Articles by Pertti Harstela

Many kinds of planning systems have been labelled decision support systems (DSS), but few meet the most important features of real DSSs in planning and control of wood procurement. It has been concluded that many reasons exist to develop DSSs for wood procurement. The purchasing of timber seems to be one of the most promising areas for DSS, because there is no formal structure for these operations and decisions deal with human behaviour. Relations between DSSs and different features of the new approaches in wood procurement are also discussed, and hypotheses for future studies suggested.

• Harstela, E-mail: ph@mm.unknown

The first aim of this study was to develop a simulation model describing the flow of different timber qualities to different firms. The second aim was to study preliminary the factors which affect timber distributions. In addition, we tested the hypothesis that in a small sawmill firm the traditional way of organizing timber procurement does not direct effectively good quality logs to the special production. The game theoretic approaching and the principles of Monte-Carlo simulation were applied in development of the simulation model. The most important factors of the model were tried to find for further studies with sensitive analysis. Empirical validation brought forth promising results in the area of one municipality. The buyer’s awareness of a marked stand, the seller’s willingness to sell a marked stand, the buyer’s ability to pay for wood and the proportion of first quality pine logs in a marked stand affected the distribution of pine logs. The results also supported the hypothesis that the traditional system, in which sawmills or their own forest departments procure themselves all timber needed, is not the most effective way to direct enough good quality timber to the special production.

• Tolvanen-Sikanen, E-mail: tt@mm.unknown
• Harstela, E-mail: ph@mm.unknown
• Sikanen, E-mail: ls@mm.unknown

The paper discussed the definitions of different Finnish and English terms concerning timber harvesting, and suggests definitions and translations of the terms.

• Kantola, E-mail: mk@mm.unknown
• Harstela, E-mail: ph@mm.unknown

The influence of horizontal whole-body vibration of fairly low intensity alone and combined with the mental load and motor action typical for the forest machine drive on heart rate variability (HRV), respiration rate (RR) and heart rate (HR) was studied by testing five subjects. Horizontal vibration had an influence on HR, HRV and RR. ’Control activities’ had the most influence on RR and HRV, but some influence on HR, too. ’Moving the control devices’ (motor action) gave the same response in HR as ’control activities’, but not in HRV and RR. ’Control activities’ together with ’vibration’ had a more effect on HRV and RR than these two factors singly, but not on HR. The possibilities of using these variables in field studies are discussed.

The PDF includes a summary in Finnish.

• Harstela, E-mail: ph@mm.unknown
• Piirainen, E-mail: kp@mm.unknown

Using literature and a simulator experiment, an ordinary processor and a grapple loader processor were compared in conditions corresponding to thinning later than the first commercial thinning. Visual bucking only was employed in the simulator experiment. The strip road spacing was 30 m and there was no preliminary skidding of the trees. The simulator experiment confirmed the view reached in the literature that work productivity of the grappler loader processor is 20–40 % greater than that of an ordinary processor provided that the stem size is under 0.2–0.4 m3.

The PDF includes a summary in English.

• Harstela, E-mail: ph@mm.unknown
• Maukkonen, E-mail: am@mm.unknown

In the first part of the study the hindrance of the remaining trees when felling trees by machines working from the strip road in selective thinning was studied on the basis of the literature. In the second part there was geometrically studied the need of schematic thinning in some type stands when bundles are pre-skidded straight-lined to the strip road. In average only 0-1 trees per pre-skidding trail needs to be removed. It was concluded that trees removed from the pre-skidding trail do not significantly increase the need of schematic thinning. Remaining trees do not limit the length of machine booms if the pre-skidding trails are planned during the felling.

The PDF includes a summary in English.

• Harstela, E-mail: ph@mm.unknown

A theoretical nomogram was made for estimating the costs of fully mechanized thinning and the driving speed of the machine. Based on this nomogram and the previous studies three harvesting methods were compared; systematic fully mechanized harvesting, selective fully mechanized harvesting, and manual felling combined with whole-tree chipping.

The third method was cheaper than the fully mechanized methods in a pole-stage stand. The choice of the most advantageous chipping station depended on conditions, but the smaller tree size and possibly the reduced damage on the remaining stand favour chipping on the strip road rather than chipping on the intermediate landing or at the mill.

Mechanized systematic thinning was the cheapest method for harvesting in the sapling stand. The required driving speed were so low that ergonomic factors should not hinder its use. Factors related to the future production of the stand do, however, limit its use. Mechanized selective thinning does not seem to be an economic method for harvesting in a sapling or pole-stage stand.

The PDF includes a summary in English.

• Harstela, E-mail: ph@mm.unknown
• Tervo, E-mail: lt@mm.unknown

The concepts central to ergonomic research connected with the amount of strain caused by work was studied. A model was made to describe the process of strain. The model includes the following concepts: load or stress, human input, worker, strain, renewal of human resources, output and their hierarchical units. Based on the quality of human input, the forest work was roughly divided into two categories: (1) work demanding primarily muscle activity and (2) neuro-sensory work. In the first group, especially in cutting work, the main part of the human input is intensive consumption of muscle energy. In addition, work load causes accidents, wear of skeletal and muscular systems and processes by noise, vibrations, and climate. Correspondingly, when operating forest machines, the human input is mainly neuro-sensory functions of the central nervous system. Work load causes directly the effects of low frequency vibration and of other work conditions. The model was tested on data from research of forest work.

The PDF includes a summary in English.

• Harstela, E-mail: ph@mm.unknown

In this study a formula has been developed to describe the influence of the change of cost level on such a mechanization prognosis, where is assumed that wages and machine costs bear compound interest. In the study there are some numerical examples.

In the formula p1 = annual per cent increase of wages, p3 = annual per cent increase of machine costs, p2 = sudden and incident per cent increase of machine costs, and tv = delay in the profitability of mechanization.

The PDF includes a summary in English.

• Harstela, E-mail: ph@mm.unknown

• 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

• Rantala, Finnish Forest Research Institute, Suonenjoki Research Unit, Juntintie 154, FI-77600 Suonenjoki, Finland E-mail: juho.rantala@metla.fi
• Harstela, Finnish Forest Research Institute, Suonenjoki Research Unit, Juntintie 154, FI-77600 Suonenjoki, Finland E-mail: ph@nn.fi
• Saarinen, Finnish Forest Research Institute, Suonenjoki Research Unit, Juntintie 154, FI-77600 Suonenjoki, Finland E-mail: vms@nn.fi
• Tervo, Finnish Forest Research Institute, Suonenjoki Research Unit, Juntintie 154, FI-77600 Suonenjoki, Finland E-mail: lt@nn.fi

• Rantala, Finnish Forest Research Institute, Suonenjoki Research Station, FIN-77600 Suonenjoki, Finland E-mail: juho.rantala@metla.fi
• Väätäinen, Finnish Forest Research Institute, Joensuu Research Centre, FIN-80101 Joensuu, Finland E-mail: kv@nn.fi
• Kiljunen, Finnish Forest Research Institute, Suonenjoki Research Station, FIN-77600 Suonenjoki, Finland E-mail: nk@nn.fi
• Harstela, Finnish Forest Research Institute, Suonenjoki Research Station, FIN-77600 Suonenjoki, Finland E-mail: ph@nn.fi