Mechanized tree planting in Nordic forestry: simulating a machine concept for continuously advancing site preparation and planting

Jussi Manner and Back Tomas Ersson
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Jussi Manner: Skogforsk, Uppsala, Sweden
Back Tomas Ersson: School of Forest Management, SLU, Skinnskatteberg, Sweden

Journal of Forest Science, 2021, vol. 67, issue 5, 242-246

Abstract: As labour for manual tree planting becomes scarcer, regeneration costs are steadily increasing in Nordic forestry. Today's intermittently advancing tree planting machines provide excellent silvicultural results, but are expensive to operate because of poor productivity. In contrast, continuously advancing planting machines, thanks to high productivities, are increasingly being regarded as a solution to these runaway regeneration costs. The Silva Nova was a historical, continuously advancing tree planting machine with high productivity. However, Silva Nova's weaknesses included high labour costs (it required two operators) and the random nature of how it chose planting spots. In contrast, SuperSilva, a purely conceptual modernisation of Silva Nova, involves both automation and microsite identification to make the machine more efficient. We used discrete-event simulation to analyse the stocking rate and spatial distribution of tree planting with SuperSilva. The simulation results showed that introducing sensors for identifying suitable microsites will allow continuously advancing planting machines (like SuperSilva) to plant seedlings in a numerically and spatially adequate manner on moraine soils. Hence, these sensors will increase the competitiveness and versatility of tree planting machines. Unfortunately, such reliable and robust sensor technology (unaffected by a wide variety of operating conditions) is not yet commercially available.

Keywords: tree planting machine; disc trenching; discrete-event simulation; reforestation; silviculture; microsite (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:caa:jnljfs:v:67:y:2021:i:5:id:203-2020-jfs

DOI: 10.17221/203/2020-JFS

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