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

Brice M. Nguelifack et al.

Journal of Agricultural and Applied Economics2025https://doi.org/10.1017/aae.2025.8article
AJG 1ABDC C
Weight
0.41

What the paper says

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.

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https://doi.org/https://doi.org/10.1017/aae.2025.8

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@article{brice2025,
  title        = {{Modeling Shrimp Income and Disease Risks Prevalence Using Econometric and Machine Learning Approaches: Evidence from Vietnam}},
  author       = {Brice M. Nguelifack et al.},
  journal      = {Journal of Agricultural and Applied Economics},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1017/aae.2025.8},
}

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

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Evidence weight

0.41

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.