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On the impact of biological risk in aquaculture valuation and decision making
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics. Inland University of Applied Sciences, Lillehammer, Norway; University of Glasgow, Adam Smith Business School, Glasgow, UK.ORCID iD: 0000-0003-3288-0164
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.ORCID iD: 0000-0003-2881-0905
2025 (English)In: Aquaculture, ISSN 0044-8486, E-ISSN 1873-5622, Vol. 603, article id 742368Article in journal (Refereed) Published
Abstract [en]

We explore the impact of stochastic mortality, health, and parasites on animal-based commodities risk management, with a specific emphasis on salmon aquaculture. Our investigation delves into the stochastic nature of mortality, fish lice infestation, and treatment plans based on comprehensive historical data from Norway. Given that salmon lice pose a significant challenge to salmon aquaculture, with associated treatment costs estimated to be comparable to feeding expenses, lice removal is imperative to ensure the survival of the salmon and comply with the Norwegian government's stipulation of maintaining an upper threshold of 0.5 lice per fish. We propose a new model that considers the relationship between hosts and parasites and determines the number of treatments required as well as the overall cost of these treatments. An important aspect of our model is its incorporation of stochastic effectiveness for each removal. After calibrating the model to our dataset, our study examines the impact of the host-parasite relationship and the required interventions on the optimal harvesting decision and draws a comparison to models that make an assumption of deterministic mortality. Our results indicate that a gain of 1.5 % in farm value (per rotation) can be obtained by employing an optimal harvesting rule based on the stochastic host-parasite model.

Place, publisher, year, edition, pages
Elsevier, 2025. Vol. 603, article id 742368
Keywords [en]
Aquaculture finance, Real options, Deep learning, Optimal stopping, Resource economics
National Category
Economics and Business Fish and Aquacultural Science
Identifiers
URN: urn:nbn:se:umu:diva-242903DOI: 10.1016/j.aquaculture.2025.742368ISI: 001442840100001Scopus ID: 2-s2.0-85219733237OAI: oai:DiVA.org:umu-242903DiVA, id: diva2:1987989
Available from: 2025-08-09 Created: 2025-08-09 Last updated: 2025-08-15Bibliographically approved

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Ewald, Christian OliverKamm, Kevin

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