Predicting the export price of shrimp is important for Vietnam’s fisheries. It not only promotes product quality but also helps policy makers determine strategies to develop the national shrimp industry. Competition in global markets is considered to be an important factor, one that significantly influences price. In this study, we predicted trends in the export price of Vietnamese shrimp based on competitive information from six leading exporters (China, India, Indonesia, Thailand, Ecuador, and Chile) who, alongside Vietnam, also export shrimp to the US. The prediction was based on a dataset collected from the US Department of Agriculture (USDA), the Food and Agriculture Organization of the United Nations (FAO), and the World Trade Organization (WTO) (May-1995 to May-2019) that included price, required farming certificates, and disease outbreak data. A super learner technique, which combined 10 single algorithms, was used to make predictions in selected base periods (3, 6, 9, and 12 months). It was found that the super learner obtained results in all base periods that were more accurate and stable than any candidate algorithms. The impacts of variables in the predictive model were interpreted by a SHapley Additive exPlanations (SHAP) analysis to determine their influence on the price of Vietnamese exports. The price of Indian, Thai, and Chinese exports highlighted the advantages of being a World Trade Organization member and the disadvantages of the prevalence of shrimp disease in Vietnam, which has had a significant impact on the Vietnamese shrimp export price.
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.
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