Nnamdi Azikiwe University Journals
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DIGITAL TRANSFORMATION AND SUSTAINABLE DEVELOPMENT OF BEVERAGE FIRMS IN PORT HARCOURT
No full textThis study investigated the connection between digital transformation and sustainable development within the context of beverage firms in Port Harcourt, Nigeria. The objective is to determine the relationship between digital transformation and sustainable Development of Food and Beverage Firms in Port Harcourt. As global business environments evolve, the adoption of digital technologies has become essential for enhancing operational efficiency, innovation capacity, and long-term sustainability. This paper applied a descriptive statistics and quantitative approach, while the population comprised of 23 registered food and beverages firms in Port Harcourt, Rivers State, Nigeria. The combination of random sampling and purposive sampling technique were adopted. The food and beverages firms were selected using purposive sampling technique, while random sampling was used to select 12 food and beverages firms in Port Harcourt. The research explores how digital tools, such as automation, data analytics, and integrated management systems are being leveraged to address the economic, environmental, and social dimensions of sustainability in the beverage industry. This paper adopted quantitative correlation approach of analysis. The findings reveal that digital transformation significantly contributes to improved resource utilization, reduced production waste, streamlined supply chain management, and enhanced customer engagement. Furthermore, digitalization supports corporate social responsibility initiatives and workforce development, thereby reinforcing the social aspect of sustainability. The paper concludes that while digital transformation is not a panacea, its strategic implementation can serve as catalyst for sustainable development in Nigeria’s beverage sector. It therefore recommends targeted investments in digital infrastructure, capacity building and policy frameworks that incentivize digital innovation aligned with sustainability goals. 
GOVERNMENT STIMULUS PACKAGES AND THE SURVIVAL RATE OF SMALL AND MEDIUM ENTERPRISES: A POST-COVID-19 STUDY IN EDO STATE
No full textThis study examined government stimulus packages and the survival rate of Small and Medium Enterprises (SMEs). The specific objective of the study was to determine whether the implementation of government stimulus packages has a significant effect on the survival rate of SMEs. Stimulus packages were dimensioned into government tax reduction and interest rate reduction. The study adopts a quantitative research technique using a data collection method through the administration of a structured questionnaire. The population of the study consists of 2,677 registered SMEs in Edo State, according to the NBS-SMEDAN National Survey of MSMEs (2017). A convenient sampling technique was used to elicit responses from 150 SME managers in Edo South. Data was analyzed using Ordinary Least Squares (OLS) analysis, the pre-estimation analyses were unit root tests, and the main analysis was done using multiple regression analysis. Findings from the analysis indicate favourable correlation between Edo State\u27s SMEs\u27 survival rate and government stimulus packages. In addition, the study discovered that the SME survival rate positively correlated with lower government taxes and interest rates, although this association was not statistically significant. The study recommends that firms with stronger financial positions were more likely to withstand the economic shock of the pandemic. As a result, the NGOs, organisations and government should introduce a wellmonitored implementation procedure for efficiency, and the post-COVID-19 stimulus initiatives for SMEs in Edo State should be continued. 
Analysis and Prediction of Sea Level Variations in River Lokoja, Kogi State, Nigeria, over the period of 2010 - 2030
No full textWith frequent fluctuations in river levels due to seasonal changes and climate factors, understanding these variations has become increasingly important for disaster management and infrastructural planning in flood-prone areas. This study utilizes historical water level and discharge data, collected from 2010 to 2020, to predict sea level trends over the next decade (2021–2030) using time series analysis in MATLAB. By employing a polynomial regression model, the study captured recurring seasonal patterns and project possible future changes in water levels and discharge rates. The findings indicate clear seasonal trends in water levels, with potential increases during peak rainy seasons that could elevate the risk of flooding. This study reveals that 2021 had the highest water level and discharge on record. Notably, a prediction indicates a substantial decline in water level and discharge by 2030. These predictions underscore the importance of preemptive measures, such as improved flood defense systems, urban planning adjustments, and community awareness programs. The study provides valuable insights for policymakers, environmental managers, and local stakeholders aiming to mitigate the adverse effects of rising water levels. Ultimately, the study contributes to a better understanding of river behavior in the region and highlights the need for sustainable approaches to address the challenges posed by sea level variations in Lokoja
Geospatial Analysis of Jetty Distribution and Accessibility in Port Harcourt Metropolis, Rivers State, Nigeria
No full textA jetty is a structure extending into a body of water to facilitate vessel berthing, cargo handling, and passenger movement. Port Harcourt Metropolis, a vital hub for oil and gas and maritime activities, lacks comprehensive spatial data on jetties, affecting urban development and maritime efficiency. This study assessed the spatial distribution of jetties in the metropolis using a Garmin 78csx GPS receiver for coordinate acquisition and QGIS 3.28 for mapping. A spatial database was developed, and compliance with Nigerian Port Authority (NPA) standards was evaluated. Results indicate 51 jetties, comprising 46 private and 5 public, with 29 functional, 21 non-functional, and 1 under construction. Additionally, 16 jetties are floating, while 35 are permanent. NPA PTOL is the largest (57,273.68 m²), while Pelfaco is the smallest (99.52 m²). The study recommends increasing jetties in strategic locations to enhance maritime operations
Geospatial Assessment of the Impacts of Precipitation and Temperature on Crop Yield at FUTA Research Farm, Ondo State, Nigeria
No full textAgricultural productivity is highly influenced by climatic factors such as temperature, and precipitation. This study geospatially evaluates the impact of climate variability on the Federal University of Technology Akure (FUTA) Research Farm over eleven planting seasons (2012–2022). Primary data, including crop yield measurements and GNSS observations for planting boundaries, were integrated with satellite imagery and climate records. Vegetative indices (NDVI and GCI) showed fluctuations, with an increase from 2014 to 2016, a decline from 2017 to 2019, and a subsequent rise in 2021–2022, indicating improved crop health. A weak negative correlation (-0.014) was found between temperature and crop yield, while precipitation exhibited a strong negative correlation (-0.821), suggesting excessive rainfall adversely impacts crop production. The correlation between GCI and yield (-0.001) was non-significant, emphasizing precipitation as a primary determinant of yield variations. These findings highlight the critical role of climate in agricultural productivity and the need for data-driven strategies to enhance farm resilience. This study provides valuable insights for sustainable farm management under changing climatic conditions. The study recommends the implementation of improved water management strategies to mitigate the adverse effects of climatic variability on crop yield at the FUTA Research Farm
Assessment of Tidal and Current Effects on Bathymetric Data Accuracy in Soku Creek, Nigeria
No full textBathymetric data collection is essential for understanding underwater topography, navigation safety, and coastal resource management. However, the accuracy of bathymetric surveys is significantly impacted by tidal and current dynamics, particularly in estuarine environments such as the Soku Creek. This study evaluates the extent to which tides and currents influence bathymetric data accuracy in Soku Creek, a critical coastal system. By integrating field measurements, tidal predictions, and current velocity analyses, the study identifies variations in depth readings caused by tidal fluctuations and flow dynamics.
The research reveals that tidal range variability introduces systematic depth deviations, leading to inconsistencies during surveys conducted at different tidal stages. Initial depth measurements, referenced to uncorrected GNSS heights, exhibited vertical deviations up to ±0.45 m, with a mean absolute error of 0.38 m, primarily attributed to unmodeled tidal height variations and lateral current shear. Post-processing included the application of harmonic tidal constituents and velocity corrections derived from in-situ ADCP (Acoustic Doppler Current Profiler) measurements. These corrections reduced depth deviations to within ±0.12 m and lowered the mean absolute error to 0.09 m, representing a 76.3% improvement in vertical accuracy. Crossline comparisons revealed a reduction in mean depth offset from 0.41 m to 0.08 m, while spatial consistency across survey lines improved by 35.7%. The findings underscore the necessity for integrating tidal corrections and current modeling during bathymetric surveys to minimize errors. This research contributes to enhancing the reliability of bathymetric data in dynamic coastal regions and provides practical recommendations for survey planning and coastal resource management. Future studies should focus on incorporating real-time tidal monitoring systems and hydrodynamic models to further improve survey accuracy in tidal-influenced areas
Comparative Analysis of Machine Learning Techniques in Identification and Mapping of Urban Road Network with Remotely Sensed Imagery in Parts of Benin City, Nigeria
No full textAccurate mapping of urban road networks is fundamental for effective urban planning, transportation management, and infrastructure development, particularly in rapidly expanding cities. This study evaluates and compares the performance of three machine learning algorithms—Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Random Forest (RF)—for automated extraction of urban road networks from high-resolution UAV imagery in Benin City, Nigeria. UAV data acquisition was conducted using a DJI Matrice 100 drone equipped with a Zen muse X5 camera, followed by extensive preprocessing including calibration, georeferencing, mosaicking, orthorectification, noise reduction, and feature extraction. The CNN model demonstrated superior performance, achieving an overall accuracy of 91.3%, precision of 0.89, recall of 0.85, F1-score of 0.87, and Intersection over Union (IoU) score of 0.834, outperforming both RF (88.7% accuracy) and SVM (87.3% accuracy). CNN’s hierarchical feature extraction effectively addressed the complexity of urban morphology, varying road widths, and edge localization, though with increased computational demands. SVM exhibited high accuracy in primary road detection but struggled with narrower and shadow-obstructed roads, while RF demonstrated balanced performance but was sensitive to urban density and occlusions. Comparative analysis with conventional remote sensing methods, which achieved 76.8% accuracy, underscores the significant advantage of machine learning-based approaches in terms of accuracy, efficiency, and scalability. The findings highlight CNN as the most robust and reliable algorithm for urban road network mapping in complex urban environments. Future work should focus on developing hybrid models that integrate the strengths of CNN, RF, and SVM, as well as implementing advanced preprocessing techniques to mitigate challenges posed by shadows and occlusions
Application of Photogrammetry for the Estimation of Tree Height in Closed Canopied Tropical Forests of Southwestern Nigeria
No full textThe study estimated tree height from high-resolution Digital Surface Models (DSMs) and Digital Terrain Models (DTMs) products derived from photogrammetric orthomosaics captured with the Sensor Optimized for Drone Applications (S.O.D.A) metric camera mounted on an Ebee Sensefly Survey Drone. This was to determine a sustainable approach for the estimation of tree height in dense forests with closed canopies. A 500-meter drone sampling plot was marked for each of the 3 tropical forest reserves that were used for this study, and 10 cm digital aerial photos were captured during the drone survey. The data sets were converted to point clouds and thereafter to Ortho mosaics for DSM and DTM, respectively, using standard processing techniques with the Pix4D software.
Results showed average tree height ranges from 0 - 50 meters for the three forest reserves with photogrammetry, and a correlation of 0.96, R2 of 97.6, and SE of 0.05 compared with height from field measurements using a laser range finder .The study concluded that Photogrammetry offers a cost-effective, scalable, and spatially continuous approach for estimating tree height over expansive areas in the area of study while enabling integration with GIS and remote sensing workflows, supporting large-scale forest monitoring, inventory, and management with higher efficiency and broader utility