Optimizing the regeneration of spruce-dominated stands suffering from Heterobasidion root rot in Finland - Holmström, Eero; Honkaniemi, Juha; Ahtikoski, Anssi; Rajala, Tuomas; Hantula, Jarkko; Piri, Tuula; Heikkinen, Juha; Suvanto, Susanne; Räsänen, Tapio; Sorsa, Juha-Antti; Riekki, Kirsi; Höglund, Henna; et al. (Elsevier, 2025)
Heterobasidion root rot is a destructive fungal disease causing extensive damage in conifer forests throughout the Northern hemisphere. The effective spreading of the causal agent Heterobasidion sp. from one tree generation to the next makes the disease a persistent problem for forestry. Here, we present a precision-forestry method for optimizing the regeneration of spruce-dominated stands suffering from Heterobasidion root rot. Our method prevents the inter-generational spread of the disease while aiming for high financial or climate change mitigation value. The method uses harvester data with non-parametric clustering or Bayesian modeling to delineate the stand into healthy and infected “microstands.” Through simulations of forest growth and Heterobasidion dynamics, the optimal species to plant in each microstand to maximize either bare land value (BLV, interest rate 2%) or net CO2 removals by living tree biomass is determined, subject to the condition that regeneration leads to disease eradication. In Finnish conditions, the method recommends pine on mesic heath sites (MT) and combinations of pine and spruce on herb-rich sites (OMT) to maximize BLV. To maximize CO2 removals, the method suggests a variety of tree species compositions including birch. In comparison to regenerating using only spruce, the predicted mean financial gain from the method is 1320 ± 40 EUR/ha on MT and 400 to 800 EUR/ha on OMT. Direct gains in CO2 removals are difficult to achieve due to prevailing management practices for infected stands. The method offers financial and carbon-wise support for decision-making while diversifying forests and cleansing sites of root rot disease.
Keywords: Heterobasidion root rot; precision forestry; microstand; harvester data; disease control; forest planning; stand dynamics; Bayesian modeling