One of the critical public health concerns worldwide is childhood obesity, which affects both developed and developing countries. Addressing this issue requires understanding its scope and analysing its causes and contributing factors. Recent studies highlight how obesity rates vary globally, with growing attention paid to the role of local socioeconomic and environmental factors. Thus, this work integrates these updated findings to propose hypotheses explaining Sheffield's observed patterns, aligning local data with broader public health insights. This study investigates the spatial distribution and prevalence of childhood obesity among Year 6 students in Sheffield, UK. Utilizing a high-granularity baseline from the 2016/2017 National Child Measurement Programme (NCMP) alongside the Income Deprivation Affecting Children Index (IDACI), the analysis examines the spatial overlap between fast-food outlet density, green space accessibility, and income deprivation. While longitudinal data (2016–2024) confirms a persistent prevalence gap between Sheffield and national averages, spatial analysis reveals a significant "East-West" gradient. The analysis reveals an average obesity prevalence of 19.21% across the city wards, with rates ranging from 7.5% to 28.2%, with statistically significant spatial autocorrelation (Global Moran’s I > 0; p < 0.05) identifying distinct obesity clusters in eastern and central wards. Findings suggest that socioeconomic deprivation acts as an effect modifier; specifically, the protective benefits of green space appear negated in high-deprivation zones characterized by "food swamp" environments. The conclusions highlight the need for targeted interventions —such as Hot Food Takeaway (HFT) exclusion zones within the Sheffield Local Plan— to address these entrenched spatial health disparities and promote equitable outcomes.Received: 2025-08-17 Revised: 2025-12-11 Accepted: 2026-06-12 Published: 2026-07-28
This study aimed to analyze and identify the dynamics of change in the degradation of vegetation cover at En-Nuhud area, West Kordofan State, Sudan, during the period from 1975-2022. Data were obtained from Landsat Multispectral Scanner (MSS), Enhanced Thematic Mapper Plus (ETM+), and LANDSAT8 OLI for the years 1975, 1985, 1995, 2002, 2012, and 2022. EARDAS and ArcGIS version 10.4 software were used to analyze and process the Normalized Difference Vegetation Index (NDVI) data of the area under study. NDVI, Land Surface Temperature (LST), and Normalized Soil Moisture Index (NSMI) models were adopted to quantify the dynamic changes in degradation of vegetation cover, while Getis-Ord Gi* statistics were calculated to identify hotspot zones. The results showed significant dynamics change in degradation of vegetation cover without a specific rate or trend over space and time. Approximately 29.56%, 44.28%, and 26.16% of the study area suffered from low, moderate, and high degradation, respectively. Furthermore, two distinctive hotspot zones were identified with a confidence level of (P ≥ 0.05). The study has further shown that variability in rainfall coupled with successive and irregular periods of drought had a significant impact on the natural vegetation cover in the area. In conclusion, geo-technologies are identified as an indispensable factor in monitoring and assessing dynamic changes in environmental degradation.Received: 2024-12-23 Revised: 2026-03-05 Accepted: 2026-06-15 Published: 2026-08-05
The Complete Systematic Land Registration (PTSL) programme represents Indonesia’s primary strategy for land policy reform, theoretically aimed at converting informal assets into capital to improve public welfare, as conceptualised by de Soto. However, in practice, its implementation often faces significant challenges, failing to resolve underlying agrarian conflicts or structural poverty. This study investigates the gap between PTSL’s normative goals and its practical implementation in Lebak Regency, Banten. Using a mixed-method case study approach in Parungsari Village, combined with the Analytical Hierarchy Process (AHP) to analyse expert and community stakeholder priorities, this research examines the praxis of PTSL and identifies pathways for policy renewal. The findings reveal that ‘community participation’ (weight 0.401) is the most critical factor for success. Furthermore, the AHP identifies ‘policy to prevent and resolve land and agrarian conflicts’ (weight 0.391) and ‘post-certification community empowerment’ (weight 0.357) as the highest-priority policy alternatives. This contrasts with the programme’s current focus, which is merely on the rapid issuance of certificates. The study concludes that PTSL, while providing tenurial security, fails to deliver economic security. It requires fundamental reform, shifting from a purely administrative-legal tool to a socio-political instrument that prioritises conflict resolution and empowerment as prerequisites for genuine, pro-poor agrarian reform.Received: 2026-05-28 Revised: 2026-07-28 Accepted: 2026-08-24 Published: 2025-08-31
Hydrological variability in river basins is strongly influenced by LULC changes, particularly in tropical regions where rapid forest transformation and agricultural expansion alter rainfall–runoff processes. Understanding how these dynamic land surface changes affect precipitation–discharge relationships is essential for sustainable water and soil resource management. This study examines the impact of multi-temporal LULC changes on precipitation–discharge interactions in the Sagileru Basin, a tropical sub-catchment of the Pennar River basin, Andhra Pradesh, India. The SWAT hydrological model was implemented for the period 1990–2020 using topography, soil characteristics, daily meteorological data, and dynamic LULC datasets from 2000, 2010, and 2020. Model calibration and validation were carried out using SWAT-CUP (SUFI-2) with fourteen sensitive hydrological parameters and observed monthly discharge data. Model performance was evaluated using the coefficient of determination (R²), Nash–Sutcliffe efficiency (NS), and percentage bias (PBIAS). In addition, precipitation–discharge correlations were analyzed at the sub-catchment scale under different LULC scenarios. The model showed good performance, achieving R² and NS values of approximately 0.8 and low PBIAS (<1.0) during both calibration and validation periods. Results indicate that forest-dominated sub-catchments exhibit stronger precipitation–discharge correlations, whereas agricultural expansion and wasteland formation reduce correlation values by 0.12–0.30 in selected sub-catchments. Compared to similar SWAT-based studies conducted at basin scales, this study highlights pronounced spatial variability at the sub-catchment level. The integration of dynamic LULC analysis with long-term precipitation–discharge relationships provide valuable insights for prioritizing soil conservation and water management strategies in tropical watersheds.Received: 2025-12-05 Revised: 2026-04-07 Accepted: 2026-05-21 Published: 2026-08-12
Rapid population growth and urban expansion in Metro City, Lampung Province, Indonesia, have intensified pressure on land resources and environmental sustainability. Therefore, this study aimed to integrate machine learning (Support Vector Machine, SVM) and deep learning (Cellular Automata–Artificial Neural Network, CA–ANN) to analyze as well as predict settlement land-use changes and assess land carrying capacity through 2038. SPOT satellite imagery from 2013, 2018, and 2023 was used for land cover classification. The results showed that settlement areas expanded from 1,087.16 ha in 2013 to 2,700.23 ha in 2023 and are projected to reach 4,336.22 ha by 2038, primarily driven by population growth and improved accessibility. The land carrying-capacity index ranged from 3.15 to 11.09, indicating that all districts remain above the minimum threshold (DDPm > 1), suggesting sufficient land availability to support projected settlement demand through 2038. Overall, the integration of SVM and CA–ANN proved effective for modeling complex urban dynamics and predicting future settlement changes. In conclusion, the results provide a scientific foundation for policymakers and urban planners to design data-driven and sustainable spatial development strategies in rapidly growing secondary cities.Received: 2025-10-23 Revised: 2026-05-04 Accepted: 2026-06-09 Published: 2026-08-05
Samarinda City as an economic, administrative center and a supporting region for the Capital City of Nusantara, It has experienced very good dynamics in both physical and socio-economic aspects. However, this dynamic development has been accompanied by an increase in the intensity and frequency of flooding disasters. The purpose of this study is to analyze the development of the city of Samarinda and its relationship to the phenomenon of flooding. The research uses the DPSIR (Driving forces-Pressures-States-Impacts-Responses) approach to examine the factors and impacts that will arise with economic, social, infrastructure, information technology, environmental, and disaster indicators. Spatial data analysis is carried out using GIS based on Landsat imagery in the period (2010-2025). The results show that the development of Samarinda City from 2010-2015 was very significant, with a transformation towards urbanization with a spatial pattern resembling an urban-rural mix towards an urban expansion zone pattern. This dynamic shows a shift in the morphology of urban space, with natural land being transformed into built-up space. The development of Samarinda from 2020-2025 indicates a phase of consolidation of built-up areas, where the pattern of urban sprawl is transforming into a regional metropolitan city. Land use change and mining expansion contribute to economic growth but cause the loss of the city's ecological functions, thereby increasing its vulnerability to flooding. Development of Samarinda. The results of the DPSIR analysis indicate that urbanization increases the demand for residential areas 10,158.12 ha (115%), Commercial Area 11941.08 ha (287%) leading to a reduction in forest area of 6,722.04 ha (64%). declining ecological functions, and vulnerability to flooding in the of Samarinda City. The results show that the development of Samarinda City from 2010-2015 was very significant, with a transformation towards urbanization with a spatial pattern resembling an urban-rural mix towards an urban expansion zone pattern.Received: 2026-01-06 Revised: 2026-04-06 Accepted: 2026-07-28 Published: 2026-08-08
Investigating thе еnеrgy pоtеntial оf аgriсultural biоmass residues hаs becоme increаsingly impоrtant in light оf the prеssing dеmand fоr rеnеwable enеrgy оptiоns that prоducе lоw carbоn emissiоns tо соmbаt climate change. In Algeria, these residues represent a potential resource for diversifying energy mix and reducing reliance on fossil fuels. This study evaluates the energy potential of agricultural residues that could be mobilized at the national scale. The assessment is grounded in a resource-based approach built from crop production, residue-to-product ratios, calorific value, and availability factor for residues using official agricultural statistics published by the Ministry of Agriculture and Rural Development. Mathematical modeling applies standard Algebraic Summation Equations to calculate Gross Crop Residue Potential (GCRP), Recoverable Residue Potential (RCRP) and Bioenergy Potential (EP). The energy potential was assigned at the provincial level (composed of 48 wilayas) and mapped using Geographic Information Systems to visualize regional disparities across the country. The results show that the total theoretical energy potential of agricultural residues in Algeria is estimated at 93,357.60 TJ. Residues from vegetable crops account for the largest share, representing 49% of the total potential, followed by cereal residues, mainly straw (37%), and arboricultural residues (14%). At the crop level, potatoes contribute the highest share of energy potential (43%), followed by wheat (26%) and barley (10%). Marked regional differences are observed in the distribution of this potential. These differences reflect variations in agricultural production closely linked to local climatic conditions. The wilayas of El Oued, Ain Defla, and Mostaganem show the highest exploitable energy potential, accounting for 11.27%, 7%, and 6%, respectively, of the national total. These results provide a quantitative and spatial basis for considering agricultural biomass within Algeria’s renewable energy strategy. Received: 2026-01-15 Revised: 2026-07-21 Accepted: 2026-08-28 Published: 2026-08-31
This study integrates population and socio-economic data into key development indicators, including the Human Development Index (HDI) and its components (income, education, and health), along with population density, poverty, illiteracy, school enrollment, and health service coverage. The objective is to construct a spatial database and assess regional development disparities across Iraqi governorates. A mixed spatial–statistical approach was applied. Statistical analysis was conducted using SPSS, including descriptive statistics, Pearson correlation, multiple linear regression, and cluster analysis to examine relationships among variables and classify governorates into homogeneous development groups. Geographic Information Systems (GIS) using ArcGIS 10.8 were employed to build a spatial database, integrate socio-economic data with administrative boundary maps, and produce thematic maps for spatial visualization of development patterns and disparities. The results reveal significant spatial inequalities in Iraq. Northern governorates show higher levels of development, with HDI reaching 0.73 in Erbil, while central and southern governorates record lower values, down to 0.58 in Muthanna. Strong negative correlations were found between HDI and both poverty and illiteracy, while positive relationships were observed with education and health indicators. The study concludes that integrating GIS with statistical analysis enhances the identification of development gaps and provides a robust evidence base for sustainable spatial planning and policy formulation.Received: 2025-11-02 Revised: 2026-06-12 Accepted: 2026-07-28 Published: 2026-08-05
The Indonesian government has designated many national strategic projects, including Yogyakarta International Airport (YIA), but the area is prone to coastal disasters, specifically abrasion and tsunamis. Greenbelt planning was proposed as a nature-based solution to mitigate these disasters. This study aimed to disclose the planning processes of YIA and the establishment of coastal forest as a greenbelt area, examine stakeholder participation, and identify the strategies used to implement the policy and plan. A total of 36 key informants were interviewed, and the policy documents were studied to analyze 1) chronology of YIA development planning processes, 2) typology of stakeholders, and 3) social network analysis (SNA) and policy strategies in establishing greenbelt area. The results showed that establishing YIA and Greenbelt required a long-term planning horizon passing through three periods between 2011 and 2020, namely Envision, Acceleration, and Mitigation. Five typologies of stakeholders were identified, including policymakers, planners, facilitators, implementers, farmer groups, and academicians. SNA showed that the forestry agency played a significant role in maintaining the communication and networks among the other stakeholders to establish greenbelt. This forestry agency used incentives, such as providing seedlings, technical guidance, and planting and maintenance costs for farmer groups. Government agencies (provincial, district, and village level) generally used a regulatory method (coercion) and dominant information to influence shrimp-pond farmers to leave the pond areas for greenbelt establishment. This study showed the importance of smart coordination in greenbelt planning using multiple strategies when complex problems arise. Received:2025-05-03Revised: 2026-03-05 Accepted: 2026-06-15 Published: 2026-08-05
This study compares Linear Regression, Random Forest (RF), CNN, and Conv1D-LSTM+Attention for estimating WorldPop-derived population density on 500 m grids in Baghdad and Basra, Iraq (2015–2020). The raw panel contained 13,530 cell-year records, with 12,678 retained after excluding zero-population cells. Alongside conventional random partitions, all four models were evaluated using leave-one-quadrant-out spatial cross-validation. Mean spatial R² was negative for every model in both cities; for example, RF achieved −0.751 ± 1.130 in Basra and −1.096 ± 0.359 in Baghdad. These results contrast with random-split Conv1D-LSTM+Attention performance (R² = 0.833 in Basra; 0.199 in Baghdad), indicating that random partitions overstate out-of-area predictive skill. Moran’s I confirmed strong spatial dependence (Baghdad = 0.847; Basra = 0.946; both p = 0.001). In Basra, a naïve persistence baseline achieved R² = 0.692. Explainability analyses agreed strongly in Basra, where CNN permutation importance and RF-SHAP both identified nighttime lights as dominant; agreement was partial in Baghdad, where CNN ranked LST first while RF-SHAP ranked nighttime lights first. Bidirectional CNN and RF transfer increased RMSE by 33.6–303.8%, indicating poor cross-city portability. Overall, spatial validation is essential for satellite-based population modeling, and WorldPop circularity means the models primarily approximate an existing population surface rather than independent census ground truth.Received: 2026-06-02 Revised: 2026-08-11 Accepted: 2026-08-24 Published: 2026-08-27
Currently, precipitation and temperature stand as the foremost indicators of climate change, exerting a profound and far-reaching influence on the global environment. This scholarly inquiry endeavors to illuminate the spatial and temporal dynamics of these climatic variables—fundamental markers of seasonal variation in the Kegalle District—spanning 41 years from 1981 to 2024. These datasets were subjected to rigorous trend analysis utilising Minitab, employing the Mann-Kendall test alongside Sen's Slope Estimator to detect and interpret temporal patterns. Among the ten selected localities, Yatiyanthota exhibits the most pronounced temperature fluctuations, with an average of 26.5°C and a minimum of 19.25°C, where temperatures reach 34.8°C. Notably, this region exhibits a decline in maximum temperatures alongside an increase in minimum temperatures. Conversely, Aranayaka maintains a relatively moderate mean temperature of 18.9°C. Rainfall observations reveal that Rambukkana experiences the highest precipitation, with an increase of 1,826 millimeters, whereas Deranyagala records the lowest. On a monthly scale, October and November receive the greatest rainfall, whereas April and March are characterised by minimal precipitation coupled with elevated temperatures. Trend analyses identify Aranayake as the locale with the lowest averages of both temperature and rainfall.Received: 2025-10-16 Revised: 2026-04-29 Accepted: 2026-07-13 Published: 2026-08-05
Accurate rice yield prediction by remote sensing data and machine learning is still a big challenge in limited resources of field survey where the sample size is often very small. This study tackles the core challenge of small sample size (n=39) in rice yield classification by proposing a hybrid feature engineering framework that combines Principal Component Analysis (PCA) and Recursive Feature Elimination (RFE) in a regularized Random Forest (RF) classifier. The methodology is based on 30 spectral features from multi-temporal Sentinel-2A imagery (15 spectral bands i.e. Bands 2, 3, 4, 5 and 8 for three acquisition dates and 15 vegetation indices). PCA revealed three principal components that explained approximately 90% of the total variance and RFE selected the five most discriminative spectral bands (SAMPLE_14, SAMPLE_15, SAMPLE_17, SAMPLE_23 and SAMPLE_26). These were fused in a compact eight-dimensional hybrid feature space. Model evaluation was conducted using repeated stratified cross-validation (5-fold x 10 repeats) and an independent test set (holdout 30%). The average cross-validation accuracy of the hybrid model was 83.36%, ROC-AUC 0.9304, independent test accuracy 91.67% and Cohen's Kappa 0.8333. There was no over-fitting of the training-validation gap to 0.10 in the learning curve. The optimal classification threshold was identified as 0.5373 in the Youden Index optimization. The RFE-selected spectral bands contributed the largest share (76%) in the feature importance analysis, and the PCA components provided a complementary 24%, confirming the synergistic value of unsupervised extraction and supervised selection. However, spatial uncertainty mapping revealed limitations to the overall extrapolation, with near-maximum values (0.999) in unsampled areas, emphasizing the need for targeted field verification in high-uncertainty zones. The study provides a replicable methodological blueprint for crop yield classification under very limited data conditions and provides practical guidance for agricultural monitoring in developing countries with logistical and financial constraints.Received: 2025-09-15 Revised: 2026-06-26 Accepted: 2026-07-28 Published: 2026-08-05
Historical landscapes maintain enduring records of human–environment interactions, which can be analyzed through spatial patterns present in the landscape. Within geographical studies, landscape ecology offers a framework for interpreting these patterns by linking human activity to ecological gradients and spatial structure. This study investigates the spatial distribution of classical period archaeological remains in Lasem, North Java, employing a landscape archaeology approach that integrates ecological variables and spatial configuration. The research conceptualizes archaeological remains as spatial patches situated within diverse ecological zones, such as volcanic uplands, alluvial plains, and coastal plain. Spatial analysis incorporated elevation, landform units, soil types, hydrological proximity, and distance-based relationships to key landscape elements. These variables were integrated with a qualitative spatial narrative to interpret how environmental conditions influenced historical land-use strategies. The results show that the distribution of archaeological patches is closely linked to specific ecological settings, especially transitional zones between upland and alluvial landscapes and areas with reliable access to water resources. Archaeological remains are not evenly distributed but instead form patterned clusters that reflect adaptive responses to topography, hydrology, and land suitability. This spatial structure indicates that landscape configuration significantly influenced human activity during the classical period in Lasem. By integrating landscape ecology with archaeological spatial data, this study highlights the value of geographical approaches for interpreting historical landscapes. The findings contribute to broader discussions on landscape structure, long-term land-use processes, and the application of ecological concepts to historical spatial analysis.Received: 2026-03-27 Revised: 2026-07-01 Accepted: 2026-04-14 Published: 2026-08-20
Land subsidence has become one of the most critical environmental hazards affecting coastal cities in Indonesia, particularly North Semarang, where rapid urban expansion, industrial development, and intensive groundwater extraction have accelerated ground deformation and increased the vulnerability of urban infrastructure. Understanding the spatial relationship between land use change and land subsidence is therefore essential for supporting sustainable urban planning and disaster risk mitigation. This study aims to evaluate land subsidence patterns using spatial interpolation techniques and to investigate the influence of land use change on subsidence dynamics in North Semarang. The study integrates Geographic Information System (GIS)-based spatial analysis with multi-temporal geospatial datasets, including land subsidence monitoring data, SPOT-6 satellite imagery, and official land use maps covering the 2013–2023 period. Land use changes were identified through image interpretation, field verification, digitization, and overlay analysis using ArcGIS. Four interpolation methods—Inverse Distance Weighted (IDW), Natural Neighbor, Kriging, and Spline—were evaluated using the Root Mean Square Error (RMSE) statistic to determine the most reliable approach for modeling the spatial distribution of land subsidence. The results indicate substantial conversion of undeveloped land into industrial, warehousing, commercial, and transportation areas, particularly around Tanjung Mas Port, demonstrating rapid urbanization during the study period. Evaluation of the interpolation methods shows that the Kriging model achieved the lowest RMSE value (0.3843), outperforming IDW (0.4101), Natural Neighbor (0.4890), and Spline (0.5428), indicating the highest predictive accuracy for representing land subsidence patterns. The spatial analysis further reveals that areas experiencing the most intensive urban development coincide with zones of higher subsidence rates, which increased from 5.47 cm/year in 2013 to 6.03 cm/year in 2023, while land use conversion reached approximately 88.45 ha/year. These findings demonstrate a strong spatial association between urban expansion and land subsidence in North Semarang. This study demonstrates that GIS-based spatial interpolation combined with multi-temporal land use analysis provides an effective framework for identifying land subsidence hotspots and evaluating their relationship with urban development. The findings offer valuable scientific evidence for spatial planning, groundwater management, and coastal risk mitigation, while supporting the development of sustainable land-use policies in rapidly urbanizing coastal environments.Received: 2024-02-16 Revised: 2024-02-23 Accepted: 2026-08-25 Published: 2026-08-31
Maternal health remains a pressing concern in India, as the country is marked by vast socio-cultural diversity. A substantial percentage of indigenous mothers in India still deliver their babies at home. Therefore, the present study aims to find out the levels and determinants of institutional delivery. To fulfill the set objectives data has been taken from nationally representative dataset- National Family Health Survey 2019-21 (NFHS-5). Appropriate univariate, bivariate and multivariate statistical techniques have been applied. The multivariate analysis indicates that education, religion, sex of household head, wealth quintile, children ever born, registration of pregnancy, antenatal care, place of residence, region and topography have a significant effect on the place of delivery. Education of mother and wealth quintile tends to have negative bearing on home delivery. Compared to Hindu indigenous mothers, all other women are more likely to deliver their babies at home. Similarly, mothers living in female-headed households and rural areas, as well as those with a higher number of children ever born, show a greater likelihood of home delivery. Conversely, pregnancy registration and antenatal care exert a negative effect on home delivery. The present study indicates that topography has a great role in determining the place of delivery. However, the belief that it is ‘not necessary’ remains the most frequently cited reason for home delivery. Hence, concerted effort is required to bring down the home delivery among the indigenous mother for attaining the Sustainable Development Goals. Received: 2025-09-19 Revised: 2025-12-11 Accepted: 2026-04-29 Published: 2026-04-30
This study examines the integration of STEM technologies into geography programs in higher education institutions to develop students' research competencies, spatial thinking, and interdisciplinary problem-solving skills. Based on a systematic analysis of 29 recent publications on STEM in geography education, the study identifies five key features of STEM-based learning: organizing research activities, developing practical and analytical skills, encouraging innovative and project-based thinking, effectively using digital tools and GIS, and gradually introducing STEM methodologies. Drawing on the international experience of countries such as Malaysia, China, and Indonesia, a STEM laboratory model for teaching geography is proposed. The results show that the structured implementation of STEM technologies significantly improves students' ability to conduct experiments, analyze data, make predictions, and develop critical thinking, thereby preparing future specialists to solve social, environmental, and economic problems. This study lays the foundation for integrating STEM into geography curricula and offers practical recommendations for creating a laboratory learning environment in universities. In general, geography, using STEM technology, can study or analyze issues such as urbanization, socioeconomic inequality, disease incidence (mapping), migration, natural.
This study aimed to explore urban influences on a highland village as a node in an urban network. The discussion focused on altering the settlement situation and the life of villagers, who were dominated by vegetable farming. The analysis was inspired by Doxiadis’s premises concerning human efforts to maximize contact with nature efficiently to fulfill living needs. A descriptive qualitative method was adopted with data collected through interviews, observation, and mapping. The interviews were conducted in two stages, including semi-structured interviews with 90 household respondents and in-depth sessions with three key informants. To ensure the quality and accuracy of the data, source and method triangulation was applied by systematically comparing information obtained from household interviews, in-depth interviews with key informants, and field observations. Based on the results, the life atmosphere of the village was becoming increasingly urban. Urban-oriented nicknames further evolved for certain places, reflecting the villagers’ new spatial mindset. Primordial spaces were also transformed into modernity, guided by economic logic and proximity to major cities through the main road. Therefore, the functional relationship between villagers and nature had diminished. Urban practices were not fully prominent as urbanization was an aspect that villagers sensed, imagined, and perhaps even anticipated. Received: 2025-03-16 Revised: 2025-12-17 Accepted: 2026-03-12 Published: 2026-04-29
This study aims to determine the amount of greenhouse gas emissions in the rice paddy agricultural sector in Simbalai Village, Loea District. The research was conducted in April 2022 in Simbalai Village, Loea District, which has 167 hectares of wet-rice fields. The methods used in this research included observation of paddy rice areas, data collected from the Agricultural Extension Center (BPP) of Loea District, and interviews. The sampling in this study employed a purposive sampling method, with key informants comprising rice farmers selected based on their knowledge and involvement in rice cultivation practices. The technique to determine the amount of greenhouse gas emissions involved using data on agricultural activities in the village, obtained from records maintained by the Loea District Agricultural Extension Center (BPP), and then analyzing the data based on PERMEN No.73 of 2017, which provides guidelines for implementing and reporting the National Greenhouse Gas Emission Inventory. The results of this study indicate that emissions from irrigated rice farming activities include 640.5 tons of CO2-eq/year of CH4, and from rain-fed rice fields, 178.5 tons of CO2-eq/year of CH4. Additionally, CO2 emissions from using urea fertilizer amounted to 787.3 tons C/year, and N2O emissions from using urea fertilizer amounted to 272.2 tons CO2-eq/year. The total emissions amounted to 1878.58 tons of CO2-eq/year. Received: 2024-10-21 Revised: 2025-08-25 Accepted: 2026-04-22 Published: 2026-04-28
The satellite gravimetry method has been employed for subsurface configuration modeling, delineating structures and boundaries of an aquifer, and determining an aquifer layer in a wide range of geological formations. However, this method is rarely used in the study of karst aquifers. This paper explores its applicability for distinguishing contact between a limestone and a volcanic rock layer and for characterizing the subsurface configuration of the karst aquifer. Satellite gravimetry GGMplus has been used in this paper. This paper constructed a subsurface model using forward modeling, executed across five sections in the study area, and identified 11 rock formations. Five outcrop locations, previous studies, and published geological maps validated the residual anomaly map and model. The model results from satellite gravimetry indicate a distinct contact between limestone (Wonosari F.) and the underlying volcanic rock, either andesitic breccia (Nglanggran F.) or tuff (Semilir F.). The mean gravity of the three formations was 2.59 g/cc, 2.3 g/cc, and 2.29 g/cc. The karst aquifer thickness obtained from the model matches the limestone thickness on the geological map. This paper unveils that satellite gravimetry can determine contact between limestone and volcanic rocks and model karst aquifer configuration. Received: 2025-12-22 Revised: 2026-03-12 Accepted: 2026-05-01 Published: 2026-04-30
The wetlands of kumadugu yobe river basin are the livelihood soul of the communities living around the wetlands site. The dominant pastoralist and agrarian population of the wetland site drives their livelihood directly from the wetlands. This study examines how the livelihoods of wetland users respond to fluctuations in wetlands. A total of 294 households were sampled. The interview questions focused on resources extracted from the wetlands, seasonal changes in volumes of wetland resources, and the impact of resource dynamics on general livelihoods. Both Descriptive and inferential statistical tools were employed in analysing the data collected. The study revealed that the wetlands are found to be the source of livelihood to 97% of the wetlands population. Degradation in wetlands productivity affects agricultural output by 61%, 48%, 55%, 71%, 65%, 73% and 47% for rice, wheat, maize, guinea corn, millet, groundnut, and cowpea, respectively, and fishing output by 77%. The shrinking of wetlands favoured wet-season livestock production, but affected dry-season grazing and increased the farmers/harder conflict. The impact of wetlands components' fluctuations on the assets and income of wetlands users is statistically significant at the 99% level. The livelihood of the KYRB wetland population responds significantly to any slight changes in the wetlands. Therefore, the Kumadugu River basin's wetlands degradation means livelihood lost to 97% of the population, thus necessitating proper management strategies to prevent the wetlands from disappearing, to prevent a social catastrophe that can affect the whole Sahel region's stability. Received: 2023-05-14 Revised: 2025-12-11 Accepted: 2026-03-05 Published: 2025-04-01
