Precision forestry for improved resource utilization and reduced wood decay in Norwegian forests

PRECISION will develop a precision forestry framework for reducing the impact of root and butt rot (RBR) in order to improve resource utilization and increase the forest sector value creation. RBR caused by fungi in the Heterobasidion genus is the most costly conifer disease in the northern hemisphere. In Europe, the annual economic losses due to Heterobasidion infection amount to 800 M Euro. In Norway, the annual direct economic losses of timber value due to RBR infection of spruce exceed 100 M NOK. Precision forestry has been defined as planning and conducting of site-specific forest management activities and operations to improve wood product quality and utilization, reduce waste, and increase profits. Precision forestry uses technology and analytical tools to support site-specific decision making. Across all sectors, technology and increased information flows are rapidly changing decision making. In forestry, site-specific data is increasing rapidly but is generally not systemized, analyzed, and applied for improving management. Forest harvesters provide one source of data that can be developed to generate information on RBR for stands and single trees. PRECISION has developed an approach for collecting root rot data from forest harvesters with high geospatial accuracy (+/- 1m) and harvesters with normal geospatial accuracy (+/- 15m), calibrating the model Rotstand to Norwegian conditions, tested how lidar data and environmental data can be used to predict RBR infections and collected hyperspectral data to asses, implemented the field experiment with precision planting for reducing RBR and assessed the overall economic losses to root rot in the Norwegian forest sector.

The three key anticipated outcomes and impacts (1) A key outcome is that the project has created a much broader knowledge about root rot in the Norwegian forest sector. The anticipated long-term effect is improved forest management. (2) A key outcome is the introduction of precision forestry based on harvester data and remote sensing to the Norwegian forest sector. The anticipated long-term impact is utilization of precision forestry in Norway with significant economic and environmental benefits. (3) From a scientific perspective a key outcome is a greatly improved state of the art in relation to precision forestry. The long-term effect will be that Norway can be a international leader in this field.

PRECISION will develop a precision forestry framework for reducing the impact of root and butt rot (RBR) in order to improve resource utilization and increase the forest sector value creation. RBR caused by fungi in the Heterobasidion genus is the most costly conifer disease in the northern hemisphere. In Europe, the annual economic losses due to Heterobasidion infection amount to 800 M Euro. In Norway, the annual direct economic losses of timber value due to RBR infection of spruce exceed 100 M NOK. Precision forestry has been defined as planning and conducting of site-specific forest management activities and operations to improve wood product quality and utilization, reduce waste, and increase profits. Precision forestry uses technology and analytical tools to support site-specific decision making. Across all sectors, technology and increased information flows are rapidly changing decision making. In forestry, site-specific data is increasing rapidly but is generally not systemized, analyzed, and applied for improving management. Forest harvesters provide one source of data that can be developed to generate information on RBR for stands and single trees. PRECISION will focus on developing a system that, during logging, maps the location of infected stumps and allows for operational collection of RBR data. Secondly, we will analyze the collected RBR data to improve modeling of Heterobasidion spread and decay dynamics and to predict the RBR infection severity in existing stands using remote sensing techniques. Third, the maps of infected stumps and the ability to model RBR spread dynamics will be utilized to design site-specific regeneration strategies focused on regeneration of resistant trees around infected stumps. In parallel, the predicted infestation severity will be used to make a framework for deciding on site-specific optimal rotation time given risk of RBR losses. Finally, the value chain gains of a potential reduction in RBR losses will be assessed.