A novel machine learning approach to predict the export price of seafood products based on competitive information: The case of the export of Vietnamese shrimp to the US market

Nguyen Minh Khiem; Yuki Takahashi; Hiroki Yasuma; Khuu Thi Phuong Dong; Tran Ngoc Hai; Nobuo Kimura

doi:10.1371/journal.pone.0275290

A novel machine learning approach to predict the export price of seafood products based on competitive information: The case of the export of Vietnamese shrimp to the US market

PLoS One. 2022 Sep 29;17(9):e0275290. doi: 10.1371/journal.pone.0275290. eCollection 2022.

Authors

Nguyen Minh Khiem^{1

2}, Yuki Takahashi³, Hiroki Yasuma³, Khuu Thi Phuong Dong⁴, Tran Ngoc Hai⁵, Nobuo Kimura³

Affiliations

¹ Graduate School of Fisheries Sciences, Hokkaido University, Hakodate, Hokkaido, Japan.
² College of Information and Communication Technology, Can Tho University, Can Tho, Vietnam.
³ Faculty of Fisheries Sciences, Hokkaido University, Hakodate, Hokkaido, Japan.
⁴ School of Economics, Can Tho University, Can Tho, Vietnam.
⁵ College of Aquaculture and Fisheries, Can Tho University, Can Tho, Vietnam.

Abstract

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Animals
Asian People
Crustacea*
Humans
Machine Learning
Seafood*
Vietnam

Grants and funding

This work was supported by the Hokkaido University DX Doctoral Fellowship [grant number JPMJSP2119]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.