The availability of water is a growing concern for flooded rice production. As such, several water-saving irrigation practices have been developed to reduce water requirements. Alternate wetting and drying and mid-season drainage have been shown to potentially reduce water requirements while maintaining rice yields when compared to continuous flooding. With the removal of permanently anaerobic conditions during the growing season, water-saving irrigation can also reduce CO₂ equivalent (CO_2eq) emissions, helping reduce the impact of greenhouse gas (GHG) emissions. However, the long-term impact of water-saving irrigation on soil organic carbon (SOC)—used here as an indicator of soil health and fertility—has not been explored. We therefore conducted a meta-analysis to assess the effects of common water-saving irrigation practices (alternate wetting and drying and mid-season drainage) on (i) SOC, and (ii) GHG emissions. Despite an extensive literature search, only 12 studies were found containing data to constrain the soil C balance in both continuous flooding and water-saving irrigation plots, highlighting the still limited understanding of long-term impacts of water-saving irrigation on soil health and GHG emissions. Water-saving irrigation was found to reduce emissions of CH₄ by 52.3% and increased those of CO₂ by 44.8%. CO_2eq emissions were thereby reduced by 18.6% but the soil-to-atmosphere carbon (C) flux increased by 25% when compared to continuous flooding. Water-saving irrigation was also found to have a negative effect on both SOC—reducing concentrations by 5.2%—and soil organic nitrogen—potentially depleting stocks by more than 100 kg N/ha per year. While negative effects of water-saving irrigation on rice yield may not be visible in short-term experiments, care should be taken when assessing the long-term sustainability of these irrigation practices because they can decrease soil fertility. Strategies need to be developed for assessing the more long-term effects of these irrigation practices by considering trade-offs between water savings and other ecosystem services.

This study describes antibiotic use by small-scale freshwater aquaculture farmers in the upper Mekong Delta in southwestern Vietnam and the knowledge and practices surrounding the cause and prevention of aquaculture disease in that region. Forty five farmers were included in the study, of which 19 (42%) cultivated tilapia Oreochromis spp., 13 (29%) Striped Catfish Pangasianodon hypophthalmus and 13 (29%) giant river prawns Macrobrachium rosenbergii. Antibiotics were used by farmers of tilapia and Striped Catfish (84% and 69% of farmers, respectively), but not by any of the prawn farmers. Most farmers (72%) used antibiotics for around 3 d when treating diseases, depending on the farmers' economic means and whether the fish recovered, as judged by the farmer. If farmers perceived that the antibiotic treatment had failed, the most common response was to change to another type of antibiotic. Some farmers also used antibiotics in the absence of clinical symptoms as a preventive measure. In the absence of rapid, cost-effective diagnostics, the likelihood for the incorrect use of antibiotics is high, which has implications for antibiotic resistance. Moreover, the sequential use of different antibiotics following therapeutic failure is a risk factor for the emergence of resistance. All farmers that were surveyed were aware of the risks associated with antibiotic use. This may lead to successful intervention toward reduced antibiotic use in freshwater fish farming in Vietnam.

This study performed flood risk assessment in urbanized areas using geospatial and remotely sensed data for 1990–2005–2020 periods using the linear unmixing model (LUM), random forests (RF), and support vector machines (SVM). The urban mapping results verified with reference data indicated close agreement, with the overall accuracies and Kappa coefficients higher than 88.9% and 0.78, respectively. A remarkable increase of 14.9% in urban area was observed for the period 2005–2020, compared to 7.2% during the period 1990–2005. The results of flood risk modeling revealed that RF produced slightly more accurate results than SVM. The flood risk areas aggregated with urban maps showed that the larger urban flood risk area was especially observed for the period 2005 − 2020. The urban areas of high/very high and medium flood risks calculated for 2020 were 19.7% and 20%, respectively. Such flood risk areas were also overlaid with the population density for urban planning and management.