2025 Frontier in Sustainable Agromaritime and Environmental Development Conference
The Frontier in Sustainable Agromaritime and Environmental Development Conference (FiSAED) 2025, organized by the Directorate of Strategic Studies and Academic Reputation, IPB University, Indonesia, continues its commitment to fostering innovation and sustainability in the agromaritime sector under the theme “Innovating Together: Collaborative Pathways for Sustainable Agromaritime Transformation.” This year’s conference highlights the power of collaboration and co-creation among researchers, policymakers, industry leaders, and communities to accelerate the transition toward sustainable agromaritime systems. FiSAED 2025 emphasizes that transformative solutions for agriculture, aquaculture, fisheries, and environmental management can only be achieved through collective innovation that bridges science, technology, and local wisdom. The conference will showcase integrative approaches that combine digital transformation, climate-smart practices, circular economy principles, and community empowerment to build resilient blue–green systems. Through interdisciplinary dialogue and global collaboration, FiSAED 2025 aspires to shape actionable pathways that strengthen the connection between scientific research, policy frameworks, and real-world implementation, advancing a shared vision for sustainable and inclusive agromaritime development aligned with the United Nations’ Sustainable Development Goals (SDGs). FiSAED 2025 is a continuation of the 2023 and 2024 conferences. FiSAED 2023, with the theme “Frontier Agromaritime Contribution to Sustainable Development Goals,” resulted in the publication of 145 papers in IOP Conference Series: Earth and Environmental Science (Scopus-indexed; https://iopscience.iop.org/issue/1755-1315/1359/1). Meanwhile, FiSAED 2024, carrying the theme “Frontier Agromaritime at the Cross-Road for Addressing Complex Issues,” led to the publication of 74 papers in BIO Web of Conferences (Scopus-indexed; https://www.bio-conferences.org/articles/bioconf/abs/2025/22/contents/contents.html). FiSAED 2025 aims to attract more than 200 paper submissions, with international contributions projected to reach 30%. The conference will delve into four core sub-themes: Science and Technology for Sustainable Agromaritime, Socio-Economic Transformation for Sustainable Agromaritime, Sustainable Management of Natural Resources and the Environment, and Environmental Health and Ecosystems. By fostering collaboration, encouraging joint authorship, and supporting global alliances, FiSAED cultivates a dynamic environment for knowledge sharing and innovation across disciplines. These partnerships are instrumental in driving systemic change, building resilient communities, and promoting the sustainable management of natural resources—key steps toward achieving broader sustainable development goals. Further information regarding the scope of each subtheme is provided below: Sustainable Natural Resources and Environmental Management Science and Technology for Sustainable Agromaritime Soil Science and Land Resources Agronomy and Horticulture Plant Protection Landscape Architecture Fisheries and Marine Science Animal Science Forestry Environmental Science and Management Agrometeorology and Geophysics Mechanical and Biosystem Engineering Food Science and Technology Agroindustrial Technology Civil and Environmental Engineering Mathematics and Natural Sciences Environmental Health and Ecosystems Socio-economic Transformation for Sustainable Agromaritime Environmental medicine Environmental Pollution and Hazards Epidemiology and Tropical Diseases Human and Animal Health Ecology, Biodiversity and Conservation Agribusiness and Management Agricultural Economics and Development Resource and Environmental Economics Family and Consumer Sciences Communication and Community Development
Ruminal methane emissions in vitro of plants differing in their main phenolic fractions
Plants containing phenolics have been known to produce less ruminal methane emissions when incubated with buffered-rumen fluid in vitro. However, little is known concerning methane mitigation effects of plants differing in their phenolic fractions. The present study was aimed to observe such effects by using several plants, namely Swietenia mahagoni (SM; rich in condensed tannins), Clidemia hirta (CH; rich in hydrolysable tannins) and Eugenia aquea (EA; rich in non-tannin phenolics). The plants were incubated either individually or mixed with a control plant with negligible phenolic contents, i.e., Carica papaya (CP; 1:1 w/w). An amount of 200 mg dry matter from each plant sample was incubated in vitro with 10 ml of rumen fluid and 20 ml of buffer solution in a Hohenheim gas test syringe. Incubations were conducted in four replicates (runs), represented by one syringe per replicate. In each run, incubation was performed for 24 h and kept at 39oC. Total gas and methane productions were measured after the incubation. Fermentation fluid was subjected to volatile fatty acid (VFA) and ammonia measurements as well as bacteria and protozoa counts. Data were analyzed by analysis of variance and subsequent Duncan’s test. Results revealed that combining each species of plant containing phenolics with CP significantly lowered methane emissions (as percentage of total gas) (P<0.05). Methane emissions of CP, CP+SM, CP+CH and CP+EA were 18.6, 14.0, 14.3 and 16.1%, respectively. Total gas production of CP decreased significantly after being mixed with SM, CH and EA (P<0.05). Similar pattern to total gas was observed for those of total VFA and ammonia concentrations. It can be concluded that methane mitigation effects of plants rich in either condensed or hydrolysable tannins are higher than that of plant rich in non-tannin phenolics.
Sustainable natural resources and environmental management
Copyright (c) 2025 2025 Frontier in Sustainable Agromaritime and Environmental Development Conference
Estimation of average daily gain in fat-tailed sheep at smallholder farms based on energy and protein intake
This study aimed to estimate average daily gain (ADG) of fat-tailed sheep based on their energy and protein intake. Data were collected from 68 fat-tailed sheep raised at three smallholder farms in West Java, including feed intake and ADG records. The evaluation was conducted by comparing the intake of dry matter (DM), crude protein (CP), and total digestible nutrient (TDN) against the nutrient requirements estimated by the model, as well as by comparing observed and estimated ADG values. The results showed that DM, CP, and TDN intakes were below the estimated standard requirements. The comparison between observed and estimated ADG indicated a high root mean square percentage error (RMSPE > 80%) and a low concordance correlation coefficient (CCC), suggesting a relatively low level of agreement. These findings imply that the model's estimates do not yet adequately reflect the actual growth performance of fat-tailed sheep under field conditions, possibly due to limitations in observational data.
Evaluation of dietary energy and protein adequacy for Priangan sheep
This study aimed to evaluate the dietary energy and protein adequacy for Priangan sheep. The evaluation was carried out by comparing the estimated average daily gain (ADG) calculated from dry matter intake (DMI), crude protein intake (CPI), and total digestible nutrient intake (TDNI) with actual observed ADG data from several smallholder farms. A total of 124 Priangan sheep were recorded from four smallholder farms located in Bogor and Bandung, West Java. The data were analyzed using linear regression, Pearson correlation analysis, paired t−test, concordance correlation coefficient (CCC), and root mean square percentage error (RMSPE). The results showed that the intake of DM, CP, and TDN on several farms was still below the estimated requirements. The validation results indicated that the ADG estimation based on crude protein intake explained 25.8% of the total variation, had a moderate correlation (0.508), better agreement (0.346), and a lower RMSPE (78.4%) compared to the dry matter and TDN estimations. However, all equations still showed low CCC values, indicating that the level of agreement between estimated and observed ADG remains inadequate and require further refinement.
Image-Based Classification of Golden Melon Ripeness Using Convolutional Neural Networks and Data Augmentation
Accurate assessment of fruit maturity is essential for postharvest quality control and determining optimal harvest timing. This study evaluates machine learning methods for classifying the maturity levels of Golden Apollo melons using image data. A self-developed dataset consisting of 230 images was collected and categorized into four maturity stages: 47, 53, 60, and 67 days after planting (DAP). Four modeling approaches were tested: PCA with Support Vector Machines (PCA + SVM), PCA with Neural Networks (PCA + NNs), a Convolutional Neural Network (CNN), and a CNN with data augmentation. The augmentation process included random rotations (≤20°), horizontal and vertical shifts (≤10%), zooming (≤20%), and horizontal flipping, applied in real time during training. Model performance was assessed using precision, recall, F1-score, and accuracy. The augmented CNN achieved the highest accuracy of 86%, outperforming the other models. Confusion matrix analysis showed that the 60 DAP class was the most challenging to classify due to its similarity to adjacent ripening stages. The contribution of this research includes both the creation of a labeled image dataset and the demonstration of deep learning superiority for agricultural maturity classification.
Science and technology for sustainable agromaritime
Circular Feed Innovation: Biotechnological Upgrading of Rice Straw and Agro-Wastes into High-Value Silage Using Fungal Enzymes
Agricultural activities in Malaysia generate large volumes of lignocellulosic biomass, particularly rice straw and oil palm residues, which are often burned or discarded, contributing to air pollution, greenhouse gas emissions, and nutrient loss. Current research indicates that biotechnological upgrading of these wastes into silage can improve feed availability while supporting sustainable environmental management. This study aims to determine whether agricultural wastes, especially rice straw, can be transformed into high-value ruminant feed through microbial fermentation combined with fungal enzyme extraction. Controlled ensiling trials were conducted using lactic acid bacteria inoculants and enzymes derived from cellulolytic fungi to enhance fiber degradation and improve silage quality. General parameters evaluated included pH reduction, crude protein enhancement, neutral detergent fiber (NDF) reduction, aerobic stability, and preliminary intake response in ruminants. The results demonstrated that rice straw treated with fungal enzymes showed significantly improved fermentative quality, lower lignocellulose content, and higher digestibility compared to untreated materials. Oil palm biomass silage also showed higher metabolic energy potential and reduced spoilage under microbial inoculation. Overall, biotechnological treatments reduced the reliance on conventional forage by up to 30–50%, improved dry matter utilization, and supported better rumen fermentation efficiency. Environmentally, this approach diverted agricultural residues from open burning and landfill, contributing to lower methane emissions and improved natural resource conservation. In conclusion, upgrading rice straw and other agro-wastes via enzymatic and microbial silage technology offers a practical circular feed strategy that enhances feed security, decreases production cost, and aligns with sustainable natural resource and environmental management goals. Further exploration of enzyme optimization and scalable application models is recommended.
Rice straw, fungal enzymes, sustainable environment
Development of a Blood HbA1c Level Detection Model Based on Support Vector Regression (SVR) Using Microtest Data
Glycated hemoglobin (HbA1c) is a key biomarker for long-term glycemic control and diagnosis of diabetes, yet standard assays remain relatively costly and infrastructure-dependent. This pilot study aimed to develop and rigorously evaluate a Support Vector Regression (SVR) model to predict HbA1c using small-scale microtest data as a potential basis for low-cost screening. Primary data were collected from 10 adults (≥40 years) who underwent three repeated oral glucose tolerance tests over 22 days, yielding 214 clinical, hemodynamic, and lifestyle-related variables per subject. Numerical features were cleaned and normalized, then modeled with SVR using a radial basis function kernel. Within a 5-fold cross-validation scheme, Spearman correlation was applied in each fold to select the 10 most informative features from the training set alone, followed by grid search over C, epsilon, and gamma to avoid overfitting and data leakage. Model performance was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and coefficient of determination (R²), and compared with a simple mean baseline model. The final SVR configuration achieved MAE 0.705, RMSE 1.285, and R² −0.162, indicating predictive performance close to the baseline, achieved under strict, transparent procedures. Features related to previous HbA1c measurements, postprandial glucose dynamics, heart rate, and impaired glucose tolerance (IGT) status were frequently selected, highlighting their potential relevance for early dysglycemia screening. Overall, the results demonstrate that SVR is technically applicable to highly constrained microtest datasets. Still, improved performance will likely require larger and more diverse samples, careful variable reduction, and better exploitation of temporal patterns before the model can be translated into a robust, field-ready decision-support tool.
Superior Food Crop Commodities in the Pawonsari Area, Boalemo Regency, Gorontalo
The Pawonsari Transmigration Area in Boalemo Regency has strategic potential for the development of food crop commodities. This study employs a two-stage analytical approach, in which the Location Quotient (LQ) method is used as an initial identification tool to determine basic commodities, while the Analytical Hierarchy Process (AHP) serves as an advanced analytical method to finalize priority commodities based on social, economic, and environmental considerations. The research data consist of food crop production statistics and expert assessments from relevant government institutions. The LQ results indicate that rice (1.7), soybean (1.5), and sweet potato (1.4) are classified as basic commodities, whereas maize (0.9), peanuts (0.9), cassava (0.7), and mung beans (0.4) fall into the non-basic category. The finalization stage through AHP identifies priority levels of stakeholders, namely the Department of Agriculture (0.498), the Regional Development Planning Agency Bappeda (0.211), the Agricultural Extension Center (0.170), the Department of Transmigration (0.082), and the Department of Industry and Trade (0.037). At the criteria level, land availability and suitability emerge as the most influential factors (0.375), followed by production and continuity (0.214), business profitability (0.196), market demand (0.138), and value added (0.049). Integration of the LQ and AHP results positions maize (0.322) and rice (0.320) as the primary priority commodities, while soybean (0.171), cassava (0.171), and sweet potato (0.095) serve as secondary priorities.
Effect of Manganese (0%, 0.5% and 1%) Doping on the Optical and Crystall Properties of Barium Titanate (BaTiO₃) Synthesised by Chemical Solution Deposition (CSD) Method
This study aims to investigate the effect of Manganese (Mn) ion addition on the optical and ferroelectric properties of Barium Titanate (BaTiO₃) materials synthesised by the Chemical Solution Deposition (CSD) method. The variations of the Mn dopant used are 0%, 0.5%, and 1% atoms to BaTiO₃. This study used a UV–Vis spectrophotometer to determine the band gap energy via the Kubelka–Munk method, and employed X-ray diffraction (XRD) to determine the lattice parameters, spontaneous polarisation, and dipole moment. The lattice parameters were found to change significantly with Mn incorporation, with BaTiO₃ exhibiting a = 4.22 Å and c = 4.61 Å, BaTi₀.₉₉₅Mn₀.₀₀₅O₃ showing increased values of a = 5.48 Å and c = 5.52 Å, and BaTi₀.₉₉Mn₀.₀₁O₃ having a = 5.13 Å and c = 5.45 Å. The value of spontaneous polarisation decreased slightly from 0.1826 C/m² to 0.1815 C/m² at 1% Mn doping, indicating a reduction in dipole-domain regularity due to Mn ion substitution at the Ti site. These results show that Mn doping can effectively modify the electronic and ferroelectric properties of BaTiO₃, potentially improving material performance for capacitor and optoelectronic sensor applications.
BaTiO₃, Band Gap, CSD, Lattice parameters, Spontaneous polarisation
Performance of Natural Plant-Based Inhibitors: Effect of Psidium Guajava Extract Concentration and Immersion Time on Corrosion Rate of Boiler Tubes
Corrosion in boiler header tubes poses a major operational challenge in steam-based power generation, especially in systems exposed to chloride-rich feedwater. Environmentally benign corrosion inhibitors have gained attention as alternatives to synthetic chemicals. This study examines the effectiveness of tannin-rich Psidium guajava (guava) leaf extract as a natural corrosion inhibitor for boiler tube carbon steel immersed in a 4% NaCl medium. The extract was produced through ethanol maceration and rotary evaporation. Specimens were exposed to inhibitor concentrations of 0, 500, 1000, 1500, and 2000 ppm for 3, 5, 7, and 10 days. Corrosion rates were measured using the weight-loss method, supported by XRF characterization and optical microscopy. Results show a consistent decrease in corrosion rate with increasing inhibitor concentration, with 2000 ppm providing the highest protection. Longer immersion increased mass loss but lowered corrosion rate due to progressive film formation. Maximum inhibition efficiency exceeded 70%, classified as “very good.” Microscopy revealed smoother surfaces and more uniform protective films on treated specimens, indicating strong adsorption of tannin compounds. Overall, Psidium guajava extract demonstrates strong potential as a green, sustainable corrosion inhibitor for boiler applications.
A Novel Advanced Technique for Dual Production of Black Soldier Fly Oil and Protein
The increasing demand for sustainable alternative feed ingredients has encouraged the utilization of Black Soldier Fly Larvae (BSFL) as a promising source of both protein and oil. In this study, larvae were reared for 14 days on a palm kernel meal–pollard substrate (1:3), then microwave-dried at 120 °C for 5 minutes and processed into larval flour. Two extraction techniques—Ultrasound-Assisted Extraction (UAE) and Hot Press Extraction—were compared using three replications to evaluate their effects on oil yield, proximate composition, fatty acid profile (GC–FID), and amino acid composition (qualitative HPLC). Independent t-tests were used to assess statistical differences. The results showed that UAE produced a significantly higher oil yield compared with the hot press extraction method. The defatted meal obtained from UAE also contained significantly higher ash, protein, and crude fiber levels, whereas the hot press method resulted in higher residual lipid content. No significant differences were observed in the fatty acid profiles between the two extraction methods, with lauric acid remaining the predominant fatty acid (~44%). The amino acid profiles were likewise comparable, with glutamate (non-essential) and leucine (essential) identified as the most abundant amino acids. Overall, extraction methods influenced oil yield and proximate composition but did not affect fatty acid or amino acid composition. UAE is recommended as the more efficient method for achieving higher oil recovery and improved quality of defatted protein meal.
The Influence of Cultural Values ​​and External Support on the Effectiveness of the GESI Approach in Increasing Young Farmer Empowerment
This study aims to analyze the influence of socio-cultural values ​​and external support on the effectiveness of the Gender Equality and Social Inclusion (GESI) approach in increasing the empowerment of young farmers in Indonesia. The research method used a quantitative approach through surveys and interviews. The respondents were 152 young farmers receiving Competitive Grants (71 women and 81 men), obtained using the Slovin formula with a 7% margin of error. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with the help of SmartPLS version 3.9.2. The results showed that socio-cultural values ​​(β = 0.349; t = 4.713; p = 0.000) and external support (β = 0.404; t = 5.939; p = 0.000) significantly influenced the effectiveness of the GESI approach. Multigroup analysis based on gender showed that among male respondents, the influence of socio-cultural values ​​was more dominant (β = 0.409), while among female respondents, external support had a stronger influence (β = 0.444). These findings emphasize the importance of inclusive socio-cultural transformation and gender-responsive external support to strengthen the empowerment of young farmers, both female and male.
Socio-economic transformation for sustainable agromaritime
Gender Equality and Social Inclusion (GESI)
The Effect of NPK–Zeolite Fertilizer on the Growth of Kopyor Coconut and Nutrient Availability in Ultisols of Tasikmalaya
Ultisols in the Tasikmalaya region have low fertility, limited cation exchange capacity, and high nutrient fixation, which cause nutrient loss and restrict the growth of kopyor coconut. This study evaluated the effects of NPK-Zeolite fertilizer on vegetative growth and soil nutrient availability across different land-slope conditions. The research was conducted at PT Cigula Bumi Mineral, Tasikmalaya, from August to December 2024 using a 2×4 factorial Randomized Complete Block Design with two factors: fertilizer type (NPK-Zeolite and Conventional NPK) and land-slope classes (0–8%, 8–15%, 15–25%, >25%). Each treatment was replicated three times, resulting in 24 experimental units receiving 300 g of fertilizer per plant every three months. Data on vegetative growth and soil chemical properties were analyzed using ANOVA followed by DMRT at 5% significance. NPK-Zeolite increased ammonium availability to 76.72 ppm and potassium availability to 88.46 ppm, reflecting the slow-release characteristics of zeolite that reduced nutrient loss. However, plant height, stem diameter, and leaf number did not differ significantly between fertilizer types. Low soil phosphorus availability was likely the main factor limiting growth. Overall, NPK-Zeolite improved nitrogen and potassium efficiency in Ultisols, but additional measures are needed to overcome phosphorus deficiency.
Development of Halal Plant-Based Cheese Using Local Ingredients for Sustainable Food Innovation
Increasing demand for sustainable, ethical, and halal-compliant food products has encouraged the development of plant-based alternatives that offer lower environmental impact and greater inclusivity. Conventional dairy cheese production is associated with high resource use and significant greenhouse gas emissions, underscoring the need for locally derived, environmentally conscious alternatives. This study aims to develop a halal plant-based cheese using local ingredients, specifically Bogor nut (Vigna subterranea), for sustainable food innovation. The methodological framework includes formulation development, processing, halal certification, and sensory evaluation. Local ingredients were processed using simple techniques to align with green processing principles. Halal assurance was incorporated through verification of ingredient sources, identification of potential halal critical points, and completion of Indonesia’s self-declare halal certification pathway, resulting in the issuance of an official halal certificate number by BPJPH. Sensory evaluation focusing on aroma, flavor, appearance, and texture served as the primary performance indicator. The findings indicate that the product achieved satisfactory sensory acceptance while fully complying with halal standards and utilizing environmentally responsible processing methods. This study provides a practical and replicable model for integrating local agro-resources, halal certification, and low-impact processing in the development of sustainable food innovations.
Mapping Potential Midas Cichlid Spread for Invasive Population Control in Indonesian Waters
Amphilophus citrinellus (Midas Cichlid) is an invasive fish species introduced to Indonesian waters through the ornamental fish trade. This species poses a serious threat to native fish populations through predation, competition, and habitat alteration. This study aims to determining priorities in the management and elimination of the Midas Cichlid in high-risk areas in Indonesian freshwater environments using species distribution modeling. The Maximum Entropy (MaxEnt) algorithm was used with 20 validated presence records and six bioclimatic variables (BIO 2, 4, 8, 15, 18, and 19), which were selected through Pearson correlation analysis and Variance Inflation Factor. Model performance was evaluated using the AUC (Area Under Curve) metric and jackknife analysis. The model showed remarkable predictive efficacy, with an average AUC value of 0.955 (SD = 0.017). Seasonal rainfall contributed 28%, followed by seasonal temperature 26.9% and cold quarter rainfall 24.2%. The optimum suitability zone is largely located in Java, Bali, Lombok, South Kalimantan, Central and Southeast Sulawesi, and several areas of Papua. These included important habitats for endemic and endangered species such as Betta burdigala, Chilatherina sentaniensis, and Oxyeleotris heterodon. The integrated implementation of population suppression techniques, including trade restrictions, habitat rehabilitation, and continuous monitoring are critical for reducing invasive populations.
Impact of Conventional Processing on Allergenic Proteins in Grasshopper-Based Meat Analog
Edible insects are increasingly recognised as sustainable protein sources due to their high nutritional value and low environmental impact. However, the allergenic potential of insect-derived ingredients, particularly after processing, is still not well understood. This study assessed the effect of conventional processing on protein distribution and allergenicity in grasshopper (Valanga nigricornis) based meat analog compared with unprocessed grasshopper powder. Protein extraction using PBS (pH 7.4) separated samples into supernatant and pellet fractions, followed by protein quantification, SDS-PAGE profiling, and immunodetection with anti-tropomyosin and anti-arginine kinase antibodies. Pellet fractions showed higher protein concentrations in both grasshopper powder (48.56 mg/mL) and meat analog (69.04 mg/mL) than supernatants (4.03 mg/mL and 2.39 mg/mL), indicating predominance of insoluble proteins. SDS-PAGE revealed reduced solubility and increased aggregation in the processed analog. Western blotting detected tropomyosin (~36–38 kDa) mainly in the supernatant, with decreased intensity after processing. Arginine kinase (~40 kDa) appeared in both fractions, suggesting partial entrapment in the insoluble matrix. Overall, processing reduces but does not eliminate allergenic proteins, highlighting the need for thorough allergen risk assessment in insect-based meat analog development.
Developing a WhatsApp-Based Extension Model for Behavioral Change in Child Feeding Practices: An Analysis Using Smart PLS-SEM
Indonesia faces persistent stunting, with a 21.5% prevalence among children aged 0–59 months (Indonesian Health Survey, 2023), which exceeds the 2024 target of 14%. Inappropriate feeding practices driven by limited caregiver knowledge remain the key determinants of poor nutrition (Aguayo & Menon, 2016; Diana et al., 2022). This study developed and validated a WhatsApp-based extension model to promote behavioral change toward balanced, local food-based child feeding.A quasi-experimental pre-post design was conducted with 128 parents of children aged 0–6 years in three East Lombok sub-districts (July–October 2025). The control group (n=64) received brochures, and the intervention group (n=64) received brochures plus weekly WhatsApp videos over 10 weeks. Complete pre-post data from all 128 participants were analyzed using change scores (Δ).Smart PLS-SEM integrated with TPB-TAM constructs. Changes in perceived ease of use exerted the strongest effect on changes in feeding behavior (β = 0.415, p < 0.001), followed by changes in perceived WhatsApp diffusion (β = 0.352, p < 0.001). The model explained 40.7% of the variance in feeding behavior change (R² = 0.407) with an acceptable model fit (SRMR = 0.082) and moderate predictive relevance (Q² = 0.226).This study provides the first empirically validated TPB-TAM framework for WhatsApp-delivered nutrition extension in Indonesia, offering a scalable low-cost digital intervention model for rural stunting reduction.
stunting, child feeding behavior, WhatsApp extension, Theory of Planned Behavior, Technology Acceptance Model, PLS-SEM
Spatial Modeling of Community Forestry (HKm) Management Options in Malang Regency
Community Forestry (HKm) is one of the Social Forestry scheme that prioritizes community welfare by providing direct legal access for local communities to manage forest areas. Effective Community Forestry management must be designed to simultaneously achieve conservation objectives and enhance community livelihoods. This study aims to formulate and map HKm management options in Malang Regency by integrating spatial modeling with the Analytical Hierarchy Process (AHP). The research was conducted among seven farmer groups across three sub-districts, namely Ngantang, Singosari, and Sumbermanjing Wetan. The assessment of HKm management suitability employed both environmental and social criteria. The environmental indicators consisted of slope, elevation, rainfall, geology, and land use, while the social indicators included distance from roads, distance from rivers, distance from settlements, population density, and deforestation vulnerability. Community Forestry management options were analyzed within two management zones: the protection zone and the utilization zone. Scoring and weighting of the criteria and indicators were carried out using expert judgment. A spatial model was developed through a mathematical formulation using the Weighted Overlay method, combining the scores and weights of each criterion and indicator through the Weighted Linear Combination (WLC) approach. The results indicate that within the protection zone of both protected and production forests, Community Forestry management is predominantly represented by Option 2A, forest-based MPTS combined with understorey agricultural MPTS, with total areas of 814.92 ha and 116.93 ha, respectively. In the utilization zone of protected forests, the dominant management option is Option 2B, forest-based MPTS combined with understorey agricultural MPTS, with a total area of 1,576.10 ha. Meanwhile, in the utilization zone of production forests, the dominant option is Option 4A, forest timber species combined with understorey agricultural MPTS, with a total area of 358.85 ha.
Developing a decision tree machine learning algorithm for coffee agroforestry detection using SPOT-7 satellite imagery in Tanggamus District
This study describes the development of a machine learning algorithm to detect and identify agroforestry coffee plants using a machine learning approach. The primary data used in this study are SPOT-7 satellite image, which are then transformed into synthetic images of NDVI, VDVI, VARI, and NRGI. To improve accuracy, this study also integrates synthetic images with socio-geo-biophysical variables, including road proximity, river proximity, settlement proximity, elevation, slope, and visual land cover. This study found that the best algorithm for detecting agroforestry and monoculture coffee plants utilizes a decision tree algorithm with information gain parameters and variables, including NDVI, VDVI, NRGI, elevation, road proximity, river proximity, and land cover. This algorithm utilizes a maximum depth of 31, without pruning or pre-pruning, a minimum leaf size of 51, a minimum size for splitting of 70, and a pre-pruning alternative of 60. This algorithm produces an overall accuracy of 94.55% and a kappa accuracy of 93.7%. The most influential variable in the detection model was land cover. Agroforestry coffee had a user accuracy (precision) of 78.8% and a producer accuracy (recall) of 80.81%. The machine learning algorithm model was able to accurately detect and identify the distribution of agroforestry and monoculture coffee.
Invasive Plant Impact in Grassland Ecosystem: A meta-analysis and its implications for the food-water-energy security
Invasive plant species can exert systemic pressures that weaken the resilience of grassland ecosystems. However, their impact on the food-energy-water security has yet to be assessed quantitatively and comprehensively. This study synthesizes 146 studies from various global grasslands to evaluate the ecological consequences of plant invasions on food, water, and energy security. The meta-analysis revealed 31-113% increase in biomass, as well as 16% and 13% increases in soil organic carbon and soil nitrogen, respectively. However, this increase is accompanied by a 32% decline in native species diversity, which reduces the long-term stability of food systems. Hydrological processes also changed, with increases in ammonium (5,5%) and nitrate (9,4%), indicating a decline in water quality. While changes in soil moisture were insignificant, shifts in microbial dynamics and soil structure were functionally important for water regulation. The most significant findings relate to energy-carbon dynamics. Though the number of studies measuring greenhouse gases remains limited, the substantial increase in soil respiration, CO2, N2O, and CH4 emissions signals substantial potential climate risk. Overall, plant invasions result in significant trade-offs in the food-energy-water security. These findings underscore the importance of controlling invasive species to ensure food-energy-water security in grassland ecosystems.
biodiversity loss, climate risk, ecosystem services
Analysis of Farmer Behavior in the Management of Used Pesticide Packaging in Karo Regency
Pesticides are essential chemical inputs in modern agriculture for maintaining productivity and food security. However, excessive use and improper disposal can pose serious threats to human health and the environment. This study aims to analyze farmers’ behavior in managing used pesticide containers in Karo Regency using the Structural Equation Modeling–Partial Least Squares (SEM–PLS) approach. The study was conducted in Raya Village, Berastagi District, Karo Regency, involving 50 respondents selected through simple random sampling. Four latent constructs were analyzed: knowledge, experience, perception, and behavior. The results showed that knowledge and experience had significant positive effects on perception, while perception positively influenced behavior in pesticide container management. The R² value for perception was 0.628, and for behavior was 0.128, indicating that knowledge and experience explain most of the variance in perception, while perception explains a small portion of the variance in safe behavior. The findings suggest that although farmers have awareness of pesticide hazards, behavioral change remains limited. Continuous training about Good Pesticide Handling Practices, collection facilities at the village level, and integrated supervision are recommended to enhance safe and sustainable pesticide management practices. Keywords: farmers’ behavior, pesticides, sustainable natural resources, used containers
Development of a Human-Elephant Conflict (HEC) Vulnerability Model in Pidie District, Aceh Province.
This paper examines and develops a model of elephant conflict vulnerability using both parametric and non-parametric approaches, integrating several anthropogenic and geo-biophysical spatial variables in Pidie Regency, Aceh Province. The parametric approach involves building a logistic regression model, while the non-parametric approach uses a decision tree machine learning. The purpose of this study is to develop a spatial model HEC vulnerability using spatial data of anthropogenic and biophysical factors. This vulnerability model is built using variables related to habitat, elephant feed sources, and disturbances, including elevation, slope, proximity to water bodies, proximity to plantations, proximity to forest boundaries, proximity to roads, and proximity to settlements. Data on the location of HEC used as response variables are obtained from previous conflict incidents, official reports from government agencies, and community reports that are reliable. This study reveals that road proximity, slope, and plantation proximity are three critical predictor variables for assessing the level of HEC vulnerability. Model evaluation using the ROC- AUC approach demonstrated good performance, with an AUC value of 0.878 and an overall accuracy of 82.54%. This study concluded that conflict vulnerability can be developed to accurately mitigate HEC, which may be used to inform the development of mitigation strategies.
Color and light transmittance studies of epoxy polymers : transparent wood composites development of biomass-based alternative glass window for tropical and coastal ecofriendly house
Transparent wood composite is an advanced and developed technology of biocomposites. This biocomposite serves as an alternative glass in building material components. One of the main development parameters of this biocomposite is epoxy polymer technology as a transparent impregnant material that fills the pore structure and wood cells. This study aims to determine the color value and light transmittance of various colored epoxy impregnants that will be developed as alternative window glass materials. The impregnant used is epoxy resin with a mixture of several colors . There are 5 color pigments, namely white (W), red (R), yellow (Y), blue (B) and green (G). The tests carried out were color tests using a color reader and transmittance tests using a Spectrophotometer UV-Vis. The test results show color differences between the samples tested, as well as the resulting light transmittance values. The basic base color values ​​obtained were (W) = 73.0; (R) = 66.7; (Y) = 69.7; (B) = 63.7 and (G) = 67. The transmittance values ​​at a wavelength of 500 nm were 19.98%; 12.89% and 1.44%. The variation in color differences and light transmittance values ​​indicates a relationship between electromagnetic waves, color, and light transmittance. This technology is a prospective environmentally friendly technology.
transparent wood, epoxy, color, light transmittance, ecofriendly house
Integration of Digital Technology in Agriculture: Smart Farming Adoption Strategies in Indonesian Farming Communities
According to data from the Central Statistics Agency (BPS), 28.64% of Indonesia’s workforce was engaged in agriculture in the first quarter of 2024, underscoring its continued importance as a major source of income. Nevertheless, challenges such as low productivity and food insecurity persist. This study aims to analyze strategies for integrating digital technology into agricultural practices, utilizing the concept of smart farming, among farming communities in Indonesia. Digital technology adoption is considered a strategic approach to improve efficiency, productivity, and sustainable resource management. The research was conducted in West Java using a quantitative approach involving 135 farmers from various agricultural communities. Data were analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS) to examine relationships among latent variables and Multivariate Analysis of Variance (MANOVA) to identify differences in perceptions and adoption levels based on demographic factors. The findings indicate that farmer characteristics, innovation attributes, external environmental support, and learning intensity significantly influence the intention and level of smart farming adoption, which subsequently impacts farm sustainability. The study provides practical insights for policymakers and stakeholders in designing technology-based training and assistance programs to strengthen the competitiveness and digital transformation of Indonesia’s agricultural sector.
Development of Mangrove Village Typology in North Sumatra, Indonesia: Biophysical and Socio-Economic Approaches
This study develops mangrove village typologies in North Sumatra, Indonesia, using biophysical and socio-economic approaches. The objective of this study is to create a typology of mangrove villages, which will inform the adaptation of management strategies, aid in identifying threats and opportunities, and assess the ecological footprint of mangrove ecosystems. Mangrove village typologies was developed using the method Agglomerative Hierarchical Clustering And Discriminant Analysis based on six key variables (1) average rate of mangrove deforestation (ha/year), (2) distance from village center to mangrove ecosystem (km), (3) area of ​​mangrove per capita (m2), (4) area of ​​damaged mangroves per capita (m2), (5) pond area per capita (m2) and (6) population density working as farmers and fishermen (persons/km2). The results of this study classify mangrove villages into three typologies: Typology 1, Densely populated, with limited environmental capacity; Typology 2, Economically driven, with high ecological degradation; Typology 3, Conservation-Oriented, with productive coastal utilization. The validation test conducted showed high accuracy based on the confusion matrix of training data (91.44%), validation data (91.07%), and cross-validation (87.64%). The typology of mangrove villages developed can be used to formulate governance policy strategies, conservation planning, and strengthen the green economy based on mangrove village communities.
Rapid Assessment Model for Fire-Induced Forest Damage: A Case Study in Pelalawan Regency, Riau
This study examines and develops a model for detecting forest damage due to fires in Riau Province, particularly in Pelalawan Regency, during the 2020-2021 period. This study aims to develop a machine learning decision tree model that can rapidly assess forest and land damage caused by fires, based on multi-spectral satellite imagery, geophysical variables, and forest and land fire incident points. The machine learning decision tree algorithm used in this study is based on spectral variables constructed using the concept of "change vector analysis." This study demonstrates that the decision tree algorithm achieves an overall accuracy (OA) of 96.18% and a Kappa (KA) of 0.949, surpassing the logistic regression approach with an OA of 93.49% and a kappa of 0.863. This study concludes that the decision tree approach produces better estimates than the logistic regression approach, with the main variables being the "magnitude" and "direction" variables generated by CVA. In the logistic regression approach, the most significant variables are identified by the education level variable and social variables, which exhibit higher uncertainty compared to the machine learning method. The development of a rapid assessment model using machine learning can assist decision-makers in determining post-forest fire restoration and rehabilitation steps
Detecting Post-Earthquake and Tsunami Mangrove Dynamics in the Nias Islands Using a Machine Learning Approach
This study presents an approach to detecting and analyzing the dynamics of mangrove vegetation cover in the Nias Islands and surrounding areas following the 2004 and 2005 earthquakes and tsunamis. A predictive model was developed using a machine learning approach with a decision tree algorithm on the 2025 dataset. This study utilized several indices derived from Landsat 5 TM and 9 OLI, along with the derivation of socio-geo-biophysical data such as elevation, slope, substrate, distance from rivers, and coastlines. The analysis of land cover change trajectories was focused on the mangrove density (moderate and dense mangrove density, non-mangrove vegetation, bare land, settlements, paddy fields, water bodies, and non-land cover categories such as clouds. The study found that the developed decision tree model can accurately detect mangrove vegetation and other land cover types. The model evaluation results showed that the F1 score for all classes exceeded 90%. The overall accuracy was 95.9%. In general, the study found that mangrove loss in the Nias Islands and surrounding areas reached 6.9% between 2003 and 2025. This study confirms that the combination of optical imagery and socio-geo-biophysics data yields good performance in detecting and mapping the dynamics of changes in mangrove vegetation cover.
Positive Impact of Organic Waste Utilization Innovation on Green Energy and Green Economy
Biogas not only reduces organic waste but also produces a highly valuable product as renewable energy. The Energy Independence Village Program (EIVP), assist Muktisari residents in converting livestock, human and tofu production waste into biogas. This study aimed to analyses the positive impacts of EIVP implementing innovation. The study employed a mixed-method explanatory sequential design to analyze the environmental, economic, and social impacts of EIVP. Positive environmental benefit showed how biogas utilization can contribute to reduce Green House Gases (GHG) 1,085.94 ton CO₂e and Bio slurry (by-product of biogas) provides a crucial environmental co-benefit by enhancing soil quality and supporting sustainable agriculture. The economic analysis revealed that biogas production significantly reduced household fuel costs and agricultural input expenses. The adoption of biogas also encouraged behavioural changes in energy use, waste management, and agricultural practices. Findings highlight the program’s contribution to circular economy practices and SDG 7 (Affordable and Clean Energy)
Analysis of The Effectiveness of Sand Filters for Raw Water Treatment in Brackish Water Aquaculture Environments
Brackish Water used in aquaculture environments often contains high concentrations of contaminants that must be treated to ensure suitability for cultivation. This study evaluated the effectiveness of sand filters in improving raw water quality at the Brackish Water Aquaculture Fisheries Center in Takalar, South Sulawesi, over a two-month monitoring period. A per-and post-filtration analysis was conducted on key water quality parameter, including Total Organic Carbon (TOC), Ammonia (NH3), Nitrite (NO2), and Total Bacterial Count (CFU/mL). Results showed that the sand filter system successfully reduce TOC by 20.99% and bacterial load by 13.18 %. However, ammonia removal was minimal (1.04%), and nitrite levels increased significantly by 23.93% after filtration. These findings suggest that while sand filters are effective for reducing organic and microbial pollutants, they are insufficient for nitrogen compound management. The novelty of this study lies in its application of sand filtration in operational, high-salinity aquaculture environments- a context that remains underexplored in the literature. Further integration with biological filtration systems is recommended to enhance nitrogen removal and improve overall treatment efficiency
sand filter; raw water treatment; sustainable aquaculture; filtration effectiveness
Multi-Sensor Decision Tree Machine Learning Algorithm For Identifying Mangrove Ecosystem
Abstract. Mangrove forests are the forest type with the highest carbon stocks (above all soil organic carbon) and fulfil numerous other important ecological and environmental functions. Monitoring of mangrove forests and providing reliable data about their development does, therefore, support the informed management of mangrove forests. This paper compares the reliability of multi-spatial resolution PlanetScope in Tapanuli Tengah, Sentinel-2A, and Landsat-8 imagery in Asahan Regency, for detecting mangroves and their vegetation density using a decision tree machine learning (DT-ML) approach. Landsat 8 and Sentinel-2A were used to identify mangrove and non-mangrove classes, while high-resolution PlanetScope was specifically examined to detect mangrove density, i.e., dense, medium, and sparse. The raw satellite data were initially transformed into synthetic images, such as NDVI, NDWI, SAVI, VARI, and ARVI, and then combined with socio-geo-bio-physical variables, including substrate type, elevation, slope, and proximity to the coastline. In general, this study demonstrates that DT-ML achieves overall accuracies of 92.4% (Landsat 8), 93.0% (Sentinel-2A), 92.1% (PlanetScope Tapanuli Site), and 94.5% (PlanetScope Langkat Site). It was identified that NDVI and substrate are the most influential variables across all datasets, particularly in distinguishing between mangrove and non-mangrove ecosystems, thereby reducing misclassification of non-mangrove classes. PlanetScope's high spatial resolution (3 m) produces superior detail, enabling better detection of canopy density. The presence of the Sentinel-2A Red Edge channel is crucial for distinguishing mangroves from other types of vegetation. The Decision Tree algorithm has been successfully adapted for multi-resolution imagery, highlighting a model with good interpretability for straightforward generalization. It is concluded that Decision Tree Machine Learning may provide better accuracy and the ability to integrate spectral variables from satellite imagery and socio-geo-biophysical non-spectral variables.
Remote sensing, Decision Tree, Machine Learning, Mangrove Ecosystem
Testicular and Epididymal Responses to Growth Variation in Prepubertal Male Peranakan Etawah Goats
This study aimed to evaluate the influence of growth rate on the reproductive characteristics of prepubertal male Peranakan Etawah (PE) goats. Six goats aged 7–8 months were divided into two groups based on body weight: high-growth (n = 3) and low-growth (n = 3). The evaluated parameters included the testicular and cauda epididymis dimensions, sperm concentration, and sperm morphology. High-growth goats exhibited greater testicular length (P > 0.05) and significantly wider testes (P < 0.05) than low-growth goats. The relative testicular length to body weight was lower in high-growth goats (0.20 cm) than in low-growth goats (0.29 cm; P < 0.05), whereas the relative testicular width did not differ significantly between groups (P > 0.05). Similarly, no significant differences were found in the cauda epididymis length, width, or their relative ratios to body weight (P > 0.05). Although not statistically significant, high-growth goats showed higher sperm concentration and lower total sperm abnormalities than low-growth goats. The proportion of sperm head abnormalities was slightly higher in high-growth goats (4.99%) than in low-growth goats (0.49%), whereas midpiece (0.87% vs. 0.89%) and tail abnormalities (53.77% vs. 62.73%) were lower in high-growth goats than in low-growth goats. In conclusion, accelerated growth during the prepubertal stage in male PE goats is associated with increased testicular size, although this improvement does not significantly affect sperm quality at this developmental stage.