Work

Modeling Shrimp Income and Disease Risks Prevalence Using Econometric and Machine Learning Approaches: Evidence from Vietnam

Year: 2025

Type: article

Abstract: Constrained econometric techniques hamper investigations of disease prevalence and income risks in the shrimp industry. We employ an econometric model and machine learning (ML) to reduce model restrictions and improve understanding of the influence of diseases and climate on income and disease risks. An interview of 534 farmers with the models enables the discernment of factors influencing shrimp income and disease risks. ML complemented the Just-Pope production model, and the partial dependency plots show nonlinear relationships between income, disease prevalence, and risk factors. Econometric and ML models generated complementary information to understand income and disease prevalence risk factors.

Source: Journal of Agricultural and Applied Economics

DOI: 10.1017/aae.2025.8

Authors: Brice M. Nguelifack, Kim Anh T. Nguyen, Tram Anh T. Nguyen, Curtis Jolly

Institutions: United States Naval Academy, Nha Trang University, Auburn University

Language: English

Topic: Fisheries and Aquaculture Studies

Subfield: General Agricultural and Biological Sciences

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDGs: Zero hunger, Decent work and economic growth

Open Access status: gold

Estimated APC paid: $2,660