Sustainable Resource Management Research Articles

Energy‐Aware Power Control Scheme For Water Quality Monitoring Systems

ABSTRACTThis research introduces an innovative power management approach for wireless sensor networks in aquatic environmental monitoring. The study presents a dynamic algorithm that optimizes energy consumption in long range (LoRa) communication nodes by adaptively adjusting transmission power based on sensor–gateway distances. Leveraging GPS data and a log‐normal shadowing model, the method enables efficient power allocation. Field testing in an aquaculture setting demonstrates the energy‐aware power control (EAPC) algorithm's efficacy. Nodes at 500 m from gateways achieve up to 39% reduction in power consumption compared to fixed‐power systems. At 1250 m, savings decrease to 22%, and at 1500 m, to 11%, while maintaining reliable communication. This research advances sustainable aquatic resource management, offering applications in aquaculture, environmental conservation, and water resource management. By optimizing power usage, the approach contributes to more effective long‐term monitoring of aquatic environments.

Groundwater potential mapping in semi-arid region of Northern Nigeria by integrating analytic hierarchy process and GIS

Groundwater resource management in drylands, characterized by climate variability and population growth, is difficult. Exploration and exploitation of groundwater, due to inadequate surface water is very costly. This study employed the analytic hierarchy process (AHP) and GIS to identify groundwater potential (GWP) areas in a semi-arid region of Nigeria. Land-use-land-cover, drainage density, slope, rainfall, static water level, soil, lithology, and aquifer were selected for GWP analysis. Parameter weights were determined using AHP and ranked based on their contribution to GWP by experts. The parameters were then integrated using the weighted overlay tool in ArcGIS 10.5 to produce a GWP map of the study area. Borehole yield data from 245 wells were collected to determine the model accuracy and model validation. Results classified the study area into very high GWP (1.9%), high GWP (8.8%), moderate GWP (62%), low GWP (20.70%) and very low GWP (6.6%). Areas with better GWP include Gurun, Dugol, Zago, Kumbo, Dukku, Doguwa, Riruwai, Dambazau, etc. Moderate GWP areas are widely spread across the state while areas with poor GWP include Sharawa, Kadewa, Koya, Dagar, Jigilawa, Galwanga, Yanganau, Kunchi, Tofa, Tsanyawa, etc. Validation of the AHP model with borehole yield data shows a correlation coefficient of 71.3% giving a good prediction. AHP and GIS can be used to successfully map GWP areas which could serve as an exploration guide for sustainable management of groundwater resources in semi-arid areas.

Finding the Nexus between Gender-Based Violence and Conservation

This review paper explores the complex and multifaceted relationship between gender-based violence (GBV) and conservation efforts, emphasizing the interconnected challenges faced by rural communities. It analyzes how GBV undermines conservation outcomes by limiting women’s participation, eroding traditional ecological knowledge, exacerbating resource exploitation, and weakening community resilience. Drawing on case studies from Northern Ghana, India, and Tanzania, the paper highlights how environmental issues such as resource scarcity, displacement, and conservation-related conflicts increase the vulnerability of women to GBV. The review also discusses strategies for addressing these challenges, including empowering women, integrating gender considerations into conservation policies, and creating safe environments for participation. Finally, it calls for more research into the links between GBV and conservation, urging the development of targeted interventions that address the root causes of both GBV and environmental degradation. By integrating gender-sensitive approaches, conservation initiatives can achieve more equitable and sustainable outcomes for all community members. Keywords: Gender Equality, Women's Empowerment, Social Cohesion, Ecological Knowledge, Sustainable Resource Management

Chromosome-level genome assembly of Indo-Pacific king mackerel (Scomberomorus guttatus).

The Indo-Pacific king mackerel, scientifically known as Scomberomorus guttatus, is a valued marine species that holds significant commercial importance in the Indo-Pacific region. However, the lack of genomic resources has hindered a comprehensive understanding of this species. In this study, we constructed a genome of Indo-Pacific king mackerel at the chromosome level using a combination of PacBio HiFi reads and a chromosome contact map (Hi-C). The resulting genome had high contig and scaffold N50 values of 8.84 Mb and 32.9 Mb, respectively. In the genome assembly, which is 797.66 Mb in size and consists of 24 chromosomes, we also identified 35.89% repetitive elements and predicted 25,886 protein-coding genes. Our study not only benefits to reveal the possible mechanism of adaptive evolution in this fish, but also offers insight for the future sustainable management of these valuable biological resources.

Convolutional long short-term memory neural network for groundwater change prediction

Forecasting groundwater changes is a crucial step towards effective water resource planning and sustainable management. Conventional models still demonstrated insufficient performance when aquifers have high spatio-temporal heterogeneity or inadequate availability of data in simulating groundwater behavior. In this regard, a spatio-temporal groundwater deep learning model is proposed to be applied for monthly groundwater prediction over the entire Choushui River Alluvial Fan in Central Taiwan. The combination of the Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM) known as Convolutional Long Short-Term Memory (CLSTM) Neural Network is proposed and investigated. Result showed that the monthly groundwater simulations from the proposed neural model were better reflective of the original observation data while producing significant improvements in comparison to only the CNN, LSTM as well as classical neural models. The study also explored the performance of the Masked CLSTM model which is designed to handle missing data by reconstructing incomplete spatio-temporal input images, enhancing groundwater forecasting through image inpainting. The findings indicated that the neural architecture can efficiently extract the relevant spatial features from the past incomplete information of hydraulic head observations under various masking scenarios while simultaneously handling the varying temporal dependencies over the entire study region. The proposed model showed strong reliability in reconstructing and simulating the spatial distribution of hydraulic heads for the following month, as evidenced by low RMSE values and high correlation coefficients when compared to observed data.

Sustainable human resource management and sustainable performance: mediating role of organization citizenship behavior

Purpose This study aims to investigate the influence of sustainable human resource management (SHRM) policies on sustainability performance of higher education institutions (HEIs). Furthermore, this study also examined the mediating role of organizational citizenship behavior toward sustainability (OCBS) between SHRM and HEIs’ sustainability performance. The ability motivation opportunity (AMO) and stakeholder theories are used to conceptualize the model. Design/methodology/approach Data were collected from 162 academic staff of 12 government and private universities in India by using snowball sampling method. The partial least square structured equation modeling technique was used to analyze the data. Findings The overall results suggest that SHRM, such as sustainability motivation, sustainability-building practices, green employee engagement initiatives and social considerations, significantly impact OCBS among academic staff and the sustainability performance of HEIs. These findings provide a better understanding of how to improve the sustainability performance of HEIs through SHRM and OCBS among academic staff. Originality/value This study significantly extends the growing literature on SHRM and sustainability performance by focusing on sustainable HR practices in the HEIs. To the best of the authors’ knowledge, this is one of the first studies to empirically investigate SHRM and sustainability performance in the context of education sector in India.

Brackish water parameters monitoring dashboard using Internet of things and industry 4.0

INTRODUCTION: Brackish water aquaculture plays a crucial role in meeting the growing global demand for seafood. It offers an opportunity to diversify aquaculture production and reduce pressure on overexploited marine resources. OBJECTIVES: By harnessing the unique properties of brackish ecosystems, this practice contributes to food security, economic growth, and sustainable resource management, while also promoting the conservation of valuable marine habitats. The development of a cutting-edge Indigenous Water Quality Monitoring Prototype named "Aqua BuoySis" for precision brackish water aquaculture utilizing machine intelligence. METHODS: The prototype integrates sensors for Dissolved Oxygen (DO), pH, Temperature, Turbidity, and Total Dissolved Solids (TDS). These sensors are calibrated using a dynamic temperature-based machine-learning approach to ensure accuracy in real-time environments. Sensor calibration constants are uploaded to a server for comprehensive data calibration. RESULTS: The system collects data at 20-second intervals, associating it with specific pond IDs. Data refinement is achieved through Long Short-Term Memory (LSTM) processing. An Android and Web application, available in native languages such as Tamil and Telugu, has been developed to provide live updates to aqua farmers, facilitating informed decision-making. CONCLUSION: This technology represents a significant step towards enhancing precision in brackish water aquaculture through the fusion of machine intelligence and water quality management.

Analysis of Water Dynamics at the Effluent Treatment Station of Cooperoque: Evaluation of Precipitation, Evaporation, and Infiltration of Effluent from a Milk Refrigeration Station

Purpose: The study aims to analyze water dynamics at the Cooperoque Effluent Treatment Station, focusing on the interactions between precipitation, evaporation, and effluent infiltration to enhance water resource management. Theoretical reference: The research references various studies and methodologies related to the quantification of evaporation from lakes and reservoirs, particularly in semi-arid regions, utilizing the Jensen-Haise Method for evaporation estimation. The research aligns with previous studies, such as Mallmann (2014), on water infiltration in soil management, emphasizing the importance of understanding water dynamics for sustainable resource management. Method: The study employs the Jensen-Haise Method to calculate evaporation rates, covering the period from January 2023 to October 2024. It examines the effects of precipitation, evaporation, and effluent infiltration on tank levels. Results and conclusion: The results reveal that tank levels are influenced by precipitation, evaporation, and effluent infiltration, with more significant declines during dry months and smaller reductions during periods of high precipitation. Evaporation rates, calculated using the Jensen-Haise Method, are higher in warmer months, contributing to lower tank levels, while these rates decrease in colder months. A notable anomaly occurred in May 2024 when effluent infiltration exceeded the volume of generated effluents and rainfall due to the activation of an irrigation system. Key findings indicate that lake levels drop more during dry months, with a significant negative water balance observed in May 2024 due to excessive infiltration during heavy rainfall. The study concludes that adaptive water management strategies are necessary to address climatic variations and improve the efficiency of the effluent treatment system. Implications of research: The research provides valuable insights for developing adaptive water management strategies to address climatic variations and improve the efficiency of effluent treatment systems. Continuous monitoring is emphasized as crucial for sustainable water resource management. Originality/value: The study highlights the complex interactions between precipitation, evaporation, and effluent dynamics, offering a foundation for improved water management practices and contributing to the preservation of regional water resources. The study contributes to the understanding of water dynamics at effluent treatment stations, offering practical insights for managing water resources effectively. It highlights the importance of adaptive strategies and continuous monitoring to maintain pond levels and optimize the treatment process.

Advances in understanding biofilm-based marine microbial corrosion

Abstract Microbially influenced corrosion and biofouling emerge as formidable challenges to the sustainable management and exploitation of marine resources. The primary instigator for these challenges lies in the insidious development of biofilm. Hence, the most direct and pivotal approach to counteracting microbial corrosion and biofouling resides in the advancement of anti-biofilm technologies. Conventional methodologies for combatting biofilm are efficient but have certain drawbacks, particularly environmental contamination and inefficacy. Research into innovative anti-biofilm technologies is imperative for more efficient use of marine resources and protection of the ecological equilibrium of the oceans. This paper offers a detailed examination of biofilm constituents, the complex processes involved in biofilm development, the various factors that affect biofilm formation, and the mechanisms underlying microbiologically influenced corrosion (MIC). Furthermore, the article summarizes emerging, eco-friendly anti-biofilm technologies, while providing the evolving landscape of anti-biofilm strategies and promising prospects.

Environmental health and human health-unifying strategies, conceptual framework and green prescriptions: a review

The intricate relationship between environmental conditions and human health is increasingly acknowledged as a critical area of public health research. As global challenges such as climate change, urbanization, and biodiversity loss become more pronounced, understanding the connections between these environmental factors and health outcomes is essential for developing effective public health strategies. This analysis delves into the complex interactions that exist between environmental influences and health, emphasizing the urgent need for integrated approaches that simultaneously promote ecological sustainability and enhance community well-being. Environmental degradation has significant implications for human health, with many deaths linked to preventable environmental health risks. These risks encompass a wide range of issues, including air and water pollution, exposure to hazardous substances, and the impacts of climate change, all of which disproportionately affect vulnerable populations. A conceptual framework is proposed to elucidate the multifaceted connections between environmental exposures, health determinants, and health outcomes, enabling a clearer understanding of how these elements interrelate. Cohesive strategies, such as the one health approach, advocate for collaboration across various sectors, including public health, veterinary health, and environmental management, to address the shared challenges of health and environmental degradation. Additionally, the principles of planetary health highlight the interdependence of ecosystems and human well-being, underscoring the need for sustainable management of natural resources to safeguard health outcomes. This article also explores the concept of "green prescriptions," which involve recommending nature-based interventions to enhance physical, mental, and social well-being. These prescriptions have demonstrated effectiveness in promoting health while fostering a sense of environmental stewardship. By synthesizing current evidence and successful case studies, this review emphasizes the significance of adopting a holistic perspective that integrates environmental considerations into healthcare practices. Ultimately, fostering collaboration among various stakeholders will contribute to the development of resilient communities that are better equipped to navigate the intertwined challenges of health and environmental sustainability.

A Systematic Review for The Development of Sustainable Tourism Destinations Based on Sports Tourism

The study purpose: The project's goal is to develop sustainable tourism destinations in Kemuning Village by reviewing the literature on sports tourism Materials and methods: An extensive literature review was carried out. Articles from 2015 to 2024 covered ways to enhance sustainable tourism by fostering the growth of sports tourism. For electronic searches, Google Scholar, PubMed, Web of Science, and Scopus are used. All the articles that discuss how to improve sports tourism-based tourist attractions are included here. Results: According to the integrated theory of tourism, sports have a role in (1) tourism management strategies, which include methods, facilities, tourist attractions, SOPs, infrastructure, additions and maintenance of facilities, sustainable management of natural resources, and visitor security and safety; (2) the role of stakeholders, which includes the government as a regulator and related institutions; and (3) the role of the authorities, which includes government agencies and institutions related to Sports tourism will increase visits to Central Java and improve the economy. Sports activities in tourist destinations can benefit local governments, enhance community welfare, and create more job opportunities if they are appropriately managed. Sports tourism and activities can be combined in Central Java. Conclusions: Combining sporting activities like river tubing, paragliding, tea gardens, and jeep communities with Kemuning Village in Ngargoyoso District, Karanganyar, Central Java, Indonesia, can help the village grow its tourism industry due to the fact that it influences the social, cultural , and economic growth of the area, promoting local advancement.

The concept of big data and predictive analytics in reservoir engineering: The future of dynamic reservoir models

Reservoir engineering is a critical discipline in the oil and gas industry, aimed at optimizing the extraction and management of subsurface resources. Traditionally, reservoir models have relied on static data and conventional simulation methods, which often fall short in capturing the dynamic complexities of reservoirs. With the advent of big data and predictive analytics, there is a significant opportunity to revolutionize reservoir modeling by integrating real-time data for continuous updates and more accurate predictions. This review introduces a theoretical exploration of how big data and predictive analytics can transform dynamic reservoir models, highlighting their potential to improve reservoir management and hydrocarbon recovery. Big data technologies enable the collection, storage, and processing of vast amounts of structured and unstructured data from multiple sources, including well logs, seismic surveys, drilling operations, and production data. Predictive analytics, through machine learning and statistical techniques, can extract actionable insights from these datasets to predict reservoir behavior, enhance decision-making, and optimize production strategies. This integration facilitates real-time monitoring and adaptive reservoir management, allowing engineers to respond to changing reservoir conditions and uncertainties more effectively. The review further explores the implications of big data-driven predictive models for enhanced hydrocarbon recovery techniques, such as water flooding and gas injection. By automating data processing and integrating real-time field data, predictive analytics can improve the accuracy of reservoir simulations, reduce downtime, and increase operational efficiency. Ultimately, the future of dynamic reservoir modeling lies in the seamless integration of big data and predictive analytics, providing a pathway to smarter, more sustainable resource management and maximized recovery factors in increasingly complex reservoir environments. Keywords: Big Data, Predictive Analytics, Reservoir Engineering, Dynamic Reservoir, Models.

Remote Sensing and GIS in Natural Resource Management: Comparing Tools and Emphasizing the Importance of In-Situ Data

Remote sensing (RS) and Geographic Information Systems (GISs) provide significant opportunities for monitoring and managing natural resources across various temporal, spectral, and spatial resolutions. There is a critical need for natural resource managers to understand the expanding capabilities of image sources, analysis techniques, and in situ validation methods. This article reviews key image analysis tools in natural resource management, highlighting their unique strengths across diverse applications such as agriculture, forestry, water resources, soil management, and natural hazard monitoring. Google Earth Engine (GEE), a cloud-based platform introduced in 2010, stands out for its vast geospatial data catalog and scalability, making it ideal for global-scale analysis and algorithm development. ENVI, known for advanced multi- and hyperspectral image processing, excels in vegetation monitoring, environmental analysis, and feature extraction. ERDAS IMAGINE specializes in radar data analysis and LiDAR processing, offering robust classification and terrain analysis capabilities. Global Mapper is recognized for its versatility, supporting over 300 data formats and excelling in 3D visualization and point cloud processing, especially in UAV applications. eCognition leverages object-based image analysis (OBIA) to enhance classification accuracy by grouping pixels into meaningful objects, making it effective in environmental monitoring and urban planning. Lastly, QGIS integrates these remote sensing tools with powerful spatial analysis functions, supporting decision-making in sustainable resource management. Together, these tools when paired with in situ data provide comprehensive solutions for managing and analyzing natural resources across scales.

Managed Aquifer Recharge for Sustainable Groundwater Management: New Developments, Challenges, and Future Prospects

The combined effect of climate change and increased water demand has put significant strain on groundwater resources globally. Managed aquifer recharge (MAR) has become an effective approach for addressing groundwater depletion problems and sustainable management of groundwater resources. This review article provides an extensive insight into the existing knowledge of MAR, including the main objectives and applications, implementation techniques (surface spreading, sub-surface, and induced recharge) being practiced over the years, risks and challenges associated with the MAR, and the developments in the field of MAR. This review also explores the potential of MAR in the Friuli Venezia Giulia (FVG) region, north-eastern Italy. An average increase in temperature and a decrease in precipitation and piezometric levels in the region suggest the development of a proper MAR plan to manage water resources in the decades to come. Additionally, a comparative analysis of studies published over the last 20 years, focusing on the quantitative and qualitative aspects of water resource management, is conducted to analyze the research trends in the field of MAR. The reviewed literature reveals a notable research trend towards the quantitative aspect compared to the qualitative one. This review also identifies a notable disparity in qualitative studies during the analysis of water quality parameters considered in different MAR studies. Based on this review, a prospective viewpoint to address the challenges and expand the scope of the field is presented. This calls for an optimized strategy that considers both water quality and quantity issues, along with incorporating environmental and socio-economic aspects within the framework of MAR.

Indigenous Inland Fisheries Development and Management in India

This paper provides a comprehensive review and analysis of indigenous inland fisheries development and management practices in India. Drawing on extensive literature review and case studies, it examines traditional ecological knowledge, community-based management systems, and the integration of indigenous practices with modern scientific approaches. The research highlights the diversity of inland fisheries across India's varied ecosystems and the critical role of indigenous communities in sustainable resource management. Key challenges including overfishing, habitat degradation, and socioeconomic pressures are analyzed. The paper proposes policy recommendations to support and scale indigenous management practices while addressing conservation needs. Findings underscore the importance of participatory, culturally-appropriate approaches that leverage local knowledge for inland fisheries development in India.

Diversity and ethnobotanical study of homegarden plants in Goba district, Oromia region, Ethiopia

Homegardens are one of the most diverse agroforestry systems and make a vital contribution to meeting various household needs, especially for smallholder farmers in developing countries. The aim of this study was to record the diversity and use of homegarden plant species in the Goba District. The households were randomly selected from five kebeles, with the number selected proportionate to the size of each kebele using the probability proportional to size (PPS) calculation. Seventy-one homegarden owners were chosen for the detailed study and data collection. Of the fifteen kebeles in the Goba District, five kebeles were chosen at random. Observations, semi-structured interviews, preference rating, direct matrix rankings, and paired comparisons were the methods used to gather ethno-botanical data. Descriptive statistical approaches, including density, frequency, relative frequency, relative density, and the Shannon and Wiener indices for species diversity were used to analyze the data. A total of 67 plant species belonging to 31 families were identified. Of the 67 plant species, 36 (or 54%) were herbs, 15 (or 22%) were tree species, 22% shrubs, and 2% were climbers. Twenty-eight (20.8%) species collected from homegardens had food and forage value, 17 (25.3%) had fence value, 12 (18%) had shade value, and 23 (34.3%) had medicinal value; moreover, 90% of the homes had home gardens, indicating that farmers value gardening plants that helps improve household food security and revenue generation. The involvement of the local population and government institutions in providing awareness through instruction or training on the sustainable use and management of plant resources is vital to conserve homegarden plants.

Assessment of diffusive recharge at regional scale in karst systems, Amazon basin, Ecuador

Karst systems play a crucial role in drinking water supply and biodiversity. Therefore, their comprehensive characterization of their recharge mechanisms is essential for safeguard water resource quality and sustainable management. This study utilizes the open-access thematic cartography of Ecuador to analyze the lithological, geomorphological, and hydrographic boundaries of groundwater karst systems. Then, employing the APLIS method, the diffusive recharge for each system is calculated. As a result, five hydrogeological karst systems and twelve small isolated karst bodies were delineated within the Amazon Region of Ecuador, collectively occupying a total area of 11112 km2. The effective infiltration of precipitation within these systems was found to vary between 11.11% and 77.11%, with a predominant low infiltration range (20%-40%) covering 48% of the karst area. One of the multiple parameters used in water balance calculations is defined by this research, serving as the initial step in creating conceptual models for evaluating karst aquifer systems in Ecuador from a hydrological perspective. The findings not only offer insights into the vulnerability and characteristics of these systems but also establish the foundation for informed decision-making in sustainable groundwater resource management and protection efforts.

Phosphatization in the São Pedro and São Paulo Archipelago (SPSPA), Equatorial Atlantic, Brazil: Insights from guano leaching and chemical weathering

This research explores the phosphatization process in the São Pedro and São Paulo Archipelago (SPSPA), located in the Equatorial Atlantic, by analyzing the geochemical interactions between guano coatings and the underlying rocks. The study also examines the geochemical characteristics of the substrates, assessing the behavior of major, minor, trace, and rare earth elements, and determines the degree of weathering degradation of guano crusts, speleothems, peridotites and carbonate sedimentary rocks affected by environmental factors as rain, wind and halmyrolysis. The distribution of chemical elements in water-soluble, adsorbed, acid-extractable/exchangeable, readily acid and reducible, acid oxidizable, and residual phases were investigated. The principal findings of this method show that elevated Na levels are leached in water-soluble fractions, thereby highlighting the occurrence of rapid and active geochemical cycling, which is influenced by seawater. In contrast, in more acidic environments (pH close 4.8 to 2.0), considerable amounts of macroelements like P, Fe, Mg, Al, and Mn are found in association with remaining phases, indicating a slower and more incremental geochemical cycle of mature guano in the input and output systems of these nutrients in the archipelago. The mineralogy, as indicated by the presence of mafic minerals such as olivine, augite, and Cr-spinel, as well as phosphates including fluorapatite, collinsite, and spheniscidite, also supports this finding. The findings underscore the complex interplay between biological activities and geochemical processes, which influence the elemental dynamics in the phosphate-rich rocks of the SPSPA at depth. This study not only enhances our understanding of phosphatization processes but also offers important insights into the sustainable management of marine resources in environmentally sensitive areas. The research underscores the necessity of considering biological and environmental variables when examining geochemical cycles, particularly in regions exhibiting distinctive geological and biological interactions that challenge the prevailing models, such as those identified in the ASPSP.

Mapping Soil Surface Moisture of an Agrophytocenosis via a Neural Network Based on Synchronized Radar and Multispectral Optoelectronic Data of SENTINEL-1,2—Case Study on Test Sites in the Lower Volga Region

In this article, the authors developed a novel method for the moisture mapping of the soil surface of agrophytocenosis using a neural network based on synchronized radar and multispectral optoelectronic data from Sentinel-1,2. The significance of this research lies in its potential to enhance precision farming practices, which are increasingly vital in addressing global agricultural challenges such as water scarcity and the need for sustainable resource management. To verify the developed method, data from two experimental plots were utilized. These plots were located on irrigated soybean crops, with the first plot situated on the right bank (plot No. 1) and the second on the left bank (plot No. 2) of the lower Volga River. Two experimental soil moisture geodatasets were created through measurements and geo-referencing points using the gravimetric method (for plot No. 1) and the proximal sensing method (for plot No. 2) employing the Soil Moisture Sensor ML3-KIT (THETAKIT, Delta). The soil moisture retrieval algorithm was based on the use of a neural network to predict the reflection coefficient of an electro-magnetic wave from the soil surface, followed by inversion into soil moisture using a dielectric model that takes into account the soil texture. The input parameter of the neural network was the ratio of the microwave radar vegetation index (calculated based on Sentinel-1 data) to the index (calculated based on the data of multispectral optoelectronic channels 8 and 11 of Sentinel-2). The retrieved soil moisture values were compared with in situ measurements, showing a determination coefficient of 0.44–0.65 and a standard deviation of 2.4–4.2% for plot No. 1 and similar metrics for plot No. 2. The conducted research laid the groundwork for developing a new technology for remote sensing of soil moisture content in agrophytocenosis, serving as a crucial component of precision farming systems and agroecology. The integration of this technology promotes sustainable agricultural practices by minimizing water consumption while maximizing crop productivity. This aligns with broader environmental goals of conserving natural resources and reducing agricultural runoff. On a larger scale, data derived from such studies can inform policy decisions related to water resource management, guiding regulations that promote efficient water use in agriculture.

Towards sustainable resource management: Graph modelling insights from Ghana's oil and gas local content ecosystem

A critical aspect of the management of oil and gas resources in Ghana is the effective promotion and development of local content to maximise the participation of local businesses and workforce in the oil and gas industry. This study examined local content drive in Ghana's oil and gas sector, analyzing its functional relations with institutional characteristics, participation, technology transfer, and economic growth via graph modelling framework. A total of 250 key stakeholders were involved in this cross-sectional study. A network analysis revealed the centrality of institutional characteristics in driving local content development, while economic growth was the least integrated domain. Diverse strengths and directionality of connections between domains were observed, which highlight the complexity of areas of influence and potential challenges. Process modelling analysis showed a significant positive direct effect of institutional characteristics on local content drive, emphasizing the critical role of robust and supportive institutions in driving local content initiatives. Conversely, economic growth exhibited a smaller yet significant negative direct effect, indicating a trade-off between economic expansion and local content focus. The broader implications of these findings are discussed, and recommendations are proposed to enhance the effectiveness of local content drive in Ghana's oil and gas sector.