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The crop simulation model Decision Support System for Agrotechnology Transfer (DSSAT) was applied over the Mekong River Delta (MRD), Southern Vietnam, to assess future (2020–2050) impacts of climate change on rice production. The DSSAT model was driven using observed station data and projected climate data derived through the dynamical downscaling of three global climate models (GCMs) using the Weather Research and Forecasting (WRF) model. The WRF model was simulated at a spatial resolution of 30 km over the study region, and the large-scale driving fields for future climates were taken from the Coupled Model Inter-Comparison Project Phase 3 (CMIP3) global models ECHAM5, CCSM3, and MIROC5 under the A2 emission scenario. Rice growth during two main seasons, namely, the winter-spring (winter) and summer-autumn (summer), were selected to quantify impacts under both irrigated and rain-fed rice cultivation. The results from this climate-crop study suggest that under rain-fed conditions, winter rice yield was likely to experience nearly 24% reduction while summer rice yield was projected to decrease by about 49%. Without irrigation, the annual rice yield was projected to decrease by about 36.5%, and under irrigated conditions, climate change is likely to reduce annual irrigated rice yields by about 1.78%. Winter rice yield was likely to decrease by 4.7% while summer rice yield was projected to marginally increase by about 0.68%. Increasing temperatures and seasonal variations of precipitation are likely to significantly reduce rice yields under rain-fed condition. In addition, (1) a decrease (increase) in the number of rainy days during the dry (wet) season and (2) positive effects of elevated CO2 for rain-fed rice growth under each of the three WRF model realizations would markedly influence rice yields. With Vietnam being one of the largest exporters of rice, these findings have serious implications for the local agricultural sector. This also serves an early warning for the policymakers and stakeholders for effective planning of not only crop production but also water resource management. The findings call for prudent diversification strategy planning by those countries which import rice.

[This corrects the article DOI: 10.1371/journal.pone.0262402.].

In many parts of the world, conditions for small scale agriculture are worsening, creating challenges in achieving consistent yields. The use of automated decision support tools, such as Bayesian Belief Networks (BBNs), can assist producers to respond to these factors. This paper describes a decision support system developed to assist farmers on the Mekong Delta, Vietnam, who grow both rice and shrimp crops in the same pond, based on an existing BBN. The BBN was previously developed in collaboration with local farmers and extension officers to represent their collective perceptions and understanding of their farming system and the risks to production that they face. This BBN can be used to provide insight into the probable consequences of farming decisions, given prevailing environmental conditions, however, it does not provide direct guidance on the optimal decision given those decisions. In this paper, the BBN is analysed using a novel, temporally-inspired data mining approach to systematically determine the agricultural decisions that farmers perceive as optimal at distinct periods in the growing and harvesting cycle, given the prevailing agricultural conditions. Using a novel form of data mining that combines with visual analytics, the results of this analysis allow the farmer to input the environmental conditions in a given growing period. They then receive recommendations that represent the collective view of the expert knowledge encoded in the BBN allowing them to maximise the probability of successful crops. Encoding the results of the data mining/inspection approach into the mobile Decision Support System helps farmers access explicit recommendations from the collective local farming community as to the optimal farming decisions, given the prevailing environmental conditions.

Advocating proper phosphorus (P) fertilisation is necessary to save this limited natural resource and to save the investment in rice cultivation. This study aimed to evaluate changes in phosphorus availability, total phosphorus in soil, phosphorus buffering capacity, and phosphorus saturation in the long-term phosphorus fertilisation in the paddy rice system. Soil samples were collected in the harvest stage after seven consecutive crops over three years at Can Tho city, Vietnam. The applied phosphorus fertiliser rates were: no phosphorus fertilisation (P0), 17.4 kg P/ha (P17.4), and 26.2 kg P/ha as farmer’s practice (P26.2). The results showed that the soil phosphorus buffering capacity in P0, P17.4 and P26.2 treatments was 9.49, 9.08 and 9.04 mg/kg, respectively. The degree of phosphorus saturation of P17.4 and P26.2 treatments ranged from 17.7% to 25.5%, showing the medium to high risk of phosphorus leaching. This study indicated that the application of phosphorus rate higher than 17.4 kg P/ha might result in the reduced soil phosphorus buffering capacity in the intensive rice cropping system in the Vietnamese Mekong Delta region. Our results implied that the application of a rate lower than 17.4 kg P/ha/crop could be extended to the other rice-growing (double/triple rice) areas in the Vietnamese Mekong Delta region or other paddy rice on alluvial soils in Asia.

In the Vietnamese Mekong Delta, soil quality and crop yield are steadily declining under rice monocultures with three crops per year. The objective of this study was to evaluate the medium-term effects of rotating rice with upland crops and adding organic amendments on rice yield, and to relate this to soil quality. A field trial with split-plot design including two factors and three replicates was carried out from 2017 to 2020, over the course of nine consecutive cropping seasons. Crop rotations and organic amendments were applied as main-plot and subplot factors, respectively. The rotations were (1) rice–rice–rice (R–R–R), (2) soybean–rice–rice (So–R–R), and (3) sesame–rice–rice (Se–R–R), while organic amendment treatments included (i) no amendment (NO-AM), (ii) compost of rice straw and cow manure (RS+CM), and (iii) sugarcane compost (SGC); the composts were applied at a rate of 2.0 t ha−1. The rotation cycle started with the so-called spring–summer (SS) season, followed by the summer–autumn (SA) season and ending with the winter–spring (WS) season. Rice yield significantly (p < 0.05) increased under organic amendments after nine growing seasons (2019–2020 WS), with an increment of 5.1% for RS+CM (7.07 ton/ha) and 6.1% for SGC (7.14 ton/ha). Contrary to our expectation, rotations with upland crops did not significantly increase rice yield. Rice yield significantly and positively correlated with an integrated soil quality index–SQI (r = 0.85) for the topsoil (0–15 cm), but not for the subsoil (15–30 cm). The increased availability of soil nutrients (Si and marginally also P) and improved soil physical properties probably induced by organic amendments, along with other soil properties under study, cumulatively attributed to enhanced rice yield. Repeated organic amendments thus becomes an effective management practice in improving soil quality under rice-based systems and could be applied to sustain rice yield in rice-producing regions with similar soil types and climatic conditions. Use of a SQI involving several soil quality indicators enables us to quantify the overall importance of soil fertility for rice yield versus other factors, and it provides an effective means of quantifying the integrated effect of improved management. Moreover, integrating a wide range of soil quality indicators in a SQI ensures its applicability across diverse settings, including different crop rotations and various soil types.

This paper presents a tracking controller for a remotely operated underwater vehicle (ROV). The kinematic and dynamic models of the BlueROV2 are introduced. The proposed control strategy is based on linear quadratic regulator (LQR) tracking, designed using the dynamic model of BlueROV2. Additionally, a co-simulation environment integrating MATLAB and Gazebo is developed to evaluate the effectiveness of the control system. Simulation results demonstrate that the proposed method enhances the stability and tracking performance of the ROV. Furthermore, a hardware-in-the-loop simulation (HILS) platform, incorporating an external joystick and the ROV within the co-simulation environment, is implemented to assess teleoperation controllability. The results confirm that the LQR tracking controller improves stability and responsiveness under complex underwater conditions.

In many parts of the world, conditions for small scale agriculture are worsening, creating challenges in achieving consistent yields. The use of automated decision support tools, such as Bayesian Belief Networks (BBNs), can assist producers to respond to these factors. This paper describes a decision support system developed to assist farmers on the Mekong Delta, Vietnam, who grow both rice and shrimp crops in the same pond, based on an existing BBN. The BBN was previously developed in collaboration with local farmers and extension officers to represent their collective perceptions and understanding of their farming system and the risks to production that they face. This BBN can be used to provide insight into the probable consequences of farming decisions, given prevailing environmental conditions, however, it does not provide direct guidance on the optimal decision given those decisions. In this paper, the BBN is analysed using a novel, temporally-inspired data mining approach to systematically determine the agricultural decisions that farmers perceive as optimal at distinct periods in the growing and harvesting cycle, given the prevailing agricultural conditions. Using a novel form of data mining that combines with visual analytics, the results of this analysis allow the farmer to input the environmental conditions in a given growing period. They then receive recommendations that represent the collective view of the expert knowledge encoded in the BBN allowing them to maximise the probability of successful crops. Encoding the results of the data mining/inspection approach into the mobile Decision Support System helps farmers access explicit recommendations from the collective local farming community as to the optimal farming decisions, given the prevailing environmental conditions.

Tuberculosis transmission continues to be a major public health challenge. In this cluster-randomized, controlled trial conducted in Vietnam, active community-wide screening for tuberculosis over 4 years is shown to decrease the prevalence of tuberculosis.

Hand, foot and mouth disease (HFMD) continues to challenge Asia with pandemic potential. In Vietnam, there have been two major outbreaks occurring during 2011-2012 (>200,000 hospitalizations and >200 deaths) and more recently in 2018 (>130,000 hospitalizations and 17 deaths). Given the high burden and the complex epidemic dynamics of HFMD, synthesizing its clinical and epidemiological data remains essential to inform the development of appropriate interventions and design public health measures. We report the results of a hospital-based study conducted during 2015-2018, covering the severe HFMD outbreak recently documented in Vietnam in 2018. The study was conducted at three major hospitals responsible for receiving HFMD patients from southern Vietnam with a population of over 40 million. A total of 19 enterovirus serotypes were detected in 1196 HFMD patients enrolled in the clinical study during 2015-2018, with enterovirus A71 (EV-A71), coxsackievirus A6 (CV-A6), CV-A10 and CV-A16 being the major causes. Despite the emergence of coxsackieviruses, EV-A71 remains the leading cause of severe HFMD in Vietnam. EV-A71 was consistently detected at a higher frequency during the second half of the years. The emergence of EV-A71 subgenogroup C4 in late 2018 was preceded by its low activity during 2017-early 2018. Compared with EV-A71 subgenogroup B5, C4 was more likely to be associated with severe HFMD, representing the first report demonstrating the difference in clinical severity between subgenogroup C4 and B5, the two predominant EV-A71 subgenogroups causing HFMD worldwide. Our data have provided significant insights into important aspects of HFMD over four years (2015-2018) in Vietnam, and emphasize active surveillance for pathogen circulation remains essential to inform the local public health authorities in the development of appropriate intervention strategies to reduce the burden of this emerging infections. Multivalent vaccines are urgently needed to control HFMD.

Despite dengue being a major public health problem, there are no antiviral or adjunctive treatments for the disease. Novel therapeutics are needed, particularly for patients at high risk of severe disease, including those living with obesity. Metformin reduces dengue viral replication in vitro through AMPK activation and may also have beneficial immunomodulatory effects. We conducted an open label trial at the Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam, enrolling 120 patients with dengue and obesity (60 treatment arm, 60 control arm receiving standard of care only). Within the treatment arm, the first 10 patients were prescribed low dose metformin, and the remaining 50 patients received weight-based dosing of 1-1.5g/day. The primary outcome was the number of adverse events (AEs), and secondary outcomes were clinical and laboratory parameters, including fever clearance time, platelet nadir, percentage of haematocrit change from baseline, maximum creatinine and highest AST/ALT, and the kinetics of plasma viraemia and NS1 antigenaemia. The majority of patients in both groups had dengue with warning signs. Six patients in the metformin group and 5 controls developed dengue shock syndrome, and no patients died. There were more AEs recorded in the metformin treated group than in the control group (mean±SD: 15 ± 4 vs. 11 ± 6), particularly the high-dose metformin group (15 ± 5). Twenty-five patients (42%) had to stop the study drug due to AEs, including severe diarrhea (n = 12), dengue shock (n = 5), increased lactate of >3mmol/L (n = 4), hypoglycemia (n = 3), and persistent vomiting (n = 1). There were no clear differences in secondary outcomes between the two groups. Metformin was poorly tolerated in patients with dengue, mainly due to gastrointestinal side effects. Metformin did not beneficially affect clinical evolution or virological parameters compared to supportive care alone. Our data does not support progression to larger phase 3 trials of metformin in patients with dengue. ClinicalTrials.gov: NCT04377451 (May 6th, 2020).