Articles containing the keyword 'mounding'

The effects of wood ash and PK fertilization on natural regeneration and sowing of Scots pine (Pinus sylvestris L.) were studied in field experiments on nitrogen-poor (N_tot 0.87–1.26%) peat substrates. The study material was derived from three drained, nutrient-poor pine mires (64°52’ N, 25°08’ E) at Muhos, near Oulu, Finland. The experimental fields were laid out in 1985 as a split-split-plot design including the following treatments; mounding, natural regeneration and sowing and fertilization; PK (400 kg ha^-1) and wood ash (5,000 kg ha^-1). The seedlings were inventoried in circles in July–August 1991.

Changes in the vegetation were small and there were no statistical differences due to the fertilization treatments in the ground vegetation. PK or ash fertilization did not cause vegetation changes harmful to Scots pine regeneration on nitrogen-poor peatlands. Both sowing and fertilization significantly increased the number of pine seedlings, but not their height. Wood ash increased seedling number more than PK fertilizer. The number of seedlings varied from 7,963 (control) to 42,781 ha^-1 (mounding + sowing + ash). The seedling number was adequate for successful regeneration even on non-mounded, non-fertilized naturally regenerated plots.

The number of birch seedlings varied more than that of pine (370–25,927 ha-¹). Mounding especially increased the number of birches. The difference between PK fertiliser and ash was less pronounced than that for pine. In addition, to the field studies the effects of ash and PK fertilizer on the germination of Scots pine seeds was studied in a greenhouse experiment. Soaking in ash solutions strongly reduced seed germination, while the PK solution was less harmful.

• Silfverberg, E-mail: ks@mm.unknown

• Nordin, pcSKOG, Grisslevägen 15, 227 32 Lund, Sweden https://orcid.org/0000-0002-2156-6615 E-mail: per.nordin@skogforsk.se
• Olofsson, Linnaeus University, Department of Forestry and Wood Technology, 351 95 Växjö, Sweden https://orcid.org/0000-0001-5844-6775 E-mail: erika.olofsson@lnu.se
• Hjelm, Swedish University of Agricultural Sciences, Southern Swedish Forest Research Centre, Box 190, 234 22 Lomma, Sweden https://orcid.org/0000-0002-6144-8250 E-mail: karin.hjelm@slu.se

In Nordic forests, consistent evidence about better seedling survival rate and increased growth due to site preparation have been obtained in numerous studies. Proper site preparation method can reduce costs of the whole regeneration chain through its effects on survival of planted seedlings, abundance of natural regeneration and competition in early stand development. This study compared the natural regeneration of birches (silver birch (Betula pendula Roth) and downy birch (B. pubescens Ehrh.)), amount of exposed mineral soil, and growth of planted seedlings between spot mounding and inverting site preparation methods. Present study was conducted in eight forest stands established in 2012 or 2015. Even though difference was not statistically significant, inverting exposed less mineral soil than spot mounding and thus reduced the natural regeneration of birch seedlings by 6135 seedlings ha^–1 compared to spot mounding. However, the variation between regeneration areas was remarkable. There was no difference in seedling mortality or growth between the site preparation methods. In order to achieve high growth of conifers, moderate amount of exposed mineral soil and thus less naturally regenerated birch, inverting should be favored over spot mounding.

• Laine, Metsä Group, P.O. Box 208, FI-70101 Kuopio, Finland E-mail: tiina.laine@metsagroup.com
• Kankaanhuhta, Natural Resources Institute Finland (Luke), Natural resources, Juntintie 154, FI-77600 Suonenjoki, Finland E-mail: ville.kankaanhuhta@luke.fi
• Rantala, Metsä Group, P.O. Box 10, FI-02020 METSÄ, Finland E-mail: juho.rantala@metsagroup.com
• Saksa, Natural Resources Institute Finland (Luke), Natural resources, Juntintie 154, FI-77600 Suonenjoki, Finland E-mail: timo.saksa@luke.fi

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

To promote the growth and survival of regenerated forests, site preparation prior to tree planting on clearcuts is necessary. This is often performed with scarifiers, either through trenching or mounding. Mounding is generally considered better in a plant survival perspective but is inefficient on obstacle-rich clearcuts. By utilising machine vision through e.g. remote sensing methods, new strategies can enable efficient mound positioning. In this paper, three realistic strategies utilizing ideal clearcut object identification through machine vision have been developed that can be used for more efficient mounding. The results show that mounding efficiency can be significantly improved with a new mound positioning strategy that employs ideal object identification, especially on obstacle-rich clearcuts.

• Lideskog, Luleå University of Technology, Department of Engineering Sciences and Mathematics, Division of Product and Production Development, SE-971 87 Luleå, Sweden E-mail: hakan.lideskog@ltu.se
• Karlberg, Luleå University of Technology, Department of Engineering Sciences and Mathematics, Division of Product and Production Development, SE-971 87 Luleå, Sweden E-mail: magnus.karlberg@ltu.se

• Heiskanen, Finnish Forest Research Institute, Suonenjoki Unit, Juntintie 154, FI-77600, Suonenjoki, Finland E-mail: juha.heiskanen@metla.fi
• Saksa, Finnish Forest Research Institute, Suonenjoki Unit, Juntintie 154, FI-77600, Suonenjoki, Finland E-mail: timo.saksa@metla.fi
• Luoranen, Finnish Forest Research Institute, Suonenjoki Unit, Juntintie 154, FI-77600, Suonenjoki, Finland E-mail: jaana.luoranen@metla.fi

• Luoranen, Finnish Forest Research Institute, Suonenjoki Research Unit, Juntintie 154, FI-77600 Suonenjoki, Finland E-mail: jaana.luoranen@metla.fi
• Rikala, Finnish Forest Research Institute, Suonenjoki Research Unit, Juntintie 154, FI-77600 Suonenjoki, Finland E-mail: rr@nn.fi
• Smolander, Finnish Forest Research Institute, Suonenjoki Research Unit, Juntintie 154, FI-77600 Suonenjoki, Finland E-mail: hs@nn.fi

• Uotila, Finnish Forest Research Institute, Suonenjoki, Finland E-mail: karri.uotila@metla.fi
• Rantala, Finnish Forest Research Institute, Suonenjoki, Finland E-mail: jr@nn.fi
• Saksa, Finnish Forest Research Institute, Suonenjoki, Finland E-mail: ts@nn.fi
• Harstela, Finnish Forest Research Institute, Suonenjoki, Finland E-mail: ph@nn.fi

• Saksa, The Finnish Forest Institute, Suonenjoki Research Station, FI-77600 Suonenjoki, Finland E-mail: ts@nn.fi
• Heiskanen, The Finnish Forest Institute, Suonenjoki Research Station, FI-77600 Suonenjoki, Finland E-mail: jh@nn.fi
• Miina, The Finnish Forest Institute, Joensuu Research Centre, P. O. Box 68, FI-80101 Joensuu, Finland E-mail: jm@nn.fi
• Tuomola, The University of Joensuu, Mekrijärvi Research Station, FI-82900 Ilomantsi, Finland E-mail: jt@nn.fi
• Kolström, The University of Joensuu, Mekrijärvi Research Station, FI-82900 Ilomantsi, Finland E-mail: tk@nn.fi