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Articles containing the keyword 'mixed linear regression'
Category : Research article
article id 982,
category
Research article
(2014).
Labour consumption models applied to motor-manual pre-commercial thinning in Finland.
Silva Fennica
vol.
48
no.
2
article id 982.
https://doi.org/10.14214/sf.982
Keywords:
productivity;
pre-commercial thinning;
forest vegetation management;
early cleaning;
release treatment;
mixed linear regression
Highlights:
When a young stand grows and gets older, the work time needed to make pre-commercial thinning increases. The stands of Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.) and hardwoods (Betula spp.) required an additional 8.2%, 5.2%, and 3.3% work-time per year, respectively.
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Author Info
Labour models were developed to estimate the time required to Pre-Commercially Thin (PCT) with a clearing saw 4- to 20-year-old stands of the main commercial tree species in Finland. Labour (i.e., work-time consumption) was estimated from the density and stem diameter of the removal of 448 stands via an existing work productivity function. The removal based estimator attained was used as the basis for a priori mixed linear regression models. The main finding was that when a young stand grows and gets older, the work time needed to make a PCT increases. The stands of Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.) and hardwoods (Betula spp.) required an additional 8.2%, 5.2%, and 3.3% work-time per year, respectively. Site fertility also played a role in that the most fertile site (mesic OMT) had an estimated labour requirement 114% higher than that for dryish VT. We also note that, per unit area, small stands require less labour than large ones and soil preparation method had a minor effect on the labour estimate. The stands which had previously gone through PCT were separately analysed. In those stands, the only significant variable concerning the labour estimate was age. The a priori models described here can help foresters to develop economic management programmes and issue quotes for forestry services.
-
Uotila,
Finnish Forest Research Institute, Juntintie 154, FI-77600 Suonenjoki, Finland
E-mail:
karri.uotila@metla.fi
- Saksa, Finnish Forest Research Institute, Juntintie 154, FI-77600 Suonenjoki, Finland E-mail: timo.saksa@metla.fi
- Rantala, Metsä Group, Lielahdenkatu 10, FI-33400 Tampere, Finland E-mail: juho.rantala@metsagroup.com
- Kiljunen, Metsähallitus, Asemakatu 7, FI-70107 Kuopio, Finland E-mail: nuutti.kiljunen@metsa.fi
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