Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Akaike H (1998) Information theory and an extension of the maximum likelihood principle. In: Parzen E, Tanabe K, Kitagawa G (eds) Selected Papers of Hirotugu Akaike. Springer Series in Statistics (Perspectives in Statistics). Springer, New York
Anders SM, Caswell JA (2009) Standards as barriers versus standards as catalyst: assessing the impact of HACCP implementation on U.S. seafood import. Am J Agric Econ. https://doi.org/10.1111/j.1467-8276.2008.01239.x
Bailey M, Packer H, Schiller L, Tlusty M, Swartz W (2018) The role of corporate social responsibility in creating a Seussian world of seafood sustainability. Fish Fish. https://doi.org/10.1111/faf.12289
Brander K (2003) What kinds of fish stock predictions do we need and what kinds of information will help us make better predictions? Sci Mar. https://doi.org/10.3989/scimar.2003.67s121
Breiman L (2001) Random forests. Mach Learn. https://doi.org/10.1023/A:1010933404324
Bring J (1994) How to standardize regression coefficients. Am Stat. https://doi.org/10.2307/2684719
COFI (2019) Fishery and aquaculture country profiles: the Socialist Republic of Viet Nam. FAO, Rome
Dong KTP, Duc NM (2012) The impacts of non-tariff barriers on the export price of Vietnamese catfish. In: Toan HT et al (eds) IFS 2012: Sharing knowledge for sustainable aquaculture and fisheries in the Southeast Asia. Agriculture Publishing House, CanTho City, Vietnam, pp 315–326
Dong KTP, Saito Y, Hoa NTN, Dan TY, Matsuishi TF (2019) Pressure–State–Response of traceability implementation in seafood-exporting countries: evidence from Vietnamese shrimp products. Aquacult Int. https://doi.org/10.1007/s10499-019-00378-2
Dong KTP, Matsuishi TF, Duc NM, Hoa NTN, Saito Y, Dan TY (2021) Does application of quality assurance certification by shrimp farmers enhance feasibility of implementing traceability along the supply chain? Evidence from Vietnam. J Appl Aquac. https://doi.org/10.1080/10454438.2020.1856751
Duc NM (2009) Economic contribution of fish culture to farm income in Southeast Vietnam. Aquacult Int. https://doi.org/10.1007/s10499-008-9176-8
Duc NM, Kinnucan HW (2007) Effects of antidumping duties with bertrand competition: some evidence for frozen catfish fillets. Agric Appl Econ Assoc. https://doi.org/10.22004/ag.econ.9893
Dudek G (2015) Short-term load forecasting using random forests. In: Filev D et al (eds) Intelligent Systems’2014 Advances in Intelligent Systems and Computing. Springer, Cham. https://doi.org/10.1007/978-3-319-11310-4_71
The English in this document has been checked by at least two professional editors, both native speakers of English. For a certificate, please see: http://www.textcheck.com/certificate/GgCgWU
FAO (2018) The State of Food and Agriculture (2018) Migration, agriculture and rural development. FAO, Rome
Flaaten O (2018) Fisheries and aquaculture economics, 2nd edn. Aquaculture: plant and industry management, Norway
Freund Y, Schapire R (1996) Experiments with a New Boosting Algorithm. Machine Learning: Proceedings of the Thirteenth International Conference, ***, pp 148–156
GSO (2018) Agriculture forestry and fishing. The General Statistics Office of Vietnam, Vietnam
Ha TTT, Bush SR (2010) The transformations of Vietnamese shrimp aquaculture policy: empirical evidence from the Mekong Delta. Environ Plann C Gov Policy. https://doi.org/10.1068/c09194
International Trade Center (2020) Trade statistics for international business development. International Trade Center, Switzerland
Kamil S, Barbara K, Wieslaw F, Witold R, Katarzyna S, Katarzyna K (2020) Applying human mobility and water consumption data for short-term water demand forecasting using classical and machine learning models. Urban Water J. https://doi.org/10.1080/1573062X.2020.1734947
Khiem NM, Takahashi Y, Oanh DTH, Hai TN, Yasuma H, Kimura N (2020) 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. Fish Sci. https://doi.org/10.1007/s12562-020-01427-z
Leung PS, Tran TL (2000) Predicting shrimp disease occurrence: artificial neural networks vs. logistic regression. Aquaculture. https://doi.org/10.1016/S0044-8486(00)00300-8
Linda C, Barry K (1998) Technical barriers to trade: a case study of phytosanitary barriers and U.S.-Japanese apple trade. J Agric Resour Econ. https://doi.org/10.22004/ag.econ.31191
Manuel M, Jose MB, Maria GP (2012) Tree-based machine learning analysis for fisheries research. Fishery Management, Chapter: Tree-based machine learning analysis for fisheries research. Nova Science Publishers Inc., pp 61–75
Natekin A, Knoll A (2013) Gradient boosting machines. Front Neurorobot 7:21. https://doi.org/10.3389/fnbot.2013.00021
Pavlyshenko BM (2019) Machine-learning models for sales time series forecasting. Data. https://doi.org/10.3390/data4010015
Pedregosa et al (2011) Scikit-learn: machine learning in Python. JMLR 12:2825–2830
Phuong NT, Oanh DTH (2010) Stripped catfish aquaculture in Vietnam: a decade of unprecedented development. In: De Silva SS, Davy FB (eds) Success stories in Asian aquaculture. Springer, Dordrecht, Netherlands, pp 131–147
Portley N (2016). Report on the shrimp sector: Asian shrimp trade and sustainability. Asian Shrimp Trade and Sustainability. Sustainable Fisheries Partnership, pp 1–74
Rahman A, Tasnim S (2014) Application of machine learning techniques in aquaculture. Int J Comput Trends Technol. https://doi.org/10.14445/22312803/IJCTT-V10P137
Shahriar MS (2014) A dynamic data-driven decision support for aquaculture farm closure. Procedia Comput Sci. https://doi.org/10.1016/j.procs.2014.05.111
Soykan CU, Eguchi T, Kohin S, Dewar H (2014) Prediction of fishing effort distributions using boosted regression trees. Ecol Appl. https://doi.org/10.1890/12-0826.1
Stoll F (2020) A Comparison of Machine Learning and Traditional Demand Forecasting Methods. All Theses. 3367. Clemson University, South Carolina
Suzuki A, Nam VH (2013) Status and constraints of costly port rejection: a case from the Vietnamese frozen seafood export industry. IDE-JETRO. Discussion Paper No.395
Suzuki A, Nam VH (2018) Better management practices and their outcomes in shrimp farming: evidence from small-scale shrimp farmers in Southern Vietnam. Aquacult Int. https://doi.org/10.1007/s10499-017-0228-9
Tran VN, Bailey C, Wilson N, Phillips M (2013) Governance of global value chains in response to food safety and certification standards: the case of shrimp from Vietnam. World Dev. https://doi.org/10.1016/j.worlddev.2013.01.025
Tucker BI (2008) Chapter 3: Market Demand and Supply. Economics for Today’s World, 5th edn. Thomson South-Western. Transcontinental-Beauceville Quebec, Canada, pp 52–57
VASEP (2018) Report on Vietnam Shrimp Sector 2009–2018. Vietnam Association of Seafood Exporters and Producers. Vietnam
VASEP (2020) Report on Vietnam Seafood Exports 2020. Vietnam Association of Seafood Exporters and Producers. Vietnam
Yu R (2006) Predicting shrimp growth: artificial neural network versus nonlinear regression models. Aquacult Eng. https://doi.org/10.1016/j.aquaeng.2005.03.003
Acknowledgements
This study is funded in part by the Can Tho University Improvement Project VN14-P6, supported by a Japanese ODA loan. We also are grateful for the advice from anonymous reviewers, who have helped the authors develop this paper.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Khiem, N.M., Takahashi, Y., Dong, K.T.P. et al. Predicting the price of Vietnamese shrimp products exported to the US market using machine learning. Fish Sci 87, 411–423 (2021). https://doi.org/10.1007/s12562-021-01498-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12562-021-01498-6