Impact of seawater intrusion control on the environment,land use and household incomes in a coastal area

Agricultural production in the coastal wetlands of Asia is often hindered by salinity intrusion caused by tidal fluctuation. This paper reports changes in environmental and socio-economic conditions that followed the phased construction and operation of sluices for controlling seawater intrusion from 1994 –2000 in a coastal area of the Mekong River Delta, Vietnam. Canal water salinity decreased rapidly upstream of sluices, allowing rice cropping intensification and increased rice production in the eastern part of the study area. However, the livelihoods of farmers in the western part were adversely affected due to cessation of supply of brackish water that was needed for brackish-water shrimp farming, while the acid sulphate soils present there posed problems for rice cultivation. The poor farmers and landless people suffered more because the fishery resource that they depended on declined sharply due to reduced salinity and increased acidity in the canal water. The findings confirmed that the environment and resource use in the coastal lands are very sensitive to external intervention. A clear understanding of the socio-economic and environmental impacts of salinity control measures in coastal areas can help planning to enhance farmers' incomes while minimizing negative environmental impacts. Land-use policy formulation, planning and management should adopt a more holistic approach, taking into account the interests of all resource users, especially the poor, instead of focusing on any particular sector.     

Use of GIS and machine learning to predict disease in shrimp farmed on the east coast of the Mekong Delta,Vietnam

Diseases in shrimp farms in the Mekong Delta of Vietnam cause significant crop losses and are therefore of great concern to producers. Once a pond becomes infected, it is difficult to prevent spread of the disease to nearby shrimp farming areas. Thus, predicting the occurrence of disease is an essential part of reducing the risk for shrimp farmers. In this study, we applied an integrated geographic information system and machine learning system to predict three serious diseases of shrimp, namely, acute hepatopancreatic necrosis, white spot syndrome disease, and Enterocytozoon hepatopenaei infection, based on data collected from shrimp farms in the Tra Vinh, Bac Lieu, Soc Trang, and Ca Mau provinces of Vietnam. We first constructed a map showing the distribution of these diseases using the locations of affected farms, and then we conducted spatial analysis to acquire the geographical features of the affected locations. This latter information was combined with environmental factors and clinical signs to form the set of independent variables affecting the outbreak of diseases. The neural network model outperformed the logistic regression, random forest, and gradient boosting methods in terms of predicting infection to estimate the probability of disease occurrence in farmed areas. Acute hepatopancreatic necrosis disease infected farms downstream of the Co Chien and Hau Rivers of Tra Vinh and west of Ca Mau. Enterocytozoon hepatopenaei infection is distributed in Soc Trang Province, while white spot syndrome virus has spread to the coastal districts of Soc Trang and Bac Lieu Provinces, where it is highly associated to water from a complex canal system.

     

GOVERNING THE ENVIRONMENTAL AND SOCIAL DIMENSIONS OF PANGASIUS PRODUCTION IN VIETNAM: A REVIEW∗

The Vietnamese government's policies for an export-led economy have supported the rapid growth of Pangasius production but have had limited success in mitigating the environmental and social impacts stemming from unplanned and ad hoc expansion. Recognising the poor performance of state regulation the industry and government have turned to private or market-based environmental and social standards for Pangasius production. The success of these private governance arrangements is dependent on the capacity of producers to comply with standards within their wider environmental, social, cultural and economic context. This paper reviews current concerns over the sustainability of Pangasius production and draws attention to the challenges private governance initiatives face in steering towards sustainable production.     

Glucose cross-linking of polyurethane copolymer and its impact on the elevation of mechanical properties and shape memory effect

Effect of glucose cross-linking on the shape memory and mechanical properties of polyurethane (PU) block copolymer was investigated. Glucose was selected due to its large number of free hydroxyl groups, easy availability, miscibility with other reactants, and cyclic structure. The glycerol cross-linking did not affect the molecular interaction and phase separation of hard and soft segments in polyurethane structure as judged from IR and DSC analysis. Viscosity of glucose cross-linked PU increased after cross-linking due to the cross-linked structure. Maximum stress drastically improved with the adoption of glucose as a cross-linker together with a slight increase in strain at break. Shape recovery also increased with the adoption of glucose as a cross-linker, and shape recovery was not diminished after four cyclic shape recovery tests. In contrast, shape retention significantly decreased if glucose was included for two different hard segment contents. Finally, glucose cross-linking was compared with other cross-linkers used in shape memory polymer and the advantage of glucose cross-linking was discussed.     

The use of machine learning to predict acute hepatopancreatic necrosis disease (AHPND) in shrimp farmed on the east coast of the Mekong Delta of Vietnam

Fisheries Science - Predicting the outbreak of disease is essential when managing shrimp farms. Acute hepatopancreatic necrosis disease (AHPND) caused by Vibrio parahaemolyticus is a serious...     

Predicting the price of Vietnamese shrimp products exported to the US market using machine learning

Accurately predicting the price of exported fishery products is an important task for fisheries because it will enable market trends to be determined, leading to the development of high-quality fishery products. In this study, we predicted prices in selected base periods (2, 3, 6, and 12 months) to investigate how historical data influenced the Vietnamese export price. A dataset (from May 1995 to May 2019) was collected from the US Department of Agriculture (USDA). We initially hypothesized that the dependent variable, Vietnamese export price, was affected by 33 independent variables, but ultimately used 15 key variables, which were chosen on the basis of Akaike information criterion (AIC) to train the models. A tree-based machine learning technique, including the random forest and gradient boosting tree algorithms, was applied for predictions. It was found that the random forest algorithm performed well for historical data for periods of more than 6 months, while the gradient boosting tree algorithm was better over short durations of less than 6 months.

     

Characterization of the ion-paired polyurethane copolymers

The polyurethane (PU) copolymer was grafted with either 3-dimethylaminopropanol as a basic pendant group or 4-hydroxylphenylacetic acid as an acidic pendant group. The two types of PU were mixed in solution to form ion-pairing between acidic and basic pendant groups. The structural change after grafting and ion-pairing was followed by acid-base titration, infrared spectra, differential scanning calorimetry, and absolute viscosity. The tensile stress can be raised as much as 70 % by the control of the ion-pairing ratio of acidic and basic PUs. Shape recovery was over 80 % and reproducible for four test cycles. The minor control of tensile properties of PU was possible through the ion-pairing method.