Search Results (71)

Sustainable agriculture is critical to sustainable development and climate resilience, yet evidence of its multidimensional environmental, social and economic benefits and trade-offs remains limited, especially in regions most vulnerable to climate change. This study addresses this gap in Vietnam’s Mekong Delta (MKD), where decades of intensive rice farming has bolstered rice yields at the expense of environmental quality and farmers’ health. This study presents a multi-criteria analysis (MCA) comparing organic rice (OR) and conventional rice (CR) farming systems in the MKD. We applied a weighted sum model to evaluate the environmental, social and economic performance of the two production systems. The assessment drew on quantitative and qualitative primary data collected through stakeholder engagements and field measurements from Vinh Long province between 2023 and 2025, as part of the OrganoRice project. Overall, OR farming performed cumulatively better (0.663 and 0.695) than CR farming (0.496 and 0.484). OR scores were higher across most sub-criteria, particularly “farmers’ income” and “biodiversity”. However, CR outperformed OR for “rice yield” and “farmers’ workload”. Comparable water and soil quality scores, due to the presence of pesticide residues in both systems, suggest that the environmental performance of OR farming was likely dampened due to cross-contamination from surrounding or upstream non-organic farms. The expansion of OR farming in the MKD has the potential to enhance environmental, social and economic performance of rice production in the region. However, in order to achieve the full scope of these benefits, short-term measures should aim to reduce the financial risks of conversion for farmers, ensure access to affordable organic inputs, increase farmer training, ensure reliable premium contracts for rice producers and support them in accessing organic markets. Longer-term measures must improve irrigation water management and infrastructure to minimise cross-contamination risks. Coordinated marketing campaigns are needed to develop a trusted regional brand for organic rice from the MKD to create new opportunities in international and domestic markets. Moreover, long-term monitoring of post-transition outcomes is important to capture the full impacts of conversion. As OR cultivation continues to expand across the MKD, evidence on its benefits and trade-offs is essential to guide this transition effectively. This study provides that evidence, offering a context-specific, multidimensional evaluation to inform OR policy and practice in the region. Full article

Microplastic pollution in tropical urban rivers has become an increasing environmental concern due to rapid urbanization, inadequate waste management, and hydrological transport processes. This study investigated the occurrence, characteristics, and spatiotemporal distribution of microplastics in the Can Tho River, Vietnam, along an urban–peri-urban–rural gradient during dry and wet seasons. Surface-water samples were collected at 15 sites and analyzed for microplastic abundance, density, shape, color, and size composition using stereomicroscopic identification and statistical analyses. Microplastics were detected at all sampling sites in both seasons, indicating widespread contamination throughout the river system. Although seasonal differences in overall abundance and density were not statistically significant at the basin scale, clear spatial variability was observed, particularly in urban and peri-urban regions. Fibers and fragments were the dominant shapes, while blue, purple, and green particles were the most common color categories. Particles larger than 1000 µm accounted for the largest proportion of detected microplastics, and continuous size-distribution analysis revealed broadly similar overall distributions, although a greater proportion of smaller particles was observed during the dry season. The results suggest that hydrological conditions, urbanization, and land-use characteristics may contribute to the observed spatial and seasonal patterns of microplastic distribution in the Can Tho River. Peri-urban zones exhibited the greatest seasonal variability, highlighting their role as transitional areas that may influence microplastic redistribution in tropical river systems. This study provides baseline information for understanding microplastic pollution in the Mekong Delta and supports future river management strategies. Full article

This study aims to explore perceptions of English as a Second Language (ESL) students on integrations of local culture into English language teaching using Artificial Intelligence approaches. Research samples included 511 ESL students of the English Faculty of four public universities in Vietnam’s Mekong Delta region. The input factor dimensions comprise demographics, level of local culture integration, facilities, and curriculum-related factors. The output factor was necessities for local culture–English-teaching integration. Two supervised machine learning algorithms, Decision Tree (DT) and Support Vector Machine (SVM), were applied with oversampling to address data imbalance issues. Results indicated that the oversampling case achieved the highest performance. The research shows that the DT model was slightly better than the SVM with an accuracy of 97% and AUC of 98%. Feature importance analysis identified curriculum, facilities, and students’ hometown as key predictors. The findings provided empirical evidence to support data-informed curriculum reform in culturally responsive English language teaching. This study also develops a novel method to explore students’ perceptions and offers practical suggestions for improving academic quality. This study is expected to enhance institutional student recruitment while contributing to the region’s sustainable development and the broader goal of preserving intangible cultural heritage in Vietnam’s Mekong Delta. Full article

Transitioning to a circular economy (CE) is now a strategic priority for countries to decouple economic growth from environmental degradation. However, in developing contexts, the readiness of environmental resource sectors to adopt CE principles is unknown due to a lack of data and uneven institutional capacity. This study presents the first regional baseline assessment of circularity readiness in Vietnam’s environmental resource sectors, focusing on land, mining, water and waste. A five-dimensional readiness framework (policy, resource management, innovation, business, awareness) was developed and applied across Vietnam’s six ecological–economic regions. A Delphi process with 12 experts was conducted in three rounds to capture and refine expert judgments, supplemented by triangulated proxy indicators (e.g., plastic recycling rates, wastewater treatment coverage). Readiness scores were aggregated at dimension and regional levels and analyzed using radar charts, heatmaps and hierarchical clustering. Results showed significant regional disparities. The Southeast (SE) and Red River Delta (RRD) have high readiness due to clearer policy frameworks, stronger institutions and more dynamic business ecosystems. The Northern Midlands and Mountains (NMM) and Central Highlands (CH) have low readiness due to infrastructural gaps, weak innovation and limited public engagement. The Mekong Delta (MD) and North Central Coast (NCC) have medium readiness, reflecting partial progress but uneven implementation. The study made three contributions: (1) a new context-specific framework for CE readiness in environmental resource sectors; (2) the value of expert-based, proxy-informed methods in data-scarce contexts; and (3) a policy roadmap for different regional readiness levels. Findings suggest that the CE should be integrated into resource planning, regional observatories should be established and CE-related research and development (R&D) should receive investment. Future research should move towards standardized quantitative indicators and predictive models to track how readiness changes under policy interventions. Full article

The Vietnam Mekong River Delta (VMRD) is a climate-sensitive region characterized by diverse ecosystems, including extensive mangrove forests that protect against sea-level rise and contribute to global carbon sequestration. Accurate land cover classification in the VMRD is essential but remains challenging due to complex landscapes and dynamic environmental conditions. The primary objective of this study is to propose a semi-supervised deep learning framework that integrates satellite indices with multi-temporal remote sensing data to address key classification challenges, particularly in situations where ground truth data is limited, as compared to unsupervised and supervised machine learning methods. Our comparative analysis across different sample sizes (500 to 6000 ground-truth data points) reveals critical insights into model performance and scalability. Supervised models, including Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN), demonstrated strong performance when sufficient labeled data were available, with CNN achieving the highest accuracy (0.97 at 6000 samples). However, at minimal sample sizes (500 sample points), these supervised approaches exhibited substantial limitations, with accuracies dropping dramatically (RF: 0.75, SVM: 0.80, CNN: 0.81). Supervised models also showed overfitting tendencies compared to official land cover statistics. In contrast, the semi-supervised approach (SoC4SS-FGVC) achieves remarkably high performance at small sample sizes (0.92 accuracy with 500 sample points), demonstrating strength under minimal data availability. The framework also showed improved capability in distinguishing spectrally similar land-cover classes and detecting environmentally sensitive types such as mangrove forests. Cross-validation with official statistics confirmed the semi-supervised model’s superior effectiveness in delineating paddy rice fields and its resistance to overfitting. The performance analysis demonstrates that SoC4SS-FGVC provides a practical and cost-effective solution for land cover mapping, particularly in regions where extensive ground-truth data collection is prohibitively expensive or logistically challenging. Full article

This study examines the socio-demographic characteristics, rice production practices, and breeding preferences of farmers across three major rice-growing regions of Vietnam: the Mekong Delta, Central Vietnam, and North Vietnam. A survey of 109 rice farmers captured information on cultivation status, livelihood activities, and preferred breeding traits for rice improvement. The results reveal clear regional differentiation in farm structure, production objectives, and varietal preferences. Rice farming in the Mekong Delta is predominantly commercially oriented, characterized by larger landholdings and greater male participation, whereas rice production in Central and Northern Vietnam is more subsistence-oriented, with higher female involvement. Farmers across regions consistently valued locally adapted rice varieties, but articulated region-specific trait priorities shaped by agro-ecological conditions. In the Mekong Delta, preferences emphasized soft grain quality and salinity tolerance, reflecting coastal production constraints. In Central Vietnam, farmers prioritized heat tolerance and resistance to pests and diseases, while in Northern Vietnam, cold tolerance and grain quality attributes, including aroma and harder texture, were most important. Major biotic stresses, particularly blast and bacterial blight, also showed significant regional variation in reported incidence. By linking these region-specific preferences to clearly defined Target Populations of Environments (TPEs), this study provides a practical framework for aligning breeding targets with real-world production conditions. The findings offer actionable guidance for participatory breeding and decentralized varietal evaluation under the Biodiversity for Opportunities, Livelihoods, and Development (BOLD) initiative, as well as other rice improvement programs. To our knowledge, this represents the first multi-region evidence from Vietnam that systematically integrates agro-ecological variation with a TPE-based breeding approach, supporting the development of climate-resilient, farmer-preferred rice varieties and more sustainable rice production systems. Full article

This study focuses on the calibration and verification of a large-scale coupled numerical model to simulate the complex hydrodynamic–wave–sediment transport processes in the coastal and estuarine regions of the Mekong River Delta (MRD), Vietnam. Using the MIKE 21/3 modeling system, the research integrates Hydrodynamics (HD), Spectral Wave (SW), and Mud Transport (MT) modules across a computational domain of 270 × 300 km. The models were rigorously tested using field measurement data from three distinct periods: May 2004 (dry season calibration), September 2017 (first verification), and June 2024 (second verification). The results from the hydrodynamic model demonstrated high accuracy in predicting water levels, with the average Root Mean Square Error (RMSE) values ranging between 4.4% and 5.8%. The wave spectral model showed reliable performance, with the average RMSE values for wave height ranging from 15.1% to 18.0%. Furthermore, the Mud Transport module successfully captured suspended sediment concentrations (SSC), yielding average RMSE values between 26.0% and 32.1% after the fine-tuning of site-specific parameters such as critical shear stress for erosion and deposition. The study highlights the critical importance of utilizing site-specific sedimentological parameters to accurately predict morphological changes in highly dynamic estuarine environments. This validated model provides a robust tool for assessing coastal erosion and developing protection measures in regions that are increasingly vulnerable to climate change and human activities. Full article

Objective: To assess the prevalence and associated factors of malnutrition—including stunting, thinness, overweight, and obesity—among Vietnamese children aged 6–17 years, and to identify demographic, geographic, and behavioral correlates to inform targeted nutrition interventions. Methods: A cross-sectional, nationally representative study was conducted from January 2024 to June 2025 using data from the MIDU Assessment Program. A multistage stratified random sampling approach recruited 43,505 children aged 6–17 years across all regions of Vietnam. Anthropometric measurements were obtained following WHO 2007 growth reference standards. Stunting was defined as height-for-age Z-score (HAZ) < −2 SD, and overweight/obesity as body mass index-for-age Z-score (BAZ) > +1 SD. Data on demographic characteristics, sleep patterns, sports participation, vitamin K₂ use, and pubertal status were collected via structured questionnaires. Multivariable logistic regression was used to identify factors associated with stunting and overweight/obesity. Results: Overall, 3.9% were stunted, 5.1% were thin, 20.7% were overweight, and 11.4% were obese; 8.6% had any undernutrition and 39.5% had any form of malnutrition. Stunting was significantly associated with being male (OR = 1.37, 95% CI: 1.24–1.52), older age—particularly 14–17 years (OR = 6.56, 95% CI: 5.48–7.84)—and residing in the Northern midlands, North Central, South Central, and Central Highlands regions. In contrast, frequent sports participation (OR = 0.76, 95% CI: 0.68–0.84), daily vitamin K₂–MK₇ use (OR = 0.82, 95% CI: 0.72–0.93), and having reached puberty (OR = 0.26, 95% CI: 0.22–0.30) were associated with lower odds of stunting. For overweight and obesity, lower odds were found among females (OR = 0.48, 95% CI: 0.46–0.51) and older children, while higher odds occurred among those living in the Southeast (OR = 1.45, 95% CI: 1.36–1.53) and Mekong River Delta (OR = 1.35, 95% CI: 1.24–1.48) regions. Early sleep (OR = 0.91, 95% CI: 0.87–0.95) and sports participation (OR = 1.07, 95% CI: 1.02–1.11) showed modest associations, whereas vitamin K₂ use and puberty were not significant predictors. Conclusions: Vietnamese school-aged children face a significant rate of malnutrition, with regional, gender, and age disparities. Full article

Organic rice farming (ORF) can support both climate change mitigation and adaptation. However, a deeper understanding of its specific benefits and challenges is needed. This paper synthesises current knowledge on the potential of ORF to enhance resilience in regions exposed to natural hazards, with particular attention to the climate-vulnerable region of the Mekong Delta (MKD), Vietnam. ORF can deliver multiple benefits: reducing production costs, revitalising degraded and pesticide-contaminated soils, improving water and soil quality, enhancing biodiversity, and contributing to human health and sustainable livelihoods. In the context of MKD, where rice production intersects with acute vulnerability to salinity intrusion, storms, and drought, ORF also presents opportunities for long-term adaptation by improving ecosystem health and reducing socio-ecological vulnerability. Despite these benefits, ORF remains limited in scale and impact due to the lack of integrated, landscape-level implementation strategies. Challenges like chemical contamination, limited access to certified organic inputs, and insufficient institutional and technical support leave many existing ORF initiatives vulnerable and constrain further expansion. To fully realise ORF’s resilience and sustainability potential, more targeted research and policy attention are needed. An integrated governance approach that considers both biophysical and socio-economic dimensions is essential to support a meaningful and scalable transition to organic rice farming in climate-sensitive regions like the MKD. Full article

Community-Based Tourism (CBT) refers to forms of tourism owned and managed by local communities, designed to enhance participation, empowerment, and equitable benefit-sharing. This study investigates how climate-induced migration and donor investment disparities shape the uneven development of CBT across Vietnam. The research pursues three aims: (1) to evaluate how macro- and micro-level funding structures influence CBT readiness; (2) to analyze how spatial justice and post-development critique illuminate structural inequalities in tourism investment; and (3) to assess the implications for climate-vulnerable and ethnic minority communities, including their underrepresentation in CBT research and policy discourse. Methodologically, the study undertakes a systematic review of CBT literature (2014–2025), a thematic analysis of donor and government reports (World Bank, ADB, IFAD), and an estimation of regional funding flows using narrative coding and text-based pattern analysis. Findings reveal a persistent geographic and institutional bias toward the Southern Mekong Delta, which benefits from climate-resilience projects and tourism-specific investments, while Northern Highlands regions remain marginalized, receiving only poverty-focused funding. The paper contributes by integrating spatial justice and post-development critique into tourism studies, demonstrating how donor-led “resilience” agendas can inadvertently reinforce spatial inequalities, and offering policy recommendations for more equitable CBT planning, funding, and scholarly attention across Vietnam’s diverse regions. Full article

In Vietnam, English proficiency is a graduation requirement and offers students great opportunities to win scholarships and employability after graduation. Universities in the Mekong Delta region (MDR) often face challenges in foresting students’ English proficiency despite continuous assistance offered. Although students have taken online supplementary courses (OSC) delivered through e-learning systems to support their English formal classes for several years, students’ successes in English tests with such supplementary courses and the predictors of this issue remain unknown. Therefore, we developed a model to predict students’ success in English final tests based on behaviors and grades in OSC using logistic regression (LR) and classification and regression tree (CART) classifiers. A total of 109 students of OSC in a target university in MDR participated in this study, and the result showed that CART (area under the curve (AUC) = 89.3%) was slightly better than LR. The outcomes of this study contribute to students’ success in English tests and the enhancement of the effectiveness of online supplementary courses for English improvements. Full article

The Mekong Delta, an important agricultural and economic hub in Vietnam, has suffered from severe water quality issues caused by both natural and anthropogenic forces. This paper aims to conduct a rational statistical approach to evaluate the current situation of surface water quality in the Mekong Delta, applying Factor Analysis (FA), Principal Component Analysis (PCA), and Agglomerative Hierarchical Clustering (AHC) to a database of 3117 samples collected by national and provincial monitoring stations. The results revealed significant contamination with organic pollutants (BOD5: 3.50–172.870 mg/L, COD: 6.493–472.984 mg/L), pesticides (e.g., DDTs: n.d to 1.227 mg/L), trace metals (As: 0.006–0.046 mg/L, Cr: n.d–1.960 mg/L), and microbial indicators (Coliforms: n.d–45,100 MPN/100 mL), often higher than the WHO drinking water threshold. PCA/AHC analysis identified the following five major pollution components: (1) organic pollution and sewage/industrial and deposited chemicals (PCA1—23.08% variance); (2) pesticide and agricultural runoff derived contamination with Hg (PCA2—15.44%); (3) microbial pollution of the water was found to correlate positively with Zn and Cu content (PCA3—8.90%); (4) salinity was found to mobilize As and Cr (PCA4—8.00%); (5) nutrient/microbial pollution presumably from agricultural and sewage inputs (PCA5—7.22%). AHC showed some spatial variability that grouped samples in urban/industrial (Cluster 1), rural/agricultural (Cluster 2), and a highly contaminated one, where water was toxic and presented with microbial and Cd contamination (Cluster 3). Levels of pesticides, Cr, and microbial pollution were higher than reported in previous Mekong Delta studies and exceeded regional trends. These results emphasize the importance of holistic water management strategies, including better wastewater treatment, pesticide control, sustainable farming, and climate-adaptive measures to reduce saltwater intrusion and safeguard drinking water quality for the Mekong Delta. Full article

Canine distemper virus (CDV) is a highly contagious pathogen and causes a fatal systemic disease in domestic dogs and wild carnivores worldwide. Despite CDV infections being monitored globally, studies on CDV in Vietnam seem to be limited. This study, therefore, investigated the epidemiological, clinical, and molecular characteristics of CDV in the Mekong Delta (MD) region of Vietnam. A total of 6687 ocular/nasal swabs were collected from CDV-suspected dogs across seven cities/provinces. CDV infection was detected in 6.19% (414 dogs) of suspected dogs using a commercially available rapid kit, with infection associated with age, roaming status, and vaccination status. Hematological and blood biochemical analysis of CDV-infected dogs revealed anemia, leukopenia, neutrophilia, thrombocytopenia, a slight increase in aspartate aminotransferase (AST) levels, and a significant increase in blood urea nitrogen (BUN) levels. Molecular characterization of partial hemagglutinin (H) and fusion (F) genes exhibited high nucleotide and amino acid homology with the Asia-1 genotype. Phylogenetic analysis confirmed that the field sequences were clustered into the Asia-1 genotype together with the neighboring countries. These findings provide important insights into the current epidemiological, clinical, and molecular features of CDV circulating in Vietnam. Full article

Graduate employability remains a crucial challenge for higher education institutions, especially in developing economies. This study investigates the key academic and vocational factors influencing early employment outcomes among recent graduates at a public university in Vietnam’s Mekong Delta region. By leveraging predictive analytics, the research explores how data-driven approaches can enhance career readiness strategies. The analysis employed AI-driven models, particularly classification and regression trees (CARTs), using a dataset of 610 recent graduates from a public university in the Mekong Delta to predict early employability. The input factors included gender, field of study, university entrance scores, and grade point average (GPA) scores for four university years. The output factor was recent graduates’ (un)employment within six months after graduation. Among all input factors, third-year GPA, university entrance scores, and final-year academic performance are the most significant predictors of early employment. Among the tested models, CARTs achieved the highest accuracy (93.6%), offering interpretable decision rules that can inform curriculum design and career support services. This study contributes to the intersection of artificial intelligence and vocational education by providing actionable insights for universities, policymakers, and employers, supporting the alignment of education with labor market demands and improving graduate employability outcomes. Full article

Background: Vietnam has made significant strides in reducing the prevalence of HIV infection and achievements in its antiretroviral treatment program. However, the COVID-19 pandemic and financial challenges in the healthcare system have posed significant obstacles to maintaining effective HIV treatment and monitoring, particularly among vulnerable populations. This study aims to evaluate the situation of HIV drug resistance among patients who have experienced treatment failure in the Mekong Delta region and to compare data from 2019 to 2022. Methods: The study material was blood plasma samples from HIV-infected individuals with ART failure: 316 collected in 2019 and 326 collected in 2022. HIV-1 genotyping and mutation detection were performed based on an analysis of the nucleotide sequences of the Pol gene region. A total of 116 HIV-infected individuals with virological failure in 2019 and 2022 were assessed for HIV drug resistance. Results: The study revealed a high proportion of participants with viral loads exceeding 1000 copies/mL, significantly increasing from 12.0% in 2019 to 23.9% in 2022 (OR = 2.3; p = 0.0001). HIV drug resistance mutations were detected in 84.21% of cases in 2019 and 92.59% in 2022. The prevalence of concurrent resistance to NRTIs and NNRTIs was 37.5% and 30.13% in 2019 and 2022, respectively. There was a statistically significant decrease in NNRTI resistance (OR = 0.32, χ² = 5.43, p < 0.05). In contrast, multi-drug resistance to protease inhibitors rose from 18.52% to 45.21% (φ* = 0.00403, p < 0.05). Triple-class resistance was identified only in 2022 (17.81%). The most common mutations included M184I/V, D67N, K103N, Y181C, and V82A/S/T, with D67N rising significantly from 3.13% to 21.92%. The predominant subtype was CRF01_AE. Conclusion: A high prevalence of viral non-suppression and HIV drug resistance was observed among patients in the Mekong Delta region, particularly after the onset of the COVID-19 pandemic. Our study highlights the ongoing challenges that the HIV/AIDS treatment program in Vietnam must address in the post-pandemic period to sustain its success and achieve the goals of the country’s HIV prevention strategies. Full article