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Researchers are developing machine learning methods to help mechanical harvesters to travel more lightly, use less fuel and leave less noticeable strip roads in the forest. Researchers at the University of Helsinki are developing machine learning methods enabling the harvester to assist the operator in choosing routes that are optimal for both harvesting and nature. The machine could then predict the relevant terrain characteristics even before the actual operation. Source: Forest fi “This information will help to optimize the route and assist the operator by telling, for example, where the ground is too soft,” said Professor Jukka Heikkonen, in charge of the project funded by the Research Council of Finland. Harvesting operations must be planned so as not to leave too invasive strip roads. The softer the ground, the more difficult it is for the harvester to travel and the more likely it will cause ground damage. “Harvesting operations must be planned so as not to leave too invasive strip roads. The softer the ground, the more difficult it is for the harvester to travel and the more likely it will cause ground damage,” Mr Heikkonen notes. Strip roads, or the tracks left by the harvester, are detrimental to forest growth, increase the risk of diseases and are an eyesore. Travelling across soft ground also increases the rolling resistance and therefore, the fuel consumption and cost. “The softer the ground, the more probably it will be damaged. The physical quantity used in the study is the rolling resistance factor of the harvester, which describes the ease of travel on a particular stretch of ground,” Mr Heikkonen says. According to the Finnish Forest Act, only one fifth of the length of a strip road created to reach a harvesting site may be over ten centimetres in depth. For peatlands, the corresponding depth is 20 centimetres. Using the figure for ease of travel and the harvester’s rolling resistance, it is possible to create a map showing the conditions most suitable for the strip roads. The map may be used to position the route of forwarders and to schedule the operation. Measurements of damage caused by strip roads may be combined with open-source forest data and harvester data. This will allow predictions of future damage from the strip roads in a harvesting operation. The predictions and modelling make use of machine learning, which is capable of combining open-source masses of data, measurement data from harvester sensors and physical models of the terrain. Combining measurements of strip road damage with open-source forest data and harvester data makes it possible to predict the degree of damage even before the operation. Research results show that the rolling resistance of the harvester is greatest in depressions and on loamy soils. Moist soils increase the resistance. Moistness, in turn, is affected by weather conditions, as well as rain, evaporation and the presence of meltwater. Data for the research was collected from actual forestry operations in southern Finland. Identifiers linked to geospatial data describing moistness and soil type indicate the ease of travel on a particular site. Using the soil moistness and the harvester’s rolling resistance data, it is possible to create a forest hydrology model for anywhere in Finland. The terrain data gathered by the harvester is generalized to cover other, corresponding terrains. “Using the soil moistness and the harvester’s rolling resistance data, it is possible to create a forest hydrology model for anywhere in Finland. The terrain data gathered by the harvester is generalized to cover other, corresponding terrains. This also makes use of forest resources data, among other things,” says Mr Heikkonen. The aim is to plan harvesting operations with the knowledge of ’where to go and when’. Knowing this would not only help the people planning the harvesting, but also the practical work of the harvester operator. The objective is to achieve an automatic route planning, which will serve as a basis for autonomous harvesters. Factors affecting the ease of travel include the load-bearing capacity of the soil, the steepness of slopes and areas where the harvester may not enter. Other important factors include type of terrain, vegetation, climate, weather, ground-level moistness and the machinery used in harvesting. Mr Heikkonen points out that once an operation has been scheduled, it must be implemented despite cold, rain, fog or rocky terrain. “For the harvester, forest is a challenging environment,” Mr Heikkonen concludes.

Seppi M USA, a leading manufacturer of mulching equipment, has introduced its new onsite balancing service. Whether you own a forestry mulcher with fixed knives or swinging hammers, Seppi M. USA has you covered. Source: Timberbiz Balancing is crucial for optimal performance and longevity of forestry mulchers. Unbalanced mulcher heads can cause excessive wear, reduced efficiency, and even damage to your equipment. Seppi M. USA’s on-site balancing service ensures that your mulcher operates smoothly, minimizing vibrations and maximizing productivity and the company can balance forestry rotors of any brand. Seppi M USA’s on-site balancing service is available across the entire US, the Cincinnati location serves as the hub for this specialized service.  

SCA’s new electric timber truck with a crane has now been premiered to a wide audience at the Elmia Lastbil fair. The truck, a collaborative project between SCA and Scania, will be the first electric timber truck in the world to collect timber in the forest for further transport to a timber terminal. Source: Timberbiz, Photo: Anton Ahlinder and Jonas Muhr/SCA “With the new truck, SCA takes the next step in sustainable timber transport. The truck has received a lot of positive feedback at Elmia,” said Anton Ahlinder, business developer at SCA Skog. Just over two years after SCA and Scania’s first electric timber truck was put into operation in Umeå, it’s time for the next big step in the work to electrify heavy transport in forestry. Unlike the truck in Umeå, which runs between a timber terminal and SCA’s paper mill in Obbola, the new electric timber truck is equipped with a crane and tandem drive so that it can load timber in the forest and transport it to a timber terminal. “We expect the truck to be in operation at the beginning of October. According to our calculations, it should have a range of just over 200 kilometres, although Scania’s calculations say 320 kilometres. Much depends on how much electricity it takes to operate the crane. We will test and see what works best,” Mr Ahlinder said. Rolf Lövgren Åkeri & Entreprenad AB owns the truck and will operate it with support from SCA. Rolf currently has two trucks and a total of five drivers, and he did not hesitate when asked by SCA to own and operate the truck. “No, I’d rather be first than last! That way, I get to be involved in the development. I haven’t actually seen the truck yet, as I couldn’t attend Elmia, but I’m looking forward to getting started and I think this will work well. I’m excited,” Mr Lövgren Åkeri said. “The truck will operate here in the Östavall area. Initially, I will be driving it, but then it will run in shifts, and by then I hope to find two more drivers.” The new electric timber truck is part of the forestry industry’s innovation project TREE, which aims for half of the forestry industry’s new trucks to be electric by 2030. “It’s exciting to be part of this important work and to contribute to a more sustainable society. When the truck is in operation, we expect it to result in 170 tons lower carbon dioxide emissions per year compared to a regular timber truck. All the attention and cheers we, Scania, and the truck received at Elmia, and also via LinkedIn, show that this is an important issue for many,” said Mr Ahlinder. “The fact that Infrastructure Minister Andreas Carlson came to see the truck was especially enjoyable. We managed to convey some requests, such as the need to allow higher vehicle weights and more flexible driving and rest times to facilitate electric vehicles. If the industry is to make a significant investment in electrification, we need the right support and reasonable conditions. For example, it’s not good if a driver has to stop for a break when there’s only fifteen minutes left to the charging station.” A significant investment in charging stations is also required for a substantial electrification of the forestry truck fleet to become a reality. “In the area where our new electric timber truck will operate, there will be four available charging stations, which reduces the risk of downtime. But if we are to have more electric timber trucks, many more charging stations are needed,” Mr Ahlinder added.