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The rapid expansion of shrimp farming in Ca Mau Province, Vietnam since the early 2000s has converted mangrove forests into aquaculture ponds, resulting in deforestation and degraded mangrove ecosystems. A system dynamics model was developed to assess the interactions and temporal changes among various economic, social, and environmental factors. The analysis was conducted under two development scenarios. In the Business as Usual scenario, shrimp farming will expand to 317,037 hectares in 2050, reducing mangrove coverage to 76,484 hectares and carbon storage to 4.8×106 MgC. However, this expansion is expected to create jobs, producing an output value of 25,153 billion VND and accounting for 25.13% of the province’s workforce. Conversely, the Policy Scenario stabilizes shrimp farming areas at 280,000 hectares, which will have alternative impacts on the environment, society, and economy. By 2050, Ca Mau’s mangrove coverage and carbon storage will reach 88,902 hectares and 5.6×106 MgC, respectively. Besides, the shrimp industry will generate an output value of 22,214 billion VND and account for 23.58% of the province’s workforce. Despite yielding lower economic growth and employment generation, policy interventions are expected to support overall positive developmental progress. Furthermore, a shift in the labor structure is anticipated due to restrictions on shrimp farming areas. The findings provide insights for policymakers to anticipate potential consequences of future development and appropriately adjust policy interventions. Several strategies, such as land use management, economic diversification, and alternative livelihood generation, are needed to balance environmental sustainability with social and economic growth in Ca Mau. Moreover, the methodology presented in this study is not limited to Ca Mau but is also applicable to other areas where the expansion of aquaculture endangers mangrove ecosystems.

This article describes data on eco-shrimp farming households in Ca Mau, Vietnam. The survey was conducted in Tam Giang Dong commune, Nam Can district, Ca Mau province from March 2023 to May 2023 with a total of 120 households engaged in eco-shrimp farming. The non-probability convenience sampling method was used to select respondents. Two responses were removed from the dataset due to uncomplete questionnaires. The dataset includes a structured questionnaire with 52 questions, along with a raw data file of 118 responses. The questionnaire collected data on (a) household demographic information, (b) eco-shrimp farming area and pond types, (c) production characteristics for the 2021–2022 eco-shrimp crop, and (d) farmers’ perspectives on the eco-shrimp farming model. The dataset is available in an Excel file format. It is expected to benefit policymakers and local authorities by providing evidence-based insights into the eco-shrimp production model and the compliance of eco-shrimp farmers with mangrove conservation policies.

Antimicrobials are a core aspect of most livestock production systems, especially in low-and middle-income countries. They underpin the efficient use of scarce feed resources and stabilize returns on capital and labor inputs. Antimicrobial use (AMU) contributes to the production of healthy animals, yet AMU in livestock is linked to antimicrobial resistance (AMR) in animals, humans and the environment.The Vietnamese Platform for Antimicrobial Reduction in Chicken Production was implemented during 2016–2019 and was one of Southeast Asia's first interventions focused on AMU reductions in livestock production. The project targeted small-scale commercial poultry farms in the Mekong Delta region of Vietnam using a “randomized before-and-after controlled” study design. It provided farmers with a locally adapted support service (farmer training plan, advisory visits, biosecurity, and antimicrobial replacement products) to help them reduce their reliance on antimicrobials. A partial budget analysis was performed comparing the control group (status-quo) and intervention group (alternative). The median net farm-level benefit of the intervention strategies with the project's support was VND 6.78 million (interquartile range (IR) VND -71.9–89 million) per farm. Without project support the benefit was reduced to VND 5.1 million (IR VND -69.1–87.2 million) to VND 5.3 million (IR -VND 68.9–87.5 million) depending on the antimicrobial alternative product used. At the project level with a focus on AMU and its reduction, subsequently influence on the resistance reduction, our results showed that achieving resistance reduction benefits with the current knowledge and technologies required investment of at least VND 9.1 million (US$ 395.10) per farm during the project's lifetime. The results highlight the positive net profit for the majority of enrolled farms and a reasonable investments from the project. The recommendation focuses on the implementation of policies on financial support, legislation, and information as potential solutions to facilitate the application of intervention strategies to reduce AMU in poultry production.

Rice is the most important crop in Vietnam’s agriculture sector for its contribution to economic stability and food security. Improving the efficiency of rice farming and expanding production towards sustainability has become one of the national economic development goals. This study aimed to empirically evaluate technical efficiency of rice farms based on their farming practices using observational data from face-to-face interviews with 152 rice farm households in Long An Province in the Mekong Delta, Vietnam. The data envelopment analysis model was applied to evaluate the technical and scale efficiencies. Results show that except for farmers applying one practice, the average technical efficiency exceeded 81 %. Farmers who adopted more sustainable practices obtained higher technical efficiency and scale efficiency with scores of 90 % and 91 % respectively. Many farmers with different combinations of farming practices operated under increasing returns to scale. The Tobit model was also applied to estimate the determinant factors of technical efficiency. The results of the Tobit model revealed that the farmer’s education level and farming experience had a significant impact on rice farming efficiency. The study also found an inverted U-shaped relationship between education level and efficiency, which indicates that if farmers had higher education levels beyond the threshold point, their farming efficiency will decrease. The significance of adopting sustainable agriculture practices in rice-producing households was emphasized by the positive correlation observed between the number of sustainable agriculture practices applied and efficiency.

Herein, we report on facile fabricating sensors based on Ag/α-Fe2O3 nanocubes harvesting by one-step pyrolyzing Prussian blue (PSB) template anchored AgNO3 at different temperatures (300~500°C) and exploiting them for electrochemical sensing of methylparaben (MP). The Ag/Fe2O3 nanocubes had crystallite sizes ranging from 300~400 nm and incorporated reduced Ag nanoparticles (AgNPs) ranging from 10.5~15.3 nm. Notably, the harvested Ag/Fe2O3 composites were constructed hetero-interfacial Ag–Fe2O3 structures inside the porous α-Fe2O3 cubes with interconnected pore matrix resulting from the thermal conversion of PSB. The carbon glass electrode (GCE) coated with an Ag/Fe2O3 sensor prepared at 400°C showed the highest oxidation peak at 0.84 V towards MP. In addition, the Ag/α-Fe2O3@GCE interface achieved an excellent detection of MP with a low LOD of 0.14 µM and a linear response range of 10~38 µM. The sensor also depicted good selectivity and stability during 10 days, demonstrating a suitable sensor for detecting and analyzing MP. This study provides a facile strategy for constructing a porous Ag/Fe2O3 heterostructural composite as an efficient electrochemical sensing material. Sensors based on Ag/α-Fe2O3 nanocube harvesting by one-step pyrolyzing Prussian blue (PSB) template anchored AgNO3 and exploited for the electrochemical sensing of methylparaben. The results suggest that Ag/α-Fe2O3 nanocube crystals comprise heterointerfaces and interconnected pores. The Ag/α-Fe2O3@GCE interface exhibited excellent detection of methylparaben, good stability, and selectivity. [ABSTRACT FROM AUTHOR]

This study presents a facile self‐healing electromagnetic interference (EMI) shielding composite by sandwiching a layer of silver nanowires (AgNWs) between two layers of self‐healing polyurethane (SPU). The AgNWs layer forms an interconnected conductive network that effectively reflects and absorbs electromagnetic radiation, providing efficient EMI shielding. The SPU layers contribute autonomous healing of mechanical damages and restoring structural integrity and conductive pathways. The composite films exhibit remarkable EMI shielding effectiveness (SE) (up to 64.6 dB), exceptional mechanical toughness (106.4 MJ/m3), and self‐healing capabilities, outperforming conventional shielding materials. The synergy between AgNWs and SPU layers offers lightweight, flexible, tough, and self‐healing properties, making this material highly promising for applications in wearable electronics, aerospace, and automotive industries where durability and long‐term reliability are critical. Highlights: Facile fabrication of sandwiched AgNWs layer between SPU films.Sandwiched AgNWs/SPU films with exceptional EMI shielding.AgNWs form conductive networks for efficient EMI shielding.Remarkable mechanical toughness of 106.4 MJ/m3 and self‐healing capability.Potential shielding materials for lightweight, wearable electronics, aerospace. [ABSTRACT FROM AUTHOR]

Fluvial floods endure as one of the most catastrophic weather-induced disasters worldwide, leading to numerous fatalities each year and significantly impacting socio-economic development and the environment. Hence, the research and development of new methods and algorithms focused on improving fluvial flood prediction and devising robust flood management strategies are essential. This study explores and assesses the potential application of 1D-Convolution Neural Networks (1D-CNN) for spatial prediction of fluvial flood in the Quang Nam province, a high-frequency tropical cyclone area in central Vietnam. To this end, a geospatial database with 4156 fluvial flood locations and 12 flood indicators was considered. The ADAM algorithm and the MSE loss function were used to train the 1D-CNN model, whereas popular performance metrics, such as Accuracy (Acc), Kappa, and AUC, were used to measure the performance. The results indicated remarkable performance by the 1D-CNN model, achieving high prediction accuracy with metrics such as Acc = 90.7%, Kappa = 0.814, and AUC = 0.963. Notably, the proposed 1D-CNN model outperformed benchmark models, including DeepNN, SVM, and LR. This achievement underscores the promise and innovation brought by 1D-CNN in the realm of susceptibility mapping for fluvial floods.