary. The results are useful to sustainable land and water management and policy implementation designed to reduce soil erosion and guarantee agricultural productivity in the long term.

Land use land cover (LULC) changes are key indicators of environmental transformation, directly influencing hydrological balance, ecosystem services and sustainable land management. Sher River basin, an agro-ecological diverse sub-basin of the Upper Narmada River system in India, is the primary hydrological and socio-economic lifeline for local communities, but no comprehensive study on Sher River basin related to long term LULC dynamics could be tracked in the literature. To fulfil this research gap, long term LULC spatiotemporal change detection and transition patterns analysis with a dual-seasonal focus on the Rabi and Kharif cropping periods over a 23-year period (2001-2023) in Sher River basin are carried out. LULC maps are created using multi-sensor Landsat data (TM, ETM+ , OLI/TIRS) for 2001, 2006, 2011, 2016 and 2023 at an interval of 5years for both Rabi and Kharif seasons using a supervised classification technique with the maximum likelihood classifier. Five LULC classes, namely, built-up, agricultural, forest, water body and barren land are delineated. All classified maps achieved overall accuracies exceeding 85% with kappa coefficients greater than 0.80. The agricultural land increased significantly in both seasons, more sharply during Rabi (5.86% to 10%) while built-up areas expanded more than fivefold (0.18% to 0.85%) reflecting rapid urbanization during 23years. Barren land declined noticeably, transitioning mainly into agricultural and urban land uses. Forest cover, after an initial decline, showed recovery post 2016 with a modest increase in the later years due to afforestation initiatives. Water bodies remained relatively stable with minor seasonal variations. This study provides critical insights into seasonal land dynamics, highlighting clear seasonal contrasts in land use behavior between Rabi and Kharif periods. The findings emphasize the need for integrated land use planning to balance agricultural growth, urban expansion and ecological sustainability in sub-humid watersheds.

Assessment of soil quality properties and crop yield after long-term implementation of sustainable management practices in semiarid rainfed conditions

Impact of sustainable land management on household resilience gaps: Evidence from China's marginalized farmers

Sustainable Land Management Practices (SLMPs) are crucial for addressing land degradation, improving agricultural productivity, and enhancing food security. Yet, adoption remains low due to a complex interplay of socio-economic and institutional constraints. While previous research has explored these constraints, there has been limited focus on how gender intersects with household structure to influence the likelihood of experiencing these barriers. This study addresses this gap by investigating whether and how constraints to SLMPs adoption differ between male and female farmers within male-headed households (MHHs) and female-headed households (FHHs). Data was collected from 480 farmers through structured surveys, complemented by qualitative insights from focus group discussions and in-depth interviews. Feminist and intersectionality theories underpinned the study’s theoretical framework. Logistic regression analyses revealed three key findings: (i) female farmers in FHHs have significantly higher odds (OR =2.185) of facing economic and financial constraints than those in MHHs; while within MHHs, male farmers are more likely than their female counterparts to experience these constraints (OR = 2.098); (ii) male farmers in MHHs have higher odds (OR = 2.402) of encountering constraints related to SLMPs characteristics than their female counterparts; and (iii) female farmers in FHHs are 62.3 % more likely to experience constraints related to land property rights than those in MHHs. These findings emphasise the unique vulnerabilities faced by female farmers in FHHs and challenge the common assumption that male farmers are always more advantaged in agricultural systems. The study’s insights underscore the need for nuanced, context-sensitive policies to effectively address these barriers and promote SLMPs adoption. • Gender and household structure intersect to shape SLMPs adoption barriers. • Female farmers in FHHs face unique vulnerabilities compared to those in MHHs. • Male farmers in MHHs also experience significant barriers to SLMPs adoption. • Findings challenge the assumption of universal male advantage in agriculture. • SLMPs policies should be context-sensitive and prioritise marginalised groups.

Land use systems significantly influence soil physical properties particularly in semi-arid alluvial soils where intensive agriculture and land degradation coexist. The present study assessed the effect of different land use systems on soil physical properties in the Gwalior–Chambal Region of Madhya Pradesh, India. Five dominant land use systems, namelyrice–wheat, vegetable, forest, agri-horticulture, and ravine, were evaluated using GPS based soil sampling. In these land use systems, composites oil samples were analysed for particle size distribution, structural stability index (SI), bulk density (BD), particle density (PD) and porosity following standard analytical procedures. The results revealed significant variations in soil properties across the systems (p  0.05). Sand fractions varied from 34.68% to 60.20%, silt from 12.71% to 21.94%, and clay from 19.38% to 32.28%, with most soils falling into sandy clay loam texture. Ravine land use system exhibited the highest sand content, whereas forest and agri-horticulture systems showed significantly higher structural stability. The BD values ranged from 1.29 to 1.48 Mg m- ³, and porosity varied between 44.45 and 51.54%. Vegetable land use system had the lowest bulk density and highest porosity, suggesting better soil structure, closely followed by forest and agri-horticulture systems. Conversely, rice– wheat and ravine systems exhibited elevated bulk density and reduced porosity, reflecting soil compaction and ongoing degradation. Overall, the research highlights the benefit of diverse and protective land use systems in improving soil physical quality and promoting sustainable land management in these vulnerable semi-arid environments.

This study applied the Markov chain model integrated with Geographic Information Systems (GIS) to analyze land use trends in Cu Chi district, Ho Chi Minh City, from 2010 to 2020 and to project land use changes for 2025 and 2030. Utilizing land use status maps in 2010, 2015, and 2020, the study developed a land use transition probability matrix to simulate future land use trends. The results indicated that from 2010 to 2030, agricultural land and perennial cropland will decline, while residential land and non-agricultural production land will expand significantly due to urbanization and industrialization. The model's accuracy was validated by comparing the 2020 forecast with actual data, yielding an overall model average of 9.08% for Mean Absolute Percentage Error (MAPE) and 428.73 ha for root mean square error (RMSE), demonstrating high reliability. By 2030, residential land is projected to increase (+973.81 ha, +28.52%), whereas perennial cropland will decline sharply (-1,946.22 ha, -12.45%), primarily being converted into urban and industrial zones, posing challenges in balancing urban expansion, economic growth, and land resource conservation. This research provides a scientific basis to support land use planning, thereby assisting policymakers and urban planners in developing sustainable land management strategies for Cu Chi district.

Purpose: This study examines the hierarchical factors influencing Green Waqf-based land development in Indonesia. It aims to identify primary barriers, outline necessary institutional changes, and formulate a strategic model for ecosystem conservation and socio-economic welfare. Methodology: Using a mixed-methods approach based on Interpretive Structural Modeling (ISM), primary data were collected via interviews and questionnaires from an expert panel of academics, nazhir, and farmers. Results: ISM analysis reveals that the most critical constraints are the lack of regulatory support, limited fintech optimization, and low nazhir professionalism. Overcoming these requires structured education, specialized training, and robust inter-agency coordination. Furthermore, establishing specific legal provisions and socialization systems are foundational objectives for systemic success. Conclusion: While Green Waqf holds immense potential for climate resilience and land rehabilitation, its current implementation Achieving sustainable impact requires legal formalization, capacity building, and cross-sectoral collaboration. Implications: Policymakers must establish specific legal frameworks and guidelines for biological waqf assets (e.g., trees). Waqf institutions should adopt fintech solutions for transparency, and local communities must be empowered as active partners in agroforestry and environmental restoration. Originality: Moving beyond normative literature, this study pioneers a structured hierarchical governance model for Green Waqf, mathematically mapping previously ill-defined causal relationships among regulatory, technological, and social variables.