Periodic Reporting for period 2 - EFFORTE (Efficient forestry by precision planning and management for sustainable environment and cost-competitive bio-based industry)
Reporting period: 2018-03-01 to 2019-08-31
Forest work is heavily disturbed by high seasonal variation. Machine utilization is especially low during periods of snow melting and when wet conditions almost completely make logging and road transport impossible. Superimposed are impacts of climate change that most likely will add to the complexity and increase the seasonal variation and increase the risks of damages to soil and water.
In this context there is large rooms for improvements not least by utilizing modern information technologies. In order to maintain the forests’ function as a carbon sink fast, cheap and reliable reforestation systems taking natural variation production into account are important. In addition there is a great potential in increased forest production based on knowledge and modern information technology useful in forestry applications; best method combined with best technique and eventually industrial and societal demands. This enables maximized biomass yield and value in the future. Further, efficiency in silviculture including pre-commercial thinning has during later year’s seen no or very minute technical development, and there is a strong need for improvements. Cost-efficient systems for pre-commercial thinning (cleaning) is very important in this context. Finally the whole process is dependent on reliable data and information including systems, softwares and routines for using Big Data in forestry.
EFFORTE is based on three key elements of technology and knowhow. First, basic understanding of fundamentals of soil mechanics, and terrain trafficability is a crucial starting point to avoid soil disturbances, accelerate machine mobility and assess persistence of soil compaction and rutting. The key findings and recommendations of trafficability related to EFFORTE can immediately be adapted in all European countries.
Second, due to decreasing cost-competitive of manual work and maturity of technology it is now perfect time to realize the potential of mechanization in silvicultural operations. EFFORTE pursues for higher productivity and efficiency in silvicultural operations such as tree planting and young stand cleaning operations.
Third, ‘Big Data’ (geospatial as well as data from forestry processes and common information e.g. weather data) provides a huge opportunity to increase the efficiency of forest operations. In addition it adds new possibilities to connect knowledge of basic conditions (e.g. trafficabillity), efficient silviculture and harvesting actions with demand and expectations from forest industries and the society. Accurate spatial information makes it possible for forestry to move from classic stand-wise management to precision forestry, i.e. micro stand level, grid cell level or tree-by-tree management. EFFORTE aims at achieving substantial influence to the implementation and improved use of Big Data within Forestry and through this increase cost-efficiency and boost new business opportunities to small and medium size enterprises (SME) in the Bio-Economy.
The general aim of the project is to develop and adopt novel technology and tools that improve efficiency and sustainability of forestry and throughout the entire forest based value chain within EU. The main objective will be accomplished through the following objectives with measurable outcome:
- To develop scientifically firm and techno-economically feasible methodology to predict trafficability of the given forest stands prior to forest operations.
- To increase forest growth and achieve a step forward in the productivity of tree planting and young stand management by accelerating technology development, testing of new innovative solutions and supporting adoption of new tools and methods.
- To develop, customize and pilot modern ‘Big data’ solutions that will increase productivity, decrease negative environmental impact and facilitate realization of a sustainable, competitive and efficient forestry through the whole forestry chain.
In this context there is large rooms for improvements not least by utilizing modern information technologies. In order to maintain the forests’ function as a carbon sink fast, cheap and reliable reforestation systems taking natural variation production into account are important. In addition there is a great potential in increased forest production based on knowledge and modern information technology useful in forestry applications; best method combined with best technique and eventually industrial and societal demands. This enables maximized biomass yield and value in the future. Further, efficiency in silviculture including pre-commercial thinning has during later year’s seen no or very minute technical development, and there is a strong need for improvements. Cost-efficient systems for pre-commercial thinning (cleaning) is very important in this context. Finally the whole process is dependent on reliable data and information including systems, softwares and routines for using Big Data in forestry.
EFFORTE is based on three key elements of technology and knowhow. First, basic understanding of fundamentals of soil mechanics, and terrain trafficability is a crucial starting point to avoid soil disturbances, accelerate machine mobility and assess persistence of soil compaction and rutting. The key findings and recommendations of trafficability related to EFFORTE can immediately be adapted in all European countries.
Second, due to decreasing cost-competitive of manual work and maturity of technology it is now perfect time to realize the potential of mechanization in silvicultural operations. EFFORTE pursues for higher productivity and efficiency in silvicultural operations such as tree planting and young stand cleaning operations.
Third, ‘Big Data’ (geospatial as well as data from forestry processes and common information e.g. weather data) provides a huge opportunity to increase the efficiency of forest operations. In addition it adds new possibilities to connect knowledge of basic conditions (e.g. trafficabillity), efficient silviculture and harvesting actions with demand and expectations from forest industries and the society. Accurate spatial information makes it possible for forestry to move from classic stand-wise management to precision forestry, i.e. micro stand level, grid cell level or tree-by-tree management. EFFORTE aims at achieving substantial influence to the implementation and improved use of Big Data within Forestry and through this increase cost-efficiency and boost new business opportunities to small and medium size enterprises (SME) in the Bio-Economy.
The general aim of the project is to develop and adopt novel technology and tools that improve efficiency and sustainability of forestry and throughout the entire forest based value chain within EU. The main objective will be accomplished through the following objectives with measurable outcome:
- To develop scientifically firm and techno-economically feasible methodology to predict trafficability of the given forest stands prior to forest operations.
- To increase forest growth and achieve a step forward in the productivity of tree planting and young stand management by accelerating technology development, testing of new innovative solutions and supporting adoption of new tools and methods.
- To develop, customize and pilot modern ‘Big data’ solutions that will increase productivity, decrease negative environmental impact and facilitate realization of a sustainable, competitive and efficient forestry through the whole forestry chain.
The EFFORTE has received fully the objectives set for the project. We have investigated principles of soil mechanics. One of the most important topics has been the significance of soil moisture content on soil strength and soil disturbances. We have developed hydrological models that predict soil moisture content for the practical operations. There are complemented with the models that predict soil moisture – strength and soil moisture and soil deformation relationship. We have also taken significant steps in understanding the effect of wheel loads on soil stress and compaction. The main results regarding soil mechanics have been converted into operational recommendations for forest practitioners.
We have achieved significant steps in advancing efficient silviculture and forest growth. System analysis indicated development potential of the continuously advancing planting machine. Biocontrol with Chondrostereum purpureum in early PCT work decreased stump sprouting but implementation into practice needs more testing in order to increase the efficacy of fugal treatment. The follow-up study of uprooted spruce plantations revealed the success of uprooting as a one-time PCT-operation in practical forestry but the timing of uprooting operation and selection of proper sites are crucial. Studies indicate that boom corridor thinning can be applied in young dense stands without jeopardizing future timber production. The local growth potential based on harvester data can be used as decision making tool when selecting optimum tree species mixture for micro compartments inside a large cutting area.
Forest operations are increasingly mastered with the help of intelligent Big Data –applications. In EFFORTE, we have made significant progress within this field. The yield and wood property predictions developed by Skogforsk have been adopted by the companies for further validation. The harvester scheduling model is at an exploitation phase, also. The different trafficability maps are now offered as services from several service providers in Sweden and Finland. The dynamic trafficability map has been demonstrated, but is not yet in operational use. The applications for optimal trail routing has been validated in EFFORTE and there are several service providers that will be commercially adopt these technologies. We have managed to develop and demonstrate a methodology for detecting and interpreting soil bearing capacity based on the relationship between engine power and travelling speed. The interpretation is executed via recording of data communication in CAN bus network of a forest machine. The methodology for customizing species selection at the regeneration phase has been demonstrated, as well.
We have achieved significant steps in advancing efficient silviculture and forest growth. System analysis indicated development potential of the continuously advancing planting machine. Biocontrol with Chondrostereum purpureum in early PCT work decreased stump sprouting but implementation into practice needs more testing in order to increase the efficacy of fugal treatment. The follow-up study of uprooted spruce plantations revealed the success of uprooting as a one-time PCT-operation in practical forestry but the timing of uprooting operation and selection of proper sites are crucial. Studies indicate that boom corridor thinning can be applied in young dense stands without jeopardizing future timber production. The local growth potential based on harvester data can be used as decision making tool when selecting optimum tree species mixture for micro compartments inside a large cutting area.
Forest operations are increasingly mastered with the help of intelligent Big Data –applications. In EFFORTE, we have made significant progress within this field. The yield and wood property predictions developed by Skogforsk have been adopted by the companies for further validation. The harvester scheduling model is at an exploitation phase, also. The different trafficability maps are now offered as services from several service providers in Sweden and Finland. The dynamic trafficability map has been demonstrated, but is not yet in operational use. The applications for optimal trail routing has been validated in EFFORTE and there are several service providers that will be commercially adopt these technologies. We have managed to develop and demonstrate a methodology for detecting and interpreting soil bearing capacity based on the relationship between engine power and travelling speed. The interpretation is executed via recording of data communication in CAN bus network of a forest machine. The methodology for customizing species selection at the regeneration phase has been demonstrated, as well.
The project has increased significantly our understanding in strength of forest soil, machine-terrain interaction and resilience of soil after forest trafficking. EFFORTE has also advanced cost-efficiency of silvicultural operations and forest growth. Huge economic gains and avoidance of soil deformations will be achieved in adoption of various big data applications in practical forestry.