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We study the effect of stochastic feeding costs on animal-based commoditieswith particular focus on aquaculture. More specifically, we use soybean futuresto infer on the stochastic behaviour of salmon feed, which we assume to followa Schwartz-2-factor model. We compare the decision of harvesting salmon using adecision rule assuming either deterministic or stochastic feeding costs, i.e.including feeding cost risk. We identify cases, where accounting for stochasticfeeding costs leads to significant improvements as well as cases wheredeterministic feeding costs are a good enough proxy. Nevertheless, in all ofthese cases, the newly derived rules show superior performance, while theadditional computational costs are negligible. From a methodological point ofview, we demonstrate how to use Deep-Neural-Networks to infer on the decisionboundary that determines harvesting or continuation, improving on moreclassical regression-based and curve-fitting methods. To achieve this we use adeep classifier, which not only improves on previous results but also scaleswell for higher dimensional problems, and in addition mitigates effects due tomodel uncertainty, which we identify in this article. effects due to modeluncertainty, which we identify in this article.

On the Impact of Feeding Cost Risk in Aquaculture Valuation and Decision Making