Salvage harvest planning for spruce budworm outbreak using multistage stochastic programming

Abstract

Forest supply chain planning must deal with many natural disturbance uncertainties such as fires, insects, and windthrow. One important consideration is wood infestation by invasive insects, as it causes environmental and economic harm. An example of invasive insects in Eastern Canada is the spruce budworm (Choristoneura fumiferana (Clemens)), which is the most destructive insect in North America’s conifer stands. In 2017, more than 5 million ha of forest were defoliated by spruce budworm in Quebec. Repeated defoliation causes tree mortality, reduction of growth rates, and reduced lumber quality. Consequently, different wood qualities with greatly varied values are found in the forest. Changes in the outbreak intensity impact wood values throughout the forest. One of the common actions to mitigate the economic and environmental damages is salvage harvesting. However, because of the large uncertainties and lack of detailed information, it is a difficult problem to model. We propose a multistage stochastic mixed-integer programming model for harvest scheduling under various outbreak intensities. The objective is to maximize revenues of wood value minus logistic costs while satisfying demand for wood in the industry. The results show that when there is an outbreak throughout the forest, the priority for salvage harvesting is to focus on forest areas with the lowest level of infestation.