Kalle Kemppainen, Jari Miina, Eetu Tarvainen, Ville Kankaanhuhta, Juha Laitila, Heli Peltola, Markus Strandström, Kalle Kärhä. (2026). Quality of drill-based site preparation and early performance of Norway spruce and Scots pine seedlings planted in different seasons. Silva Fennica vol. 60 no. 3 article id 26011. https://doi.org/10.14214/sf.26011

Keywords: forest regeneration; seedling survival; manual planting; mechanical soil preparation

Highlights: A drill-based device for mechanical site preparation significantly reduces soil exposure; The drill created mostly good (61%) or satisfactory (34%) planting spots for conifer seedlings; Two years after planting, Norway spruce seedlings performed better than Scots pine seedlings.

Abstract | Full text in HTML | Full text in PDF | Author Info

Mechanical site preparation (MSP) is essential for successful forest regeneration. However, excessively exposed soil may harm the environment and increase the total costs of seedling stand management. A drill-based MSP device (drill), designed to reduce soil exposure, was recently tested in Finland. This study investigated the quality of drill-based MSP and the early performance of Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.) seedlings planted in different seasons on five mineral soil stands. The findings showed that the mean area of the drilling spots (0.18 m²) was about 20% of that reported for mounding. The drill mostly resulted in good (61%) or satisfactory (34%) planting spot quality (e.g., good spots were covered with mineral soil). Ground and soil obstacles, such as roots, stones, stumps, and logging residues, negatively affected planting spot quality. About 13% of the planting spots did not have a pure mineral soil cover, which is required to mitigate pine weevil (Hylobius abietis L.) damage. Most seedlings were still alive one year after planting, but the proportion of healthy seedlings was 71% for Norway spruce and 48% for Scots pine two years after planting. Overall, drill-based MSP may be most feasible for drier sites with less competitive ground vegetation. However, the productivity and costs, the performance of the planted seedlings, and other potential benefits of drill-based MSP should be further tested under varying operating conditions and compared with those of conventional MSP methods.

Kemppainen, School of Forest Sciences, University of Eastern Finland, Yliopistokatu 7, FI-80100 Joensuu, Finland; Natural Resources Institute Finland, Yliopistokatu 6, FI-80100 Joensuu, Finland https://orcid.org/0009-0000-6184-8812 E-mail: kalle.kemppainen@uef.fi
Miina, Natural Resources Institute Finland, Yliopistokatu 6, FI-80100 Joensuu, Finland https://orcid.org/0000-0002-8639-4383 E-mail: jari.miina@luke.fi
Tarvainen, School of Forest Sciences, University of Eastern Finland, Yliopistokatu 7, FI-80100 Joensuu, Finland E-mail: eetu.tarvainen@harvestia.fi
Kankaanhuhta, Natural Resources Institute Finland, Yliopistokatu 6, FI-80100 Joensuu, Finland https://orcid.org/0000-0001-5785-5972 E-mail: ville.kankaanhuhta@luke.fi
Laitila, Natural Resources Institute Finland, Yliopistokatu 6, FI-80100 Joensuu, Finland https://orcid.org/0000-0003-4431-3319 E-mail: juha.laitila@luke.fi
Peltola, Natural Resources Institute Finland, Yliopistokatu 6, FI-80100 Joensuu, Finland https://orcid.org/0000-0003-1384-9153 E-mail: heli.peltola@uef.fi
Strandström, Metsäteho Oy, Vernissakatu 1, FI-01300 Vantaa, Finland https://orcid.org/0009-0004-0868-3042 E-mail: markus.strandstrom@metsateho.fi
Kärhä, School of Forest Sciences, University of Eastern Finland, Yliopistokatu 7, FI-80100 Joensuu, Finland https://orcid.org/0000-0002-8455-2974 E-mail: kalle.karha@uef.fi

article id 10068, category Research article

Lari Melander, Risto Ritala, Markus Strandström. (2019). Classifying soil stoniness based on the excavator boom vibration data in mounding operations. Silva Fennica vol. 53 no. 2 article id 10068. https://doi.org/10.14214/sf.10068

Keywords: spot mounding; activity recognition; stoniness classification; supervised machine learning

Highlights: An excavator was equipped with an inertial measurement unit for taking automatic measurements of soil stoniness during mounding work; Supervised machine-learning classifiers were trained utilizing both the automatically measured data and manual stoniness measurements; The class prediction for the soil stoniness achieved an accuracy of 70% when assigned to constant grid cells.

Abstract | Full text in HTML | Full text in PDF | Author Info

The stoniness index of forest soil describes the stone content in the upper soil layer at depths of 20–30 centimeters. This index is not available in any existing map databases, and traditional measurements for the stoniness of the soil have always necessitated laborious soil-penetration methods. Knowledge of the stone content of a forest site could be of use in a variety of forestry operations. This paper presents a novel approach to obtaining automatic measurements of soil stoniness during an excavator-based mounding operation. The excavator was equipped with only a low-cost inertial measurement unit and a satellite navigation receiver. Using the data from these sensors and manually conducted soil stoniness measurements, supervised machine learning methods were utilized to build a model that is capable of predicting the stoniness class of a given mounding location. This study compares different classifiers and feature selection methods to find the most promising solution for this learning problem. The discussion includes a proposition for a meaningful measurement resolution of the soil’s stoniness, and a practical method for evaluating the variability of the stone content of the soil. The results indicate that it is possible to predict the soil stoniness class with 70% accuracy using only the inertial and location measurements.

Melander, Automation Technology and Mechanical Engineering, Tampere University, FI-33014 Tampere University, Finland http://orcid.org/0000-0003-3662-5187 E-mail: lari.melander@tuni.fi
Ritala, Automation Technology and Mechanical Engineering, Tampere University, FI-33014 Tampere University, Finland http://orcid.org/0000-0003-0721-9948 E-mail: risto.ritala@tuni.fi
Strandström, Metsäteho Oy, Vernissakatu 1, FI-01300 Vantaa, Finland E-mail: markus.strandstrom@metsateho.fi

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