Journal of Environmental and Agricultural Studies
Journal of Environmental and Agricultural Studies (ISSN: 2710-1401) is an open access, free and peer reviewed monthly journal published by Al-Kindi Center for Research and Development. The journal publishes papers in all aspects of Agriculture and Environmental Sciences. All manuscripts are initially screened on their topic suitability and linguistic quality. The journal is also devoted to publish original, peer-reviewed research papers on main aspects of environmental studies.
Al-Kindi Center for Research and Development
A Robust and Explainable Approach to Crop Recommendation Using a Balanced Multi-Crop Agronomic Dataset
Crop recommendation systems play a critical role in supporting sustainable agricultural decision making under increasing climate variability. Modern machine learning approaches offer high predictive accuracy, yet their adoption in real-world agri-tech systems depends equally on robustness to environmental change and transparency of decision logic. Using a balanced multi-crop agronomic dataset, this study evaluates classical machine learning models, ensemble methods, and inherently interpretable rule-based learners under two evaluation settings: standard k-fold cross-validation and a rainfall-quartile protocol that simulates shifts in precipitation regimes. The results show that high accuracy under random data splits can substantially overestimate real-world performance when rainfall patterns change. To address this gap, we analyse the accuracy–explainability trade-off by comparing black-box ensembles with interpretable rule-based models. Feature attribution analysis based on SHAP further confirms that rainfall, humidity, and soil potassium are the most influential drivers of crop suitability. The findings provide a data-driven and explainable framework for developing climate-resilient crop recommendation systems that support environmental sustainability, resource-efficient farming, and informed decision making in precision agriculture.
Crop recommendation, digital agriculture, explainable artificial intelligence, environmental sustainability, robustness, SHAP, data-driven decision making
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
Rice Production as Influenced by Automated Alternate Wetting and Drying Technology in Different Plant Spacing Configurations
This study investigated optimal automated alternate wetting and drying (AWD) depths and planting spacings to enhance water conservation and rice yield under upland conditions. Using a two-factor strip-block design, AWD at 5 cm above, 10 cm below, and 15 cm below the soil surface was compared against continuous flooding, with spacings of 15×15 cm, 20×20 cm, and 25×25 cm on NSIC Rc222 variety. Results showed that 5 cm AWD depth with 15×15 cm spacing produced the highest grain yield (6.64 t/ha), water use efficiency (1.15 kg/m³), and net return (PHP 55,955/ha). The automated system maintained 98.7% sensor accuracy. Significant interactions between AWD depth and planting spacing were observed for plant height, shoot-root ratio, panicle length, harvest index, and yield. These findings confirm that automated AWD technology can achieve yields comparable to those of lowland systems using only shallow-well water, offering a sustainable alternative for upland rice farmers.
Copyright (c) 2026 Journal of Environmental and Agricultural Studies
Drought-Induced Modulation of Bioactive Compounds in Rice: A Comparative Study of Tolerant and Susceptible Varieties
In this research, the correlation between bioactive compounds and drought stress was studied in 11 rice varieties. The rice varieties were divided into treatment and control groups, and the plants were subjected to different drought conditions, including 3, 5, and 7 days without irrigation. Among the rice varieties, K8 was the most tolerant, whereas K11 was the most susceptible to water deficit. The results showed that total phenolic content increased dramatically in K8 and K11 under a 7-day treatment (71.397 and 51.381 mg gallic acid equivalent (GAE)/g, respectively). K8 showed higher antioxidant activities (DPPH = 8.832 µg/mL, ABTS = 1161.8 µg/mL, and reducing power = 1168.2 µg/mL) after 7 days of no irrigation. Contrastingly, the IC50 values indicate that K11 showed lower antioxidant activity (DPPH=16.261 µg/mL, ABTS=1944.5 µg/mL, and reducing power=3721 µg/mL) for the same variable.
Rice, Drought Tolerance and Susceptibility, Total phenolic and Flavonoid contents, Antioxidant activities.
Genetic Uniformity and Residual Divergence in Apomictic Mangosteen Garcinia mangostana L.) Revealed by RAPD Markers
Mangosteen (Garcinia mangostana L.) is a high-value tropical fruit whose genetic improvement is constrained by obligate apomictic reproduction, which limits meiotic recombination and narrows detectable diversity. This study assessed the genetic uniformity and residual divergence among mangosteen accessions to support objective mother-tree selection and germplasm management. Twenty local accessions from Bulukumpa District, Bulukumba Regency (South Sulawesi, Indonesia), and two commercial reference genotypes (Kali Gesing and Lotan) were evaluated using five RAPD primers (OPA-01, OPA-04, OPB-01, OPB-04, and OPB-18). Genomic DNA was extracted using a modified CTAB protocol, amplified under optimized RAPD-PCR conditions, and electrophoresed on a 2% agarose gel. To ensure robustness, each primer–accession combination was amplified in duplicate; only clear, consistently reproducible bands were scored in a binary matrix (1/0), with no-template controls included to monitor for contamination. Genetic similarity was estimated using Jaccard’s coefficient and clustered using UPGMA (SAHN) in the NTSYS-pc 2.1. All primers produced clear and reproducible banding patterns, yielding two to six loci per primer. However, the polymorphism was highly limited: four primers generated exclusively monomorphic profiles, whereas primer OPA-04 produced five loci with one polymorphic locus. Overall polymorphism was ~20%, with moderate marker informativeness indicated by PIC and expected heterozygosity values of approximately 0.40 for OPA-04. Similarity coefficients were very high (0.95–1.00), reflecting a strongly homogeneous genetic structure. UPGMA clustering resolved two groups at a similarity threshold of 0.95: a major cluster containing most accessions, along with Kali Gesing and Lotan, and a minor sub-cluster comprising four accessions (MGS_9, MGS_13, MGS_17, and MGS_18) differentiated by the single polymorphic locus. These findings confirm an extremely narrow genetic base consistent with apomictic reproduction, while highlighting a small set of relatively distinct accessions that may be prioritized for conservation and evidence-based parent selection in mangosteen improvement programs.
Gender Integration in Climate-Smart Agriculture (CSA) Education in Saudi Arabia: A Review
Climate-Smart Agriculture (CSA) aligns directly with the goal of Saudi Vision 2030 by optimising resource use and building climate resilience for environmental resilience, economic diversification and sustainable development. As technological agriculture becomes more advanced, an effective education and capacity-building system is vital for promoting adoption and sustainable CSA practices. The participation of women in agriculture in Saudi Arabia is getting more recognition through small livestock rearing, beekeeping, nurseries, greenhouse production and value addition, information on gender integration in CSA education and training program is limited. The article synthesises peer-reviewed literatures, policy documents, and institutional reports to examine the gender integration of CSA education in Saudi Arabia. The article identifies the possible transformation and agricultural development through feminist theory, gender-responsive pedagogy, intersectionality, and Agricultural Knowledge and Information Systems (AKIS). Additionally, the persistent gendered barriers which include male-dominated knowledge systems, limited representation of women’s, agricultural roles, unequal access to extension and digital tools, and weak institutional coordination was highlighted in this review. Simultaneously, the article shows the emerging opportunities for gender-responsive and transformative CSA education that aligns with national policy. Conclusively, women’s agency, learning, and leadership in climate-resilient agricultural systems could be enhance through integrating gender in CSA education.
agricultural education, gender, Saudi Arabia, climate smart, pedagogy, women participation
Seaweed Aquaculture and Waste Stream Integration for Blue Carbon: A Systematic Review of Carbon Pathways and Mitigation Strategies
Seaweed aquaculture integrated with waste streams is a promising blue-carbon pathway. This review goes beyond narrative synthesis by introducing three contributions: (i) a cross-system taxonomy of integration modalities, (ii) a concise measurement, reporting, and verification (MRV) protocol accompanied by a practitioner checklist, and (iii) a Crediting Readiness Scorecard (CRS) that operationalizes additionality, durability, leakage, and governability. We systematically reviewed studies published between 2016 and 2025 on coupling seaweed cultivation with aquaculture effluents, municipal wastewater, and flue-gas CO₂, following PRISMA screening and a simple risk-of-bias appraisal; where data permitted, we performed a small quantitative synthesis for nutrient removal and carbon-capture indicators. Our aim is to evaluate operational conditions, carbon pathways (harvest, particulate organic carbon export, dissolved and recalcitrant dissolved organic carbon), and MRV feasibility, while proposing an implementable protocol and scoring tool that standardize assessment across sites and species. Findings indicate that integration generally elevates productivity and carbon capture, but outcomes hinge on nutrient management, residence time and flow distribution, thermal control, and pretreatment addressing pathogens, metals, and co-pollutants. Using the CRS, aquaculture-effluent integrations emerge as nearer-term candidates for conservative crediting; municipal-wastewater and flue-gas routes can approach comparable readiness with stronger pretreatment, clearer system boundaries, and leakage safeguards. MRV remains challenged by air–sea flux attribution, spatial heterogeneity, and inconsistent standards; we map fit-for-purpose metrics net ecosystem exchange/eddy covariance, pCO₂ sensors, ¹³C tracers, and traps/cores to farm, bay, and shelf boundaries with QA/QC routines to constrain uncertainty. Technology assessments highlight artificial upwelling, CO₂ dosing, longline offshore arrays, and automated sensing as promising yet constrained by energy demand, reliability, and permitting; we synthesize indicative readiness ranges to guide deployment. Overall, the review converts disparate findings into actionable guidance through the taxonomy, MRV protocol, and CRS, and sets priorities for MRV optimization, standardized LCA distinctions between avoided emissions and removals, and scaling strategies suited to data-limited tropical contexts.
Seaweed aquaculture; blue carbon; waste stream integration; MRV protocol; Crediting Readiness Scorecard (CRS)
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Environmental Impacts of River Aggregate Quarrying in Masiu, Lanao del Sur: Water Quality Degradation and Community-Based Perception
River aggregate quarrying is increasingly practiced in rural river systems to support infrastructure development, yet its environmental impacts remain inadequately documented in many resource-dependent communities. This study assessed the environmental impacts of river aggregate quarrying in Masiu, Lanao del Sur by integrating laboratory-based water quality analysis with community-based environmental perception data to determine the extent of water quality degradation and its correspondence with lived experience. A mixed-methods approach was employed, involving physico-chemical and bacteriological analysis of nine river water samples collected from left bank, midstream, and right bank sections, alongside a structured survey administered to 200 household respondents across six barangays at varying distances from quarrying zones, supplemented by semi-structured interviews. Results revealed that key water quality parameters, particularly turbidity, total dissolved solids, apparent color, and pH, frequently exceeded national drinking water standards in quarry-influenced sections, with the most pronounced degradation observed in midstream areas.
Aggregate quarrying; Community perception; Environmental impact; River water quality; Rural livelihoods
Copyright (c) 2025 Journal of Environmental and Agricultural Studies
AI-Driven Forecasting of U.S. Biofuel Production and Feedstock Demand: A Machine Learning Analysis
Biofuel production in the United States is shaped by shifting policy requirements, fluctuating commodity markets, and changes within agricultural supply systems. Forecasting production volumes and feedstock needs is important for decisions made by producers, refiners, and agricultural planners. This study applies machine learning to predict monthly ethanol and biodiesel output alongside demand for key feedstocks such as corn and soybean oil. The analysis integrates data from federal energy and agricultural sources with market signals, weather conditions, and policy indicators. Several models are evaluated, including gradient boosted decision trees, long short-term memory networks, and hybrid ensembles, and their performance is compared against standard econometric baselines using cross-validated error metrics. The results show that machine learning models capture nonlinear relationships that conventional approaches fail to represent, leading to lower errors across short and medium-term forecasting windows. Feature analyses indicate strong influence from feedstock prices, planted acreage, refinery utilization patterns, and Renewable Fuel Standard volumes. The forecasts point to continued growth in ethanol production and moderate gains in biodiesel output, accompanied by rising demand for corn and soybean oil. The overall findings show that data-driven forecasting can improve planning and risk assessment in an evolving bioenergy sector.
Effects of repeated immersive virtual reality exposure on attitudes and intentions to avoid single-use plastics: moderating role of environmental concern
The aim of this study was to examine whether repeated exposure to an immersive virtual reality experience—as opposed to a single exposure—could reinforce individuals' attitudes and intentions to avoid single-use plastics, incorporating environmental concern as a moderating variable. A laboratory experiment was conducted with 107 students. Participants were exposed to an immersive virtual reality video (vs. non-immersive), and their attitudes and intentions were measured at two distinct points in time: before and after exposure. The study's findings reveal that repeated exposure to immersive virtual reality has a significant effect on participants' attitudes and intentions to avoid single-use plastics, greater than that of a single exposure. Furthermore, it appears that environmental concern positively moderates the relationship between attitude and intention: the more concerned individuals are about the environment, the greater the impact of their attitude on their intention to avoid single-use plastics.
Rebranding Siquijor Produce to Organic Farming: Opportunities and Challenges
This study investigates how branding and marketing influence organic certification readiness and market access among registered partner-farmers of the Department of Agriculture in Siquijor Province, Philippines. Anchored on the Pagente Organic Farming Model (2025), the research highlights the role of integrated practices—pest management, fertilizer use, record-keeping, and product presentation—in enhancing farmer participation in sustainable organic agriculture. Employing a descriptive-comparative quantitative design, the study surveyed the entire population of DA-registered partner-farmers in Siquijor using a structured and validated questionnaire (Cronbach’s Alpha = 0.864). Data were analyzed using descriptive statistics, weighted means, and path analysis to identify key predictors of organic labelling readiness. Results showed strong adoption of organic practices in pest control (M = 4.96), fertilizer use (M = 4.81), and record-keeping (M = 4.51), while branding and marketing scored lower (M = 3.87), suggesting critical gaps in market-oriented competencies. Path analysis identified branding and marketing (β = 0.34, p = 0.01) and pest control (β = 0.33, p = 0.01) as the most significant predictors of certification readiness. Despite farmers’ strong ecological practices, limited access to certification, market platforms, and branding tools remains a barrier to full organic integration. The study recommends targeted interventions such as branding workshops, cooperative marketing hubs, simplified certification pathways, low-cost organic fertilizer promotion, and improved market access through digital and local channels. Funded solely by the author, the research calls for future studies to explore the long-term socio-economic and technological impacts of these strategies in Siquijor and similar emerging organic farming communities.
Organic farming; Certification readiness; Branding and marketing; Market access; Sustainable agriculture; Path analysis; Participatory Guarantee System (PGS); Siquijor Province; Department of Agriculture (Philippines)
Strawberry Agrotourism as a Solution for Farmer Income Diversification in Indonesia: Potential and Challenges
This study aims to explore the potential of Strawberry Agrotourism in Indonesia as a solution to diversify farmers' incomes while identifying the challenges faced in developing this sector. This research uses a qualitative approach with descriptive methods through in-depth interviews, documentation, and field observations, with content analysis using NVivo 12 Plus. Gowa Regency was used as the locus in this study because the highlands of Gowa are considered not only as a vegetable-producing area but also a fairly well-known tourist destination. These findings show that even though strawberry production experienced a significant decline in 2020, Gowa Regency remains the area with the most significant strawberry production in South Sulawesi. This research found that developing strawberry agrotourism through activities such as strawberry picking tourism could be a solution to diversify farmers' income and attract more tourists. The main challenges identified include limited infrastructure, commodity price volatility, dependence on harvest seasons, and competition from other tourist destinations, which require attention for more sustainable development. Investment in supporting infrastructure, more effective marketing strategies, and business diversification programs for farmers are needed to reduce dependence on the harvest season and increase the competitiveness of agrotourism in the region.
Agrotourism, agricultural production, farmer income, price volatility, income diversification
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
Effects of Repeated Exposure and Nature Type in Immersive Virtual Reality on Pro-Environmental Behavioral Intention
The current study aims to examine the effects of immersive VR exposure type (single vs. repeated) and nature type presented (intact vs. destroyed) on nature relatedness and pro-environmental behavioral intention, while also taking into account the sense of environmental indebtedness as a moderating factor. A laboratory experiment (N= 207) was conducted using a 2 (exposure type to immersive 360° video: single vs. repeated) × 2 (Nature type: intact vs. destroyed) between-subjects design. The results showed that repeated exposure to immersive VR videos depicting destroyed natural environments led to the highest scores of nature relatedness. Moderated mediation analyses revealed that nature relatedness fully mediate the relationship between VR exposure characteristics (repetition and content type) and pro-environmental behavioral intention, and that sense of environmental indebtedness positively moderates the effect of nature relatedness on pro-environmental behavioral intention. This study stands out for its novel combination of two dimensions rarely explored together in immersive VR: the number of exposures (single vs. repeated) and the type of environmental content (intact vs. destroyed). It highlights not only the direct effect of these characteristics on nature relatedness, but also the full mediating role of this variable in the formation of pro-environmental intentions. The introduction of the sense of environmental indebtedness as a moderating factor constitutes an original contribution, as this concept is still rarely used in the literature.
Advancements in Renewable Energy Technologies for Environmental Sustainability
The worldwide change from fossil fuels to renewable energy sources has become vital for addressing climate change and promoting environmental sustainability. A key answer to the twin problems of supplying the world's expanding energy needs while lowering greenhouse gas emissions and slowing down climate change is the use of renewable energy sources. Improved renewable energy dispersion can minimize carbon emissions by up to 40% and promote energy security and economic growth. With an emphasis on their developments, difficulties, and opportunities in the current energy environment, this scholarly article thoroughly examines a range of renewable energy sources, such as solar, wind, hydropower, geothermal, and biomass. This paper examines renewable energy technologies, their environmental and socioeconomic effects, and real-life case studies from different regions. The research report ends with international partnership proposals alongside money spent on development research and helpful governmental policies that aim to accelerate worldwide renewable energy progress.
Renewable Energy, Sustainability, Solar Energy, Wind Energy, Energy Efficiency
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Little Zab River water suitability assessment applying three water quality index mathematical models (Horton, National Sanitation Foundation (NSF) and Malaysia)
Surface water is considered one of the most common sources of water supply, due to the various environmental challenges. Therefore, it has become necessary to conduct monitoring of surface water quality to ensure it is safe for use. This led to increasing interest in models-based research for water quality determination. This research investigated the evaluation of water quality index (WQI) for the Little Zab River in the Northern region of Iraq for 14 years. Several parameters were selected including (pH, DO, BOD5, PO4-3, NO3- , alkalinity, TDS, Cl- and temperature). The results showed that the (temperature, pH, alkalinity, NO3-, Cl- and TDS parameters were all within the permissible limits, while (DO, BOD5, and PO4-3) were sometime higher in some years. Three mathematical models were selected in WQI evaluation including (Horton, National Sanitation Foundation (NSF) and Malaysia). The resulting WQI values for the three proposed models are 45.45, 77 and (28-30) % respectively. Also, the river suitability for irrigation is assessed through several indices including (Kelley's Index (KI), Sodium Adsorption Ratio (SAR), Magnesium Hazard (MH), Soluble Sodium Percentage (SSP), Permeability Index (PI), and Total Hardness (TH)). The results fall within the permissible limits, except for PI in some years. Future studies should prioritize tasks the development of water quality assessment models, using machine learning and artificial intelligence for predictive modelling and risk evaluation, in a sustainable and environmentally friendly approach.
WQI, Little Zab River, Irrigation Suitability, Mathematical Modeling
Leveraging AI for Sustainable and Cost-Effective Decentralized Energy in Rural U.S. Regions
Sustainable and affordable decentralized power in rural parts of the U.S. is difficult to accomplish because of inadequate infrastructure and transmission costs, and centralized grids are susceptible. This paper suggests an artificial intelligence (AI) hybrid framework combining XGBoost and a neural network to provide precise predictions of renewable generation and operational expenses. More than 13000 electric power plants databases in the United States underwent preprocessing in order to solve the problem of missing data, heterogeneity, and rural-urban disparities. The proposed model (XGBoost+NN) was compared with KNN, Auto encoder, PINN, CNN-LSTM, and BiLSTM-Attention. It out-performed all baselines with MSE of 0.0003, RMSE of 0.0178, R2 of 0.999, and MAPE of 1.36%. The error in predicting costs was only 0.08% and this is both a technical strength and an economic importance. These findings indicate the model’s scalability, interpretability, and possible use to facilitate equitable access to clean and affordable energy in underserved rural populations.
Assessing the Impact of Climate Change on the Livelihoods of Communities in Burhakaba District
The purpose of this study was to assess the impact of climate change on the livelihoods of communities in Burhakaba District, Somalia, an area particularly vulnerable to climatic variations and significantly affected by them. The specific objectives of the study were to understand the effects of climate change on community livelihoods and the role of local authorities in responding to climate-induced crises and to examine the general perceptions of climate change adaptation and its mitigation strategies. The study employed a quantitative research design and collected data through an administered questionnaire survey involving 80 respondents selected via purposive sampling. The data was analysed using SPSS. The findings indicate that 76.25% of participants strongly agreed that climate change adversely affects their livelihoods, with severe water shortages, declining agricultural yields, and an increased frequency of droughts identified as critical challenges. Furthermore, the results underscore the urgent need for educational initiatives focused on adaptation strategies and enhanced public awareness programs to empower affected communities and mitigate the socio-economic impacts of climate change. This research provides valuable localized insights into the social, economic, and environmental consequences of climate change in Burhakaba, addressing significant gaps in the existing literature.
Climate Change, Livelihoods, Adaption Strategies, Droughts, and Somalia
Guyana’s Strides: Food Security, Agriculture Productivity and Public Health Management
The paper examines the strides the Government of Guyana has made in the drive towards food security and the roles of agriculture in food security towards the promotion of public health. This research was conducted using the qualitative approach through literature review and content analyses. The data were obtained from secondary open access sources. Data were collected, interpreted and analyzed from existing literature, on food security, agriculture productivity and public health in Guyana. The Agriculture and Health sectors have been working collaboratively to reduce the prevalence of factors leading to non-communicable diseases that are modifiable. Hence, these sectors are very active in the inclusion of food security through agriculture productivity towards public health management. An examination of some of the most recent strides that Guyana has made in ensuring that food security is enhanced and the promotion of public health, are; the establishment of the school feeding programme where local farmers are tasked with providing produce for the programme, alignment of the National Development Pathway to focus on food security, a decrease in the number of Guyanese who are unable to afford a healthy diet and the reduction in the prevalence of undernourishment. Guyana has included in its constitution the right to food for all Guyanese, the establishment of the “One Health Approach” where food safety is at the helm of the initiative, the construction of Guyana’s first food hub, increase in the production of traditional and non-traditional crops, high-value and value-added products through the use of technology and the development of infrastructure to enable farmers to access markets with their fresh produce. Guyana’ government has been making strides to ensure that food security is well established through collaborations with local, regional and international agencies. The impact of agriculture productivity on food security is critical and ultimately influences public health. The interplay of Agriculture Productivity and Food Security is very critical for the survival of the country’s population while at the same time ensuring that the health status of the population is maintained by the reduction in the prevalence of diseases, decrease in mortality rates and access to nutritious foods.
Agriculture Productivity, Food Security, Nutrition, Public Health
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Energy Demand Forecasting Using Machine Learning: Optimizing Smart Grid Efficiency with Time-Series Analytics
With the pace of the global transition toward smart grid technologies, more precise and responsive energy forecasting systems are essential to securing sustainable and effective power distribution. Smart grids, defined by their bidirectional communications and built-in sensing technologies, depend on time-series data analytics to control energy flow, forecast consumption patterns, and counter fluctuations. The chief objective of this research was to leverage the potential of machine learning algorithms to maximize the precision and flexibility of energy demand prediction within smart grid networks. The data used for this study consisted of high-resolution, time-stamped energy consumption data captured at 15-minute intervals for two years, both residential and commercial usage patterns. Every record contained the precise timestamp of consumption, which made it possible to undertake fine-grained temporal analysis that captures strong hourly cycles, daily patterns, and seasonal variations that are representative of user behavior and climatic factors. The authors selected three different models for effective energy demand level classification in this study. Our models received a time-based train-test split for their dataset to guarantee their robustness. A complete set of performance metrics for assessing our classification models included Accuracy alongside Precision, Recall, and F1-Score in addition to implementing a Confusion Matrix evaluation. Both KNN and SVM demonstrated a strong balance in precision against recall because their Accuracy and F1 Score bars overlap almost completely in the evaluation graph. The results indicate that KNN and SVM perform superior to Logistic Regression with very equivalent outcomes in this classification activity. One of the most direct and significant advantages of using machine learning-based energy demand forecasting for smart grids is improved operating efficiency through more intelligent scheduling of energy delivery. Furthermore, machine learning-driven operational efficiency makes an important contribution to cost savings throughout the energy value chain. By being able to forecast short-interval demand fluctuations, utilities are better positioned to more efficiently execute their procurement strategy, such as strategically selling and buying energy on wholesale markets. Load management is another essential function where machine learning-based forecasting greatly improves smart grid operations. Preplanning for grid expansion and maintenance is another strategic advantage that arises from the predictive potential of machine-learning-powered smart grids. In the future, various opportunities exist for enhancing the functionality of machine learning-based energy forecasting for the smart grid. First, incorporating more contextual sources of data, including online meteorological information, IoT sensor streams (e.g., appliance usage patterns, occupancy levels), and local grid status monitors, holds the potential to add to the feature inventory available to models.
Copyright (c) 2024 Journal of Environmental and Agricultural Studies
Farmers' Challenges Arising from the Promotion and Distribution of Low-Quality Improved Seeds in Takhar Province
This study investigates the challenges faced by farmers in Takhar Province, Afghanistan, resulting from the distribution of low-quality improved seed varieties. Takhar Province represents one of Afghanistan’s primary agricultural regions, where farming constitutes the livelihood for a substantial portion of the population. Using a quantitative methodology with a descriptive-analytical approach, data was collected from 80 farmers across four districts (Darqad, Baharak, Bangi, and Taloqan) through structured questionnaires and interviews. The research identified multiple deficiencies in the improved seeds distributed by extension institutions, including inadequate physical characteristics (volume and weight), insufficient resistance to environmental and climatic conditions, limited adaptability to diverse soil types, and susceptibility to pests and plant diseases. Statistical analysis revealed that 40% of respondents expressed dissatisfaction with extension services, while 20% indicated they would discontinue using improved seeds from these institutions in the future. Furthermore, 82.5% of farmers reported that extension institutions had not conducted field surveys to evaluate seed performance. The findings suggest that without significant improvements in seed quality and institutional accountability, farmer dissatisfaction may increase substantially, potentially undermining agricultural productivity and economic stability in the region. This research highlights the critical need for enhanced quality control mechanisms, greater transparency in seed promotion, and improved compatibility testing for local environmental conditions.
Harnessing Machine Learning to Analyze Energy Generation and Capacity Trends in the USA: A Comprehensive Study
The constraints in conventional energy forecasting models in the USA are increasingly being appreciated as the energy landscape evolves. Such models are generally constructed on linear assumptions that do not capture energy generation and consumption patterns as dynamic and multi-dimensional. This research project aimed to develop effective machine-learning models to interpret historical trends in energy generation and capacity in the United States. With access to massive datasets for various energy sources—fuel-based to renewable and nuclear energy—this research endeavors to discover actionable information that can optimize energy output and improve grid stability. The dataset used is a comprehensive overview of energy production in America, integrating information from a variety of sources, ranging from fossil fuels to renewables and nuclear energy. This extensive dataset is sourced from reliable sources such as the U.S. Energy Information Administration (EIA) which supplies vast historical and current data on energy consumption and production patterns; the Federal Energy Regulatory Commission (FERC) which supplies information on regulatory frameworks and market forces that affect energy production; and smart grid management systems that deliver real-time data on energy flows and grid performance. Selecting appropriate machine learning models is crucial to process engineered features and derive actionable insights effectively. For this purpose, we use Logistic Regression, Random Forest Classifier, and the Support Vector Machines. The model performance table indicates how well three machine learning algorithms—Logistic Regression, Random Forest, and SVM—classify between renewable and non-renewable energy sources. All three models are highly accurate, with SVM having a slight edge, followed by Random Forest and lastly Logistic Regression. AI-powered energy forecasting is revolutionizing energy infrastructure planning in America through the provision of sophisticated information to guide investment in grid upgrades and expansion. As America transitions to dependence on renewable energy, AI-based forecasting is essential to optimize the integration of solar and wind energy production onto the grid. The variability of these renewable resources poses special challenges for grid managers, who need to balance demand and supply in real time. Moreover, AI can be used to identify gaps in renewable energy capacity and storage. The infusion of AI-based insights into the energy sector has far-reaching implications for U.S. policymakers, equipping them with fact-based tools to efficiently apply energy reforms. With evolving energy patterns, legislators need to adjust regulations to facilitate a seamless transition to a more sustainable energy system.
Machine Learning-Based Prediction of U.S. CO2 Emissions: Developing Models for Forecasting and Sustainable Policy Formulation
The exponential escalation of carbon dioxide (CO2) emissions in the U.S. presents a pressing environmental challenge with substantial implications for climate change and public health. The principal objective of this study was to devise robust machine learning algorithms particularly designed for forecasting CO2 emissions in the United States. This focused exclusively on CO2 emission data pertinent to America, reflecting the economic, unique environmental, and regulatory context of the nation. The dataset for analysis consisted of a broad-based set of information focused on the main contributors of CO2 emissions in the United States, ranging from energy consumption and industrial activity to transportation and historical CO2 emission data. The energy consumption data included facts on electricity generated, fuel consumed, and absolute energy consumption among different sectors of the economy, and industrial activities information provides data on specific outputs from such processes and their emissions. It also included transportation facts on vehicle trends, fuel intensity, and energy-related emissions associated with the sector. These three datasets have been garnered from reliable resources, including the US. These range from detailed EPA emissions inventories and energy reports from the U.S. The analyst deployed credible algorithms such as Random Forest, Logistic Regression, and Support Vector Classifier which had different strengths that can be leveraged based on characteristics of the dataset. According to their accuracy scores, the Random Forest model led the race compared to the other two models, with a higher accuracy rate. With such large integrations of machine learning predictions into climate policy, great opportunities might develop vis-à-vis sustainable development goals in the USA. Advanced analytics will let the policy analyst capture emission and resource trends with greater insight than ever before into the effectiveness of existing regulations; this will let it plug into the SDGs on Climate Action, Sustainable Cities, and Responsible Consumption. For enhancing environmental monitoring systems' efficiency, environmental planning should be incorporated with machine learning models.
Copyright (c) 2023 Journal of Environmental and Agricultural Studies
Global Plastic Waste Management: Analyzing Trends, Economic and Social Implications, and Predictive Modeling Using Artificial Intelligence
Plastic waste, which is a result of human activities in America, has become one of the most critical environmental issues in the 21st century, and this calls for an urgent prescription of strategies at a global management level. The pervasiveness of plastic in modern life has created an unparalleled surge in plastic waste, which, unless adequately managed, is poised to pose severe threats to ecosystems, human health, and the global economy. The utmost objective of this study was to perform an extensive analysis of global plastic waste management practices in the USA, with a specific concentration on pinpointing the economic and social implications of these practices. This research project therefore intends to probe into the waste management practice applied in different countries for understanding the various best practices, challenges, and areas of improvement. The research project also aimed to employ AI-driven predictive models, notably, gradient boosting algorithm, linear regression, and random forest to predict the future trends in plastic wastes generated. Diverse datasets were used, to ensure that the study of global plastic waste management practices was comprehensive. Primary data on the conditions of global plastic waste generation was obtained through the World Bank's database, which provides detailed data on waste composition, generation rate, and methods of disposal in many countries. Also, the sources of economic indicators were OECD reports and UNEP publications on the hidden economic costs of plastic waste to municipal budgets. Data on its social impact, such as health effects and metrics involving environmental pollution, were provided by the World Health Organization through studies it conducted along with reports from environmental NGOs such as Greenpeace. The Gradient Boosting model performed the best with relatively high accuracy, followed by Logistic Regression and Random Forest Classifier. Besides, the Gradient Boosting model offered the highest Macro Average F1-score, which suggests better overall performance in balancing precision and recall for all classes. Predictive insights provided by the proposed models are valuable tools to expect future trends and patterns in plastic waste generation. Advanced analytics and machine learning can help predict the volume of plastic waste generated across different geographies and sectors. Application of the predictive models in plastic waste management contexts has huge potential about information and shaping of policy decisions. Predictive analytics can use historical trends on production, consumption, and generation of waste and recycling rates of plastics to create forecasts about the future and define high-risk areas.
Global Plastic-Waste Management; Plastic Generation Trends; Predictive Modelling; Artificial Intelligence; Machine Learning
IoT Technologies Application and Effect on Urban Infrastructure: Intelligent Waste Management, Energy Efficiency, and Traffic Management
The Internet of Things (IoT) is revolutionizing urban infrastructure by significantly enhancing traffic management, waste management, and energy efficiency. The tremendous effects and revolutionary potential of IoT technology in these crucial urban sectors are examined in this thesis This thesis investigates IoT technologies' profound impact and transformative potential within these critical urban domains. Through an in-depth analysis of diverse case studies, an extensive literature review, and insightful expert interviews, this research explores how IoT applications optimize traffic flow, reduce congestion, and improve public transportation systems. In waste management, IoT solutions enable real-time monitoring and efficient collection processes, minimizing environmental impact and operational costs. For energy efficiency, smart grids and IoT-enabled sensors contribute to more sustainable energy consumption and distribution, reducing overall carbon footprints. The study also addresses the technical and regulatory challenges associated with IoT deployment, such as data security, privacy concerns, and the need for standardized protocols. By providing a comprehensive understanding of IoT's role in urban transformation, this research highlights the potential for creating smarter, more efficient, and sustainable cities, ultimately enhancing the quality of life for urban residents.
Internet of Things (IoT), waste management, data security, traffic management, energy efficiency
Performance of 200 Liter of Continuous Anaerobic Digester for Processing Animal Waste of Cattle Dung
This study aims to explore continuous anaerobic digesters that normally use cattle dung as a substrate. In this work, a 200-liter continues anaerobic digester was established to conduct the research. The digester was filled with mixture of cattle dung and water (50%:50%). The digester was operated in the batch system for about 1 month at the beginning. The digester was then operated by continuously feeding every day with 5 liter mixture of cattle dung and water (50%:50%) for 1 month observation. The digester was stirred during feeding time for easy flow of the substrate inside the anaerobic digester and also avoid the formation of scum. The biogas production was measured, methane and carbon dioxide composition were detected, and temperature inside digester as well as ambient temperature outside the digester was recorded. The pH inside the digester were measured to recognize the process that was involved. Biogas production is found to almost linearly increase with a rate around 15 liter/day. The average methane concentration is around 65.4 %. The average CO2 content is found around 30.8%. The average temperature inside the digester is 33.6oc and average ambient temperature outside digester is 30.1oC. The average pH is found to be at a value of 6.7. The 200 liter continuous anaerobic digester is found sophisticated by feeding with 5 liter slurry of cattle dung every day.
Performance, animal, waste, cattle dung, continuous, anaerobic, digester
Effect of RNA/DNA in Tilapia Fish Larvae with Administering Daphnia sp. enriched with Amino Acid
The quality and availability of seeds are the main problems in tilapia cultivation. One of the parameters that can be used to evaluate the quality of tilapia fish seeds is the RNA/DNA ratio. This research analyzes the influence of feeding Daphnia sp. enriched with amino acids on RNA/DNA performance in tilapia fish larvae. This research was carried out at the Nutrition Laboratory of the Aquaculture Study Program, Bosowa University, Makassar. RNA/DNA ratio analysis was carried out in October 2023 at the Takalar Brackish Water Aquaculture Fisheries Center Test Laboratory. This research used a Completely Randomized Design (CRD) with four treatments and three replications as a quantitative experimental method. The treatments are : A (Daphnia sp. + amino acids 1 mL/liter); B (Daphnia sp. + amino acids 3 mL/liter); C (Daphnia sp. + amino acids 5 mL/liter) and D (Daphnia sp). The results showed that providing Daphnia sp. enriched with 3 mL/liter amino acids can increase tilapia larvae's RNA/DNA ratio, which amounted to 0.866. This research helps increase the availability of quality tilapia seeds.
Improving Rainfall Prediction Accuracy in the USA Using Advanced Machine Learning Techniques
The key aim of this research project is to design and evaluate advanced machine learning models for increasing accuracy in rainfall forecasting over the USA. We intended to investigate nonlinear relationships typical of the atmospheric variables using state-of-the-art ML methods for more accurate rainfall predictions. For this research project on rainfall forecasting in the USA, we utilized an extensive dataset that comprises historical rainfall data collected from the National Oceanic and Atmospheric Administration (NOAA) and other meteorological agencies. The main dataset we use in this paper consists of daily rainfall measurements across various geographical locations of the USA, thus capturing the wide-ranging historical data necessary for both training and validation of the model. Besides measuring rainfall, we included meteorological data from sources such as NOAA's Global Historical Climatology Network and NASA's Modern-Era Retrospective Analysis for Research and Applications. These datasets further provided key variables that are known to affect rain, including temperature, humidity, wind speed, and atmospheric pressure. The performance metrics used in this research work for the models considered include accuracy, precision, recall, and F1 score. The above table shows that the Random Forest Classifier outperformed the other models, achieving perfect accuracy. That indicated that it rightly classified all the instances in the test set. The Logistic Regression and Support Vector Machine models gave a quite good performance by giving above average accuracy but had lower precision and recall for the rainfall prediction. Accurate rainfall forecasting has direct consequences on agriculture, greatly empowering farmers and agricultural planners to make more effective decisions regarding planting, harvesting, and crop management. The forecasts of rainfall are also of critical importance in disaster management regarding planning for flood emergencies. Moreover, precise forecasting of rainfall, particularly in sustainable water resources management, presents the most important data in planning for and conserving these resources.
Improving Rainfall Prediction; Forecasting Accuracy; Meteorological factors Advance Machine Learning; Agricultural Sustainability
Integrating Genomic Selection and Machine Learning: A Data-Driven Approach to Enhance Corn Yield Resilience Under Climate Change
Genomic selection is a revolutionary approach in breeding, exploiting genetic markers to forecast breeding values and hence accelerating the pace of traits associated with resilience, like drought tolerance, heat resistance, and pest resistance. This study addresses these challenges through ML algorithms such as random forests, support vector machines, and neural networks thereby enhancing prediction accuracy while handling complicated genomic as well as environmental datasets. Relevant ML algorithms for genomic selection are considered in this discussion, as well as strategies for data processing, feature selection, and environmental factors, including climate conditions and soil parameters. These are brought together to form predictive models that indeed cater to genotype-by-environment interactions vital for crop performance evaluation over different environmental conditions. A proposed framework integrates genomic selection with machine learning, benefiting both disciplines by developing a data-driven methodology for yield prediction in corn. The critical machine learning models to be used include multi-layer perceptron and ensemble models. A case study shows the practical applicability of the GS-ML framework, describing the dataset prepared, model testing and validation procedures, and yield resilience prediction results. The conclusion of the study states that GS and ML combined hold great promise in supporting sustainable agriculture and climate resilience. It requires further research, infrastructure development, and policy support to scale this approach across different crops and diverse climate scenarios. The combined use of genomic and ML approaches is profoundly innovative in predictive breeding and will help develop resilient agricultural systems critical for global food security under a changing climate.
Corn (Zea mays L.), Genomic Selection, Machine Learning, Yield Resilience
Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/
Predictive Analytics in Plant Biotechnology: Using Data Science to Drive Crop Resilience and Productivity
Data science and predictive analytics are revolutionizing plant biotechnology by revealing crop performance and tolerances. Data science is important in a global context where agricultural demand is rising and crops' yields, resilience, and sustainable resource usage are maximized daily. We explore predictive models in plant biotechnology and how they may be developed utilizing agronomic, environmental, phenotypic, and genomic data to improve agricultural solutions. Predictive analytics extrapolates genome, transcriptomics, and proteomics data to promote precision farming and climate-resilient crop adaptive breeding. Agricultural data science uses IoT sensors, drones, and image technologies, but integration and data quality are still difficulties. The review also explores machine learning approaches including decision trees, neural networks, regression, and others to help predictive analytics overcome restrictions. These models can quantify resilience and response to biotic and abiotic stresses, predict yields, and choose breeding genes. Examples demonstrate how predictive models can boost crop resilience, yields, and water and pest management early intervention. Predictive analytics in plant biotechnology faces data shortages, processing needs, and model interpretability challenges. These barriers may prohibit many agricultural stakeholders from adopting advanced models like deep neural networks. The study concludes that plant scientists, data scientists, and agronomists must work together, integrate AI with multi-omics for advanced predictive modeling, and use blockchain for data security. These advances can help predictive analytics improve sustainable agriculture by fostering resilient crop growth and resource efficiency for a more predictable food supply.
Crop Improvement, Data Science, Plant Biotechnology, Predictive Analytics
Farming Development Strategies of Purwaceng (Pimpinella pruatjan Molk.): Endemic Medicinal Plants in the Dieng Plateau, Central of Java, Indonesia
Purwaceng is a typical plant that grows in the Dieng Plateau and is efficacious for human health but is still cultivated on a limited basis. This study aims to determine the strategy for developing purwaceng farming in the Dieng Plateau. The study used a survey method. Key informants in this study were purwaceng farmers in Patak Banteng District, Sikunang District, and Batur District, Field Extension Officers from the Angestitani Agricultural Extension Center, Kejajar District, purwaceng processed product entrepreneurs, and the Head of the Batur District Farmer Group. Data collection through interviews and focus group discussions (FGD). Data analysis using the IFE matrix, EFE matrix and SWOT. The study produced 10 alternative strategies that can be applied to the development of purwaceng farming in the Dieng Plateau, namely SO strategies: marketing purwaceng through digital marketing, legal registration of purwaceng processed products, increasing the quantity and continuity of purwaceng crop yields, making purwaceng houses; ST strategies: branding the benefits of purwaceng; WO strategies: formation and strengthening of purwaceng farmer groups, the synergy between government agencies and purwaceng farmers, education of the Dieng Plateau community regarding purwaceng farming; WT strategies: consumer education regarding the benefits of purwaceng, and preparation of Standard Operating Procedures for purwaceng farming. Purwaceng farming in the Dieng Plateau can focus on SO (Strength-Opportunity) strategies by supporting aggressive strategy, namely utilizing existing strengths to take existing opportunities.
Comparison of Phytoplankton Composition and Abundance (River, Estuary dan Coast) as Potential Water Contamination Bio-Indicators in Tallo Watershed Area
This study aims to compare phytoplankton composition and abundance in the Tallo watershed area, evaluate its ecological factors and water quality, and their correlation to particular types of water contamination. The study was performed from June to August 2021 at three stations in the Tallo watershed, including the river (1), estuary (2), and coast (3). The results suggested that the ecological parameters were still within the tolerable limit for the organisms to live. 4 classes of phytoplankton found in the Tallo watershed included Bacillariophyceae, Cyanophyceae, Dinophyceae, and Chlorophyceae, with a total of 15 genera. No Dinophyceae was found in the river during the high and low tides and there were 9 genera found during the high tide and 10 genera found during the low tide. As many as 3 classes with 12 genera were found in the estuary during the high tide, and 4 classes with 12 genera were found during the low tide. No Chlorophyceae was found on the coast during both the low and high tides. A total of 10 genera were found during the high tide and 9 genera were found during the low tide. Analysis of variance indicated that Phytoplankton Composition there is a difference in phytoplankton abundance in 3 habitats. Phytoplankton abundance on the coast was the highest, followed by the estuary and the river (coast > estuary > river). From the abundance and the composition of phytoplankton in 3 habitats, there was a strong indication of inorganic nitrogen, heavy metal, and microplastic contamination that occurred in the Tallo watershed.