Caraga State University

Karstic terrains are formed by the dissolution of carbonate rocks and are essential zones for groundwater reservoirs but are susceptible to geological and climatic conditions. Thus, delineation and characterization of potential karst development sites are necessary, especially in areas with limited data on karst development, which hinders accurate groundwater assessment, hazard mitigation, and sustainable land-use planning, especially in remote areas such as Siargao Island, Philippines. By applying a data-driven geospatial framework that combines statistical analysis with Geographic Information System (GIS) techniques, it is possible to evaluate the island’s karstification potential as support for future water resource management strategies.Principal Component Analysis (PCA) was applied to eight initially selected variables, which were then reduced to four key components: geology, slope, precipitation, and vegetation. These components were used for GIS-based multi-criteria evaluation to generate a karst potential map of Siargao Island. Results show strong spatial variability in karst development wherein high to very high potential zones are in the southern and southeastern regions, characterized by mature cockpit karsts, caves, and sinkholes. The eastern and western parts of the island, where transitional stages of karst development are present, exhibit moderate karstification potential. Non-carbonate areas with minimal karst expression in the central and northern regions showed low to very low potential zones. Field observations, existing geomorphological maps, and sinkhole inventory data were utilized for model validation, resulting in an overall accuracy of 80.6% and a Kappa coefficient of 0.44, indicating moderate agreement between the predicted and observed karst features.Through this approach, a cost-effective monitoring strategy for assessing groundwater resources and geohazards in data-scarce, remote areas with karstic terrains, such as Siargao Island, can be developed. The generated karst potential map provides a baseline for sustainable water resource management, groundwater protection, and land-use planning. Furthermore, this study demonstrates the use of geospatial and decision-support methods to strengthen hydrological management in remote environments.

Conditional distribution estimation (CDE) is central in nonparametric forecasting and risk analysis. While considerable progress has been made for finite-dimensional and stationary settings, functional data and nonstationary settings pose new challenges. We propose a Nadaraya-Watson (NW) conditional quantile estimator for regularly mixing locally stationary functional time series (LSFTS). It incorporates three kernel functions: one for time rescaling, another for the functional covariates, and an integrated kernel to act as a cumulative distribution function (CDF) of the response variable. A theoretical framework and the uniform convergence of the estimator were provided. To demonstrate the consistency of the estimator, a numerical experiment was conducted. Finally, we apply the method to financial data, specifically the Nikkei 225.

The increasing demand for alternative energy sources in the Philippines is driven by population growth and rising fossil fuel prices. Accurate information on locally available biomass resources is essential for assessing energy generation potential and for formulating sustainable resource management strategies. This study estimates the energy generation potential of rice hull residues in Butuan City, Agusan del Norte, Philippines, using a Geographic Information System (GIS)–based approach. Rice production data derived from the Philippine Agricultural Resource Mapping (PARMap) of the Phil-LiDAR 2 Project were used as the baseline for spatial analysis, while field surveys were conducted to validate biomass availability and collection efficiency. Empirical equations were applied to compute both theoretical and available biomass potential, and spatial maps were generated to illustrate residue distribution. Results indicate that the total theoretical potential of rice hulls in Butuan City is 6,623.94 metric tons. Based on field validation, the available energy potential was estimated at 496 MJ/ha for 35% biomass availability, increasing to 1,417 MJ/ha under a 100% availability scenario. Assuming full utilization, the total energy content of rice hull residues was calculated to be 95,086,658.70 MJ, equivalent to 26,412,981.88 kWh, which could potentially supply electricity to approximately 3,201 households annually. These findings demonstrate that rice hull biomass represents a significant renewable energy resource and that GIS-based assessment provides an effective framework for evaluating biomass energy potential to support local energy planning.

This study assessed the innovation capacity of Community Food Farms (CFFs) established by Gawad Kalinga in Barangay Bitaug and Barangay Cangcohoy, Siargao Island, Philippines. Using a descriptive research design, data were collected from 19 participants through a structured survey instrument measuring five innovation capacity dimensions: navigating complexity, collaboration, reflection and learning, engagement in strategic and political processes, and technical skills. Descriptive statistics (mean and standard deviation) and Coxcomb plot visualizations were used for analysis.Findings indicate moderate to high levels of innovation capacity across all five dimensions, with particularly strong performance in collaboration and technical skills. Participants demonstrated effective problem-solving, stakeholder engagement, and adaptive learning practices. Comparative analysis further revealed variation in capacity levels based on education and gender, suggesting the need for tailored capacity-building interventions.The study concludes that Community Food Farms in Siargao possess foundational innovation capacities that contribute to sustainable community development and resilience. Strategic investments in structured training, stakeholder coordination, and institutionalized monitoring systems are recommended to strengthen long-term impact.

This study evaluated the diagnostic efficacy of two methods for detecting Banana Bunchy Top Virus (BBTV) in abaca: conventional Polymerase Chain Reaction (PCR) and a novel colorimetric Loop-Mediated Isothermal Amplification (LAMP) assay. The study's purpose was to determine which method is more effective for field-based surveillance in the Philippines, a country with significant economic reliance on abaca production. A total of 243 leaf samples from key abaca-producing municipalities in the Caraga Region were collected and tested.The LAMP assay exhibited a significantly higher detection rate, identifying 85.2% of samples as positive compared to only 35.8% by conventional PCR (χ2 = 121.92, df = 1, p < 0.001). This diagnostic superiority was consistent across all four provinces surveyed, with the most pronounced difference in Surigao del Norte, where LAMP detected 86.04% of infections compared to a mere 2.33% by PCR. These findings reveal a substantial, previously hidden, reservoir of asymptomatic and low-titer BBTV infections that evade detection by traditional methods.The novelty of this study lies in its demonstration that LAMP is not only more sensitive but also uniquely suited for resource-limited settings due to its simple visual readout and lack of reliance on complex laboratory equipment. The results highlight that relying solely on symptom-based surveillance severely underestimates the true disease prevalence. The study concludes that adopting LAMP as a cornerstone of national surveillance efforts could provide a critical advantage in curbing the silent spread of BBTV, ensuring the sustainability of the abaca industry in the Philippines.

The Agusan Marsh Wildlife Sanctuary (AMWS) plays a crucial role in biodiversity conservation, climate regulation, and the sustenance of wetland-dependent livelihoods. This study assessed the management effectiveness of AMWS using the Management Effectiveness Tracking Tool version 4 (METT-4) to identify prevailing threats and evaluate the performance of existing governance and management strategies. Data were collected through Key Informant Interviews with 44 members of the AMWS Protected Area Management Board (PAMB), complemented by secondary data from management plans, spatial datasets, and institutional records. Threat analysis results indicate that while overall threats were perceived as low, emerging pressures are increasing, particularly human intrusion and disturbance (x̅ = 0.523), agriculture and aquaculture expansion (x̅ = 0.432), and energy production and mining activities in adjacent areas (x̅ = 0.386). These pressures are largely concentrated in Multiple Use Zones (MUZs) and are driven by population growth, land-use conversion, and external extractive activities. The overall METT score for AMWS was 75.52%, corresponding to an excellent rating for management effectiveness. The context component scored highest (100%), reflecting a strong legal and institutional foundation under the Republic Act 7586, as amended by RA 11038, and the recognition of ancestral domains within the protected area. Planning, outputs, and outcomes achieved good ratings (76.63%, 72.73%, and 70.19%, respectively), supported by the presence of comprehensive management plans, active Indigenous Peoples’ co-management, Biodiversity Monitoring Systems (BMS), and livelihood-oriented conservation initiatives. Inputs (65.32%) and processes (68.23%) also received good ratings but revealed constraints related to financial sustainability, logistical capacity, monitoring coverage, and the absence of a formally established buffer zone. Overall, AMWS demonstrates strong governance and participatory management but requires adaptive strategies, strengthened enforcement, and sustainable financing. Periodic METT assessments are recommended to support evidence-based decision-making and long-term conservation of this globally significant wetland ecosystem.

Butuan City, Philippines, is prone to seismic events due to its proximity to numerous earthquake generators, resulting in a range of hazards such as liquefaction. This study investigates the liquefaction potential in Caraga State University - Main Campus by analyzing the PF: Surigao and PF: Esperanza segments, an unnamed fault west of Butuan City, and the Philippine Trench. The maximum magnitudes Mw 7.4, Mw 7.1, Mw 6.5, and Mw 8.1, respectively, along with the minimum recorded Mw 5.1, which triggered liquefaction in the Philippines, were considered. A deterministic approach was employed to calculate ground attenuation values. Electrical resistivity tomography (ERT) profiles identified areas prone to liquefaction using resistivity meters, and borehole data were used to calculate the liquefaction potential index (LPI). The calculated LPI varies with the earthquake magnitude and PGA, ranging from high (5-15 LPI) to very high potential (>15 LPI) based on maximum magnitudes and low to no liquefaction based on the smallest magnitude. These findings underscore the need for effective mitigation strategies to reduce the potential damage caused by liquefaction in the study area.

Road traffic accidents remain a persistent public safety concern in the Caraga Region. This study analyzes and forecasts road traffic accidents in the Caraga Region by integrating dimensionality reduction, comparative clustering, and seasonal time-series modeling. A dataset of 18,568 incidents (2019–2024) from the Philippine National Police Caraga was preprocessed and transformed using Principal Component Analysis (PCA), retaining ≈100% of variance (PC1 = 51.80%, PC2 = 48.20%). Four clustering algorithms, The Divisive Analysis (DIANA), Agglomerative Nesting (AGNES), K-Means, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) were compared using the Silhouette Score and Davies–Bouldin Index (DBI). DBSCAN achieved the highest Silhouette Score, while K-Means obtained the lowest DBI (0.813) with competitive cohesion, and was selected for downstream forecasting due to its balanced performance and interpretable severity-based clusters (Low, Moderate, High). Seasonal Autoregressive Integrated Moving Average (SARIMA) models were trained on 2019–2024 data to generate a five-year outlook (2024–2029). Forecast accuracy, measured by Root Mean Square Error (RMSE), was 41.37 for Low, 48.97 for Moderate, and 65.75 for High severity clusters. Results reveal seasonal peaks holidays and rainy months, persistent high-volume low-severity incidents, and volatile high-severity spikes. These findings inform a cluster-specific intervention program combining targeted enforcement, seasonal operations, and infrastructure upgrades. This work constitutes the first documented application of comparative clustering techniques integrated with SARIMA-based forecasting in the Caraga Region, establishing a novel, data-driven framework for localized accident prediction and the formulation of evidence-based road safety interventions.